diff --git "a/SenseVoiceSmall_fp32.mlmodelc/model.mil" "b/SenseVoiceSmall_fp32.mlmodelc/model.mil" new file mode 100644--- /dev/null +++ "b/SenseVoiceSmall_fp32.mlmodelc/model.mil" @@ -0,0 +1,5097 @@ +program(1.0) +[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.5.1"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.3.0"}})] +{ + func main(tensor language, tensor speech, tensor speech_lengths, tensor textnorm) { + tensor embed_weight = const()[name = tensor("embed_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; + tensor encoder_encoders0_0_self_attn_linear_q_k_v_bias = const()[name = tensor("encoder_encoders0_0_self_attn_linear_q_k_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(35968)))]; + tensor encoder_encoders0_0_self_attn_linear_q_k_v_weight = const()[name = tensor("encoder_encoders0_0_self_attn_linear_q_k_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42176)))]; + tensor encoder_encoders0_0_self_attn_fsmn_block_weight = const()[name = tensor("encoder_encoders0_0_self_attn_fsmn_block_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3482880)))]; + tensor encoder_encoders0_0_self_attn_linear_out_bias = const()[name = tensor("encoder_encoders0_0_self_attn_linear_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3505472)))]; + tensor encoder_encoders0_0_self_attn_linear_out_weight = const()[name = tensor("encoder_encoders0_0_self_attn_linear_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3507584)))]; + tensor encoder_encoders0_0_feed_forward_w_1_bias = const()[name = tensor("encoder_encoders0_0_feed_forward_w_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4556224)))]; + tensor encoder_encoders0_0_feed_forward_w_1_weight = const()[name = tensor("encoder_encoders0_0_feed_forward_w_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4564480)))]; + tensor encoder_encoders0_0_feed_forward_w_2_bias = const()[name = tensor("encoder_encoders0_0_feed_forward_w_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8758848)))]; + tensor encoder_encoders0_0_feed_forward_w_2_weight = const()[name = tensor("encoder_encoders0_0_feed_forward_w_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8760960)))]; + tensor encoder_encoders_0_self_attn_linear_q_k_v_bias = const()[name = tensor("encoder_encoders_0_self_attn_linear_q_k_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(12955328)))]; + tensor encoder_encoders_0_self_attn_linear_q_k_v_weight = const()[name = tensor("encoder_encoders_0_self_attn_linear_q_k_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(12961536)))]; + tensor encoder_encoders_0_self_attn_fsmn_block_weight = const()[name = tensor("encoder_encoders_0_self_attn_fsmn_block_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16107328)))]; + tensor encoder_encoders_0_self_attn_linear_out_bias = const()[name = tensor("encoder_encoders_0_self_attn_linear_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16129920)))]; + tensor encoder_encoders_0_self_attn_linear_out_weight = const()[name = tensor("encoder_encoders_0_self_attn_linear_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16132032)))]; + tensor encoder_encoders_0_feed_forward_w_1_bias = const()[name = tensor("encoder_encoders_0_feed_forward_w_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(17180672)))]; + tensor encoder_encoders_0_feed_forward_w_1_weight = const()[name = tensor("encoder_encoders_0_feed_forward_w_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(17188928)))]; + tensor encoder_encoders_0_feed_forward_w_2_bias = const()[name = tensor("encoder_encoders_0_feed_forward_w_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21383296)))]; + tensor encoder_encoders_0_feed_forward_w_2_weight = const()[name = tensor("encoder_encoders_0_feed_forward_w_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21385408)))]; + tensor encoder_encoders_1_self_attn_linear_q_k_v_bias = const()[name = tensor("encoder_encoders_1_self_attn_linear_q_k_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25579776)))]; + tensor encoder_encoders_1_self_attn_linear_q_k_v_weight = const()[name = tensor("encoder_encoders_1_self_attn_linear_q_k_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25585984)))]; + tensor encoder_encoders_1_self_attn_fsmn_block_weight = const()[name = tensor("encoder_encoders_1_self_attn_fsmn_block_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28731776)))]; + tensor encoder_encoders_1_self_attn_linear_out_bias = const()[name = tensor("encoder_encoders_1_self_attn_linear_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28754368)))]; + tensor encoder_encoders_1_self_attn_linear_out_weight = const()[name = tensor("encoder_encoders_1_self_attn_linear_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28756480)))]; + tensor encoder_encoders_1_feed_forward_w_1_bias = const()[name = tensor("encoder_encoders_1_feed_forward_w_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29805120)))]; + tensor encoder_encoders_1_feed_forward_w_1_weight = const()[name = tensor("encoder_encoders_1_feed_forward_w_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29813376)))]; + tensor encoder_encoders_1_feed_forward_w_2_bias = const()[name = tensor("encoder_encoders_1_feed_forward_w_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(34007744)))]; + tensor encoder_encoders_1_feed_forward_w_2_weight = const()[name = tensor("encoder_encoders_1_feed_forward_w_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(34009856)))]; + tensor encoder_encoders_2_self_attn_linear_q_k_v_bias = const()[name = tensor("encoder_encoders_2_self_attn_linear_q_k_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38204224)))]; + tensor encoder_encoders_2_self_attn_linear_q_k_v_weight = const()[name = tensor("encoder_encoders_2_self_attn_linear_q_k_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38210432)))]; + tensor encoder_encoders_2_self_attn_fsmn_block_weight = const()[name = tensor("encoder_encoders_2_self_attn_fsmn_block_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41356224)))]; + tensor encoder_encoders_2_self_attn_linear_out_bias = const()[name = tensor("encoder_encoders_2_self_attn_linear_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41378816)))]; + tensor encoder_encoders_2_self_attn_linear_out_weight = const()[name = tensor("encoder_encoders_2_self_attn_linear_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41380928)))]; + tensor encoder_encoders_2_feed_forward_w_1_bias = const()[name = tensor("encoder_encoders_2_feed_forward_w_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42429568)))]; + tensor encoder_encoders_2_feed_forward_w_1_weight = const()[name = tensor("encoder_encoders_2_feed_forward_w_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42437824)))]; + tensor encoder_encoders_2_feed_forward_w_2_bias = const()[name = tensor("encoder_encoders_2_feed_forward_w_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46632192)))]; + tensor encoder_encoders_2_feed_forward_w_2_weight = const()[name = tensor("encoder_encoders_2_feed_forward_w_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46634304)))]; + tensor encoder_encoders_3_self_attn_linear_q_k_v_bias = const()[name = tensor("encoder_encoders_3_self_attn_linear_q_k_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50828672)))]; + tensor encoder_encoders_3_self_attn_linear_q_k_v_weight = const()[name = tensor("encoder_encoders_3_self_attn_linear_q_k_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50834880)))]; + tensor encoder_encoders_3_self_attn_fsmn_block_weight = const()[name = tensor("encoder_encoders_3_self_attn_fsmn_block_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(53980672)))]; + tensor encoder_encoders_3_self_attn_linear_out_bias = const()[name = tensor("encoder_encoders_3_self_attn_linear_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(54003264)))]; + tensor encoder_encoders_3_self_attn_linear_out_weight = const()[name = tensor("encoder_encoders_3_self_attn_linear_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(54005376)))]; + tensor encoder_encoders_3_feed_forward_w_1_bias = const()[name = tensor("encoder_encoders_3_feed_forward_w_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(55054016)))]; + tensor encoder_encoders_3_feed_forward_w_1_weight = const()[name = tensor("encoder_encoders_3_feed_forward_w_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(55062272)))]; + tensor encoder_encoders_3_feed_forward_w_2_bias = const()[name = tensor("encoder_encoders_3_feed_forward_w_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(59256640)))]; + tensor encoder_encoders_3_feed_forward_w_2_weight = const()[name = tensor("encoder_encoders_3_feed_forward_w_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(59258752)))]; + tensor encoder_encoders_4_self_attn_linear_q_k_v_bias = const()[name = tensor("encoder_encoders_4_self_attn_linear_q_k_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(63453120)))]; + tensor encoder_encoders_4_self_attn_linear_q_k_v_weight = const()[name = tensor("encoder_encoders_4_self_attn_linear_q_k_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(63459328)))]; + tensor encoder_encoders_4_self_attn_fsmn_block_weight = const()[name = tensor("encoder_encoders_4_self_attn_fsmn_block_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(66605120)))]; + tensor encoder_encoders_4_self_attn_linear_out_bias = const()[name = tensor("encoder_encoders_4_self_attn_linear_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(66627712)))]; + tensor encoder_encoders_4_self_attn_linear_out_weight = const()[name = tensor("encoder_encoders_4_self_attn_linear_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(66629824)))]; + tensor encoder_encoders_4_feed_forward_w_1_bias = const()[name = tensor("encoder_encoders_4_feed_forward_w_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67678464)))]; + tensor encoder_encoders_4_feed_forward_w_1_weight = const()[name = tensor("encoder_encoders_4_feed_forward_w_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67686720)))]; + tensor encoder_encoders_4_feed_forward_w_2_bias = const()[name = tensor("encoder_encoders_4_feed_forward_w_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(71881088)))]; + tensor encoder_encoders_4_feed_forward_w_2_weight = const()[name = tensor("encoder_encoders_4_feed_forward_w_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(71883200)))]; + tensor encoder_encoders_5_self_attn_linear_q_k_v_bias = const()[name = tensor("encoder_encoders_5_self_attn_linear_q_k_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(76077568)))]; + tensor encoder_encoders_5_self_attn_linear_q_k_v_weight = const()[name = tensor("encoder_encoders_5_self_attn_linear_q_k_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(76083776)))]; + tensor encoder_encoders_5_self_attn_fsmn_block_weight = const()[name = tensor("encoder_encoders_5_self_attn_fsmn_block_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79229568)))]; + tensor encoder_encoders_5_self_attn_linear_out_bias = const()[name = tensor("encoder_encoders_5_self_attn_linear_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79252160)))]; + tensor encoder_encoders_5_self_attn_linear_out_weight = const()[name = tensor("encoder_encoders_5_self_attn_linear_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79254272)))]; + tensor encoder_encoders_5_feed_forward_w_1_bias = const()[name = tensor("encoder_encoders_5_feed_forward_w_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(80302912)))]; + tensor encoder_encoders_5_feed_forward_w_1_weight = const()[name = tensor("encoder_encoders_5_feed_forward_w_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(80311168)))]; + tensor encoder_encoders_5_feed_forward_w_2_bias = const()[name = tensor("encoder_encoders_5_feed_forward_w_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(84505536)))]; + tensor encoder_encoders_5_feed_forward_w_2_weight = const()[name = tensor("encoder_encoders_5_feed_forward_w_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(84507648)))]; + tensor encoder_encoders_6_self_attn_linear_q_k_v_bias = const()[name = tensor("encoder_encoders_6_self_attn_linear_q_k_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(88702016)))]; + tensor encoder_encoders_6_self_attn_linear_q_k_v_weight = const()[name = tensor("encoder_encoders_6_self_attn_linear_q_k_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(88708224)))]; + tensor encoder_encoders_6_self_attn_fsmn_block_weight = const()[name = tensor("encoder_encoders_6_self_attn_fsmn_block_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(91854016)))]; + tensor encoder_encoders_6_self_attn_linear_out_bias = const()[name = tensor("encoder_encoders_6_self_attn_linear_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(91876608)))]; + tensor encoder_encoders_6_self_attn_linear_out_weight = const()[name = tensor("encoder_encoders_6_self_attn_linear_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(91878720)))]; + tensor encoder_encoders_6_feed_forward_w_1_bias = const()[name = tensor("encoder_encoders_6_feed_forward_w_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(92927360)))]; + tensor encoder_encoders_6_feed_forward_w_1_weight = const()[name = tensor("encoder_encoders_6_feed_forward_w_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(92935616)))]; + tensor encoder_encoders_6_feed_forward_w_2_bias = const()[name = tensor("encoder_encoders_6_feed_forward_w_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97129984)))]; + tensor encoder_encoders_6_feed_forward_w_2_weight = const()[name = tensor("encoder_encoders_6_feed_forward_w_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97132096)))]; + tensor encoder_encoders_7_self_attn_linear_q_k_v_bias = const()[name = tensor("encoder_encoders_7_self_attn_linear_q_k_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(101326464)))]; + tensor encoder_encoders_7_self_attn_linear_q_k_v_weight = const()[name = tensor("encoder_encoders_7_self_attn_linear_q_k_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(101332672)))]; + tensor encoder_encoders_7_self_attn_fsmn_block_weight = const()[name = tensor("encoder_encoders_7_self_attn_fsmn_block_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(104478464)))]; + tensor encoder_encoders_7_self_attn_linear_out_bias = const()[name = tensor("encoder_encoders_7_self_attn_linear_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(104501056)))]; + tensor encoder_encoders_7_self_attn_linear_out_weight = const()[name = tensor("encoder_encoders_7_self_attn_linear_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(104503168)))]; + tensor encoder_encoders_7_feed_forward_w_1_bias = const()[name = tensor("encoder_encoders_7_feed_forward_w_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(105551808)))]; + tensor encoder_encoders_7_feed_forward_w_1_weight = const()[name = tensor("encoder_encoders_7_feed_forward_w_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(105560064)))]; + tensor encoder_encoders_7_feed_forward_w_2_bias = const()[name = tensor("encoder_encoders_7_feed_forward_w_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(109754432)))]; + tensor encoder_encoders_7_feed_forward_w_2_weight = const()[name = tensor("encoder_encoders_7_feed_forward_w_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(109756544)))]; + tensor encoder_encoders_8_self_attn_linear_q_k_v_bias = const()[name = tensor("encoder_encoders_8_self_attn_linear_q_k_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(113950912)))]; + tensor encoder_encoders_8_self_attn_linear_q_k_v_weight = const()[name = tensor("encoder_encoders_8_self_attn_linear_q_k_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(113957120)))]; + tensor encoder_encoders_8_self_attn_fsmn_block_weight = const()[name = tensor("encoder_encoders_8_self_attn_fsmn_block_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(117102912)))]; + tensor encoder_encoders_8_self_attn_linear_out_bias = const()[name = tensor("encoder_encoders_8_self_attn_linear_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(117125504)))]; + tensor encoder_encoders_8_self_attn_linear_out_weight = const()[name = tensor("encoder_encoders_8_self_attn_linear_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(117127616)))]; + tensor encoder_encoders_8_feed_forward_w_1_bias = const()[name = tensor("encoder_encoders_8_feed_forward_w_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(118176256)))]; + tensor encoder_encoders_8_feed_forward_w_1_weight = const()[name = tensor("encoder_encoders_8_feed_forward_w_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(118184512)))]; + tensor encoder_encoders_8_feed_forward_w_2_bias = const()[name = tensor("encoder_encoders_8_feed_forward_w_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(122378880)))]; + tensor encoder_encoders_8_feed_forward_w_2_weight = const()[name = tensor("encoder_encoders_8_feed_forward_w_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(122380992)))]; + tensor encoder_encoders_9_self_attn_linear_q_k_v_bias = const()[name = tensor("encoder_encoders_9_self_attn_linear_q_k_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(126575360)))]; + tensor encoder_encoders_9_self_attn_linear_q_k_v_weight = const()[name = tensor("encoder_encoders_9_self_attn_linear_q_k_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(126581568)))]; + tensor encoder_encoders_9_self_attn_fsmn_block_weight = const()[name = tensor("encoder_encoders_9_self_attn_fsmn_block_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(129727360)))]; + tensor encoder_encoders_9_self_attn_linear_out_bias = const()[name = tensor("encoder_encoders_9_self_attn_linear_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(129749952)))]; + tensor encoder_encoders_9_self_attn_linear_out_weight = const()[name = tensor("encoder_encoders_9_self_attn_linear_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(129752064)))]; + tensor encoder_encoders_9_feed_forward_w_1_bias = const()[name = tensor("encoder_encoders_9_feed_forward_w_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(130800704)))]; + tensor encoder_encoders_9_feed_forward_w_1_weight = const()[name = tensor("encoder_encoders_9_feed_forward_w_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(130808960)))]; + tensor encoder_encoders_9_feed_forward_w_2_bias = const()[name = tensor("encoder_encoders_9_feed_forward_w_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(135003328)))]; + tensor encoder_encoders_9_feed_forward_w_2_weight = const()[name = tensor("encoder_encoders_9_feed_forward_w_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(135005440)))]; + tensor encoder_encoders_10_self_attn_linear_q_k_v_bias = const()[name = tensor("encoder_encoders_10_self_attn_linear_q_k_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(139199808)))]; + tensor encoder_encoders_10_self_attn_linear_q_k_v_weight = const()[name = tensor("encoder_encoders_10_self_attn_linear_q_k_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(139206016)))]; + tensor encoder_encoders_10_self_attn_fsmn_block_weight = const()[name = tensor("encoder_encoders_10_self_attn_fsmn_block_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(142351808)))]; + tensor encoder_encoders_10_self_attn_linear_out_bias = const()[name = tensor("encoder_encoders_10_self_attn_linear_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(142374400)))]; + tensor encoder_encoders_10_self_attn_linear_out_weight = const()[name = tensor("encoder_encoders_10_self_attn_linear_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(142376512)))]; + tensor encoder_encoders_10_feed_forward_w_1_bias = const()[name = tensor("encoder_encoders_10_feed_forward_w_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(143425152)))]; + tensor encoder_encoders_10_feed_forward_w_1_weight = const()[name = tensor("encoder_encoders_10_feed_forward_w_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(143433408)))]; + tensor encoder_encoders_10_feed_forward_w_2_bias = const()[name = tensor("encoder_encoders_10_feed_forward_w_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(147627776)))]; + tensor encoder_encoders_10_feed_forward_w_2_weight = const()[name = tensor("encoder_encoders_10_feed_forward_w_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(147629888)))]; + tensor encoder_encoders_11_self_attn_linear_q_k_v_bias = const()[name = tensor("encoder_encoders_11_self_attn_linear_q_k_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(151824256)))]; + tensor encoder_encoders_11_self_attn_linear_q_k_v_weight = const()[name = tensor("encoder_encoders_11_self_attn_linear_q_k_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(151830464)))]; + tensor encoder_encoders_11_self_attn_fsmn_block_weight = const()[name = tensor("encoder_encoders_11_self_attn_fsmn_block_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(154976256)))]; + tensor encoder_encoders_11_self_attn_linear_out_bias = const()[name = tensor("encoder_encoders_11_self_attn_linear_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(154998848)))]; + tensor encoder_encoders_11_self_attn_linear_out_weight = const()[name = tensor("encoder_encoders_11_self_attn_linear_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(155000960)))]; + tensor encoder_encoders_11_feed_forward_w_1_bias = const()[name = tensor("encoder_encoders_11_feed_forward_w_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(156049600)))]; + tensor encoder_encoders_11_feed_forward_w_1_weight = const()[name = tensor("encoder_encoders_11_feed_forward_w_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(156057856)))]; + tensor encoder_encoders_11_feed_forward_w_2_bias = const()[name = tensor("encoder_encoders_11_feed_forward_w_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(160252224)))]; + tensor encoder_encoders_11_feed_forward_w_2_weight = const()[name = tensor("encoder_encoders_11_feed_forward_w_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(160254336)))]; + tensor encoder_encoders_12_self_attn_linear_q_k_v_bias = const()[name = tensor("encoder_encoders_12_self_attn_linear_q_k_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(164448704)))]; + tensor encoder_encoders_12_self_attn_linear_q_k_v_weight = const()[name = tensor("encoder_encoders_12_self_attn_linear_q_k_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(164454912)))]; + tensor encoder_encoders_12_self_attn_fsmn_block_weight = const()[name = tensor("encoder_encoders_12_self_attn_fsmn_block_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(167600704)))]; + tensor encoder_encoders_12_self_attn_linear_out_bias = const()[name = tensor("encoder_encoders_12_self_attn_linear_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(167623296)))]; + tensor encoder_encoders_12_self_attn_linear_out_weight = const()[name = tensor("encoder_encoders_12_self_attn_linear_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(167625408)))]; + tensor encoder_encoders_12_feed_forward_w_1_bias = const()[name = tensor("encoder_encoders_12_feed_forward_w_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(168674048)))]; + tensor encoder_encoders_12_feed_forward_w_1_weight = const()[name = tensor("encoder_encoders_12_feed_forward_w_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(168682304)))]; + tensor encoder_encoders_12_feed_forward_w_2_bias = const()[name = tensor("encoder_encoders_12_feed_forward_w_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(172876672)))]; + tensor encoder_encoders_12_feed_forward_w_2_weight = const()[name = tensor("encoder_encoders_12_feed_forward_w_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(172878784)))]; + tensor encoder_encoders_13_self_attn_linear_q_k_v_bias = const()[name = tensor("encoder_encoders_13_self_attn_linear_q_k_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(177073152)))]; + tensor encoder_encoders_13_self_attn_linear_q_k_v_weight = const()[name = tensor("encoder_encoders_13_self_attn_linear_q_k_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(177079360)))]; + tensor encoder_encoders_13_self_attn_fsmn_block_weight = const()[name = tensor("encoder_encoders_13_self_attn_fsmn_block_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(180225152)))]; + tensor encoder_encoders_13_self_attn_linear_out_bias = const()[name = tensor("encoder_encoders_13_self_attn_linear_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(180247744)))]; + tensor encoder_encoders_13_self_attn_linear_out_weight = const()[name = tensor("encoder_encoders_13_self_attn_linear_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(180249856)))]; + tensor encoder_encoders_13_feed_forward_w_1_bias = const()[name = tensor("encoder_encoders_13_feed_forward_w_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(181298496)))]; + tensor encoder_encoders_13_feed_forward_w_1_weight = const()[name = tensor("encoder_encoders_13_feed_forward_w_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(181306752)))]; + tensor encoder_encoders_13_feed_forward_w_2_bias = const()[name = tensor("encoder_encoders_13_feed_forward_w_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(185501120)))]; + tensor encoder_encoders_13_feed_forward_w_2_weight = const()[name = tensor("encoder_encoders_13_feed_forward_w_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(185503232)))]; + tensor encoder_encoders_14_self_attn_linear_q_k_v_bias = const()[name = tensor("encoder_encoders_14_self_attn_linear_q_k_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(189697600)))]; + tensor encoder_encoders_14_self_attn_linear_q_k_v_weight = const()[name = tensor("encoder_encoders_14_self_attn_linear_q_k_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(189703808)))]; + tensor encoder_encoders_14_self_attn_fsmn_block_weight = const()[name = tensor("encoder_encoders_14_self_attn_fsmn_block_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(192849600)))]; + tensor encoder_encoders_14_self_attn_linear_out_bias = const()[name = tensor("encoder_encoders_14_self_attn_linear_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(192872192)))]; + tensor encoder_encoders_14_self_attn_linear_out_weight = const()[name = tensor("encoder_encoders_14_self_attn_linear_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(192874304)))]; + tensor encoder_encoders_14_feed_forward_w_1_bias = const()[name = tensor("encoder_encoders_14_feed_forward_w_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(193922944)))]; + tensor encoder_encoders_14_feed_forward_w_1_weight = const()[name = tensor("encoder_encoders_14_feed_forward_w_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(193931200)))]; + tensor encoder_encoders_14_feed_forward_w_2_bias = const()[name = tensor("encoder_encoders_14_feed_forward_w_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(198125568)))]; + tensor encoder_encoders_14_feed_forward_w_2_weight = const()[name = tensor("encoder_encoders_14_feed_forward_w_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(198127680)))]; + tensor encoder_encoders_15_self_attn_linear_q_k_v_bias = const()[name = tensor("encoder_encoders_15_self_attn_linear_q_k_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(202322048)))]; + tensor encoder_encoders_15_self_attn_linear_q_k_v_weight = const()[name = tensor("encoder_encoders_15_self_attn_linear_q_k_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(202328256)))]; + tensor encoder_encoders_15_self_attn_fsmn_block_weight = const()[name = tensor("encoder_encoders_15_self_attn_fsmn_block_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(205474048)))]; + tensor encoder_encoders_15_self_attn_linear_out_bias = const()[name = tensor("encoder_encoders_15_self_attn_linear_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(205496640)))]; + tensor encoder_encoders_15_self_attn_linear_out_weight = const()[name = tensor("encoder_encoders_15_self_attn_linear_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(205498752)))]; + tensor encoder_encoders_15_feed_forward_w_1_bias = const()[name = tensor("encoder_encoders_15_feed_forward_w_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(206547392)))]; + tensor encoder_encoders_15_feed_forward_w_1_weight = const()[name = tensor("encoder_encoders_15_feed_forward_w_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(206555648)))]; + tensor encoder_encoders_15_feed_forward_w_2_bias = const()[name = tensor("encoder_encoders_15_feed_forward_w_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(210750016)))]; + tensor encoder_encoders_15_feed_forward_w_2_weight = const()[name = tensor("encoder_encoders_15_feed_forward_w_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(210752128)))]; + tensor encoder_encoders_16_self_attn_linear_q_k_v_bias = const()[name = tensor("encoder_encoders_16_self_attn_linear_q_k_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(214946496)))]; + tensor encoder_encoders_16_self_attn_linear_q_k_v_weight = const()[name = tensor("encoder_encoders_16_self_attn_linear_q_k_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(214952704)))]; + tensor encoder_encoders_16_self_attn_fsmn_block_weight = const()[name = tensor("encoder_encoders_16_self_attn_fsmn_block_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(218098496)))]; + tensor encoder_encoders_16_self_attn_linear_out_bias = const()[name = tensor("encoder_encoders_16_self_attn_linear_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(218121088)))]; + tensor encoder_encoders_16_self_attn_linear_out_weight = const()[name = tensor("encoder_encoders_16_self_attn_linear_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(218123200)))]; + tensor encoder_encoders_16_feed_forward_w_1_bias = const()[name = tensor("encoder_encoders_16_feed_forward_w_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(219171840)))]; + tensor encoder_encoders_16_feed_forward_w_1_weight = const()[name = tensor("encoder_encoders_16_feed_forward_w_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(219180096)))]; + tensor encoder_encoders_16_feed_forward_w_2_bias = const()[name = tensor("encoder_encoders_16_feed_forward_w_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(223374464)))]; + tensor encoder_encoders_16_feed_forward_w_2_weight = const()[name = tensor("encoder_encoders_16_feed_forward_w_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(223376576)))]; + tensor encoder_encoders_17_self_attn_linear_q_k_v_bias = const()[name = tensor("encoder_encoders_17_self_attn_linear_q_k_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(227570944)))]; + tensor encoder_encoders_17_self_attn_linear_q_k_v_weight = const()[name = tensor("encoder_encoders_17_self_attn_linear_q_k_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(227577152)))]; + tensor encoder_encoders_17_self_attn_fsmn_block_weight = const()[name = tensor("encoder_encoders_17_self_attn_fsmn_block_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(230722944)))]; + tensor encoder_encoders_17_self_attn_linear_out_bias = const()[name = tensor("encoder_encoders_17_self_attn_linear_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(230745536)))]; + tensor encoder_encoders_17_self_attn_linear_out_weight = const()[name = tensor("encoder_encoders_17_self_attn_linear_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(230747648)))]; + tensor encoder_encoders_17_feed_forward_w_1_bias = const()[name = tensor("encoder_encoders_17_feed_forward_w_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(231796288)))]; + tensor encoder_encoders_17_feed_forward_w_1_weight = const()[name = tensor("encoder_encoders_17_feed_forward_w_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(231804544)))]; + tensor encoder_encoders_17_feed_forward_w_2_bias = const()[name = tensor("encoder_encoders_17_feed_forward_w_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(235998912)))]; + tensor encoder_encoders_17_feed_forward_w_2_weight = const()[name = tensor("encoder_encoders_17_feed_forward_w_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(236001024)))]; + tensor encoder_encoders_18_self_attn_linear_q_k_v_bias = const()[name = tensor("encoder_encoders_18_self_attn_linear_q_k_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(240195392)))]; + tensor encoder_encoders_18_self_attn_linear_q_k_v_weight = const()[name = tensor("encoder_encoders_18_self_attn_linear_q_k_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(240201600)))]; + tensor encoder_encoders_18_self_attn_fsmn_block_weight = const()[name = tensor("encoder_encoders_18_self_attn_fsmn_block_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(243347392)))]; + tensor encoder_encoders_18_self_attn_linear_out_bias = const()[name = tensor("encoder_encoders_18_self_attn_linear_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(243369984)))]; + tensor encoder_encoders_18_self_attn_linear_out_weight = const()[name = tensor("encoder_encoders_18_self_attn_linear_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(243372096)))]; + tensor encoder_encoders_18_feed_forward_w_1_bias = const()[name = tensor("encoder_encoders_18_feed_forward_w_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(244420736)))]; + tensor encoder_encoders_18_feed_forward_w_1_weight = const()[name = tensor("encoder_encoders_18_feed_forward_w_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(244428992)))]; + tensor encoder_encoders_18_feed_forward_w_2_bias = const()[name = tensor("encoder_encoders_18_feed_forward_w_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(248623360)))]; + tensor encoder_encoders_18_feed_forward_w_2_weight = const()[name = tensor("encoder_encoders_18_feed_forward_w_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(248625472)))]; + tensor encoder_encoders_19_self_attn_linear_q_k_v_bias = const()[name = tensor("encoder_encoders_19_self_attn_linear_q_k_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(252819840)))]; + tensor encoder_encoders_19_self_attn_linear_q_k_v_weight = const()[name = tensor("encoder_encoders_19_self_attn_linear_q_k_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(252826048)))]; + tensor encoder_encoders_19_self_attn_fsmn_block_weight = const()[name = tensor("encoder_encoders_19_self_attn_fsmn_block_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(255971840)))]; + tensor encoder_encoders_19_self_attn_linear_out_bias = const()[name = tensor("encoder_encoders_19_self_attn_linear_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(255994432)))]; + tensor encoder_encoders_19_self_attn_linear_out_weight = const()[name = tensor("encoder_encoders_19_self_attn_linear_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(255996544)))]; + tensor encoder_encoders_19_feed_forward_w_1_bias = const()[name = tensor("encoder_encoders_19_feed_forward_w_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(257045184)))]; + tensor encoder_encoders_19_feed_forward_w_1_weight = const()[name = tensor("encoder_encoders_19_feed_forward_w_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(257053440)))]; + tensor encoder_encoders_19_feed_forward_w_2_bias = const()[name = tensor("encoder_encoders_19_feed_forward_w_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(261247808)))]; + tensor encoder_encoders_19_feed_forward_w_2_weight = const()[name = tensor("encoder_encoders_19_feed_forward_w_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(261249920)))]; + tensor encoder_encoders_20_self_attn_linear_q_k_v_bias = const()[name = tensor("encoder_encoders_20_self_attn_linear_q_k_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(265444288)))]; + tensor encoder_encoders_20_self_attn_linear_q_k_v_weight = const()[name = tensor("encoder_encoders_20_self_attn_linear_q_k_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(265450496)))]; + tensor encoder_encoders_20_self_attn_fsmn_block_weight = const()[name = tensor("encoder_encoders_20_self_attn_fsmn_block_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(268596288)))]; + tensor encoder_encoders_20_self_attn_linear_out_bias = const()[name = tensor("encoder_encoders_20_self_attn_linear_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(268618880)))]; + tensor encoder_encoders_20_self_attn_linear_out_weight = const()[name = tensor("encoder_encoders_20_self_attn_linear_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(268620992)))]; + tensor encoder_encoders_20_feed_forward_w_1_bias = const()[name = tensor("encoder_encoders_20_feed_forward_w_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(269669632)))]; + tensor encoder_encoders_20_feed_forward_w_1_weight = const()[name = tensor("encoder_encoders_20_feed_forward_w_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(269677888)))]; + tensor encoder_encoders_20_feed_forward_w_2_bias = const()[name = tensor("encoder_encoders_20_feed_forward_w_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(273872256)))]; + tensor encoder_encoders_20_feed_forward_w_2_weight = const()[name = tensor("encoder_encoders_20_feed_forward_w_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(273874368)))]; + tensor encoder_encoders_21_self_attn_linear_q_k_v_bias = const()[name = tensor("encoder_encoders_21_self_attn_linear_q_k_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(278068736)))]; + tensor encoder_encoders_21_self_attn_linear_q_k_v_weight = const()[name = tensor("encoder_encoders_21_self_attn_linear_q_k_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(278074944)))]; + tensor encoder_encoders_21_self_attn_fsmn_block_weight = const()[name = tensor("encoder_encoders_21_self_attn_fsmn_block_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(281220736)))]; + tensor encoder_encoders_21_self_attn_linear_out_bias = const()[name = tensor("encoder_encoders_21_self_attn_linear_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(281243328)))]; + tensor encoder_encoders_21_self_attn_linear_out_weight = const()[name = tensor("encoder_encoders_21_self_attn_linear_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(281245440)))]; + tensor encoder_encoders_21_feed_forward_w_1_bias = const()[name = tensor("encoder_encoders_21_feed_forward_w_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(282294080)))]; + tensor encoder_encoders_21_feed_forward_w_1_weight = const()[name = tensor("encoder_encoders_21_feed_forward_w_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(282302336)))]; + tensor encoder_encoders_21_feed_forward_w_2_bias = const()[name = tensor("encoder_encoders_21_feed_forward_w_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(286496704)))]; + tensor encoder_encoders_21_feed_forward_w_2_weight = const()[name = tensor("encoder_encoders_21_feed_forward_w_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(286498816)))]; + tensor encoder_encoders_22_self_attn_linear_q_k_v_bias = const()[name = tensor("encoder_encoders_22_self_attn_linear_q_k_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(290693184)))]; + tensor encoder_encoders_22_self_attn_linear_q_k_v_weight = const()[name = tensor("encoder_encoders_22_self_attn_linear_q_k_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(290699392)))]; + tensor encoder_encoders_22_self_attn_fsmn_block_weight = const()[name = tensor("encoder_encoders_22_self_attn_fsmn_block_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(293845184)))]; + tensor encoder_encoders_22_self_attn_linear_out_bias = const()[name = tensor("encoder_encoders_22_self_attn_linear_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(293867776)))]; + tensor encoder_encoders_22_self_attn_linear_out_weight = const()[name = tensor("encoder_encoders_22_self_attn_linear_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(293869888)))]; + tensor encoder_encoders_22_feed_forward_w_1_bias = const()[name = tensor("encoder_encoders_22_feed_forward_w_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(294918528)))]; + tensor encoder_encoders_22_feed_forward_w_1_weight = const()[name = tensor("encoder_encoders_22_feed_forward_w_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(294926784)))]; + tensor encoder_encoders_22_feed_forward_w_2_bias = const()[name = tensor("encoder_encoders_22_feed_forward_w_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(299121152)))]; + tensor encoder_encoders_22_feed_forward_w_2_weight = const()[name = tensor("encoder_encoders_22_feed_forward_w_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(299123264)))]; + tensor encoder_encoders_23_self_attn_linear_q_k_v_bias = const()[name = tensor("encoder_encoders_23_self_attn_linear_q_k_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(303317632)))]; + tensor encoder_encoders_23_self_attn_linear_q_k_v_weight = const()[name = tensor("encoder_encoders_23_self_attn_linear_q_k_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(303323840)))]; + tensor encoder_encoders_23_self_attn_fsmn_block_weight = const()[name = tensor("encoder_encoders_23_self_attn_fsmn_block_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(306469632)))]; + tensor encoder_encoders_23_self_attn_linear_out_bias = const()[name = tensor("encoder_encoders_23_self_attn_linear_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(306492224)))]; + tensor encoder_encoders_23_self_attn_linear_out_weight = const()[name = tensor("encoder_encoders_23_self_attn_linear_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(306494336)))]; + tensor encoder_encoders_23_feed_forward_w_1_bias = const()[name = tensor("encoder_encoders_23_feed_forward_w_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(307542976)))]; + tensor encoder_encoders_23_feed_forward_w_1_weight = const()[name = tensor("encoder_encoders_23_feed_forward_w_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(307551232)))]; + tensor encoder_encoders_23_feed_forward_w_2_bias = const()[name = tensor("encoder_encoders_23_feed_forward_w_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(311745600)))]; + tensor encoder_encoders_23_feed_forward_w_2_weight = const()[name = tensor("encoder_encoders_23_feed_forward_w_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(311747712)))]; + tensor encoder_encoders_24_self_attn_linear_q_k_v_bias = const()[name = tensor("encoder_encoders_24_self_attn_linear_q_k_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(315942080)))]; + tensor encoder_encoders_24_self_attn_linear_q_k_v_weight = const()[name = tensor("encoder_encoders_24_self_attn_linear_q_k_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(315948288)))]; + tensor encoder_encoders_24_self_attn_fsmn_block_weight = const()[name = tensor("encoder_encoders_24_self_attn_fsmn_block_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(319094080)))]; + tensor encoder_encoders_24_self_attn_linear_out_bias = const()[name = tensor("encoder_encoders_24_self_attn_linear_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(319116672)))]; + tensor encoder_encoders_24_self_attn_linear_out_weight = const()[name = tensor("encoder_encoders_24_self_attn_linear_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(319118784)))]; + tensor encoder_encoders_24_feed_forward_w_1_bias = const()[name = tensor("encoder_encoders_24_feed_forward_w_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(320167424)))]; + tensor encoder_encoders_24_feed_forward_w_1_weight = const()[name = tensor("encoder_encoders_24_feed_forward_w_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(320175680)))]; + tensor encoder_encoders_24_feed_forward_w_2_bias = const()[name = tensor("encoder_encoders_24_feed_forward_w_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(324370048)))]; + tensor encoder_encoders_24_feed_forward_w_2_weight = const()[name = tensor("encoder_encoders_24_feed_forward_w_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(324372160)))]; + tensor encoder_encoders_25_self_attn_linear_q_k_v_bias = const()[name = tensor("encoder_encoders_25_self_attn_linear_q_k_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(328566528)))]; + tensor encoder_encoders_25_self_attn_linear_q_k_v_weight = const()[name = tensor("encoder_encoders_25_self_attn_linear_q_k_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(328572736)))]; + tensor encoder_encoders_25_self_attn_fsmn_block_weight = const()[name = tensor("encoder_encoders_25_self_attn_fsmn_block_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(331718528)))]; + tensor encoder_encoders_25_self_attn_linear_out_bias = const()[name = tensor("encoder_encoders_25_self_attn_linear_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(331741120)))]; + tensor encoder_encoders_25_self_attn_linear_out_weight = const()[name = tensor("encoder_encoders_25_self_attn_linear_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(331743232)))]; + tensor encoder_encoders_25_feed_forward_w_1_bias = const()[name = tensor("encoder_encoders_25_feed_forward_w_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(332791872)))]; + tensor encoder_encoders_25_feed_forward_w_1_weight = const()[name = tensor("encoder_encoders_25_feed_forward_w_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(332800128)))]; + tensor encoder_encoders_25_feed_forward_w_2_bias = const()[name = tensor("encoder_encoders_25_feed_forward_w_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(336994496)))]; + tensor encoder_encoders_25_feed_forward_w_2_weight = const()[name = tensor("encoder_encoders_25_feed_forward_w_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(336996608)))]; + tensor encoder_encoders_26_self_attn_linear_q_k_v_bias = const()[name = tensor("encoder_encoders_26_self_attn_linear_q_k_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(341190976)))]; + tensor encoder_encoders_26_self_attn_linear_q_k_v_weight = const()[name = tensor("encoder_encoders_26_self_attn_linear_q_k_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(341197184)))]; + tensor encoder_encoders_26_self_attn_fsmn_block_weight = const()[name = tensor("encoder_encoders_26_self_attn_fsmn_block_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(344342976)))]; + tensor encoder_encoders_26_self_attn_linear_out_bias = const()[name = tensor("encoder_encoders_26_self_attn_linear_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(344365568)))]; + tensor encoder_encoders_26_self_attn_linear_out_weight = const()[name = tensor("encoder_encoders_26_self_attn_linear_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(344367680)))]; + tensor encoder_encoders_26_feed_forward_w_1_bias = const()[name = tensor("encoder_encoders_26_feed_forward_w_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(345416320)))]; + tensor encoder_encoders_26_feed_forward_w_1_weight = const()[name = tensor("encoder_encoders_26_feed_forward_w_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(345424576)))]; + tensor encoder_encoders_26_feed_forward_w_2_bias = const()[name = tensor("encoder_encoders_26_feed_forward_w_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(349618944)))]; + tensor encoder_encoders_26_feed_forward_w_2_weight = const()[name = tensor("encoder_encoders_26_feed_forward_w_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(349621056)))]; + tensor encoder_encoders_27_self_attn_linear_q_k_v_bias = const()[name = tensor("encoder_encoders_27_self_attn_linear_q_k_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(353815424)))]; + tensor encoder_encoders_27_self_attn_linear_q_k_v_weight = const()[name = tensor("encoder_encoders_27_self_attn_linear_q_k_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(353821632)))]; + tensor encoder_encoders_27_self_attn_fsmn_block_weight = const()[name = tensor("encoder_encoders_27_self_attn_fsmn_block_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(356967424)))]; + tensor encoder_encoders_27_self_attn_linear_out_bias = const()[name = tensor("encoder_encoders_27_self_attn_linear_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(356990016)))]; + tensor encoder_encoders_27_self_attn_linear_out_weight = const()[name = tensor("encoder_encoders_27_self_attn_linear_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(356992128)))]; + tensor encoder_encoders_27_feed_forward_w_1_bias = const()[name = tensor("encoder_encoders_27_feed_forward_w_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(358040768)))]; + tensor encoder_encoders_27_feed_forward_w_1_weight = const()[name = tensor("encoder_encoders_27_feed_forward_w_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(358049024)))]; + tensor encoder_encoders_27_feed_forward_w_2_bias = const()[name = tensor("encoder_encoders_27_feed_forward_w_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(362243392)))]; + tensor encoder_encoders_27_feed_forward_w_2_weight = const()[name = tensor("encoder_encoders_27_feed_forward_w_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(362245504)))]; + tensor encoder_encoders_28_self_attn_linear_q_k_v_bias = const()[name = tensor("encoder_encoders_28_self_attn_linear_q_k_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(366439872)))]; + tensor encoder_encoders_28_self_attn_linear_q_k_v_weight = const()[name = tensor("encoder_encoders_28_self_attn_linear_q_k_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(366446080)))]; + tensor encoder_encoders_28_self_attn_fsmn_block_weight = const()[name = tensor("encoder_encoders_28_self_attn_fsmn_block_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(369591872)))]; + tensor encoder_encoders_28_self_attn_linear_out_bias = const()[name = tensor("encoder_encoders_28_self_attn_linear_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(369614464)))]; + tensor encoder_encoders_28_self_attn_linear_out_weight = const()[name = tensor("encoder_encoders_28_self_attn_linear_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(369616576)))]; + tensor encoder_encoders_28_feed_forward_w_1_bias = const()[name = tensor("encoder_encoders_28_feed_forward_w_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(370665216)))]; + tensor encoder_encoders_28_feed_forward_w_1_weight = const()[name = tensor("encoder_encoders_28_feed_forward_w_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(370673472)))]; + tensor encoder_encoders_28_feed_forward_w_2_bias = const()[name = tensor("encoder_encoders_28_feed_forward_w_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(374867840)))]; + tensor encoder_encoders_28_feed_forward_w_2_weight = const()[name = tensor("encoder_encoders_28_feed_forward_w_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(374869952)))]; + tensor encoder_encoders_29_self_attn_linear_q_k_v_bias = const()[name = tensor("encoder_encoders_29_self_attn_linear_q_k_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(379064320)))]; + tensor encoder_encoders_29_self_attn_linear_q_k_v_weight = const()[name = tensor("encoder_encoders_29_self_attn_linear_q_k_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(379070528)))]; + tensor encoder_encoders_29_self_attn_fsmn_block_weight = const()[name = tensor("encoder_encoders_29_self_attn_fsmn_block_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(382216320)))]; + tensor encoder_encoders_29_self_attn_linear_out_bias = const()[name = tensor("encoder_encoders_29_self_attn_linear_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(382238912)))]; + tensor encoder_encoders_29_self_attn_linear_out_weight = const()[name = tensor("encoder_encoders_29_self_attn_linear_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(382241024)))]; + tensor encoder_encoders_29_feed_forward_w_1_bias = const()[name = tensor("encoder_encoders_29_feed_forward_w_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(383289664)))]; + tensor encoder_encoders_29_feed_forward_w_1_weight = const()[name = tensor("encoder_encoders_29_feed_forward_w_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(383297920)))]; + tensor encoder_encoders_29_feed_forward_w_2_bias = const()[name = tensor("encoder_encoders_29_feed_forward_w_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(387492288)))]; + tensor encoder_encoders_29_feed_forward_w_2_weight = const()[name = tensor("encoder_encoders_29_feed_forward_w_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(387494400)))]; + tensor encoder_encoders_30_self_attn_linear_q_k_v_bias = const()[name = tensor("encoder_encoders_30_self_attn_linear_q_k_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(391688768)))]; + tensor encoder_encoders_30_self_attn_linear_q_k_v_weight = const()[name = tensor("encoder_encoders_30_self_attn_linear_q_k_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(391694976)))]; + tensor encoder_encoders_30_self_attn_fsmn_block_weight = const()[name = tensor("encoder_encoders_30_self_attn_fsmn_block_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(394840768)))]; + tensor encoder_encoders_30_self_attn_linear_out_bias = const()[name = tensor("encoder_encoders_30_self_attn_linear_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(394863360)))]; + tensor encoder_encoders_30_self_attn_linear_out_weight = const()[name = tensor("encoder_encoders_30_self_attn_linear_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(394865472)))]; + tensor encoder_encoders_30_feed_forward_w_1_bias = const()[name = tensor("encoder_encoders_30_feed_forward_w_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(395914112)))]; + tensor encoder_encoders_30_feed_forward_w_1_weight = const()[name = tensor("encoder_encoders_30_feed_forward_w_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(395922368)))]; + tensor encoder_encoders_30_feed_forward_w_2_bias = const()[name = tensor("encoder_encoders_30_feed_forward_w_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(400116736)))]; + tensor encoder_encoders_30_feed_forward_w_2_weight = const()[name = tensor("encoder_encoders_30_feed_forward_w_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(400118848)))]; + tensor encoder_encoders_31_self_attn_linear_q_k_v_bias = const()[name = tensor("encoder_encoders_31_self_attn_linear_q_k_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(404313216)))]; + tensor encoder_encoders_31_self_attn_linear_q_k_v_weight = const()[name = tensor("encoder_encoders_31_self_attn_linear_q_k_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(404319424)))]; + tensor encoder_encoders_31_self_attn_fsmn_block_weight = const()[name = tensor("encoder_encoders_31_self_attn_fsmn_block_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(407465216)))]; + tensor encoder_encoders_31_self_attn_linear_out_bias = const()[name = tensor("encoder_encoders_31_self_attn_linear_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(407487808)))]; + tensor encoder_encoders_31_self_attn_linear_out_weight = const()[name = tensor("encoder_encoders_31_self_attn_linear_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(407489920)))]; + tensor encoder_encoders_31_feed_forward_w_1_bias = const()[name = tensor("encoder_encoders_31_feed_forward_w_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(408538560)))]; + tensor encoder_encoders_31_feed_forward_w_1_weight = const()[name = tensor("encoder_encoders_31_feed_forward_w_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(408546816)))]; + tensor encoder_encoders_31_feed_forward_w_2_bias = const()[name = tensor("encoder_encoders_31_feed_forward_w_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(412741184)))]; + tensor encoder_encoders_31_feed_forward_w_2_weight = const()[name = tensor("encoder_encoders_31_feed_forward_w_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(412743296)))]; + tensor encoder_encoders_32_self_attn_linear_q_k_v_bias = const()[name = tensor("encoder_encoders_32_self_attn_linear_q_k_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(416937664)))]; + tensor encoder_encoders_32_self_attn_linear_q_k_v_weight = const()[name = tensor("encoder_encoders_32_self_attn_linear_q_k_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(416943872)))]; + tensor encoder_encoders_32_self_attn_fsmn_block_weight = const()[name = tensor("encoder_encoders_32_self_attn_fsmn_block_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(420089664)))]; + tensor encoder_encoders_32_self_attn_linear_out_bias = const()[name = tensor("encoder_encoders_32_self_attn_linear_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(420112256)))]; + tensor encoder_encoders_32_self_attn_linear_out_weight = const()[name = tensor("encoder_encoders_32_self_attn_linear_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(420114368)))]; + tensor encoder_encoders_32_feed_forward_w_1_bias = const()[name = tensor("encoder_encoders_32_feed_forward_w_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(421163008)))]; + tensor encoder_encoders_32_feed_forward_w_1_weight = const()[name = tensor("encoder_encoders_32_feed_forward_w_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(421171264)))]; + tensor encoder_encoders_32_feed_forward_w_2_bias = const()[name = tensor("encoder_encoders_32_feed_forward_w_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(425365632)))]; + tensor encoder_encoders_32_feed_forward_w_2_weight = const()[name = tensor("encoder_encoders_32_feed_forward_w_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(425367744)))]; + tensor encoder_encoders_33_self_attn_linear_q_k_v_bias = const()[name = tensor("encoder_encoders_33_self_attn_linear_q_k_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(429562112)))]; + tensor encoder_encoders_33_self_attn_linear_q_k_v_weight = const()[name = tensor("encoder_encoders_33_self_attn_linear_q_k_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(429568320)))]; + tensor encoder_encoders_33_self_attn_fsmn_block_weight = const()[name = tensor("encoder_encoders_33_self_attn_fsmn_block_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(432714112)))]; + tensor encoder_encoders_33_self_attn_linear_out_bias = const()[name = tensor("encoder_encoders_33_self_attn_linear_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(432736704)))]; + tensor encoder_encoders_33_self_attn_linear_out_weight = const()[name = tensor("encoder_encoders_33_self_attn_linear_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(432738816)))]; + tensor encoder_encoders_33_feed_forward_w_1_bias = const()[name = tensor("encoder_encoders_33_feed_forward_w_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(433787456)))]; + tensor encoder_encoders_33_feed_forward_w_1_weight = const()[name = tensor("encoder_encoders_33_feed_forward_w_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(433795712)))]; + tensor encoder_encoders_33_feed_forward_w_2_bias = const()[name = tensor("encoder_encoders_33_feed_forward_w_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(437990080)))]; + tensor encoder_encoders_33_feed_forward_w_2_weight = const()[name = tensor("encoder_encoders_33_feed_forward_w_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(437992192)))]; + tensor encoder_encoders_34_self_attn_linear_q_k_v_bias = const()[name = tensor("encoder_encoders_34_self_attn_linear_q_k_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(442186560)))]; + tensor encoder_encoders_34_self_attn_linear_q_k_v_weight = const()[name = tensor("encoder_encoders_34_self_attn_linear_q_k_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(442192768)))]; + tensor encoder_encoders_34_self_attn_fsmn_block_weight = const()[name = tensor("encoder_encoders_34_self_attn_fsmn_block_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(445338560)))]; + tensor encoder_encoders_34_self_attn_linear_out_bias = const()[name = tensor("encoder_encoders_34_self_attn_linear_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(445361152)))]; + tensor encoder_encoders_34_self_attn_linear_out_weight = const()[name = tensor("encoder_encoders_34_self_attn_linear_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(445363264)))]; + tensor encoder_encoders_34_feed_forward_w_1_bias = const()[name = tensor("encoder_encoders_34_feed_forward_w_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(446411904)))]; + tensor encoder_encoders_34_feed_forward_w_1_weight = const()[name = tensor("encoder_encoders_34_feed_forward_w_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(446420160)))]; + tensor encoder_encoders_34_feed_forward_w_2_bias = const()[name = tensor("encoder_encoders_34_feed_forward_w_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(450614528)))]; + tensor encoder_encoders_34_feed_forward_w_2_weight = const()[name = tensor("encoder_encoders_34_feed_forward_w_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(450616640)))]; + tensor encoder_encoders_35_self_attn_linear_q_k_v_bias = const()[name = tensor("encoder_encoders_35_self_attn_linear_q_k_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(454811008)))]; + tensor encoder_encoders_35_self_attn_linear_q_k_v_weight = const()[name = tensor("encoder_encoders_35_self_attn_linear_q_k_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(454817216)))]; + tensor encoder_encoders_35_self_attn_fsmn_block_weight = const()[name = tensor("encoder_encoders_35_self_attn_fsmn_block_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(457963008)))]; + tensor encoder_encoders_35_self_attn_linear_out_bias = const()[name = tensor("encoder_encoders_35_self_attn_linear_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(457985600)))]; + tensor encoder_encoders_35_self_attn_linear_out_weight = const()[name = tensor("encoder_encoders_35_self_attn_linear_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(457987712)))]; + tensor encoder_encoders_35_feed_forward_w_1_bias = const()[name = tensor("encoder_encoders_35_feed_forward_w_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(459036352)))]; + tensor encoder_encoders_35_feed_forward_w_1_weight = const()[name = tensor("encoder_encoders_35_feed_forward_w_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(459044608)))]; + tensor encoder_encoders_35_feed_forward_w_2_bias = const()[name = tensor("encoder_encoders_35_feed_forward_w_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(463238976)))]; + tensor encoder_encoders_35_feed_forward_w_2_weight = const()[name = tensor("encoder_encoders_35_feed_forward_w_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(463241088)))]; + tensor encoder_encoders_36_self_attn_linear_q_k_v_bias = const()[name = tensor("encoder_encoders_36_self_attn_linear_q_k_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(467435456)))]; + tensor encoder_encoders_36_self_attn_linear_q_k_v_weight = const()[name = tensor("encoder_encoders_36_self_attn_linear_q_k_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(467441664)))]; + tensor encoder_encoders_36_self_attn_fsmn_block_weight = const()[name = tensor("encoder_encoders_36_self_attn_fsmn_block_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(470587456)))]; + tensor encoder_encoders_36_self_attn_linear_out_bias = const()[name = tensor("encoder_encoders_36_self_attn_linear_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(470610048)))]; + tensor encoder_encoders_36_self_attn_linear_out_weight = const()[name = tensor("encoder_encoders_36_self_attn_linear_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(470612160)))]; + tensor encoder_encoders_36_feed_forward_w_1_bias = const()[name = tensor("encoder_encoders_36_feed_forward_w_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(471660800)))]; + tensor encoder_encoders_36_feed_forward_w_1_weight = const()[name = tensor("encoder_encoders_36_feed_forward_w_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(471669056)))]; + tensor encoder_encoders_36_feed_forward_w_2_bias = const()[name = tensor("encoder_encoders_36_feed_forward_w_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(475863424)))]; + tensor encoder_encoders_36_feed_forward_w_2_weight = const()[name = tensor("encoder_encoders_36_feed_forward_w_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(475865536)))]; + tensor encoder_encoders_37_self_attn_linear_q_k_v_bias = const()[name = tensor("encoder_encoders_37_self_attn_linear_q_k_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(480059904)))]; + tensor encoder_encoders_37_self_attn_linear_q_k_v_weight = const()[name = tensor("encoder_encoders_37_self_attn_linear_q_k_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(480066112)))]; + tensor encoder_encoders_37_self_attn_fsmn_block_weight = const()[name = tensor("encoder_encoders_37_self_attn_fsmn_block_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(483211904)))]; + tensor encoder_encoders_37_self_attn_linear_out_bias = const()[name = tensor("encoder_encoders_37_self_attn_linear_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(483234496)))]; + tensor encoder_encoders_37_self_attn_linear_out_weight = const()[name = tensor("encoder_encoders_37_self_attn_linear_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(483236608)))]; + tensor encoder_encoders_37_feed_forward_w_1_bias = const()[name = tensor("encoder_encoders_37_feed_forward_w_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(484285248)))]; + tensor encoder_encoders_37_feed_forward_w_1_weight = const()[name = tensor("encoder_encoders_37_feed_forward_w_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(484293504)))]; + tensor encoder_encoders_37_feed_forward_w_2_bias = const()[name = tensor("encoder_encoders_37_feed_forward_w_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(488487872)))]; + tensor encoder_encoders_37_feed_forward_w_2_weight = const()[name = tensor("encoder_encoders_37_feed_forward_w_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(488489984)))]; + tensor encoder_encoders_38_self_attn_linear_q_k_v_bias = const()[name = tensor("encoder_encoders_38_self_attn_linear_q_k_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(492684352)))]; + tensor encoder_encoders_38_self_attn_linear_q_k_v_weight = const()[name = tensor("encoder_encoders_38_self_attn_linear_q_k_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(492690560)))]; + tensor encoder_encoders_38_self_attn_fsmn_block_weight = const()[name = tensor("encoder_encoders_38_self_attn_fsmn_block_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(495836352)))]; + tensor encoder_encoders_38_self_attn_linear_out_bias = const()[name = tensor("encoder_encoders_38_self_attn_linear_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(495858944)))]; + tensor encoder_encoders_38_self_attn_linear_out_weight = const()[name = tensor("encoder_encoders_38_self_attn_linear_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(495861056)))]; + tensor encoder_encoders_38_feed_forward_w_1_bias = const()[name = tensor("encoder_encoders_38_feed_forward_w_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(496909696)))]; + tensor encoder_encoders_38_feed_forward_w_1_weight = const()[name = tensor("encoder_encoders_38_feed_forward_w_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(496917952)))]; + tensor encoder_encoders_38_feed_forward_w_2_bias = const()[name = tensor("encoder_encoders_38_feed_forward_w_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(501112320)))]; + tensor encoder_encoders_38_feed_forward_w_2_weight = const()[name = tensor("encoder_encoders_38_feed_forward_w_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(501114432)))]; + tensor encoder_encoders_39_self_attn_linear_q_k_v_bias = const()[name = tensor("encoder_encoders_39_self_attn_linear_q_k_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(505308800)))]; + tensor encoder_encoders_39_self_attn_linear_q_k_v_weight = const()[name = tensor("encoder_encoders_39_self_attn_linear_q_k_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(505315008)))]; + tensor encoder_encoders_39_self_attn_fsmn_block_weight = const()[name = tensor("encoder_encoders_39_self_attn_fsmn_block_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(508460800)))]; + tensor encoder_encoders_39_self_attn_linear_out_bias = const()[name = tensor("encoder_encoders_39_self_attn_linear_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(508483392)))]; + tensor encoder_encoders_39_self_attn_linear_out_weight = const()[name = tensor("encoder_encoders_39_self_attn_linear_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(508485504)))]; + tensor encoder_encoders_39_feed_forward_w_1_bias = const()[name = tensor("encoder_encoders_39_feed_forward_w_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(509534144)))]; + tensor encoder_encoders_39_feed_forward_w_1_weight = const()[name = tensor("encoder_encoders_39_feed_forward_w_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(509542400)))]; + tensor encoder_encoders_39_feed_forward_w_2_bias = const()[name = tensor("encoder_encoders_39_feed_forward_w_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(513736768)))]; + tensor encoder_encoders_39_feed_forward_w_2_weight = const()[name = tensor("encoder_encoders_39_feed_forward_w_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(513738880)))]; + tensor encoder_encoders_40_self_attn_linear_q_k_v_bias = const()[name = tensor("encoder_encoders_40_self_attn_linear_q_k_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(517933248)))]; + tensor encoder_encoders_40_self_attn_linear_q_k_v_weight = const()[name = tensor("encoder_encoders_40_self_attn_linear_q_k_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(517939456)))]; + tensor encoder_encoders_40_self_attn_fsmn_block_weight = const()[name = tensor("encoder_encoders_40_self_attn_fsmn_block_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(521085248)))]; + tensor encoder_encoders_40_self_attn_linear_out_bias = const()[name = tensor("encoder_encoders_40_self_attn_linear_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(521107840)))]; + tensor encoder_encoders_40_self_attn_linear_out_weight = const()[name = tensor("encoder_encoders_40_self_attn_linear_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(521109952)))]; + tensor encoder_encoders_40_feed_forward_w_1_bias = const()[name = tensor("encoder_encoders_40_feed_forward_w_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(522158592)))]; + tensor encoder_encoders_40_feed_forward_w_1_weight = const()[name = tensor("encoder_encoders_40_feed_forward_w_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(522166848)))]; + tensor encoder_encoders_40_feed_forward_w_2_bias = const()[name = tensor("encoder_encoders_40_feed_forward_w_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(526361216)))]; + tensor encoder_encoders_40_feed_forward_w_2_weight = const()[name = tensor("encoder_encoders_40_feed_forward_w_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(526363328)))]; + tensor encoder_encoders_41_self_attn_linear_q_k_v_bias = const()[name = tensor("encoder_encoders_41_self_attn_linear_q_k_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(530557696)))]; + tensor encoder_encoders_41_self_attn_linear_q_k_v_weight = const()[name = tensor("encoder_encoders_41_self_attn_linear_q_k_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(530563904)))]; + tensor encoder_encoders_41_self_attn_fsmn_block_weight = const()[name = tensor("encoder_encoders_41_self_attn_fsmn_block_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(533709696)))]; + tensor encoder_encoders_41_self_attn_linear_out_bias = const()[name = tensor("encoder_encoders_41_self_attn_linear_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(533732288)))]; + tensor encoder_encoders_41_self_attn_linear_out_weight = const()[name = tensor("encoder_encoders_41_self_attn_linear_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(533734400)))]; + tensor encoder_encoders_41_feed_forward_w_1_bias = const()[name = tensor("encoder_encoders_41_feed_forward_w_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(534783040)))]; + tensor encoder_encoders_41_feed_forward_w_1_weight = const()[name = tensor("encoder_encoders_41_feed_forward_w_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(534791296)))]; + tensor encoder_encoders_41_feed_forward_w_2_bias = const()[name = tensor("encoder_encoders_41_feed_forward_w_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(538985664)))]; + tensor encoder_encoders_41_feed_forward_w_2_weight = const()[name = tensor("encoder_encoders_41_feed_forward_w_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(538987776)))]; + tensor encoder_encoders_42_self_attn_linear_q_k_v_bias = const()[name = tensor("encoder_encoders_42_self_attn_linear_q_k_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(543182144)))]; + tensor encoder_encoders_42_self_attn_linear_q_k_v_weight = const()[name = tensor("encoder_encoders_42_self_attn_linear_q_k_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(543188352)))]; + tensor encoder_encoders_42_self_attn_fsmn_block_weight = const()[name = tensor("encoder_encoders_42_self_attn_fsmn_block_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(546334144)))]; + tensor encoder_encoders_42_self_attn_linear_out_bias = const()[name = tensor("encoder_encoders_42_self_attn_linear_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(546356736)))]; + tensor encoder_encoders_42_self_attn_linear_out_weight = const()[name = tensor("encoder_encoders_42_self_attn_linear_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(546358848)))]; + tensor encoder_encoders_42_feed_forward_w_1_bias = const()[name = tensor("encoder_encoders_42_feed_forward_w_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(547407488)))]; + tensor encoder_encoders_42_feed_forward_w_1_weight = const()[name = tensor("encoder_encoders_42_feed_forward_w_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(547415744)))]; + tensor encoder_encoders_42_feed_forward_w_2_bias = const()[name = tensor("encoder_encoders_42_feed_forward_w_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(551610112)))]; + tensor encoder_encoders_42_feed_forward_w_2_weight = const()[name = tensor("encoder_encoders_42_feed_forward_w_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(551612224)))]; + tensor encoder_encoders_43_self_attn_linear_q_k_v_bias = const()[name = tensor("encoder_encoders_43_self_attn_linear_q_k_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(555806592)))]; + tensor encoder_encoders_43_self_attn_linear_q_k_v_weight = const()[name = tensor("encoder_encoders_43_self_attn_linear_q_k_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(555812800)))]; + tensor encoder_encoders_43_self_attn_fsmn_block_weight = const()[name = tensor("encoder_encoders_43_self_attn_fsmn_block_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(558958592)))]; + tensor encoder_encoders_43_self_attn_linear_out_bias = const()[name = tensor("encoder_encoders_43_self_attn_linear_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(558981184)))]; + tensor encoder_encoders_43_self_attn_linear_out_weight = const()[name = tensor("encoder_encoders_43_self_attn_linear_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(558983296)))]; + tensor encoder_encoders_43_feed_forward_w_1_bias = const()[name = tensor("encoder_encoders_43_feed_forward_w_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(560031936)))]; + tensor encoder_encoders_43_feed_forward_w_1_weight = const()[name = tensor("encoder_encoders_43_feed_forward_w_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(560040192)))]; + tensor encoder_encoders_43_feed_forward_w_2_bias = const()[name = tensor("encoder_encoders_43_feed_forward_w_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(564234560)))]; + tensor encoder_encoders_43_feed_forward_w_2_weight = const()[name = tensor("encoder_encoders_43_feed_forward_w_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(564236672)))]; + tensor encoder_encoders_44_self_attn_linear_q_k_v_bias = const()[name = tensor("encoder_encoders_44_self_attn_linear_q_k_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(568431040)))]; + tensor encoder_encoders_44_self_attn_linear_q_k_v_weight = const()[name = tensor("encoder_encoders_44_self_attn_linear_q_k_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(568437248)))]; + tensor encoder_encoders_44_self_attn_fsmn_block_weight = const()[name = tensor("encoder_encoders_44_self_attn_fsmn_block_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(571583040)))]; + tensor encoder_encoders_44_self_attn_linear_out_bias = const()[name = tensor("encoder_encoders_44_self_attn_linear_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(571605632)))]; + tensor encoder_encoders_44_self_attn_linear_out_weight = const()[name = tensor("encoder_encoders_44_self_attn_linear_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(571607744)))]; + tensor encoder_encoders_44_feed_forward_w_1_bias = const()[name = tensor("encoder_encoders_44_feed_forward_w_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(572656384)))]; + tensor encoder_encoders_44_feed_forward_w_1_weight = const()[name = tensor("encoder_encoders_44_feed_forward_w_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(572664640)))]; + tensor encoder_encoders_44_feed_forward_w_2_bias = const()[name = tensor("encoder_encoders_44_feed_forward_w_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(576859008)))]; + tensor encoder_encoders_44_feed_forward_w_2_weight = const()[name = tensor("encoder_encoders_44_feed_forward_w_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(576861120)))]; + tensor encoder_encoders_45_self_attn_linear_q_k_v_bias = const()[name = tensor("encoder_encoders_45_self_attn_linear_q_k_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(581055488)))]; + tensor encoder_encoders_45_self_attn_linear_q_k_v_weight = const()[name = tensor("encoder_encoders_45_self_attn_linear_q_k_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(581061696)))]; + tensor encoder_encoders_45_self_attn_fsmn_block_weight = const()[name = tensor("encoder_encoders_45_self_attn_fsmn_block_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(584207488)))]; + tensor encoder_encoders_45_self_attn_linear_out_bias = const()[name = tensor("encoder_encoders_45_self_attn_linear_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(584230080)))]; + tensor encoder_encoders_45_self_attn_linear_out_weight = const()[name = tensor("encoder_encoders_45_self_attn_linear_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(584232192)))]; + tensor encoder_encoders_45_feed_forward_w_1_bias = const()[name = tensor("encoder_encoders_45_feed_forward_w_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(585280832)))]; + tensor encoder_encoders_45_feed_forward_w_1_weight = const()[name = tensor("encoder_encoders_45_feed_forward_w_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(585289088)))]; + tensor encoder_encoders_45_feed_forward_w_2_bias = const()[name = tensor("encoder_encoders_45_feed_forward_w_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(589483456)))]; + tensor encoder_encoders_45_feed_forward_w_2_weight = const()[name = tensor("encoder_encoders_45_feed_forward_w_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(589485568)))]; + tensor encoder_encoders_46_self_attn_linear_q_k_v_bias = const()[name = tensor("encoder_encoders_46_self_attn_linear_q_k_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(593679936)))]; + tensor encoder_encoders_46_self_attn_linear_q_k_v_weight = const()[name = tensor("encoder_encoders_46_self_attn_linear_q_k_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(593686144)))]; + tensor encoder_encoders_46_self_attn_fsmn_block_weight = const()[name = tensor("encoder_encoders_46_self_attn_fsmn_block_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(596831936)))]; + tensor encoder_encoders_46_self_attn_linear_out_bias = const()[name = tensor("encoder_encoders_46_self_attn_linear_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(596854528)))]; + tensor encoder_encoders_46_self_attn_linear_out_weight = const()[name = tensor("encoder_encoders_46_self_attn_linear_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(596856640)))]; + tensor encoder_encoders_46_feed_forward_w_1_bias = const()[name = tensor("encoder_encoders_46_feed_forward_w_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(597905280)))]; + tensor encoder_encoders_46_feed_forward_w_1_weight = const()[name = tensor("encoder_encoders_46_feed_forward_w_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(597913536)))]; + tensor encoder_encoders_46_feed_forward_w_2_bias = const()[name = tensor("encoder_encoders_46_feed_forward_w_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(602107904)))]; + tensor encoder_encoders_46_feed_forward_w_2_weight = const()[name = tensor("encoder_encoders_46_feed_forward_w_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(602110016)))]; + tensor encoder_encoders_47_self_attn_linear_q_k_v_bias = const()[name = tensor("encoder_encoders_47_self_attn_linear_q_k_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(606304384)))]; + tensor encoder_encoders_47_self_attn_linear_q_k_v_weight = const()[name = tensor("encoder_encoders_47_self_attn_linear_q_k_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(606310592)))]; + tensor encoder_encoders_47_self_attn_fsmn_block_weight = const()[name = tensor("encoder_encoders_47_self_attn_fsmn_block_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(609456384)))]; + tensor encoder_encoders_47_self_attn_linear_out_bias = const()[name = tensor("encoder_encoders_47_self_attn_linear_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(609478976)))]; + tensor encoder_encoders_47_self_attn_linear_out_weight = const()[name = tensor("encoder_encoders_47_self_attn_linear_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(609481088)))]; + tensor encoder_encoders_47_feed_forward_w_1_bias = const()[name = tensor("encoder_encoders_47_feed_forward_w_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(610529728)))]; + tensor encoder_encoders_47_feed_forward_w_1_weight = const()[name = tensor("encoder_encoders_47_feed_forward_w_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(610537984)))]; + tensor encoder_encoders_47_feed_forward_w_2_bias = const()[name = tensor("encoder_encoders_47_feed_forward_w_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(614732352)))]; + tensor encoder_encoders_47_feed_forward_w_2_weight = const()[name = tensor("encoder_encoders_47_feed_forward_w_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(614734464)))]; + tensor encoder_encoders_48_self_attn_linear_q_k_v_bias = const()[name = tensor("encoder_encoders_48_self_attn_linear_q_k_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(618928832)))]; + tensor encoder_encoders_48_self_attn_linear_q_k_v_weight = const()[name = tensor("encoder_encoders_48_self_attn_linear_q_k_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(618935040)))]; + tensor encoder_encoders_48_self_attn_fsmn_block_weight = const()[name = tensor("encoder_encoders_48_self_attn_fsmn_block_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(622080832)))]; + tensor encoder_encoders_48_self_attn_linear_out_bias = const()[name = tensor("encoder_encoders_48_self_attn_linear_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(622103424)))]; + tensor encoder_encoders_48_self_attn_linear_out_weight = const()[name = tensor("encoder_encoders_48_self_attn_linear_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(622105536)))]; + tensor encoder_encoders_48_feed_forward_w_1_bias = const()[name = tensor("encoder_encoders_48_feed_forward_w_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(623154176)))]; + tensor encoder_encoders_48_feed_forward_w_1_weight = const()[name = tensor("encoder_encoders_48_feed_forward_w_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(623162432)))]; + tensor encoder_encoders_48_feed_forward_w_2_bias = const()[name = tensor("encoder_encoders_48_feed_forward_w_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(627356800)))]; + tensor encoder_encoders_48_feed_forward_w_2_weight = const()[name = tensor("encoder_encoders_48_feed_forward_w_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(627358912)))]; + tensor encoder_tp_encoders_0_self_attn_linear_q_k_v_bias = const()[name = tensor("encoder_tp_encoders_0_self_attn_linear_q_k_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(631553280)))]; + tensor encoder_tp_encoders_0_self_attn_linear_q_k_v_weight = const()[name = tensor("encoder_tp_encoders_0_self_attn_linear_q_k_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(631559488)))]; + tensor encoder_tp_encoders_0_self_attn_fsmn_block_weight = const()[name = tensor("encoder_tp_encoders_0_self_attn_fsmn_block_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(634705280)))]; + tensor encoder_tp_encoders_0_self_attn_linear_out_bias = const()[name = tensor("encoder_tp_encoders_0_self_attn_linear_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(634727872)))]; + tensor encoder_tp_encoders_0_self_attn_linear_out_weight = const()[name = tensor("encoder_tp_encoders_0_self_attn_linear_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(634729984)))]; + tensor encoder_tp_encoders_0_feed_forward_w_1_bias = const()[name = tensor("encoder_tp_encoders_0_feed_forward_w_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(635778624)))]; + tensor encoder_tp_encoders_0_feed_forward_w_1_weight = const()[name = tensor("encoder_tp_encoders_0_feed_forward_w_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(635786880)))]; + tensor encoder_tp_encoders_0_feed_forward_w_2_bias = const()[name = tensor("encoder_tp_encoders_0_feed_forward_w_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(639981248)))]; + tensor encoder_tp_encoders_0_feed_forward_w_2_weight = const()[name = tensor("encoder_tp_encoders_0_feed_forward_w_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(639983360)))]; + tensor encoder_tp_encoders_1_self_attn_linear_q_k_v_bias = const()[name = tensor("encoder_tp_encoders_1_self_attn_linear_q_k_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(644177728)))]; + tensor encoder_tp_encoders_1_self_attn_linear_q_k_v_weight = const()[name = tensor("encoder_tp_encoders_1_self_attn_linear_q_k_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(644183936)))]; + tensor encoder_tp_encoders_1_self_attn_fsmn_block_weight = const()[name = tensor("encoder_tp_encoders_1_self_attn_fsmn_block_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(647329728)))]; + tensor encoder_tp_encoders_1_self_attn_linear_out_bias = const()[name = tensor("encoder_tp_encoders_1_self_attn_linear_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(647352320)))]; + tensor encoder_tp_encoders_1_self_attn_linear_out_weight = const()[name = tensor("encoder_tp_encoders_1_self_attn_linear_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(647354432)))]; + tensor encoder_tp_encoders_1_feed_forward_w_1_bias = const()[name = tensor("encoder_tp_encoders_1_feed_forward_w_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(648403072)))]; + tensor encoder_tp_encoders_1_feed_forward_w_1_weight = const()[name = tensor("encoder_tp_encoders_1_feed_forward_w_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(648411328)))]; + tensor encoder_tp_encoders_1_feed_forward_w_2_bias = const()[name = tensor("encoder_tp_encoders_1_feed_forward_w_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(652605696)))]; + tensor encoder_tp_encoders_1_feed_forward_w_2_weight = const()[name = tensor("encoder_tp_encoders_1_feed_forward_w_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(652607808)))]; + tensor encoder_tp_encoders_2_self_attn_linear_q_k_v_bias = const()[name = tensor("encoder_tp_encoders_2_self_attn_linear_q_k_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(656802176)))]; + tensor encoder_tp_encoders_2_self_attn_linear_q_k_v_weight = const()[name = tensor("encoder_tp_encoders_2_self_attn_linear_q_k_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(656808384)))]; + tensor encoder_tp_encoders_2_self_attn_fsmn_block_weight = const()[name = tensor("encoder_tp_encoders_2_self_attn_fsmn_block_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(659954176)))]; + tensor encoder_tp_encoders_2_self_attn_linear_out_bias = const()[name = tensor("encoder_tp_encoders_2_self_attn_linear_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(659976768)))]; + tensor encoder_tp_encoders_2_self_attn_linear_out_weight = const()[name = tensor("encoder_tp_encoders_2_self_attn_linear_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(659978880)))]; + tensor encoder_tp_encoders_2_feed_forward_w_1_bias = const()[name = tensor("encoder_tp_encoders_2_feed_forward_w_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(661027520)))]; + tensor encoder_tp_encoders_2_feed_forward_w_1_weight = const()[name = tensor("encoder_tp_encoders_2_feed_forward_w_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(661035776)))]; + tensor encoder_tp_encoders_2_feed_forward_w_2_bias = const()[name = tensor("encoder_tp_encoders_2_feed_forward_w_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(665230144)))]; + tensor encoder_tp_encoders_2_feed_forward_w_2_weight = const()[name = tensor("encoder_tp_encoders_2_feed_forward_w_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(665232256)))]; + tensor encoder_tp_encoders_3_self_attn_linear_q_k_v_bias = const()[name = tensor("encoder_tp_encoders_3_self_attn_linear_q_k_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(669426624)))]; + tensor encoder_tp_encoders_3_self_attn_linear_q_k_v_weight = const()[name = tensor("encoder_tp_encoders_3_self_attn_linear_q_k_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(669432832)))]; + tensor encoder_tp_encoders_3_self_attn_fsmn_block_weight = const()[name = tensor("encoder_tp_encoders_3_self_attn_fsmn_block_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(672578624)))]; + tensor encoder_tp_encoders_3_self_attn_linear_out_bias = const()[name = tensor("encoder_tp_encoders_3_self_attn_linear_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(672601216)))]; + tensor encoder_tp_encoders_3_self_attn_linear_out_weight = const()[name = tensor("encoder_tp_encoders_3_self_attn_linear_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(672603328)))]; + tensor encoder_tp_encoders_3_feed_forward_w_1_bias = const()[name = tensor("encoder_tp_encoders_3_feed_forward_w_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(673651968)))]; + tensor encoder_tp_encoders_3_feed_forward_w_1_weight = const()[name = tensor("encoder_tp_encoders_3_feed_forward_w_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(673660224)))]; + tensor encoder_tp_encoders_3_feed_forward_w_2_bias = const()[name = tensor("encoder_tp_encoders_3_feed_forward_w_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(677854592)))]; + tensor encoder_tp_encoders_3_feed_forward_w_2_weight = const()[name = tensor("encoder_tp_encoders_3_feed_forward_w_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(677856704)))]; + tensor encoder_tp_encoders_4_self_attn_linear_q_k_v_bias = const()[name = tensor("encoder_tp_encoders_4_self_attn_linear_q_k_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(682051072)))]; + tensor encoder_tp_encoders_4_self_attn_linear_q_k_v_weight = const()[name = tensor("encoder_tp_encoders_4_self_attn_linear_q_k_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(682057280)))]; + tensor encoder_tp_encoders_4_self_attn_fsmn_block_weight = const()[name = tensor("encoder_tp_encoders_4_self_attn_fsmn_block_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(685203072)))]; + tensor encoder_tp_encoders_4_self_attn_linear_out_bias = const()[name = tensor("encoder_tp_encoders_4_self_attn_linear_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(685225664)))]; + tensor encoder_tp_encoders_4_self_attn_linear_out_weight = const()[name = tensor("encoder_tp_encoders_4_self_attn_linear_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(685227776)))]; + tensor encoder_tp_encoders_4_feed_forward_w_1_bias = const()[name = tensor("encoder_tp_encoders_4_feed_forward_w_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(686276416)))]; + tensor encoder_tp_encoders_4_feed_forward_w_1_weight = const()[name = tensor("encoder_tp_encoders_4_feed_forward_w_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(686284672)))]; + tensor encoder_tp_encoders_4_feed_forward_w_2_bias = const()[name = tensor("encoder_tp_encoders_4_feed_forward_w_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(690479040)))]; + tensor encoder_tp_encoders_4_feed_forward_w_2_weight = const()[name = tensor("encoder_tp_encoders_4_feed_forward_w_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(690481152)))]; + tensor encoder_tp_encoders_5_self_attn_linear_q_k_v_bias = const()[name = tensor("encoder_tp_encoders_5_self_attn_linear_q_k_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(694675520)))]; + tensor encoder_tp_encoders_5_self_attn_linear_q_k_v_weight = const()[name = tensor("encoder_tp_encoders_5_self_attn_linear_q_k_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(694681728)))]; + tensor encoder_tp_encoders_5_self_attn_fsmn_block_weight = const()[name = tensor("encoder_tp_encoders_5_self_attn_fsmn_block_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(697827520)))]; + tensor encoder_tp_encoders_5_self_attn_linear_out_bias = const()[name = tensor("encoder_tp_encoders_5_self_attn_linear_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(697850112)))]; + tensor encoder_tp_encoders_5_self_attn_linear_out_weight = const()[name = tensor("encoder_tp_encoders_5_self_attn_linear_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(697852224)))]; + tensor encoder_tp_encoders_5_feed_forward_w_1_bias = const()[name = tensor("encoder_tp_encoders_5_feed_forward_w_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(698900864)))]; + tensor encoder_tp_encoders_5_feed_forward_w_1_weight = const()[name = tensor("encoder_tp_encoders_5_feed_forward_w_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(698909120)))]; + tensor encoder_tp_encoders_5_feed_forward_w_2_bias = const()[name = tensor("encoder_tp_encoders_5_feed_forward_w_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(703103488)))]; + tensor encoder_tp_encoders_5_feed_forward_w_2_weight = const()[name = tensor("encoder_tp_encoders_5_feed_forward_w_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(703105600)))]; + tensor encoder_tp_encoders_6_self_attn_linear_q_k_v_bias = const()[name = tensor("encoder_tp_encoders_6_self_attn_linear_q_k_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(707299968)))]; + tensor encoder_tp_encoders_6_self_attn_linear_q_k_v_weight = const()[name = tensor("encoder_tp_encoders_6_self_attn_linear_q_k_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(707306176)))]; + tensor encoder_tp_encoders_6_self_attn_fsmn_block_weight = const()[name = tensor("encoder_tp_encoders_6_self_attn_fsmn_block_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(710451968)))]; + tensor encoder_tp_encoders_6_self_attn_linear_out_bias = const()[name = tensor("encoder_tp_encoders_6_self_attn_linear_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(710474560)))]; + tensor encoder_tp_encoders_6_self_attn_linear_out_weight = const()[name = tensor("encoder_tp_encoders_6_self_attn_linear_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(710476672)))]; + tensor encoder_tp_encoders_6_feed_forward_w_1_bias = const()[name = tensor("encoder_tp_encoders_6_feed_forward_w_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(711525312)))]; + tensor encoder_tp_encoders_6_feed_forward_w_1_weight = const()[name = tensor("encoder_tp_encoders_6_feed_forward_w_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(711533568)))]; + tensor encoder_tp_encoders_6_feed_forward_w_2_bias = const()[name = tensor("encoder_tp_encoders_6_feed_forward_w_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(715727936)))]; + tensor encoder_tp_encoders_6_feed_forward_w_2_weight = const()[name = tensor("encoder_tp_encoders_6_feed_forward_w_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(715730048)))]; + tensor encoder_tp_encoders_7_self_attn_linear_q_k_v_bias = const()[name = tensor("encoder_tp_encoders_7_self_attn_linear_q_k_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(719924416)))]; + tensor encoder_tp_encoders_7_self_attn_linear_q_k_v_weight = const()[name = tensor("encoder_tp_encoders_7_self_attn_linear_q_k_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(719930624)))]; + tensor encoder_tp_encoders_7_self_attn_fsmn_block_weight = const()[name = tensor("encoder_tp_encoders_7_self_attn_fsmn_block_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(723076416)))]; + tensor encoder_tp_encoders_7_self_attn_linear_out_bias = const()[name = tensor("encoder_tp_encoders_7_self_attn_linear_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(723099008)))]; + tensor encoder_tp_encoders_7_self_attn_linear_out_weight = const()[name = tensor("encoder_tp_encoders_7_self_attn_linear_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(723101120)))]; + tensor encoder_tp_encoders_7_feed_forward_w_1_bias = const()[name = tensor("encoder_tp_encoders_7_feed_forward_w_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(724149760)))]; + tensor encoder_tp_encoders_7_feed_forward_w_1_weight = const()[name = tensor("encoder_tp_encoders_7_feed_forward_w_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(724158016)))]; + tensor encoder_tp_encoders_7_feed_forward_w_2_bias = const()[name = tensor("encoder_tp_encoders_7_feed_forward_w_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(728352384)))]; + tensor encoder_tp_encoders_7_feed_forward_w_2_weight = const()[name = tensor("encoder_tp_encoders_7_feed_forward_w_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(728354496)))]; + tensor encoder_tp_encoders_8_self_attn_linear_q_k_v_bias = const()[name = tensor("encoder_tp_encoders_8_self_attn_linear_q_k_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(732548864)))]; + tensor encoder_tp_encoders_8_self_attn_linear_q_k_v_weight = const()[name = tensor("encoder_tp_encoders_8_self_attn_linear_q_k_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(732555072)))]; + tensor encoder_tp_encoders_8_self_attn_fsmn_block_weight = const()[name = tensor("encoder_tp_encoders_8_self_attn_fsmn_block_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(735700864)))]; + tensor encoder_tp_encoders_8_self_attn_linear_out_bias = const()[name = tensor("encoder_tp_encoders_8_self_attn_linear_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(735723456)))]; + tensor encoder_tp_encoders_8_self_attn_linear_out_weight = const()[name = tensor("encoder_tp_encoders_8_self_attn_linear_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(735725568)))]; + tensor encoder_tp_encoders_8_feed_forward_w_1_bias = const()[name = tensor("encoder_tp_encoders_8_feed_forward_w_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(736774208)))]; + tensor encoder_tp_encoders_8_feed_forward_w_1_weight = const()[name = tensor("encoder_tp_encoders_8_feed_forward_w_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(736782464)))]; + tensor encoder_tp_encoders_8_feed_forward_w_2_bias = const()[name = tensor("encoder_tp_encoders_8_feed_forward_w_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(740976832)))]; + tensor encoder_tp_encoders_8_feed_forward_w_2_weight = const()[name = tensor("encoder_tp_encoders_8_feed_forward_w_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(740978944)))]; + tensor encoder_tp_encoders_9_self_attn_linear_q_k_v_bias = const()[name = tensor("encoder_tp_encoders_9_self_attn_linear_q_k_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(745173312)))]; + tensor encoder_tp_encoders_9_self_attn_linear_q_k_v_weight = const()[name = tensor("encoder_tp_encoders_9_self_attn_linear_q_k_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(745179520)))]; + tensor encoder_tp_encoders_9_self_attn_fsmn_block_weight = const()[name = tensor("encoder_tp_encoders_9_self_attn_fsmn_block_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(748325312)))]; + tensor encoder_tp_encoders_9_self_attn_linear_out_bias = const()[name = tensor("encoder_tp_encoders_9_self_attn_linear_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(748347904)))]; + tensor encoder_tp_encoders_9_self_attn_linear_out_weight = const()[name = tensor("encoder_tp_encoders_9_self_attn_linear_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(748350016)))]; + tensor encoder_tp_encoders_9_feed_forward_w_1_bias = const()[name = tensor("encoder_tp_encoders_9_feed_forward_w_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(749398656)))]; + tensor encoder_tp_encoders_9_feed_forward_w_1_weight = const()[name = tensor("encoder_tp_encoders_9_feed_forward_w_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(749406912)))]; + tensor encoder_tp_encoders_9_feed_forward_w_2_bias = const()[name = tensor("encoder_tp_encoders_9_feed_forward_w_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(753601280)))]; + tensor encoder_tp_encoders_9_feed_forward_w_2_weight = const()[name = tensor("encoder_tp_encoders_9_feed_forward_w_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(753603392)))]; + tensor encoder_tp_encoders_10_self_attn_linear_q_k_v_bias = const()[name = tensor("encoder_tp_encoders_10_self_attn_linear_q_k_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(757797760)))]; + tensor encoder_tp_encoders_10_self_attn_linear_q_k_v_weight = const()[name = tensor("encoder_tp_encoders_10_self_attn_linear_q_k_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(757803968)))]; + tensor encoder_tp_encoders_10_self_attn_fsmn_block_weight = const()[name = tensor("encoder_tp_encoders_10_self_attn_fsmn_block_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(760949760)))]; + tensor encoder_tp_encoders_10_self_attn_linear_out_bias = const()[name = tensor("encoder_tp_encoders_10_self_attn_linear_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(760972352)))]; + tensor encoder_tp_encoders_10_self_attn_linear_out_weight = const()[name = tensor("encoder_tp_encoders_10_self_attn_linear_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(760974464)))]; + tensor encoder_tp_encoders_10_feed_forward_w_1_bias = const()[name = tensor("encoder_tp_encoders_10_feed_forward_w_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(762023104)))]; + tensor encoder_tp_encoders_10_feed_forward_w_1_weight = const()[name = tensor("encoder_tp_encoders_10_feed_forward_w_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(762031360)))]; + tensor encoder_tp_encoders_10_feed_forward_w_2_bias = const()[name = tensor("encoder_tp_encoders_10_feed_forward_w_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(766225728)))]; + tensor encoder_tp_encoders_10_feed_forward_w_2_weight = const()[name = tensor("encoder_tp_encoders_10_feed_forward_w_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(766227840)))]; + tensor encoder_tp_encoders_11_self_attn_linear_q_k_v_bias = const()[name = tensor("encoder_tp_encoders_11_self_attn_linear_q_k_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(770422208)))]; + tensor encoder_tp_encoders_11_self_attn_linear_q_k_v_weight = const()[name = tensor("encoder_tp_encoders_11_self_attn_linear_q_k_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(770428416)))]; + tensor encoder_tp_encoders_11_self_attn_fsmn_block_weight = const()[name = tensor("encoder_tp_encoders_11_self_attn_fsmn_block_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(773574208)))]; + tensor encoder_tp_encoders_11_self_attn_linear_out_bias = const()[name = tensor("encoder_tp_encoders_11_self_attn_linear_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(773596800)))]; + tensor encoder_tp_encoders_11_self_attn_linear_out_weight = const()[name = tensor("encoder_tp_encoders_11_self_attn_linear_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(773598912)))]; + tensor encoder_tp_encoders_11_feed_forward_w_1_bias = const()[name = tensor("encoder_tp_encoders_11_feed_forward_w_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(774647552)))]; + tensor encoder_tp_encoders_11_feed_forward_w_1_weight = const()[name = tensor("encoder_tp_encoders_11_feed_forward_w_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(774655808)))]; + tensor encoder_tp_encoders_11_feed_forward_w_2_bias = const()[name = tensor("encoder_tp_encoders_11_feed_forward_w_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(778850176)))]; + tensor encoder_tp_encoders_11_feed_forward_w_2_weight = const()[name = tensor("encoder_tp_encoders_11_feed_forward_w_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(778852288)))]; + tensor encoder_tp_encoders_12_self_attn_linear_q_k_v_bias = const()[name = tensor("encoder_tp_encoders_12_self_attn_linear_q_k_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(783046656)))]; + tensor encoder_tp_encoders_12_self_attn_linear_q_k_v_weight = const()[name = tensor("encoder_tp_encoders_12_self_attn_linear_q_k_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(783052864)))]; + tensor encoder_tp_encoders_12_self_attn_fsmn_block_weight = const()[name = tensor("encoder_tp_encoders_12_self_attn_fsmn_block_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(786198656)))]; + tensor encoder_tp_encoders_12_self_attn_linear_out_bias = const()[name = tensor("encoder_tp_encoders_12_self_attn_linear_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(786221248)))]; + tensor encoder_tp_encoders_12_self_attn_linear_out_weight = const()[name = tensor("encoder_tp_encoders_12_self_attn_linear_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(786223360)))]; + tensor encoder_tp_encoders_12_feed_forward_w_1_bias = const()[name = tensor("encoder_tp_encoders_12_feed_forward_w_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(787272000)))]; + tensor encoder_tp_encoders_12_feed_forward_w_1_weight = const()[name = tensor("encoder_tp_encoders_12_feed_forward_w_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(787280256)))]; + tensor encoder_tp_encoders_12_feed_forward_w_2_bias = const()[name = tensor("encoder_tp_encoders_12_feed_forward_w_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(791474624)))]; + tensor encoder_tp_encoders_12_feed_forward_w_2_weight = const()[name = tensor("encoder_tp_encoders_12_feed_forward_w_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(791476736)))]; + tensor encoder_tp_encoders_13_self_attn_linear_q_k_v_bias = const()[name = tensor("encoder_tp_encoders_13_self_attn_linear_q_k_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(795671104)))]; + tensor encoder_tp_encoders_13_self_attn_linear_q_k_v_weight = const()[name = tensor("encoder_tp_encoders_13_self_attn_linear_q_k_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(795677312)))]; + tensor encoder_tp_encoders_13_self_attn_fsmn_block_weight = const()[name = tensor("encoder_tp_encoders_13_self_attn_fsmn_block_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(798823104)))]; + tensor encoder_tp_encoders_13_self_attn_linear_out_bias = const()[name = tensor("encoder_tp_encoders_13_self_attn_linear_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(798845696)))]; + tensor encoder_tp_encoders_13_self_attn_linear_out_weight = const()[name = tensor("encoder_tp_encoders_13_self_attn_linear_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(798847808)))]; + tensor encoder_tp_encoders_13_feed_forward_w_1_bias = const()[name = tensor("encoder_tp_encoders_13_feed_forward_w_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(799896448)))]; + tensor encoder_tp_encoders_13_feed_forward_w_1_weight = const()[name = tensor("encoder_tp_encoders_13_feed_forward_w_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(799904704)))]; + tensor encoder_tp_encoders_13_feed_forward_w_2_bias = const()[name = tensor("encoder_tp_encoders_13_feed_forward_w_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(804099072)))]; + tensor encoder_tp_encoders_13_feed_forward_w_2_weight = const()[name = tensor("encoder_tp_encoders_13_feed_forward_w_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(804101184)))]; + tensor encoder_tp_encoders_14_self_attn_linear_q_k_v_bias = const()[name = tensor("encoder_tp_encoders_14_self_attn_linear_q_k_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(808295552)))]; + tensor encoder_tp_encoders_14_self_attn_linear_q_k_v_weight = const()[name = tensor("encoder_tp_encoders_14_self_attn_linear_q_k_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(808301760)))]; + tensor encoder_tp_encoders_14_self_attn_fsmn_block_weight = const()[name = tensor("encoder_tp_encoders_14_self_attn_fsmn_block_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(811447552)))]; + tensor encoder_tp_encoders_14_self_attn_linear_out_bias = const()[name = tensor("encoder_tp_encoders_14_self_attn_linear_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(811470144)))]; + tensor encoder_tp_encoders_14_self_attn_linear_out_weight = const()[name = tensor("encoder_tp_encoders_14_self_attn_linear_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(811472256)))]; + tensor encoder_tp_encoders_14_feed_forward_w_1_bias = const()[name = tensor("encoder_tp_encoders_14_feed_forward_w_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(812520896)))]; + tensor encoder_tp_encoders_14_feed_forward_w_1_weight = const()[name = tensor("encoder_tp_encoders_14_feed_forward_w_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(812529152)))]; + tensor encoder_tp_encoders_14_feed_forward_w_2_bias = const()[name = tensor("encoder_tp_encoders_14_feed_forward_w_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(816723520)))]; + tensor encoder_tp_encoders_14_feed_forward_w_2_weight = const()[name = tensor("encoder_tp_encoders_14_feed_forward_w_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(816725632)))]; + tensor encoder_tp_encoders_15_self_attn_linear_q_k_v_bias = const()[name = tensor("encoder_tp_encoders_15_self_attn_linear_q_k_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(820920000)))]; + tensor encoder_tp_encoders_15_self_attn_linear_q_k_v_weight = const()[name = tensor("encoder_tp_encoders_15_self_attn_linear_q_k_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(820926208)))]; + tensor encoder_tp_encoders_15_self_attn_fsmn_block_weight = const()[name = tensor("encoder_tp_encoders_15_self_attn_fsmn_block_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(824072000)))]; + tensor encoder_tp_encoders_15_self_attn_linear_out_bias = const()[name = tensor("encoder_tp_encoders_15_self_attn_linear_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(824094592)))]; + tensor encoder_tp_encoders_15_self_attn_linear_out_weight = const()[name = tensor("encoder_tp_encoders_15_self_attn_linear_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(824096704)))]; + tensor encoder_tp_encoders_15_feed_forward_w_1_bias = const()[name = tensor("encoder_tp_encoders_15_feed_forward_w_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(825145344)))]; + tensor encoder_tp_encoders_15_feed_forward_w_1_weight = const()[name = tensor("encoder_tp_encoders_15_feed_forward_w_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(825153600)))]; + tensor encoder_tp_encoders_15_feed_forward_w_2_bias = const()[name = tensor("encoder_tp_encoders_15_feed_forward_w_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(829347968)))]; + tensor encoder_tp_encoders_15_feed_forward_w_2_weight = const()[name = tensor("encoder_tp_encoders_15_feed_forward_w_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(829350080)))]; + tensor encoder_tp_encoders_16_self_attn_linear_q_k_v_bias = const()[name = tensor("encoder_tp_encoders_16_self_attn_linear_q_k_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(833544448)))]; + tensor encoder_tp_encoders_16_self_attn_linear_q_k_v_weight = const()[name = tensor("encoder_tp_encoders_16_self_attn_linear_q_k_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(833550656)))]; + tensor encoder_tp_encoders_16_self_attn_fsmn_block_weight = const()[name = tensor("encoder_tp_encoders_16_self_attn_fsmn_block_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(836696448)))]; + tensor encoder_tp_encoders_16_self_attn_linear_out_bias = const()[name = tensor("encoder_tp_encoders_16_self_attn_linear_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(836719040)))]; + tensor encoder_tp_encoders_16_self_attn_linear_out_weight = const()[name = tensor("encoder_tp_encoders_16_self_attn_linear_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(836721152)))]; + tensor encoder_tp_encoders_16_feed_forward_w_1_bias = const()[name = tensor("encoder_tp_encoders_16_feed_forward_w_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(837769792)))]; + tensor encoder_tp_encoders_16_feed_forward_w_1_weight = const()[name = tensor("encoder_tp_encoders_16_feed_forward_w_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(837778048)))]; + tensor encoder_tp_encoders_16_feed_forward_w_2_bias = const()[name = tensor("encoder_tp_encoders_16_feed_forward_w_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(841972416)))]; + tensor encoder_tp_encoders_16_feed_forward_w_2_weight = const()[name = tensor("encoder_tp_encoders_16_feed_forward_w_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(841974528)))]; + tensor encoder_tp_encoders_17_self_attn_linear_q_k_v_bias = const()[name = tensor("encoder_tp_encoders_17_self_attn_linear_q_k_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(846168896)))]; + tensor encoder_tp_encoders_17_self_attn_linear_q_k_v_weight = const()[name = tensor("encoder_tp_encoders_17_self_attn_linear_q_k_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(846175104)))]; + tensor encoder_tp_encoders_17_self_attn_fsmn_block_weight = const()[name = tensor("encoder_tp_encoders_17_self_attn_fsmn_block_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(849320896)))]; + tensor encoder_tp_encoders_17_self_attn_linear_out_bias = const()[name = tensor("encoder_tp_encoders_17_self_attn_linear_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(849343488)))]; + tensor encoder_tp_encoders_17_self_attn_linear_out_weight = const()[name = tensor("encoder_tp_encoders_17_self_attn_linear_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(849345600)))]; + tensor encoder_tp_encoders_17_feed_forward_w_1_bias = const()[name = tensor("encoder_tp_encoders_17_feed_forward_w_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(850394240)))]; + tensor encoder_tp_encoders_17_feed_forward_w_1_weight = const()[name = tensor("encoder_tp_encoders_17_feed_forward_w_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(850402496)))]; + tensor encoder_tp_encoders_17_feed_forward_w_2_bias = const()[name = tensor("encoder_tp_encoders_17_feed_forward_w_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(854596864)))]; + tensor encoder_tp_encoders_17_feed_forward_w_2_weight = const()[name = tensor("encoder_tp_encoders_17_feed_forward_w_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(854598976)))]; + tensor encoder_tp_encoders_18_self_attn_linear_q_k_v_bias = const()[name = tensor("encoder_tp_encoders_18_self_attn_linear_q_k_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(858793344)))]; + tensor encoder_tp_encoders_18_self_attn_linear_q_k_v_weight = const()[name = tensor("encoder_tp_encoders_18_self_attn_linear_q_k_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(858799552)))]; + tensor encoder_tp_encoders_18_self_attn_fsmn_block_weight = const()[name = tensor("encoder_tp_encoders_18_self_attn_fsmn_block_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(861945344)))]; + tensor encoder_tp_encoders_18_self_attn_linear_out_bias = const()[name = tensor("encoder_tp_encoders_18_self_attn_linear_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(861967936)))]; + tensor encoder_tp_encoders_18_self_attn_linear_out_weight = const()[name = tensor("encoder_tp_encoders_18_self_attn_linear_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(861970048)))]; + tensor encoder_tp_encoders_18_feed_forward_w_1_bias = const()[name = tensor("encoder_tp_encoders_18_feed_forward_w_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(863018688)))]; + tensor encoder_tp_encoders_18_feed_forward_w_1_weight = const()[name = tensor("encoder_tp_encoders_18_feed_forward_w_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(863026944)))]; + tensor encoder_tp_encoders_18_feed_forward_w_2_bias = const()[name = tensor("encoder_tp_encoders_18_feed_forward_w_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(867221312)))]; + tensor encoder_tp_encoders_18_feed_forward_w_2_weight = const()[name = tensor("encoder_tp_encoders_18_feed_forward_w_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(867223424)))]; + tensor encoder_tp_encoders_19_self_attn_linear_q_k_v_bias = const()[name = tensor("encoder_tp_encoders_19_self_attn_linear_q_k_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(871417792)))]; + tensor encoder_tp_encoders_19_self_attn_linear_q_k_v_weight = const()[name = tensor("encoder_tp_encoders_19_self_attn_linear_q_k_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(871424000)))]; + tensor encoder_tp_encoders_19_self_attn_fsmn_block_weight = const()[name = tensor("encoder_tp_encoders_19_self_attn_fsmn_block_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(874569792)))]; + tensor encoder_tp_encoders_19_self_attn_linear_out_bias = const()[name = tensor("encoder_tp_encoders_19_self_attn_linear_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(874592384)))]; + tensor encoder_tp_encoders_19_self_attn_linear_out_weight = const()[name = tensor("encoder_tp_encoders_19_self_attn_linear_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(874594496)))]; + tensor encoder_tp_encoders_19_feed_forward_w_1_bias = const()[name = tensor("encoder_tp_encoders_19_feed_forward_w_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(875643136)))]; + tensor encoder_tp_encoders_19_feed_forward_w_1_weight = const()[name = tensor("encoder_tp_encoders_19_feed_forward_w_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(875651392)))]; + tensor encoder_tp_encoders_19_feed_forward_w_2_bias = const()[name = tensor("encoder_tp_encoders_19_feed_forward_w_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(879845760)))]; + tensor encoder_tp_encoders_19_feed_forward_w_2_weight = const()[name = tensor("encoder_tp_encoders_19_feed_forward_w_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(879847872)))]; + tensor ctc_lo_bias = const()[name = tensor("ctc_lo_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(884042240)))]; + tensor ctc_lo_weight = const()[name = tensor("ctc_lo_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(884142528)))]; + tensor var_16 = const()[name = tensor("op_16"), val = tensor([1, 1])]; + tensor input_1 = reshape(shape = var_16, x = language)[name = tensor("input_1")]; + tensor lang_q_axis_0 = const()[name = tensor("lang_q_axis_0"), val = tensor(0)]; + tensor lang_q_batch_dims_0 = const()[name = tensor("lang_q_batch_dims_0"), val = tensor(0)]; + tensor lang_q_validate_indices_0 = const()[name = tensor("lang_q_validate_indices_0"), val = tensor(false)]; + tensor lang_q = gather(axis = lang_q_axis_0, batch_dims = lang_q_batch_dims_0, indices = input_1, validate_indices = lang_q_validate_indices_0, x = embed_weight)[name = tensor("lang_q")]; + tensor var_29 = const()[name = tensor("op_29"), val = tensor([1, 1])]; + tensor input_3 = reshape(shape = var_29, x = textnorm)[name = tensor("input_3")]; + tensor style_q_axis_0 = const()[name = tensor("style_q_axis_0"), val = tensor(0)]; + tensor style_q_batch_dims_0 = const()[name = tensor("style_q_batch_dims_0"), val = tensor(0)]; + tensor style_q_validate_indices_0 = const()[name = tensor("style_q_validate_indices_0"), val = tensor(false)]; + tensor style_q = gather(axis = style_q_axis_0, batch_dims = style_q_batch_dims_0, indices = input_3, validate_indices = style_q_validate_indices_0, x = embed_weight)[name = tensor("style_q")]; + tensor event_q = const()[name = tensor("event_q"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(935455232)))]; + tensor var_40 = const()[name = tensor("op_40"), val = tensor(1)]; + tensor xs_pad_interleave_0 = const()[name = tensor("xs_pad_interleave_0"), val = tensor(false)]; + tensor xs_pad = concat(axis = var_40, interleave = xs_pad_interleave_0, values = (lang_q, event_q, style_q, speech))[name = tensor("xs_pad")]; + tensor var_43 = const()[name = tensor("op_43"), val = tensor(4)]; + tensor lengths = add(x = speech_lengths, y = var_43)[name = tensor("lengths")]; + tensor var_46 = const()[name = tensor("op_46"), val = tensor(0x1.4f8b58p-17)]; + tensor var_48 = const()[name = tensor("op_48"), val = tensor(-0x1.ff933cp+127)]; + tensor var_53 = const()[name = tensor("op_53"), val = tensor(0x0p+0)]; + tensor var_57 = const()[name = tensor("op_57"), val = tensor(0x1.6a09e6p+4)]; + tensor var_61 = const()[name = tensor("op_61"), val = tensor(-1)]; + tensor const_1 = const()[name = tensor("const_1"), val = tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 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1401, 1402, 1403, 1404, 1405, 1406, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1415, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426, 1427, 1428, 1429, 1430, 1431, 1432, 1433, 1434, 1435, 1436, 1437, 1438, 1439, 1440, 1441, 1442, 1443, 1444, 1445, 1446, 1447, 1448, 1449, 1450, 1451, 1452, 1453, 1454, 1455, 1456, 1457, 1458, 1459, 1460, 1461, 1462, 1463, 1464, 1465, 1466, 1467, 1468, 1469, 1470, 1471, 1472, 1473, 1474, 1475, 1476, 1477, 1478, 1479, 1480, 1481, 1482, 1483, 1484, 1485, 1486, 1487, 1488, 1489, 1490, 1491, 1492, 1493, 1494, 1495, 1496, 1497, 1498, 1499, 1500, 1501, 1502, 1503, 1504, 1505, 1506, 1507, 1508, 1509, 1510, 1511, 1512, 1513, 1514, 1515, 1516, 1517, 1518, 1519, 1520, 1521, 1522, 1523, 1524, 1525, 1526, 1527, 1528, 1529, 1530, 1531, 1532, 1533, 1534, 1535, 1536, 1537, 1538, 1539, 1540, 1541, 1542, 1543, 1544, 1545, 1546, 1547, 1548, 1549, 1550, 1551, 1552, 1553, 1554, 1555, 1556, 1557, 1558, 1559, 1560, 1561, 1562, 1563, 1564, 1565, 1566, 1567, 1568, 1569, 1570, 1571, 1572, 1573, 1574, 1575, 1576, 1577, 1578, 1579, 1580, 1581, 1582, 1583, 1584, 1585, 1586, 1587, 1588, 1589, 1590, 1591, 1592, 1593, 1594, 1595, 1596, 1597, 1598, 1599, 1600, 1601, 1602, 1603, 1604, 1605, 1606, 1607, 1608, 1609, 1610, 1611, 1612, 1613, 1614, 1615, 1616, 1617, 1618, 1619, 1620, 1621, 1622, 1623, 1624, 1625, 1626, 1627, 1628, 1629, 1630, 1631, 1632, 1633, 1634, 1635, 1636, 1637, 1638, 1639, 1640, 1641, 1642, 1643, 1644, 1645, 1646, 1647, 1648, 1649, 1650, 1651, 1652, 1653, 1654, 1655, 1656, 1657, 1658, 1659, 1660, 1661, 1662, 1663, 1664, 1665, 1666, 1667, 1668, 1669, 1670, 1671, 1672, 1673, 1674, 1675, 1676, 1677, 1678, 1679, 1680, 1681, 1682, 1683, 1684, 1685, 1686, 1687, 1688, 1689, 1690, 1691, 1692, 1693, 1694, 1695, 1696, 1697, 1698, 1699, 1700, 1701, 1702, 1703, 1704, 1705, 1706, 1707, 1708, 1709, 1710, 1711, 1712, 1713, 1714, 1715, 1716, 1717, 1718, 1719, 1720, 1721, 1722, 1723, 1724, 1725, 1726, 1727, 1728, 1729, 1730, 1731, 1732, 1733, 1734, 1735, 1736, 1737, 1738, 1739, 1740, 1741, 1742, 1743, 1744, 1745, 1746, 1747, 1748, 1749, 1750, 1751, 1752, 1753, 1754, 1755, 1756, 1757, 1758, 1759, 1760, 1761, 1762, 1763, 1764, 1765, 1766, 1767, 1768, 1769, 1770, 1771, 1772, 1773, 1774, 1775, 1776, 1777, 1778, 1779, 1780, 1781, 1782, 1783, 1784, 1785, 1786, 1787, 1788, 1789, 1790, 1791, 1792, 1793, 1794, 1795, 1796, 1797, 1798, 1799, 1800, 1801, 1802, 1803])]; + tensor matrix_axes_0 = const()[name = tensor("matrix_axes_0"), val = tensor([-1])]; + tensor matrix = expand_dims(axes = matrix_axes_0, x = lengths)[name = tensor("matrix")]; + tensor mask_1 = less(x = const_1, y = matrix)[name = tensor("mask_1")]; + tensor cast_7_dtype_0 = const()[name = tensor("cast_7_dtype_0"), val = tensor("fp32")]; + tensor var_219_axes_0 = const()[name = tensor("op_219_axes_0"), val = tensor([1])]; + tensor cast_7 = cast(dtype = cast_7_dtype_0, x = mask_1)[name = tensor("cast_510")]; + tensor var_219 = expand_dims(axes = var_219_axes_0, x = cast_7)[name = tensor("op_219")]; + tensor x_1 = mul(x = xs_pad, y = var_57)[name = tensor("x_1")]; + tensor const_13 = const()[name = tensor("const_13"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(935459776)))]; + tensor input_7 = add(x = x_1, y = const_13)[name = tensor("input_7")]; + tensor const_14 = const()[name = tensor("const_14"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939500800)))]; + tensor const_15 = const()[name = tensor("const_15"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939503104)))]; + tensor output_1_axes_0 = const()[name = tensor("output_1_axes_0"), val = tensor([-1])]; + tensor output_1 = layer_norm(axes = output_1_axes_0, beta = const_15, epsilon = var_46, gamma = const_14, x = input_7)[name = tensor("output_1")]; + tensor var_283 = linear(bias = encoder_encoders0_0_self_attn_linear_q_k_v_bias, weight = encoder_encoders0_0_self_attn_linear_q_k_v_weight, x = output_1)[name = tensor("linear_0")]; + tensor tile_0 = const()[name = tensor("tile_0"), val = tensor([512, 512, 512])]; + tensor var_284_axis_0 = const()[name = tensor("op_284_axis_0"), val = tensor(-1)]; + tensor var_284_0, tensor var_284_1, tensor var_284_2 = split(axis = var_284_axis_0, split_sizes = tile_0, x = var_283)[name = tensor("op_284")]; + tensor var_288 = const()[name = tensor("op_288"), val = tensor([1, 1804, 4, 128])]; + tensor var_289 = reshape(shape = var_288, x = var_284_0)[name = tensor("op_289")]; + tensor var_291 = const()[name = tensor("op_291"), val = tensor([1, 1804, 4, 128])]; + tensor var_292 = reshape(shape = var_291, x = var_284_1)[name = tensor("op_292")]; + tensor var_294 = const()[name = tensor("op_294"), val = tensor([1, 1804, 4, 128])]; + tensor var_295 = reshape(shape = var_294, x = var_284_2)[name = tensor("op_295")]; + tensor value_1_perm_0 = const()[name = tensor("value_1_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_298 = const()[name = tensor("op_298"), val = tensor([1, -1, 1])]; + tensor mask_7 = reshape(shape = var_298, x = var_219)[name = tensor("mask_7")]; + tensor inputs_1 = mul(x = var_284_2, y = mask_7)[name = tensor("inputs_1")]; + tensor input_11_perm_0 = const()[name = tensor("input_11_perm_0"), val = tensor([0, 2, 1])]; + tensor const_21 = const()[name = tensor("const_21"), val = tensor(0x0p+0)]; + tensor input_13_pad_0 = const()[name = tensor("input_13_pad_0"), val = tensor([0, 0, 0, 0, 5, 5])]; + tensor input_13_mode_0 = const()[name = tensor("input_13_mode_0"), val = tensor("constant")]; + tensor input_11 = transpose(perm = input_11_perm_0, x = inputs_1)[name = tensor("transpose_768")]; + tensor input_13 = pad(constant_val = const_21, mode = input_13_mode_0, pad = input_13_pad_0, x = input_11)[name = tensor("input_13")]; + tensor x_5_pad_type_0 = const()[name = tensor("x_5_pad_type_0"), val = tensor("valid")]; + tensor x_5_groups_0 = const()[name = tensor("x_5_groups_0"), val = tensor(512)]; + tensor x_5_strides_0 = const()[name = tensor("x_5_strides_0"), val = tensor([1])]; + tensor x_5_pad_0 = const()[name = tensor("x_5_pad_0"), val = tensor([0, 0])]; + tensor x_5_dilations_0 = const()[name = tensor("x_5_dilations_0"), val = tensor([1])]; + tensor x_5 = conv(dilations = x_5_dilations_0, groups = x_5_groups_0, pad = x_5_pad_0, pad_type = x_5_pad_type_0, strides = x_5_strides_0, weight = encoder_encoders0_0_self_attn_fsmn_block_weight, x = input_13)[name = tensor("x_5")]; + tensor x_7_perm_0 = const()[name = tensor("x_7_perm_0"), val = tensor([0, 2, 1])]; + tensor x_7 = transpose(perm = x_7_perm_0, x = x_5)[name = tensor("transpose_767")]; + tensor input_15 = add(x = x_7, y = inputs_1)[name = tensor("input_15")]; + tensor fsmn_memory_1 = mul(x = input_15, y = mask_7)[name = tensor("fsmn_memory_1")]; + tensor var_314 = const()[name = tensor("op_314"), val = tensor(0x1.6a09e6p-4)]; + tensor q_h_3 = mul(x = var_289, y = var_314)[name = tensor("q_h_3")]; + tensor scores_1_transpose_x_0 = const()[name = tensor("scores_1_transpose_x_0"), val = tensor(false)]; + tensor scores_1_transpose_y_0 = const()[name = tensor("scores_1_transpose_y_0"), val = tensor(false)]; + tensor transpose_210_perm_0 = const()[name = tensor("transpose_210_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_211_perm_0 = const()[name = tensor("transpose_211_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_211 = transpose(perm = transpose_211_perm_0, x = var_292)[name = tensor("transpose_765")]; + tensor transpose_210 = transpose(perm = transpose_210_perm_0, x = q_h_3)[name = tensor("transpose_766")]; + tensor scores_1 = matmul(transpose_x = scores_1_transpose_x_0, transpose_y = scores_1_transpose_y_0, x = transpose_210, y = transpose_211)[name = tensor("scores_1")]; + tensor var_319_axes_0 = const()[name = tensor("op_319_axes_0"), val = tensor([1])]; + tensor var_319 = expand_dims(axes = var_319_axes_0, x = var_219)[name = tensor("op_319")]; + tensor var_66_promoted = const()[name = tensor("op_66_promoted"), val = tensor(0x0p+0)]; + tensor mask_9 = equal(x = var_319, y = var_66_promoted)[name = tensor("mask_9")]; + tensor scores_3 = select(a = var_48, b = scores_1, cond = mask_9)[name = tensor("scores_3")]; + tensor var_322 = softmax(axis = var_61, x = scores_3)[name = tensor("op_322")]; + tensor input_17 = select(a = var_53, b = var_322, cond = mask_9)[name = tensor("input_17")]; + tensor x_11_transpose_x_0 = const()[name = tensor("x_11_transpose_x_0"), val = tensor(false)]; + tensor x_11_transpose_y_0 = const()[name = tensor("x_11_transpose_y_0"), val = tensor(false)]; + tensor value_1 = transpose(perm = value_1_perm_0, x = var_295)[name = tensor("transpose_769")]; + tensor x_11 = matmul(transpose_x = x_11_transpose_x_0, transpose_y = x_11_transpose_y_0, x = input_17, y = value_1)[name = tensor("x_11")]; + tensor var_326_perm_0 = const()[name = tensor("op_326_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_328 = const()[name = tensor("op_328"), val = tensor([1, -1, 512])]; + tensor var_326 = transpose(perm = var_326_perm_0, x = x_11)[name = tensor("transpose_764")]; + tensor input_19 = reshape(shape = var_328, x = var_326)[name = tensor("input_19")]; + tensor att_outs_1 = linear(bias = encoder_encoders0_0_self_attn_linear_out_bias, weight = encoder_encoders0_0_self_attn_linear_out_weight, x = input_19)[name = tensor("linear_1")]; + tensor input_21 = add(x = att_outs_1, y = fsmn_memory_1)[name = tensor("input_21")]; + tensor const_23 = const()[name = tensor("const_23"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939505408)))]; + tensor const_24 = const()[name = tensor("const_24"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939507520)))]; + tensor output_3_axes_0 = const()[name = tensor("output_3_axes_0"), val = tensor([-1])]; + tensor output_3 = layer_norm(axes = output_3_axes_0, beta = const_24, epsilon = var_46, gamma = const_23, x = input_21)[name = tensor("output_3")]; + tensor input_29 = linear(bias = encoder_encoders0_0_feed_forward_w_1_bias, weight = encoder_encoders0_0_feed_forward_w_1_weight, x = output_3)[name = tensor("linear_2")]; + tensor input_31 = relu(x = input_29)[name = tensor("input_31")]; + tensor input_35 = linear(bias = encoder_encoders0_0_feed_forward_w_2_bias, weight = encoder_encoders0_0_feed_forward_w_2_weight, x = input_31)[name = tensor("linear_3")]; + tensor input_37 = add(x = input_21, y = input_35)[name = tensor("input_37")]; + tensor const_25 = const()[name = tensor("const_25"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939509632)))]; + tensor const_26 = const()[name = tensor("const_26"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939511744)))]; + tensor output_5_axes_0 = const()[name = tensor("output_5_axes_0"), val = tensor([-1])]; + tensor output_5 = layer_norm(axes = output_5_axes_0, beta = const_26, epsilon = var_46, gamma = const_25, x = input_37)[name = tensor("output_5")]; + tensor var_384 = linear(bias = encoder_encoders_0_self_attn_linear_q_k_v_bias, weight = encoder_encoders_0_self_attn_linear_q_k_v_weight, x = output_5)[name = tensor("linear_4")]; + tensor tile_1 = const()[name = tensor("tile_1"), val = tensor([512, 512, 512])]; + tensor var_385_axis_0 = const()[name = tensor("op_385_axis_0"), val = tensor(-1)]; + tensor var_385_0, tensor var_385_1, tensor var_385_2 = split(axis = var_385_axis_0, split_sizes = tile_1, x = var_384)[name = tensor("op_385")]; + tensor var_389 = const()[name = tensor("op_389"), val = tensor([1, 1804, 4, 128])]; + tensor var_390 = reshape(shape = var_389, x = var_385_0)[name = tensor("op_390")]; + tensor var_392 = const()[name = tensor("op_392"), val = tensor([1, 1804, 4, 128])]; + tensor var_393 = reshape(shape = var_392, x = var_385_1)[name = tensor("op_393")]; + tensor var_395 = const()[name = tensor("op_395"), val = tensor([1, 1804, 4, 128])]; + tensor var_396 = reshape(shape = var_395, x = var_385_2)[name = tensor("op_396")]; + tensor value_3_perm_0 = const()[name = tensor("value_3_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor inputs_3 = mul(x = var_385_2, y = mask_7)[name = tensor("inputs_3")]; + tensor input_41_perm_0 = const()[name = tensor("input_41_perm_0"), val = tensor([0, 2, 1])]; + tensor const_32 = const()[name = tensor("const_32"), val = tensor(0x0p+0)]; + tensor input_43_pad_0 = const()[name = tensor("input_43_pad_0"), val = tensor([0, 0, 0, 0, 5, 5])]; + tensor input_43_mode_0 = const()[name = tensor("input_43_mode_0"), val = tensor("constant")]; + tensor input_41 = transpose(perm = input_41_perm_0, x = inputs_3)[name = tensor("transpose_762")]; + tensor input_43 = pad(constant_val = const_32, mode = input_43_mode_0, pad = input_43_pad_0, x = input_41)[name = tensor("input_43")]; + tensor x_15_pad_type_0 = const()[name = tensor("x_15_pad_type_0"), val = tensor("valid")]; + tensor x_15_groups_0 = const()[name = tensor("x_15_groups_0"), val = tensor(512)]; + tensor x_15_strides_0 = const()[name = tensor("x_15_strides_0"), val = tensor([1])]; + tensor x_15_pad_0 = const()[name = tensor("x_15_pad_0"), val = tensor([0, 0])]; + tensor x_15_dilations_0 = const()[name = tensor("x_15_dilations_0"), val = tensor([1])]; + tensor x_15 = conv(dilations = x_15_dilations_0, groups = x_15_groups_0, pad = x_15_pad_0, pad_type = x_15_pad_type_0, strides = x_15_strides_0, weight = encoder_encoders_0_self_attn_fsmn_block_weight, x = input_43)[name = tensor("x_15")]; + tensor x_17_perm_0 = const()[name = tensor("x_17_perm_0"), val = tensor([0, 2, 1])]; + tensor x_17 = transpose(perm = x_17_perm_0, x = x_15)[name = tensor("transpose_761")]; + tensor input_45 = add(x = x_17, y = inputs_3)[name = tensor("input_45")]; + tensor fsmn_memory_3 = mul(x = input_45, y = mask_7)[name = tensor("fsmn_memory_3")]; + tensor var_415 = const()[name = tensor("op_415"), val = tensor(0x1.6a09e6p-4)]; + tensor q_h_7 = mul(x = var_390, y = var_415)[name = tensor("q_h_7")]; + tensor scores_5_transpose_x_0 = const()[name = tensor("scores_5_transpose_x_0"), val = tensor(false)]; + tensor scores_5_transpose_y_0 = const()[name = tensor("scores_5_transpose_y_0"), val = tensor(false)]; + tensor transpose_212_perm_0 = const()[name = tensor("transpose_212_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_213_perm_0 = const()[name = tensor("transpose_213_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_213 = transpose(perm = transpose_213_perm_0, x = var_393)[name = tensor("transpose_759")]; + tensor transpose_212 = transpose(perm = transpose_212_perm_0, x = q_h_7)[name = tensor("transpose_760")]; + tensor scores_5 = matmul(transpose_x = scores_5_transpose_x_0, transpose_y = scores_5_transpose_y_0, x = transpose_212, y = transpose_213)[name = tensor("scores_5")]; + tensor scores_7 = select(a = var_48, b = scores_5, cond = mask_9)[name = tensor("scores_7")]; + tensor var_423 = softmax(axis = var_61, x = scores_7)[name = tensor("op_423")]; + tensor input_47 = select(a = var_53, b = var_423, cond = mask_9)[name = tensor("input_47")]; + tensor x_21_transpose_x_0 = const()[name = tensor("x_21_transpose_x_0"), val = tensor(false)]; + tensor x_21_transpose_y_0 = const()[name = tensor("x_21_transpose_y_0"), val = tensor(false)]; + tensor value_3 = transpose(perm = value_3_perm_0, x = var_396)[name = tensor("transpose_763")]; + tensor x_21 = matmul(transpose_x = x_21_transpose_x_0, transpose_y = x_21_transpose_y_0, x = input_47, y = value_3)[name = tensor("x_21")]; + tensor var_427_perm_0 = const()[name = tensor("op_427_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_429 = const()[name = tensor("op_429"), val = tensor([1, -1, 512])]; + tensor var_427 = transpose(perm = var_427_perm_0, x = x_21)[name = tensor("transpose_758")]; + tensor input_49 = reshape(shape = var_429, x = var_427)[name = tensor("input_49")]; + tensor att_outs_3 = linear(bias = encoder_encoders_0_self_attn_linear_out_bias, weight = encoder_encoders_0_self_attn_linear_out_weight, x = input_49)[name = tensor("linear_5")]; + tensor input_51 = add(x = att_outs_3, y = fsmn_memory_3)[name = tensor("input_51")]; + tensor input_53 = add(x = input_37, y = input_51)[name = tensor("input_53")]; + tensor const_34 = const()[name = tensor("const_34"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939513856)))]; + tensor const_35 = const()[name = tensor("const_35"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939515968)))]; + tensor output_7_axes_0 = const()[name = tensor("output_7_axes_0"), val = tensor([-1])]; + tensor output_7 = layer_norm(axes = output_7_axes_0, beta = const_35, epsilon = var_46, gamma = const_34, x = input_53)[name = tensor("output_7")]; + tensor input_59 = linear(bias = encoder_encoders_0_feed_forward_w_1_bias, weight = encoder_encoders_0_feed_forward_w_1_weight, x = output_7)[name = tensor("linear_6")]; + tensor input_61 = relu(x = input_59)[name = tensor("input_61")]; + tensor input_65 = linear(bias = encoder_encoders_0_feed_forward_w_2_bias, weight = encoder_encoders_0_feed_forward_w_2_weight, x = input_61)[name = tensor("linear_7")]; + tensor input_67 = add(x = input_53, y = input_65)[name = tensor("input_67")]; + tensor const_36 = const()[name = tensor("const_36"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939518080)))]; + tensor const_37 = const()[name = tensor("const_37"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939520192)))]; + tensor output_9_axes_0 = const()[name = tensor("output_9_axes_0"), val = tensor([-1])]; + tensor output_9 = layer_norm(axes = output_9_axes_0, beta = const_37, epsilon = var_46, gamma = const_36, x = input_67)[name = tensor("output_9")]; + tensor var_486 = linear(bias = encoder_encoders_1_self_attn_linear_q_k_v_bias, weight = encoder_encoders_1_self_attn_linear_q_k_v_weight, x = output_9)[name = tensor("linear_8")]; + tensor tile_2 = const()[name = tensor("tile_2"), val = tensor([512, 512, 512])]; + tensor var_487_axis_0 = const()[name = tensor("op_487_axis_0"), val = tensor(-1)]; + tensor var_487_0, tensor var_487_1, tensor var_487_2 = split(axis = var_487_axis_0, split_sizes = tile_2, x = var_486)[name = tensor("op_487")]; + tensor var_491 = const()[name = tensor("op_491"), val = tensor([1, 1804, 4, 128])]; + tensor var_492 = reshape(shape = var_491, x = var_487_0)[name = tensor("op_492")]; + tensor var_494 = const()[name = tensor("op_494"), val = tensor([1, 1804, 4, 128])]; + tensor var_495 = reshape(shape = var_494, x = var_487_1)[name = tensor("op_495")]; + tensor var_497 = const()[name = tensor("op_497"), val = tensor([1, 1804, 4, 128])]; + tensor var_498 = reshape(shape = var_497, x = var_487_2)[name = tensor("op_498")]; + tensor value_5_perm_0 = const()[name = tensor("value_5_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor inputs_5 = mul(x = var_487_2, y = mask_7)[name = tensor("inputs_5")]; + tensor input_71_perm_0 = const()[name = tensor("input_71_perm_0"), val = tensor([0, 2, 1])]; + tensor const_43 = const()[name = tensor("const_43"), val = tensor(0x0p+0)]; + tensor input_73_pad_0 = const()[name = tensor("input_73_pad_0"), val = tensor([0, 0, 0, 0, 5, 5])]; + tensor input_73_mode_0 = const()[name = tensor("input_73_mode_0"), val = tensor("constant")]; + tensor input_71 = transpose(perm = input_71_perm_0, x = inputs_5)[name = tensor("transpose_756")]; + tensor input_73 = pad(constant_val = const_43, mode = input_73_mode_0, pad = input_73_pad_0, x = input_71)[name = tensor("input_73")]; + tensor x_25_pad_type_0 = const()[name = tensor("x_25_pad_type_0"), val = tensor("valid")]; + tensor x_25_groups_0 = const()[name = tensor("x_25_groups_0"), val = tensor(512)]; + tensor x_25_strides_0 = const()[name = tensor("x_25_strides_0"), val = tensor([1])]; + tensor x_25_pad_0 = const()[name = tensor("x_25_pad_0"), val = tensor([0, 0])]; + tensor x_25_dilations_0 = const()[name = tensor("x_25_dilations_0"), val = tensor([1])]; + tensor x_25 = conv(dilations = x_25_dilations_0, groups = x_25_groups_0, pad = x_25_pad_0, pad_type = x_25_pad_type_0, strides = x_25_strides_0, weight = encoder_encoders_1_self_attn_fsmn_block_weight, x = input_73)[name = tensor("x_25")]; + tensor x_27_perm_0 = const()[name = tensor("x_27_perm_0"), val = tensor([0, 2, 1])]; + tensor x_27 = transpose(perm = x_27_perm_0, x = x_25)[name = tensor("transpose_755")]; + tensor input_75 = add(x = x_27, y = inputs_5)[name = tensor("input_75")]; + tensor fsmn_memory_5 = mul(x = input_75, y = mask_7)[name = tensor("fsmn_memory_5")]; + tensor var_517 = const()[name = tensor("op_517"), val = tensor(0x1.6a09e6p-4)]; + tensor q_h_11 = mul(x = var_492, y = var_517)[name = tensor("q_h_11")]; + tensor scores_9_transpose_x_0 = const()[name = tensor("scores_9_transpose_x_0"), val = tensor(false)]; + tensor scores_9_transpose_y_0 = const()[name = tensor("scores_9_transpose_y_0"), val = tensor(false)]; + tensor transpose_214_perm_0 = const()[name = tensor("transpose_214_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_215_perm_0 = const()[name = tensor("transpose_215_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_215 = transpose(perm = transpose_215_perm_0, x = var_495)[name = tensor("transpose_753")]; + tensor transpose_214 = transpose(perm = transpose_214_perm_0, x = q_h_11)[name = tensor("transpose_754")]; + tensor scores_9 = matmul(transpose_x = scores_9_transpose_x_0, transpose_y = scores_9_transpose_y_0, x = transpose_214, y = transpose_215)[name = tensor("scores_9")]; + tensor scores_11 = select(a = var_48, b = scores_9, cond = mask_9)[name = tensor("scores_11")]; + tensor var_525 = softmax(axis = var_61, x = scores_11)[name = tensor("op_525")]; + tensor input_77 = select(a = var_53, b = var_525, cond = mask_9)[name = tensor("input_77")]; + tensor x_31_transpose_x_0 = const()[name = tensor("x_31_transpose_x_0"), val = tensor(false)]; + tensor x_31_transpose_y_0 = const()[name = tensor("x_31_transpose_y_0"), val = tensor(false)]; + tensor value_5 = transpose(perm = value_5_perm_0, x = var_498)[name = tensor("transpose_757")]; + tensor x_31 = matmul(transpose_x = x_31_transpose_x_0, transpose_y = x_31_transpose_y_0, x = input_77, y = value_5)[name = tensor("x_31")]; + tensor var_529_perm_0 = const()[name = tensor("op_529_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_531 = const()[name = tensor("op_531"), val = tensor([1, -1, 512])]; + tensor var_529 = transpose(perm = var_529_perm_0, x = x_31)[name = tensor("transpose_752")]; + tensor input_79 = reshape(shape = var_531, x = var_529)[name = tensor("input_79")]; + tensor att_outs_5 = linear(bias = encoder_encoders_1_self_attn_linear_out_bias, weight = encoder_encoders_1_self_attn_linear_out_weight, x = input_79)[name = tensor("linear_9")]; + tensor input_81 = add(x = att_outs_5, y = fsmn_memory_5)[name = tensor("input_81")]; + tensor input_83 = add(x = input_67, y = input_81)[name = tensor("input_83")]; + tensor const_45 = const()[name = tensor("const_45"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939522304)))]; + tensor const_46 = const()[name = tensor("const_46"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939524416)))]; + tensor output_11_axes_0 = const()[name = tensor("output_11_axes_0"), val = tensor([-1])]; + tensor output_11 = layer_norm(axes = output_11_axes_0, beta = const_46, epsilon = var_46, gamma = const_45, x = input_83)[name = tensor("output_11")]; + tensor input_89 = linear(bias = encoder_encoders_1_feed_forward_w_1_bias, weight = encoder_encoders_1_feed_forward_w_1_weight, x = output_11)[name = tensor("linear_10")]; + tensor input_91 = relu(x = input_89)[name = tensor("input_91")]; + tensor input_95 = linear(bias = encoder_encoders_1_feed_forward_w_2_bias, weight = encoder_encoders_1_feed_forward_w_2_weight, x = input_91)[name = tensor("linear_11")]; + tensor input_97 = add(x = input_83, y = input_95)[name = tensor("input_97")]; + tensor const_47 = const()[name = tensor("const_47"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939526528)))]; + tensor const_48 = const()[name = tensor("const_48"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939528640)))]; + tensor output_13_axes_0 = const()[name = tensor("output_13_axes_0"), val = tensor([-1])]; + tensor output_13 = layer_norm(axes = output_13_axes_0, beta = const_48, epsilon = var_46, gamma = const_47, x = input_97)[name = tensor("output_13")]; + tensor var_588 = linear(bias = encoder_encoders_2_self_attn_linear_q_k_v_bias, weight = encoder_encoders_2_self_attn_linear_q_k_v_weight, x = output_13)[name = tensor("linear_12")]; + tensor tile_3 = const()[name = tensor("tile_3"), val = tensor([512, 512, 512])]; + tensor var_589_axis_0 = const()[name = tensor("op_589_axis_0"), val = tensor(-1)]; + tensor var_589_0, tensor var_589_1, tensor var_589_2 = split(axis = var_589_axis_0, split_sizes = tile_3, x = var_588)[name = tensor("op_589")]; + tensor var_593 = const()[name = tensor("op_593"), val = tensor([1, 1804, 4, 128])]; + tensor var_594 = reshape(shape = var_593, x = var_589_0)[name = tensor("op_594")]; + tensor var_596 = const()[name = tensor("op_596"), val = tensor([1, 1804, 4, 128])]; + tensor var_597 = reshape(shape = var_596, x = var_589_1)[name = tensor("op_597")]; + tensor var_599 = const()[name = tensor("op_599"), val = tensor([1, 1804, 4, 128])]; + tensor var_600 = reshape(shape = var_599, x = var_589_2)[name = tensor("op_600")]; + tensor value_7_perm_0 = const()[name = tensor("value_7_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor inputs_7 = mul(x = var_589_2, y = mask_7)[name = tensor("inputs_7")]; + tensor input_101_perm_0 = const()[name = tensor("input_101_perm_0"), val = tensor([0, 2, 1])]; + tensor const_54 = const()[name = tensor("const_54"), val = tensor(0x0p+0)]; + tensor input_103_pad_0 = const()[name = tensor("input_103_pad_0"), val = tensor([0, 0, 0, 0, 5, 5])]; + tensor input_103_mode_0 = const()[name = tensor("input_103_mode_0"), val = tensor("constant")]; + tensor input_101 = transpose(perm = input_101_perm_0, x = inputs_7)[name = tensor("transpose_750")]; + tensor input_103 = pad(constant_val = const_54, mode = input_103_mode_0, pad = input_103_pad_0, x = input_101)[name = tensor("input_103")]; + tensor x_35_pad_type_0 = const()[name = tensor("x_35_pad_type_0"), val = tensor("valid")]; + tensor x_35_groups_0 = const()[name = tensor("x_35_groups_0"), val = tensor(512)]; + tensor x_35_strides_0 = const()[name = tensor("x_35_strides_0"), val = tensor([1])]; + tensor x_35_pad_0 = const()[name = tensor("x_35_pad_0"), val = tensor([0, 0])]; + tensor x_35_dilations_0 = const()[name = tensor("x_35_dilations_0"), val = tensor([1])]; + tensor x_35 = conv(dilations = x_35_dilations_0, groups = x_35_groups_0, pad = x_35_pad_0, pad_type = x_35_pad_type_0, strides = x_35_strides_0, weight = encoder_encoders_2_self_attn_fsmn_block_weight, x = input_103)[name = tensor("x_35")]; + tensor x_37_perm_0 = const()[name = tensor("x_37_perm_0"), val = tensor([0, 2, 1])]; + tensor x_37 = transpose(perm = x_37_perm_0, x = x_35)[name = tensor("transpose_749")]; + tensor input_105 = add(x = x_37, y = inputs_7)[name = tensor("input_105")]; + tensor fsmn_memory_7 = mul(x = input_105, y = mask_7)[name = tensor("fsmn_memory_7")]; + tensor var_619 = const()[name = tensor("op_619"), val = tensor(0x1.6a09e6p-4)]; + tensor q_h_15 = mul(x = var_594, y = var_619)[name = tensor("q_h_15")]; + tensor scores_13_transpose_x_0 = const()[name = tensor("scores_13_transpose_x_0"), val = tensor(false)]; + tensor scores_13_transpose_y_0 = const()[name = tensor("scores_13_transpose_y_0"), val = tensor(false)]; + tensor transpose_216_perm_0 = const()[name = tensor("transpose_216_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_217_perm_0 = const()[name = tensor("transpose_217_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_217 = transpose(perm = transpose_217_perm_0, x = var_597)[name = tensor("transpose_747")]; + tensor transpose_216 = transpose(perm = transpose_216_perm_0, x = q_h_15)[name = tensor("transpose_748")]; + tensor scores_13 = matmul(transpose_x = scores_13_transpose_x_0, transpose_y = scores_13_transpose_y_0, x = transpose_216, y = transpose_217)[name = tensor("scores_13")]; + tensor scores_15 = select(a = var_48, b = scores_13, cond = mask_9)[name = tensor("scores_15")]; + tensor var_627 = softmax(axis = var_61, x = scores_15)[name = tensor("op_627")]; + tensor input_107 = select(a = var_53, b = var_627, cond = mask_9)[name = tensor("input_107")]; + tensor x_41_transpose_x_0 = const()[name = tensor("x_41_transpose_x_0"), val = tensor(false)]; + tensor x_41_transpose_y_0 = const()[name = tensor("x_41_transpose_y_0"), val = tensor(false)]; + tensor value_7 = transpose(perm = value_7_perm_0, x = var_600)[name = tensor("transpose_751")]; + tensor x_41 = matmul(transpose_x = x_41_transpose_x_0, transpose_y = x_41_transpose_y_0, x = input_107, y = value_7)[name = tensor("x_41")]; + tensor var_631_perm_0 = const()[name = tensor("op_631_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_633 = const()[name = tensor("op_633"), val = tensor([1, -1, 512])]; + tensor var_631 = transpose(perm = var_631_perm_0, x = x_41)[name = tensor("transpose_746")]; + tensor input_109 = reshape(shape = var_633, x = var_631)[name = tensor("input_109")]; + tensor att_outs_7 = linear(bias = encoder_encoders_2_self_attn_linear_out_bias, weight = encoder_encoders_2_self_attn_linear_out_weight, x = input_109)[name = tensor("linear_13")]; + tensor input_111 = add(x = att_outs_7, y = fsmn_memory_7)[name = tensor("input_111")]; + tensor input_113 = add(x = input_97, y = input_111)[name = tensor("input_113")]; + tensor const_56 = const()[name = tensor("const_56"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939530752)))]; + tensor const_57 = const()[name = tensor("const_57"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939532864)))]; + tensor output_15_axes_0 = const()[name = tensor("output_15_axes_0"), val = tensor([-1])]; + tensor output_15 = layer_norm(axes = output_15_axes_0, beta = const_57, epsilon = var_46, gamma = const_56, x = input_113)[name = tensor("output_15")]; + tensor input_119 = linear(bias = encoder_encoders_2_feed_forward_w_1_bias, weight = encoder_encoders_2_feed_forward_w_1_weight, x = output_15)[name = tensor("linear_14")]; + tensor input_121 = relu(x = input_119)[name = tensor("input_121")]; + tensor input_125 = linear(bias = encoder_encoders_2_feed_forward_w_2_bias, weight = encoder_encoders_2_feed_forward_w_2_weight, x = input_121)[name = tensor("linear_15")]; + tensor input_127 = add(x = input_113, y = input_125)[name = tensor("input_127")]; + tensor const_58 = const()[name = tensor("const_58"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939534976)))]; + tensor const_59 = const()[name = tensor("const_59"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939537088)))]; + tensor output_17_axes_0 = const()[name = tensor("output_17_axes_0"), val = tensor([-1])]; + tensor output_17 = layer_norm(axes = output_17_axes_0, beta = const_59, epsilon = var_46, gamma = const_58, x = input_127)[name = tensor("output_17")]; + tensor var_690 = linear(bias = encoder_encoders_3_self_attn_linear_q_k_v_bias, weight = encoder_encoders_3_self_attn_linear_q_k_v_weight, x = output_17)[name = tensor("linear_16")]; + tensor tile_4 = const()[name = tensor("tile_4"), val = tensor([512, 512, 512])]; + tensor var_691_axis_0 = const()[name = tensor("op_691_axis_0"), val = tensor(-1)]; + tensor var_691_0, tensor var_691_1, tensor var_691_2 = split(axis = var_691_axis_0, split_sizes = tile_4, x = var_690)[name = tensor("op_691")]; + tensor var_695 = const()[name = tensor("op_695"), val = tensor([1, 1804, 4, 128])]; + tensor var_696 = reshape(shape = var_695, x = var_691_0)[name = tensor("op_696")]; + tensor var_698 = const()[name = tensor("op_698"), val = tensor([1, 1804, 4, 128])]; + tensor var_699 = reshape(shape = var_698, x = var_691_1)[name = tensor("op_699")]; + tensor var_701 = const()[name = tensor("op_701"), val = tensor([1, 1804, 4, 128])]; + tensor var_702 = reshape(shape = var_701, x = var_691_2)[name = tensor("op_702")]; + tensor value_9_perm_0 = const()[name = tensor("value_9_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor inputs_9 = mul(x = var_691_2, y = mask_7)[name = tensor("inputs_9")]; + tensor input_131_perm_0 = const()[name = tensor("input_131_perm_0"), val = tensor([0, 2, 1])]; + tensor const_65 = const()[name = tensor("const_65"), val = tensor(0x0p+0)]; + tensor input_133_pad_0 = const()[name = tensor("input_133_pad_0"), val = tensor([0, 0, 0, 0, 5, 5])]; + tensor input_133_mode_0 = const()[name = tensor("input_133_mode_0"), val = tensor("constant")]; + tensor input_131 = transpose(perm = input_131_perm_0, x = inputs_9)[name = tensor("transpose_744")]; + tensor input_133 = pad(constant_val = const_65, mode = input_133_mode_0, pad = input_133_pad_0, x = input_131)[name = tensor("input_133")]; + tensor x_45_pad_type_0 = const()[name = tensor("x_45_pad_type_0"), val = tensor("valid")]; + tensor x_45_groups_0 = const()[name = tensor("x_45_groups_0"), val = tensor(512)]; + tensor x_45_strides_0 = const()[name = tensor("x_45_strides_0"), val = tensor([1])]; + tensor x_45_pad_0 = const()[name = tensor("x_45_pad_0"), val = tensor([0, 0])]; + tensor x_45_dilations_0 = const()[name = tensor("x_45_dilations_0"), val = tensor([1])]; + tensor x_45 = conv(dilations = x_45_dilations_0, groups = x_45_groups_0, pad = x_45_pad_0, pad_type = x_45_pad_type_0, strides = x_45_strides_0, weight = encoder_encoders_3_self_attn_fsmn_block_weight, x = input_133)[name = tensor("x_45")]; + tensor x_47_perm_0 = const()[name = tensor("x_47_perm_0"), val = tensor([0, 2, 1])]; + tensor x_47 = transpose(perm = x_47_perm_0, x = x_45)[name = tensor("transpose_743")]; + tensor input_135 = add(x = x_47, y = inputs_9)[name = tensor("input_135")]; + tensor fsmn_memory_9 = mul(x = input_135, y = mask_7)[name = tensor("fsmn_memory_9")]; + tensor var_721 = const()[name = tensor("op_721"), val = tensor(0x1.6a09e6p-4)]; + tensor q_h_19 = mul(x = var_696, y = var_721)[name = tensor("q_h_19")]; + tensor scores_17_transpose_x_0 = const()[name = tensor("scores_17_transpose_x_0"), val = tensor(false)]; + tensor scores_17_transpose_y_0 = const()[name = tensor("scores_17_transpose_y_0"), val = tensor(false)]; + tensor transpose_218_perm_0 = const()[name = tensor("transpose_218_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_219_perm_0 = const()[name = tensor("transpose_219_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_219 = transpose(perm = transpose_219_perm_0, x = var_699)[name = tensor("transpose_741")]; + tensor transpose_218 = transpose(perm = transpose_218_perm_0, x = q_h_19)[name = tensor("transpose_742")]; + tensor scores_17 = matmul(transpose_x = scores_17_transpose_x_0, transpose_y = scores_17_transpose_y_0, x = transpose_218, y = transpose_219)[name = tensor("scores_17")]; + tensor scores_19 = select(a = var_48, b = scores_17, cond = mask_9)[name = tensor("scores_19")]; + tensor var_729 = softmax(axis = var_61, x = scores_19)[name = tensor("op_729")]; + tensor input_137 = select(a = var_53, b = var_729, cond = mask_9)[name = tensor("input_137")]; + tensor x_51_transpose_x_0 = const()[name = tensor("x_51_transpose_x_0"), val = tensor(false)]; + tensor x_51_transpose_y_0 = const()[name = tensor("x_51_transpose_y_0"), val = tensor(false)]; + tensor value_9 = transpose(perm = value_9_perm_0, x = var_702)[name = tensor("transpose_745")]; + tensor x_51 = matmul(transpose_x = x_51_transpose_x_0, transpose_y = x_51_transpose_y_0, x = input_137, y = value_9)[name = tensor("x_51")]; + tensor var_733_perm_0 = const()[name = tensor("op_733_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_735 = const()[name = tensor("op_735"), val = tensor([1, -1, 512])]; + tensor var_733 = transpose(perm = var_733_perm_0, x = x_51)[name = tensor("transpose_740")]; + tensor input_139 = reshape(shape = var_735, x = var_733)[name = tensor("input_139")]; + tensor att_outs_9 = linear(bias = encoder_encoders_3_self_attn_linear_out_bias, weight = encoder_encoders_3_self_attn_linear_out_weight, x = input_139)[name = tensor("linear_17")]; + tensor input_141 = add(x = att_outs_9, y = fsmn_memory_9)[name = tensor("input_141")]; + tensor input_143 = add(x = input_127, y = input_141)[name = tensor("input_143")]; + tensor const_67 = const()[name = tensor("const_67"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939539200)))]; + tensor const_68 = const()[name = tensor("const_68"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939541312)))]; + tensor output_19_axes_0 = const()[name = tensor("output_19_axes_0"), val = tensor([-1])]; + tensor output_19 = layer_norm(axes = output_19_axes_0, beta = const_68, epsilon = var_46, gamma = const_67, x = input_143)[name = tensor("output_19")]; + tensor input_149 = linear(bias = encoder_encoders_3_feed_forward_w_1_bias, weight = encoder_encoders_3_feed_forward_w_1_weight, x = output_19)[name = tensor("linear_18")]; + tensor input_151 = relu(x = input_149)[name = tensor("input_151")]; + tensor input_155 = linear(bias = encoder_encoders_3_feed_forward_w_2_bias, weight = encoder_encoders_3_feed_forward_w_2_weight, x = input_151)[name = tensor("linear_19")]; + tensor input_157 = add(x = input_143, y = input_155)[name = tensor("input_157")]; + tensor const_69 = const()[name = tensor("const_69"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939543424)))]; + tensor const_70 = const()[name = tensor("const_70"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939545536)))]; + tensor output_21_axes_0 = const()[name = tensor("output_21_axes_0"), val = tensor([-1])]; + tensor output_21 = layer_norm(axes = output_21_axes_0, beta = const_70, epsilon = var_46, gamma = const_69, x = input_157)[name = tensor("output_21")]; + tensor var_792 = linear(bias = encoder_encoders_4_self_attn_linear_q_k_v_bias, weight = encoder_encoders_4_self_attn_linear_q_k_v_weight, x = output_21)[name = tensor("linear_20")]; + tensor tile_5 = const()[name = tensor("tile_5"), val = tensor([512, 512, 512])]; + tensor var_793_axis_0 = const()[name = tensor("op_793_axis_0"), val = tensor(-1)]; + tensor var_793_0, tensor var_793_1, tensor var_793_2 = split(axis = var_793_axis_0, split_sizes = tile_5, x = var_792)[name = tensor("op_793")]; + tensor var_797 = const()[name = tensor("op_797"), val = tensor([1, 1804, 4, 128])]; + tensor var_798 = reshape(shape = var_797, x = var_793_0)[name = tensor("op_798")]; + tensor var_800 = const()[name = tensor("op_800"), val = tensor([1, 1804, 4, 128])]; + tensor var_801 = reshape(shape = var_800, x = var_793_1)[name = tensor("op_801")]; + tensor var_803 = const()[name = tensor("op_803"), val = tensor([1, 1804, 4, 128])]; + tensor var_804 = reshape(shape = var_803, x = var_793_2)[name = tensor("op_804")]; + tensor value_11_perm_0 = const()[name = tensor("value_11_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor inputs_11 = mul(x = var_793_2, y = mask_7)[name = tensor("inputs_11")]; + tensor input_161_perm_0 = const()[name = tensor("input_161_perm_0"), val = tensor([0, 2, 1])]; + tensor const_76 = const()[name = tensor("const_76"), val = tensor(0x0p+0)]; + tensor input_163_pad_0 = const()[name = tensor("input_163_pad_0"), val = tensor([0, 0, 0, 0, 5, 5])]; + tensor input_163_mode_0 = const()[name = tensor("input_163_mode_0"), val = tensor("constant")]; + tensor input_161 = transpose(perm = input_161_perm_0, x = inputs_11)[name = tensor("transpose_738")]; + tensor input_163 = pad(constant_val = const_76, mode = input_163_mode_0, pad = input_163_pad_0, x = input_161)[name = tensor("input_163")]; + tensor x_55_pad_type_0 = const()[name = tensor("x_55_pad_type_0"), val = tensor("valid")]; + tensor x_55_groups_0 = const()[name = tensor("x_55_groups_0"), val = tensor(512)]; + tensor x_55_strides_0 = const()[name = tensor("x_55_strides_0"), val = tensor([1])]; + tensor x_55_pad_0 = const()[name = tensor("x_55_pad_0"), val = tensor([0, 0])]; + tensor x_55_dilations_0 = const()[name = tensor("x_55_dilations_0"), val = tensor([1])]; + tensor x_55 = conv(dilations = x_55_dilations_0, groups = x_55_groups_0, pad = x_55_pad_0, pad_type = x_55_pad_type_0, strides = x_55_strides_0, weight = encoder_encoders_4_self_attn_fsmn_block_weight, x = input_163)[name = tensor("x_55")]; + tensor x_57_perm_0 = const()[name = tensor("x_57_perm_0"), val = tensor([0, 2, 1])]; + tensor x_57 = transpose(perm = x_57_perm_0, x = x_55)[name = tensor("transpose_737")]; + tensor input_165 = add(x = x_57, y = inputs_11)[name = tensor("input_165")]; + tensor fsmn_memory_11 = mul(x = input_165, y = mask_7)[name = tensor("fsmn_memory_11")]; + tensor var_823 = const()[name = tensor("op_823"), val = tensor(0x1.6a09e6p-4)]; + tensor q_h_23 = mul(x = var_798, y = var_823)[name = tensor("q_h_23")]; + tensor scores_21_transpose_x_0 = const()[name = tensor("scores_21_transpose_x_0"), val = tensor(false)]; + tensor scores_21_transpose_y_0 = const()[name = tensor("scores_21_transpose_y_0"), val = tensor(false)]; + tensor transpose_220_perm_0 = const()[name = tensor("transpose_220_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_221_perm_0 = const()[name = tensor("transpose_221_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_221 = transpose(perm = transpose_221_perm_0, x = var_801)[name = tensor("transpose_735")]; + tensor transpose_220 = transpose(perm = transpose_220_perm_0, x = q_h_23)[name = tensor("transpose_736")]; + tensor scores_21 = matmul(transpose_x = scores_21_transpose_x_0, transpose_y = scores_21_transpose_y_0, x = transpose_220, y = transpose_221)[name = tensor("scores_21")]; + tensor scores_23 = select(a = var_48, b = scores_21, cond = mask_9)[name = tensor("scores_23")]; + tensor var_831 = softmax(axis = var_61, x = scores_23)[name = tensor("op_831")]; + tensor input_167 = select(a = var_53, b = var_831, cond = mask_9)[name = tensor("input_167")]; + tensor x_61_transpose_x_0 = const()[name = tensor("x_61_transpose_x_0"), val = tensor(false)]; + tensor x_61_transpose_y_0 = const()[name = tensor("x_61_transpose_y_0"), val = tensor(false)]; + tensor value_11 = transpose(perm = value_11_perm_0, x = var_804)[name = tensor("transpose_739")]; + tensor x_61 = matmul(transpose_x = x_61_transpose_x_0, transpose_y = x_61_transpose_y_0, x = input_167, y = value_11)[name = tensor("x_61")]; + tensor var_835_perm_0 = const()[name = tensor("op_835_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_837 = const()[name = tensor("op_837"), val = tensor([1, -1, 512])]; + tensor var_835 = transpose(perm = var_835_perm_0, x = x_61)[name = tensor("transpose_734")]; + tensor input_169 = reshape(shape = var_837, x = var_835)[name = tensor("input_169")]; + tensor att_outs_11 = linear(bias = encoder_encoders_4_self_attn_linear_out_bias, weight = encoder_encoders_4_self_attn_linear_out_weight, x = input_169)[name = tensor("linear_21")]; + tensor input_171 = add(x = att_outs_11, y = fsmn_memory_11)[name = tensor("input_171")]; + tensor input_173 = add(x = input_157, y = input_171)[name = tensor("input_173")]; + tensor const_78 = const()[name = tensor("const_78"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939547648)))]; + tensor const_79 = const()[name = tensor("const_79"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939549760)))]; + tensor output_23_axes_0 = const()[name = tensor("output_23_axes_0"), val = tensor([-1])]; + tensor output_23 = layer_norm(axes = output_23_axes_0, beta = const_79, epsilon = var_46, gamma = const_78, x = input_173)[name = tensor("output_23")]; + tensor input_179 = linear(bias = encoder_encoders_4_feed_forward_w_1_bias, weight = encoder_encoders_4_feed_forward_w_1_weight, x = output_23)[name = tensor("linear_22")]; + tensor input_181 = relu(x = input_179)[name = tensor("input_181")]; + tensor input_185 = linear(bias = encoder_encoders_4_feed_forward_w_2_bias, weight = encoder_encoders_4_feed_forward_w_2_weight, x = input_181)[name = tensor("linear_23")]; + tensor input_187 = add(x = input_173, y = input_185)[name = tensor("input_187")]; + tensor const_80 = const()[name = tensor("const_80"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939551872)))]; + tensor const_81 = const()[name = tensor("const_81"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939553984)))]; + tensor output_25_axes_0 = const()[name = tensor("output_25_axes_0"), val = tensor([-1])]; + tensor output_25 = layer_norm(axes = output_25_axes_0, beta = const_81, epsilon = var_46, gamma = const_80, x = input_187)[name = tensor("output_25")]; + tensor var_894 = linear(bias = encoder_encoders_5_self_attn_linear_q_k_v_bias, weight = encoder_encoders_5_self_attn_linear_q_k_v_weight, x = output_25)[name = tensor("linear_24")]; + tensor tile_6 = const()[name = tensor("tile_6"), val = tensor([512, 512, 512])]; + tensor var_895_axis_0 = const()[name = tensor("op_895_axis_0"), val = tensor(-1)]; + tensor var_895_0, tensor var_895_1, tensor var_895_2 = split(axis = var_895_axis_0, split_sizes = tile_6, x = var_894)[name = tensor("op_895")]; + tensor var_899 = const()[name = tensor("op_899"), val = tensor([1, 1804, 4, 128])]; + tensor var_900 = reshape(shape = var_899, x = var_895_0)[name = tensor("op_900")]; + tensor var_902 = const()[name = tensor("op_902"), val = tensor([1, 1804, 4, 128])]; + tensor var_903 = reshape(shape = var_902, x = var_895_1)[name = tensor("op_903")]; + tensor var_905 = const()[name = tensor("op_905"), val = tensor([1, 1804, 4, 128])]; + tensor var_906 = reshape(shape = var_905, x = var_895_2)[name = tensor("op_906")]; + tensor value_13_perm_0 = const()[name = tensor("value_13_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor inputs_13 = mul(x = var_895_2, y = mask_7)[name = tensor("inputs_13")]; + tensor input_191_perm_0 = const()[name = tensor("input_191_perm_0"), val = tensor([0, 2, 1])]; + tensor const_87 = const()[name = tensor("const_87"), val = tensor(0x0p+0)]; + tensor input_193_pad_0 = const()[name = tensor("input_193_pad_0"), val = tensor([0, 0, 0, 0, 5, 5])]; + tensor input_193_mode_0 = const()[name = tensor("input_193_mode_0"), val = tensor("constant")]; + tensor input_191 = transpose(perm = input_191_perm_0, x = inputs_13)[name = tensor("transpose_732")]; + tensor input_193 = pad(constant_val = const_87, mode = input_193_mode_0, pad = input_193_pad_0, x = input_191)[name = tensor("input_193")]; + tensor x_65_pad_type_0 = const()[name = tensor("x_65_pad_type_0"), val = tensor("valid")]; + tensor x_65_groups_0 = const()[name = tensor("x_65_groups_0"), val = tensor(512)]; + tensor x_65_strides_0 = const()[name = tensor("x_65_strides_0"), val = tensor([1])]; + tensor x_65_pad_0 = const()[name = tensor("x_65_pad_0"), val = tensor([0, 0])]; + tensor x_65_dilations_0 = const()[name = tensor("x_65_dilations_0"), val = tensor([1])]; + tensor x_65 = conv(dilations = x_65_dilations_0, groups = x_65_groups_0, pad = x_65_pad_0, pad_type = x_65_pad_type_0, strides = x_65_strides_0, weight = encoder_encoders_5_self_attn_fsmn_block_weight, x = input_193)[name = tensor("x_65")]; + tensor x_67_perm_0 = const()[name = tensor("x_67_perm_0"), val = tensor([0, 2, 1])]; + tensor x_67 = transpose(perm = x_67_perm_0, x = x_65)[name = tensor("transpose_731")]; + tensor input_195 = add(x = x_67, y = inputs_13)[name = tensor("input_195")]; + tensor fsmn_memory_13 = mul(x = input_195, y = mask_7)[name = tensor("fsmn_memory_13")]; + tensor var_925 = const()[name = tensor("op_925"), val = tensor(0x1.6a09e6p-4)]; + tensor q_h_27 = mul(x = var_900, y = var_925)[name = tensor("q_h_27")]; + tensor scores_25_transpose_x_0 = const()[name = tensor("scores_25_transpose_x_0"), val = tensor(false)]; + tensor scores_25_transpose_y_0 = const()[name = tensor("scores_25_transpose_y_0"), val = tensor(false)]; + tensor transpose_222_perm_0 = const()[name = tensor("transpose_222_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_223_perm_0 = const()[name = tensor("transpose_223_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_223 = transpose(perm = transpose_223_perm_0, x = var_903)[name = tensor("transpose_729")]; + tensor transpose_222 = transpose(perm = transpose_222_perm_0, x = q_h_27)[name = tensor("transpose_730")]; + tensor scores_25 = matmul(transpose_x = scores_25_transpose_x_0, transpose_y = scores_25_transpose_y_0, x = transpose_222, y = transpose_223)[name = tensor("scores_25")]; + tensor scores_27 = select(a = var_48, b = scores_25, cond = mask_9)[name = tensor("scores_27")]; + tensor var_933 = softmax(axis = var_61, x = scores_27)[name = tensor("op_933")]; + tensor input_197 = select(a = var_53, b = var_933, cond = mask_9)[name = tensor("input_197")]; + tensor x_71_transpose_x_0 = const()[name = tensor("x_71_transpose_x_0"), val = tensor(false)]; + tensor x_71_transpose_y_0 = const()[name = tensor("x_71_transpose_y_0"), val = tensor(false)]; + tensor value_13 = transpose(perm = value_13_perm_0, x = var_906)[name = tensor("transpose_733")]; + tensor x_71 = matmul(transpose_x = x_71_transpose_x_0, transpose_y = x_71_transpose_y_0, x = input_197, y = value_13)[name = tensor("x_71")]; + tensor var_937_perm_0 = const()[name = tensor("op_937_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_939 = const()[name = tensor("op_939"), val = tensor([1, -1, 512])]; + tensor var_937 = transpose(perm = var_937_perm_0, x = x_71)[name = tensor("transpose_728")]; + tensor input_199 = reshape(shape = var_939, x = var_937)[name = tensor("input_199")]; + tensor att_outs_13 = linear(bias = encoder_encoders_5_self_attn_linear_out_bias, weight = encoder_encoders_5_self_attn_linear_out_weight, x = input_199)[name = tensor("linear_25")]; + tensor input_201 = add(x = att_outs_13, y = fsmn_memory_13)[name = tensor("input_201")]; + tensor input_203 = add(x = input_187, y = input_201)[name = tensor("input_203")]; + tensor const_89 = const()[name = tensor("const_89"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939556096)))]; + tensor const_90 = const()[name = tensor("const_90"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939558208)))]; + tensor output_27_axes_0 = const()[name = tensor("output_27_axes_0"), val = tensor([-1])]; + tensor output_27 = layer_norm(axes = output_27_axes_0, beta = const_90, epsilon = var_46, gamma = const_89, x = input_203)[name = tensor("output_27")]; + tensor input_209 = linear(bias = encoder_encoders_5_feed_forward_w_1_bias, weight = encoder_encoders_5_feed_forward_w_1_weight, x = output_27)[name = tensor("linear_26")]; + tensor input_211 = relu(x = input_209)[name = tensor("input_211")]; + tensor input_215 = linear(bias = encoder_encoders_5_feed_forward_w_2_bias, weight = encoder_encoders_5_feed_forward_w_2_weight, x = input_211)[name = tensor("linear_27")]; + tensor input_217 = add(x = input_203, y = input_215)[name = tensor("input_217")]; + tensor const_91 = const()[name = tensor("const_91"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939560320)))]; + tensor const_92 = const()[name = tensor("const_92"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939562432)))]; + tensor output_29_axes_0 = const()[name = tensor("output_29_axes_0"), val = tensor([-1])]; + tensor output_29 = layer_norm(axes = output_29_axes_0, beta = const_92, epsilon = var_46, gamma = const_91, x = input_217)[name = tensor("output_29")]; + tensor var_996 = linear(bias = encoder_encoders_6_self_attn_linear_q_k_v_bias, weight = encoder_encoders_6_self_attn_linear_q_k_v_weight, x = output_29)[name = tensor("linear_28")]; + tensor tile_7 = const()[name = tensor("tile_7"), val = tensor([512, 512, 512])]; + tensor var_997_axis_0 = const()[name = tensor("op_997_axis_0"), val = tensor(-1)]; + tensor var_997_0, tensor var_997_1, tensor var_997_2 = split(axis = var_997_axis_0, split_sizes = tile_7, x = var_996)[name = tensor("op_997")]; + tensor var_1001 = const()[name = tensor("op_1001"), val = tensor([1, 1804, 4, 128])]; + tensor var_1002 = reshape(shape = var_1001, x = var_997_0)[name = tensor("op_1002")]; + tensor var_1004 = const()[name = tensor("op_1004"), val = tensor([1, 1804, 4, 128])]; + tensor var_1005 = reshape(shape = var_1004, x = var_997_1)[name = tensor("op_1005")]; + tensor var_1007 = const()[name = tensor("op_1007"), val = tensor([1, 1804, 4, 128])]; + tensor var_1008 = reshape(shape = var_1007, x = var_997_2)[name = tensor("op_1008")]; + tensor value_15_perm_0 = const()[name = tensor("value_15_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor inputs_15 = mul(x = var_997_2, y = mask_7)[name = tensor("inputs_15")]; + tensor input_221_perm_0 = const()[name = tensor("input_221_perm_0"), val = tensor([0, 2, 1])]; + tensor const_98 = const()[name = tensor("const_98"), val = tensor(0x0p+0)]; + tensor input_223_pad_0 = const()[name = tensor("input_223_pad_0"), val = tensor([0, 0, 0, 0, 5, 5])]; + tensor input_223_mode_0 = const()[name = tensor("input_223_mode_0"), val = tensor("constant")]; + tensor input_221 = transpose(perm = input_221_perm_0, x = inputs_15)[name = tensor("transpose_726")]; + tensor input_223 = pad(constant_val = const_98, mode = input_223_mode_0, pad = input_223_pad_0, x = input_221)[name = tensor("input_223")]; + tensor x_75_pad_type_0 = const()[name = tensor("x_75_pad_type_0"), val = tensor("valid")]; + tensor x_75_groups_0 = const()[name = tensor("x_75_groups_0"), val = tensor(512)]; + tensor x_75_strides_0 = const()[name = tensor("x_75_strides_0"), val = tensor([1])]; + tensor x_75_pad_0 = const()[name = tensor("x_75_pad_0"), val = tensor([0, 0])]; + tensor x_75_dilations_0 = const()[name = tensor("x_75_dilations_0"), val = tensor([1])]; + tensor x_75 = conv(dilations = x_75_dilations_0, groups = x_75_groups_0, pad = x_75_pad_0, pad_type = x_75_pad_type_0, strides = x_75_strides_0, weight = encoder_encoders_6_self_attn_fsmn_block_weight, x = input_223)[name = tensor("x_75")]; + tensor x_77_perm_0 = const()[name = tensor("x_77_perm_0"), val = tensor([0, 2, 1])]; + tensor x_77 = transpose(perm = x_77_perm_0, x = x_75)[name = tensor("transpose_725")]; + tensor input_225 = add(x = x_77, y = inputs_15)[name = tensor("input_225")]; + tensor fsmn_memory_15 = mul(x = input_225, y = mask_7)[name = tensor("fsmn_memory_15")]; + tensor var_1027 = const()[name = tensor("op_1027"), val = tensor(0x1.6a09e6p-4)]; + tensor q_h_31 = mul(x = var_1002, y = var_1027)[name = tensor("q_h_31")]; + tensor scores_29_transpose_x_0 = const()[name = tensor("scores_29_transpose_x_0"), val = tensor(false)]; + tensor scores_29_transpose_y_0 = const()[name = tensor("scores_29_transpose_y_0"), val = tensor(false)]; + tensor transpose_224_perm_0 = const()[name = tensor("transpose_224_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_225_perm_0 = const()[name = tensor("transpose_225_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_225 = transpose(perm = transpose_225_perm_0, x = var_1005)[name = tensor("transpose_723")]; + tensor transpose_224 = transpose(perm = transpose_224_perm_0, x = q_h_31)[name = tensor("transpose_724")]; + tensor scores_29 = matmul(transpose_x = scores_29_transpose_x_0, transpose_y = scores_29_transpose_y_0, x = transpose_224, y = transpose_225)[name = tensor("scores_29")]; + tensor scores_31 = select(a = var_48, b = scores_29, cond = mask_9)[name = tensor("scores_31")]; + tensor var_1035 = softmax(axis = var_61, x = scores_31)[name = tensor("op_1035")]; + tensor input_227 = select(a = var_53, b = var_1035, cond = mask_9)[name = tensor("input_227")]; + tensor x_81_transpose_x_0 = const()[name = tensor("x_81_transpose_x_0"), val = tensor(false)]; + tensor x_81_transpose_y_0 = const()[name = tensor("x_81_transpose_y_0"), val = tensor(false)]; + tensor value_15 = transpose(perm = value_15_perm_0, x = var_1008)[name = tensor("transpose_727")]; + tensor x_81 = matmul(transpose_x = x_81_transpose_x_0, transpose_y = x_81_transpose_y_0, x = input_227, y = value_15)[name = tensor("x_81")]; + tensor var_1039_perm_0 = const()[name = tensor("op_1039_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1041 = const()[name = tensor("op_1041"), val = tensor([1, -1, 512])]; + tensor var_1039 = transpose(perm = var_1039_perm_0, x = x_81)[name = tensor("transpose_722")]; + tensor input_229 = reshape(shape = var_1041, x = var_1039)[name = tensor("input_229")]; + tensor att_outs_15 = linear(bias = encoder_encoders_6_self_attn_linear_out_bias, weight = encoder_encoders_6_self_attn_linear_out_weight, x = input_229)[name = tensor("linear_29")]; + tensor input_231 = add(x = att_outs_15, y = fsmn_memory_15)[name = tensor("input_231")]; + tensor input_233 = add(x = input_217, y = input_231)[name = tensor("input_233")]; + tensor const_100 = const()[name = tensor("const_100"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939564544)))]; + tensor const_101 = const()[name = tensor("const_101"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939566656)))]; + tensor output_31_axes_0 = const()[name = tensor("output_31_axes_0"), val = tensor([-1])]; + tensor output_31 = layer_norm(axes = output_31_axes_0, beta = const_101, epsilon = var_46, gamma = const_100, x = input_233)[name = tensor("output_31")]; + tensor input_239 = linear(bias = encoder_encoders_6_feed_forward_w_1_bias, weight = encoder_encoders_6_feed_forward_w_1_weight, x = output_31)[name = tensor("linear_30")]; + tensor input_241 = relu(x = input_239)[name = tensor("input_241")]; + tensor input_245 = linear(bias = encoder_encoders_6_feed_forward_w_2_bias, weight = encoder_encoders_6_feed_forward_w_2_weight, x = input_241)[name = tensor("linear_31")]; + tensor input_247 = add(x = input_233, y = input_245)[name = tensor("input_247")]; + tensor const_102 = const()[name = tensor("const_102"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939568768)))]; + tensor const_103 = const()[name = tensor("const_103"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939570880)))]; + tensor output_33_axes_0 = const()[name = tensor("output_33_axes_0"), val = tensor([-1])]; + tensor output_33 = layer_norm(axes = output_33_axes_0, beta = const_103, epsilon = var_46, gamma = const_102, x = input_247)[name = tensor("output_33")]; + tensor var_1098 = linear(bias = encoder_encoders_7_self_attn_linear_q_k_v_bias, weight = encoder_encoders_7_self_attn_linear_q_k_v_weight, x = output_33)[name = tensor("linear_32")]; + tensor tile_8 = const()[name = tensor("tile_8"), val = tensor([512, 512, 512])]; + tensor var_1099_axis_0 = const()[name = tensor("op_1099_axis_0"), val = tensor(-1)]; + tensor var_1099_0, tensor var_1099_1, tensor var_1099_2 = split(axis = var_1099_axis_0, split_sizes = tile_8, x = var_1098)[name = tensor("op_1099")]; + tensor var_1103 = const()[name = tensor("op_1103"), val = tensor([1, 1804, 4, 128])]; + tensor var_1104 = reshape(shape = var_1103, x = var_1099_0)[name = tensor("op_1104")]; + tensor var_1106 = const()[name = tensor("op_1106"), val = tensor([1, 1804, 4, 128])]; + tensor var_1107 = reshape(shape = var_1106, x = var_1099_1)[name = tensor("op_1107")]; + tensor var_1109 = const()[name = tensor("op_1109"), val = tensor([1, 1804, 4, 128])]; + tensor var_1110 = reshape(shape = var_1109, x = var_1099_2)[name = tensor("op_1110")]; + tensor value_17_perm_0 = const()[name = tensor("value_17_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor inputs_17 = mul(x = var_1099_2, y = mask_7)[name = tensor("inputs_17")]; + tensor input_251_perm_0 = const()[name = tensor("input_251_perm_0"), val = tensor([0, 2, 1])]; + tensor const_109 = const()[name = tensor("const_109"), val = tensor(0x0p+0)]; + tensor input_253_pad_0 = const()[name = tensor("input_253_pad_0"), val = tensor([0, 0, 0, 0, 5, 5])]; + tensor input_253_mode_0 = const()[name = tensor("input_253_mode_0"), val = tensor("constant")]; + tensor input_251 = transpose(perm = input_251_perm_0, x = inputs_17)[name = tensor("transpose_720")]; + tensor input_253 = pad(constant_val = const_109, mode = input_253_mode_0, pad = input_253_pad_0, x = input_251)[name = tensor("input_253")]; + tensor x_85_pad_type_0 = const()[name = tensor("x_85_pad_type_0"), val = tensor("valid")]; + tensor x_85_groups_0 = const()[name = tensor("x_85_groups_0"), val = tensor(512)]; + tensor x_85_strides_0 = const()[name = tensor("x_85_strides_0"), val = tensor([1])]; + tensor x_85_pad_0 = const()[name = tensor("x_85_pad_0"), val = tensor([0, 0])]; + tensor x_85_dilations_0 = const()[name = tensor("x_85_dilations_0"), val = tensor([1])]; + tensor x_85 = conv(dilations = x_85_dilations_0, groups = x_85_groups_0, pad = x_85_pad_0, pad_type = x_85_pad_type_0, strides = x_85_strides_0, weight = encoder_encoders_7_self_attn_fsmn_block_weight, x = input_253)[name = tensor("x_85")]; + tensor x_87_perm_0 = const()[name = tensor("x_87_perm_0"), val = tensor([0, 2, 1])]; + tensor x_87 = transpose(perm = x_87_perm_0, x = x_85)[name = tensor("transpose_719")]; + tensor input_255 = add(x = x_87, y = inputs_17)[name = tensor("input_255")]; + tensor fsmn_memory_17 = mul(x = input_255, y = mask_7)[name = tensor("fsmn_memory_17")]; + tensor var_1129 = const()[name = tensor("op_1129"), val = tensor(0x1.6a09e6p-4)]; + tensor q_h_35 = mul(x = var_1104, y = var_1129)[name = tensor("q_h_35")]; + tensor scores_33_transpose_x_0 = const()[name = tensor("scores_33_transpose_x_0"), val = tensor(false)]; + tensor scores_33_transpose_y_0 = const()[name = tensor("scores_33_transpose_y_0"), val = tensor(false)]; + tensor transpose_226_perm_0 = const()[name = tensor("transpose_226_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_227_perm_0 = const()[name = tensor("transpose_227_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_227 = transpose(perm = transpose_227_perm_0, x = var_1107)[name = tensor("transpose_717")]; + tensor transpose_226 = transpose(perm = transpose_226_perm_0, x = q_h_35)[name = tensor("transpose_718")]; + tensor scores_33 = matmul(transpose_x = scores_33_transpose_x_0, transpose_y = scores_33_transpose_y_0, x = transpose_226, y = transpose_227)[name = tensor("scores_33")]; + tensor scores_35 = select(a = var_48, b = scores_33, cond = mask_9)[name = tensor("scores_35")]; + tensor var_1137 = softmax(axis = var_61, x = scores_35)[name = tensor("op_1137")]; + tensor input_257 = select(a = var_53, b = var_1137, cond = mask_9)[name = tensor("input_257")]; + tensor x_91_transpose_x_0 = const()[name = tensor("x_91_transpose_x_0"), val = tensor(false)]; + tensor x_91_transpose_y_0 = const()[name = tensor("x_91_transpose_y_0"), val = tensor(false)]; + tensor value_17 = transpose(perm = value_17_perm_0, x = var_1110)[name = tensor("transpose_721")]; + tensor x_91 = matmul(transpose_x = x_91_transpose_x_0, transpose_y = x_91_transpose_y_0, x = input_257, y = value_17)[name = tensor("x_91")]; + tensor var_1141_perm_0 = const()[name = tensor("op_1141_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1143 = const()[name = tensor("op_1143"), val = tensor([1, -1, 512])]; + tensor var_1141 = transpose(perm = var_1141_perm_0, x = x_91)[name = tensor("transpose_716")]; + tensor input_259 = reshape(shape = var_1143, x = var_1141)[name = tensor("input_259")]; + tensor att_outs_17 = linear(bias = encoder_encoders_7_self_attn_linear_out_bias, weight = encoder_encoders_7_self_attn_linear_out_weight, x = input_259)[name = tensor("linear_33")]; + tensor input_261 = add(x = att_outs_17, y = fsmn_memory_17)[name = tensor("input_261")]; + tensor input_263 = add(x = input_247, y = input_261)[name = tensor("input_263")]; + tensor const_111 = const()[name = tensor("const_111"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939572992)))]; + tensor const_112 = const()[name = tensor("const_112"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939575104)))]; + tensor output_35_axes_0 = const()[name = tensor("output_35_axes_0"), val = tensor([-1])]; + tensor output_35 = layer_norm(axes = output_35_axes_0, beta = const_112, epsilon = var_46, gamma = const_111, x = input_263)[name = tensor("output_35")]; + tensor input_269 = linear(bias = encoder_encoders_7_feed_forward_w_1_bias, weight = encoder_encoders_7_feed_forward_w_1_weight, x = output_35)[name = tensor("linear_34")]; + tensor input_271 = relu(x = input_269)[name = tensor("input_271")]; + tensor input_275 = linear(bias = encoder_encoders_7_feed_forward_w_2_bias, weight = encoder_encoders_7_feed_forward_w_2_weight, x = input_271)[name = tensor("linear_35")]; + tensor input_277 = add(x = input_263, y = input_275)[name = tensor("input_277")]; + tensor const_113 = const()[name = tensor("const_113"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939577216)))]; + tensor const_114 = const()[name = tensor("const_114"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939579328)))]; + tensor output_37_axes_0 = const()[name = tensor("output_37_axes_0"), val = tensor([-1])]; + tensor output_37 = layer_norm(axes = output_37_axes_0, beta = const_114, epsilon = var_46, gamma = const_113, x = input_277)[name = tensor("output_37")]; + tensor var_1200 = linear(bias = encoder_encoders_8_self_attn_linear_q_k_v_bias, weight = encoder_encoders_8_self_attn_linear_q_k_v_weight, x = output_37)[name = tensor("linear_36")]; + tensor tile_9 = const()[name = tensor("tile_9"), val = tensor([512, 512, 512])]; + tensor var_1201_axis_0 = const()[name = tensor("op_1201_axis_0"), val = tensor(-1)]; + tensor var_1201_0, tensor var_1201_1, tensor var_1201_2 = split(axis = var_1201_axis_0, split_sizes = tile_9, x = var_1200)[name = tensor("op_1201")]; + tensor var_1205 = const()[name = tensor("op_1205"), val = tensor([1, 1804, 4, 128])]; + tensor var_1206 = reshape(shape = var_1205, x = var_1201_0)[name = tensor("op_1206")]; + tensor var_1208 = const()[name = tensor("op_1208"), val = tensor([1, 1804, 4, 128])]; + tensor var_1209 = reshape(shape = var_1208, x = var_1201_1)[name = tensor("op_1209")]; + tensor var_1211 = const()[name = tensor("op_1211"), val = tensor([1, 1804, 4, 128])]; + tensor var_1212 = reshape(shape = var_1211, x = var_1201_2)[name = tensor("op_1212")]; + tensor value_19_perm_0 = const()[name = tensor("value_19_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor inputs_19 = mul(x = var_1201_2, y = mask_7)[name = tensor("inputs_19")]; + tensor input_281_perm_0 = const()[name = tensor("input_281_perm_0"), val = tensor([0, 2, 1])]; + tensor const_120 = const()[name = tensor("const_120"), val = tensor(0x0p+0)]; + tensor input_283_pad_0 = const()[name = tensor("input_283_pad_0"), val = tensor([0, 0, 0, 0, 5, 5])]; + tensor input_283_mode_0 = const()[name = tensor("input_283_mode_0"), val = tensor("constant")]; + tensor input_281 = transpose(perm = input_281_perm_0, x = inputs_19)[name = tensor("transpose_714")]; + tensor input_283 = pad(constant_val = const_120, mode = input_283_mode_0, pad = input_283_pad_0, x = input_281)[name = tensor("input_283")]; + tensor x_95_pad_type_0 = const()[name = tensor("x_95_pad_type_0"), val = tensor("valid")]; + tensor x_95_groups_0 = const()[name = tensor("x_95_groups_0"), val = tensor(512)]; + tensor x_95_strides_0 = const()[name = tensor("x_95_strides_0"), val = tensor([1])]; + tensor x_95_pad_0 = const()[name = tensor("x_95_pad_0"), val = tensor([0, 0])]; + tensor x_95_dilations_0 = const()[name = tensor("x_95_dilations_0"), val = tensor([1])]; + tensor x_95 = conv(dilations = x_95_dilations_0, groups = x_95_groups_0, pad = x_95_pad_0, pad_type = x_95_pad_type_0, strides = x_95_strides_0, weight = encoder_encoders_8_self_attn_fsmn_block_weight, x = input_283)[name = tensor("x_95")]; + tensor x_97_perm_0 = const()[name = tensor("x_97_perm_0"), val = tensor([0, 2, 1])]; + tensor x_97 = transpose(perm = x_97_perm_0, x = x_95)[name = tensor("transpose_713")]; + tensor input_285 = add(x = x_97, y = inputs_19)[name = tensor("input_285")]; + tensor fsmn_memory_19 = mul(x = input_285, y = mask_7)[name = tensor("fsmn_memory_19")]; + tensor var_1231 = const()[name = tensor("op_1231"), val = tensor(0x1.6a09e6p-4)]; + tensor q_h_39 = mul(x = var_1206, y = var_1231)[name = tensor("q_h_39")]; + tensor scores_37_transpose_x_0 = const()[name = tensor("scores_37_transpose_x_0"), val = tensor(false)]; + tensor scores_37_transpose_y_0 = const()[name = tensor("scores_37_transpose_y_0"), val = tensor(false)]; + tensor transpose_228_perm_0 = const()[name = tensor("transpose_228_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_229_perm_0 = const()[name = tensor("transpose_229_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_229 = transpose(perm = transpose_229_perm_0, x = var_1209)[name = tensor("transpose_711")]; + tensor transpose_228 = transpose(perm = transpose_228_perm_0, x = q_h_39)[name = tensor("transpose_712")]; + tensor scores_37 = matmul(transpose_x = scores_37_transpose_x_0, transpose_y = scores_37_transpose_y_0, x = transpose_228, y = transpose_229)[name = tensor("scores_37")]; + tensor scores_39 = select(a = var_48, b = scores_37, cond = mask_9)[name = tensor("scores_39")]; + tensor var_1239 = softmax(axis = var_61, x = scores_39)[name = tensor("op_1239")]; + tensor input_287 = select(a = var_53, b = var_1239, cond = mask_9)[name = tensor("input_287")]; + tensor x_101_transpose_x_0 = const()[name = tensor("x_101_transpose_x_0"), val = tensor(false)]; + tensor x_101_transpose_y_0 = const()[name = tensor("x_101_transpose_y_0"), val = tensor(false)]; + tensor value_19 = transpose(perm = value_19_perm_0, x = var_1212)[name = tensor("transpose_715")]; + tensor x_101 = matmul(transpose_x = x_101_transpose_x_0, transpose_y = x_101_transpose_y_0, x = input_287, y = value_19)[name = tensor("x_101")]; + tensor var_1243_perm_0 = const()[name = tensor("op_1243_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1245 = const()[name = tensor("op_1245"), val = tensor([1, -1, 512])]; + tensor var_1243 = transpose(perm = var_1243_perm_0, x = x_101)[name = tensor("transpose_710")]; + tensor input_289 = reshape(shape = var_1245, x = var_1243)[name = tensor("input_289")]; + tensor att_outs_19 = linear(bias = encoder_encoders_8_self_attn_linear_out_bias, weight = encoder_encoders_8_self_attn_linear_out_weight, x = input_289)[name = tensor("linear_37")]; + tensor input_291 = add(x = att_outs_19, y = fsmn_memory_19)[name = tensor("input_291")]; + tensor input_293 = add(x = input_277, y = input_291)[name = tensor("input_293")]; + tensor const_122 = const()[name = tensor("const_122"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939581440)))]; + tensor const_123 = const()[name = tensor("const_123"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939583552)))]; + tensor output_39_axes_0 = const()[name = tensor("output_39_axes_0"), val = tensor([-1])]; + tensor output_39 = layer_norm(axes = output_39_axes_0, beta = const_123, epsilon = var_46, gamma = const_122, x = input_293)[name = tensor("output_39")]; + tensor input_299 = linear(bias = encoder_encoders_8_feed_forward_w_1_bias, weight = encoder_encoders_8_feed_forward_w_1_weight, x = output_39)[name = tensor("linear_38")]; + tensor input_301 = relu(x = input_299)[name = tensor("input_301")]; + tensor input_305 = linear(bias = encoder_encoders_8_feed_forward_w_2_bias, weight = encoder_encoders_8_feed_forward_w_2_weight, x = input_301)[name = tensor("linear_39")]; + tensor input_307 = add(x = input_293, y = input_305)[name = tensor("input_307")]; + tensor const_124 = const()[name = tensor("const_124"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939585664)))]; + tensor const_125 = const()[name = tensor("const_125"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939587776)))]; + tensor output_41_axes_0 = const()[name = tensor("output_41_axes_0"), val = tensor([-1])]; + tensor output_41 = layer_norm(axes = output_41_axes_0, beta = const_125, epsilon = var_46, gamma = const_124, x = input_307)[name = tensor("output_41")]; + tensor var_1302 = linear(bias = encoder_encoders_9_self_attn_linear_q_k_v_bias, weight = encoder_encoders_9_self_attn_linear_q_k_v_weight, x = output_41)[name = tensor("linear_40")]; + tensor tile_10 = const()[name = tensor("tile_10"), val = tensor([512, 512, 512])]; + tensor var_1303_axis_0 = const()[name = tensor("op_1303_axis_0"), val = tensor(-1)]; + tensor var_1303_0, tensor var_1303_1, tensor var_1303_2 = split(axis = var_1303_axis_0, split_sizes = tile_10, x = var_1302)[name = tensor("op_1303")]; + tensor var_1307 = const()[name = tensor("op_1307"), val = tensor([1, 1804, 4, 128])]; + tensor var_1308 = reshape(shape = var_1307, x = var_1303_0)[name = tensor("op_1308")]; + tensor var_1310 = const()[name = tensor("op_1310"), val = tensor([1, 1804, 4, 128])]; + tensor var_1311 = reshape(shape = var_1310, x = var_1303_1)[name = tensor("op_1311")]; + tensor var_1313 = const()[name = tensor("op_1313"), val = tensor([1, 1804, 4, 128])]; + tensor var_1314 = reshape(shape = var_1313, x = var_1303_2)[name = tensor("op_1314")]; + tensor value_21_perm_0 = const()[name = tensor("value_21_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor inputs_21 = mul(x = var_1303_2, y = mask_7)[name = tensor("inputs_21")]; + tensor input_311_perm_0 = const()[name = tensor("input_311_perm_0"), val = tensor([0, 2, 1])]; + tensor const_131 = const()[name = tensor("const_131"), val = tensor(0x0p+0)]; + tensor input_313_pad_0 = const()[name = tensor("input_313_pad_0"), val = tensor([0, 0, 0, 0, 5, 5])]; + tensor input_313_mode_0 = const()[name = tensor("input_313_mode_0"), val = tensor("constant")]; + tensor input_311 = transpose(perm = input_311_perm_0, x = inputs_21)[name = tensor("transpose_708")]; + tensor input_313 = pad(constant_val = const_131, mode = input_313_mode_0, pad = input_313_pad_0, x = input_311)[name = tensor("input_313")]; + tensor x_105_pad_type_0 = const()[name = tensor("x_105_pad_type_0"), val = tensor("valid")]; + tensor x_105_groups_0 = const()[name = tensor("x_105_groups_0"), val = tensor(512)]; + tensor x_105_strides_0 = const()[name = tensor("x_105_strides_0"), val = tensor([1])]; + tensor x_105_pad_0 = const()[name = tensor("x_105_pad_0"), val = tensor([0, 0])]; + tensor x_105_dilations_0 = const()[name = tensor("x_105_dilations_0"), val = tensor([1])]; + tensor x_105 = conv(dilations = x_105_dilations_0, groups = x_105_groups_0, pad = x_105_pad_0, pad_type = x_105_pad_type_0, strides = x_105_strides_0, weight = encoder_encoders_9_self_attn_fsmn_block_weight, x = input_313)[name = tensor("x_105")]; + tensor x_107_perm_0 = const()[name = tensor("x_107_perm_0"), val = tensor([0, 2, 1])]; + tensor x_107 = transpose(perm = x_107_perm_0, x = x_105)[name = tensor("transpose_707")]; + tensor input_315 = add(x = x_107, y = inputs_21)[name = tensor("input_315")]; + tensor fsmn_memory_21 = mul(x = input_315, y = mask_7)[name = tensor("fsmn_memory_21")]; + tensor var_1333 = const()[name = tensor("op_1333"), val = tensor(0x1.6a09e6p-4)]; + tensor q_h_43 = mul(x = var_1308, y = var_1333)[name = tensor("q_h_43")]; + tensor scores_41_transpose_x_0 = const()[name = tensor("scores_41_transpose_x_0"), val = tensor(false)]; + tensor scores_41_transpose_y_0 = const()[name = tensor("scores_41_transpose_y_0"), val = tensor(false)]; + tensor transpose_230_perm_0 = const()[name = tensor("transpose_230_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_231_perm_0 = const()[name = tensor("transpose_231_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_231 = transpose(perm = transpose_231_perm_0, x = var_1311)[name = tensor("transpose_705")]; + tensor transpose_230 = transpose(perm = transpose_230_perm_0, x = q_h_43)[name = tensor("transpose_706")]; + tensor scores_41 = matmul(transpose_x = scores_41_transpose_x_0, transpose_y = scores_41_transpose_y_0, x = transpose_230, y = transpose_231)[name = tensor("scores_41")]; + tensor scores_43 = select(a = var_48, b = scores_41, cond = mask_9)[name = tensor("scores_43")]; + tensor var_1341 = softmax(axis = var_61, x = scores_43)[name = tensor("op_1341")]; + tensor input_317 = select(a = var_53, b = var_1341, cond = mask_9)[name = tensor("input_317")]; + tensor x_111_transpose_x_0 = const()[name = tensor("x_111_transpose_x_0"), val = tensor(false)]; + tensor x_111_transpose_y_0 = const()[name = tensor("x_111_transpose_y_0"), val = tensor(false)]; + tensor value_21 = transpose(perm = value_21_perm_0, x = var_1314)[name = tensor("transpose_709")]; + tensor x_111 = matmul(transpose_x = x_111_transpose_x_0, transpose_y = x_111_transpose_y_0, x = input_317, y = value_21)[name = tensor("x_111")]; + tensor var_1345_perm_0 = const()[name = tensor("op_1345_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1347 = const()[name = tensor("op_1347"), val = tensor([1, -1, 512])]; + tensor var_1345 = transpose(perm = var_1345_perm_0, x = x_111)[name = tensor("transpose_704")]; + tensor input_319 = reshape(shape = var_1347, x = var_1345)[name = tensor("input_319")]; + tensor att_outs_21 = linear(bias = encoder_encoders_9_self_attn_linear_out_bias, weight = encoder_encoders_9_self_attn_linear_out_weight, x = input_319)[name = tensor("linear_41")]; + tensor input_321 = add(x = att_outs_21, y = fsmn_memory_21)[name = tensor("input_321")]; + tensor input_323 = add(x = input_307, y = input_321)[name = tensor("input_323")]; + tensor const_133 = const()[name = tensor("const_133"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939589888)))]; + tensor const_134 = const()[name = tensor("const_134"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939592000)))]; + tensor output_43_axes_0 = const()[name = tensor("output_43_axes_0"), val = tensor([-1])]; + tensor output_43 = layer_norm(axes = output_43_axes_0, beta = const_134, epsilon = var_46, gamma = const_133, x = input_323)[name = tensor("output_43")]; + tensor input_329 = linear(bias = encoder_encoders_9_feed_forward_w_1_bias, weight = encoder_encoders_9_feed_forward_w_1_weight, x = output_43)[name = tensor("linear_42")]; + tensor input_331 = relu(x = input_329)[name = tensor("input_331")]; + tensor input_335 = linear(bias = encoder_encoders_9_feed_forward_w_2_bias, weight = encoder_encoders_9_feed_forward_w_2_weight, x = input_331)[name = tensor("linear_43")]; + tensor input_337 = add(x = input_323, y = input_335)[name = tensor("input_337")]; + tensor const_135 = const()[name = tensor("const_135"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939594112)))]; + tensor const_136 = const()[name = tensor("const_136"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939596224)))]; + tensor output_45_axes_0 = const()[name = tensor("output_45_axes_0"), val = tensor([-1])]; + tensor output_45 = layer_norm(axes = output_45_axes_0, beta = const_136, epsilon = var_46, gamma = const_135, x = input_337)[name = tensor("output_45")]; + tensor var_1404 = linear(bias = encoder_encoders_10_self_attn_linear_q_k_v_bias, weight = encoder_encoders_10_self_attn_linear_q_k_v_weight, x = output_45)[name = tensor("linear_44")]; + tensor tile_11 = const()[name = tensor("tile_11"), val = tensor([512, 512, 512])]; + tensor var_1405_axis_0 = const()[name = tensor("op_1405_axis_0"), val = tensor(-1)]; + tensor var_1405_0, tensor var_1405_1, tensor var_1405_2 = split(axis = var_1405_axis_0, split_sizes = tile_11, x = var_1404)[name = tensor("op_1405")]; + tensor var_1409 = const()[name = tensor("op_1409"), val = tensor([1, 1804, 4, 128])]; + tensor var_1410 = reshape(shape = var_1409, x = var_1405_0)[name = tensor("op_1410")]; + tensor var_1412 = const()[name = tensor("op_1412"), val = tensor([1, 1804, 4, 128])]; + tensor var_1413 = reshape(shape = var_1412, x = var_1405_1)[name = tensor("op_1413")]; + tensor var_1415 = const()[name = tensor("op_1415"), val = tensor([1, 1804, 4, 128])]; + tensor var_1416 = reshape(shape = var_1415, x = var_1405_2)[name = tensor("op_1416")]; + tensor value_23_perm_0 = const()[name = tensor("value_23_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor inputs_23 = mul(x = var_1405_2, y = mask_7)[name = tensor("inputs_23")]; + tensor input_341_perm_0 = const()[name = tensor("input_341_perm_0"), val = tensor([0, 2, 1])]; + tensor const_142 = const()[name = tensor("const_142"), val = tensor(0x0p+0)]; + tensor input_343_pad_0 = const()[name = tensor("input_343_pad_0"), val = tensor([0, 0, 0, 0, 5, 5])]; + tensor input_343_mode_0 = const()[name = tensor("input_343_mode_0"), val = tensor("constant")]; + tensor input_341 = transpose(perm = input_341_perm_0, x = inputs_23)[name = tensor("transpose_702")]; + tensor input_343 = pad(constant_val = const_142, mode = input_343_mode_0, pad = input_343_pad_0, x = input_341)[name = tensor("input_343")]; + tensor x_115_pad_type_0 = const()[name = tensor("x_115_pad_type_0"), val = tensor("valid")]; + tensor x_115_groups_0 = const()[name = tensor("x_115_groups_0"), val = tensor(512)]; + tensor x_115_strides_0 = const()[name = tensor("x_115_strides_0"), val = tensor([1])]; + tensor x_115_pad_0 = const()[name = tensor("x_115_pad_0"), val = tensor([0, 0])]; + tensor x_115_dilations_0 = const()[name = tensor("x_115_dilations_0"), val = tensor([1])]; + tensor x_115 = conv(dilations = x_115_dilations_0, groups = x_115_groups_0, pad = x_115_pad_0, pad_type = x_115_pad_type_0, strides = x_115_strides_0, weight = encoder_encoders_10_self_attn_fsmn_block_weight, x = input_343)[name = tensor("x_115")]; + tensor x_117_perm_0 = const()[name = tensor("x_117_perm_0"), val = tensor([0, 2, 1])]; + tensor x_117 = transpose(perm = x_117_perm_0, x = x_115)[name = tensor("transpose_701")]; + tensor input_345 = add(x = x_117, y = inputs_23)[name = tensor("input_345")]; + tensor fsmn_memory_23 = mul(x = input_345, y = mask_7)[name = tensor("fsmn_memory_23")]; + tensor var_1435 = const()[name = tensor("op_1435"), val = tensor(0x1.6a09e6p-4)]; + tensor q_h_47 = mul(x = var_1410, y = var_1435)[name = tensor("q_h_47")]; + tensor scores_45_transpose_x_0 = const()[name = tensor("scores_45_transpose_x_0"), val = tensor(false)]; + tensor scores_45_transpose_y_0 = const()[name = tensor("scores_45_transpose_y_0"), val = tensor(false)]; + tensor transpose_232_perm_0 = const()[name = tensor("transpose_232_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_233_perm_0 = const()[name = tensor("transpose_233_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_233 = transpose(perm = transpose_233_perm_0, x = var_1413)[name = tensor("transpose_699")]; + tensor transpose_232 = transpose(perm = transpose_232_perm_0, x = q_h_47)[name = tensor("transpose_700")]; + tensor scores_45 = matmul(transpose_x = scores_45_transpose_x_0, transpose_y = scores_45_transpose_y_0, x = transpose_232, y = transpose_233)[name = tensor("scores_45")]; + tensor scores_47 = select(a = var_48, b = scores_45, cond = mask_9)[name = tensor("scores_47")]; + tensor var_1443 = softmax(axis = var_61, x = scores_47)[name = tensor("op_1443")]; + tensor input_347 = select(a = var_53, b = var_1443, cond = mask_9)[name = tensor("input_347")]; + tensor x_121_transpose_x_0 = const()[name = tensor("x_121_transpose_x_0"), val = tensor(false)]; + tensor x_121_transpose_y_0 = const()[name = tensor("x_121_transpose_y_0"), val = tensor(false)]; + tensor value_23 = transpose(perm = value_23_perm_0, x = var_1416)[name = tensor("transpose_703")]; + tensor x_121 = matmul(transpose_x = x_121_transpose_x_0, transpose_y = x_121_transpose_y_0, x = input_347, y = value_23)[name = tensor("x_121")]; + tensor var_1447_perm_0 = const()[name = tensor("op_1447_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1449 = const()[name = tensor("op_1449"), val = tensor([1, -1, 512])]; + tensor var_1447 = transpose(perm = var_1447_perm_0, x = x_121)[name = tensor("transpose_698")]; + tensor input_349 = reshape(shape = var_1449, x = var_1447)[name = tensor("input_349")]; + tensor att_outs_23 = linear(bias = encoder_encoders_10_self_attn_linear_out_bias, weight = encoder_encoders_10_self_attn_linear_out_weight, x = input_349)[name = tensor("linear_45")]; + tensor input_351 = add(x = att_outs_23, y = fsmn_memory_23)[name = tensor("input_351")]; + tensor input_353 = add(x = input_337, y = input_351)[name = tensor("input_353")]; + tensor const_144 = const()[name = tensor("const_144"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939598336)))]; + tensor const_145 = const()[name = tensor("const_145"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939600448)))]; + tensor output_47_axes_0 = const()[name = tensor("output_47_axes_0"), val = tensor([-1])]; + tensor output_47 = layer_norm(axes = output_47_axes_0, beta = const_145, epsilon = var_46, gamma = const_144, x = input_353)[name = tensor("output_47")]; + tensor input_359 = linear(bias = encoder_encoders_10_feed_forward_w_1_bias, weight = encoder_encoders_10_feed_forward_w_1_weight, x = output_47)[name = tensor("linear_46")]; + tensor input_361 = relu(x = input_359)[name = tensor("input_361")]; + tensor input_365 = linear(bias = encoder_encoders_10_feed_forward_w_2_bias, weight = encoder_encoders_10_feed_forward_w_2_weight, x = input_361)[name = tensor("linear_47")]; + tensor input_367 = add(x = input_353, y = input_365)[name = tensor("input_367")]; + tensor const_146 = const()[name = tensor("const_146"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939602560)))]; + tensor const_147 = const()[name = tensor("const_147"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939604672)))]; + tensor output_49_axes_0 = const()[name = tensor("output_49_axes_0"), val = tensor([-1])]; + tensor output_49 = layer_norm(axes = output_49_axes_0, beta = const_147, epsilon = var_46, gamma = const_146, x = input_367)[name = tensor("output_49")]; + tensor var_1506 = linear(bias = encoder_encoders_11_self_attn_linear_q_k_v_bias, weight = encoder_encoders_11_self_attn_linear_q_k_v_weight, x = output_49)[name = tensor("linear_48")]; + tensor tile_12 = const()[name = tensor("tile_12"), val = tensor([512, 512, 512])]; + tensor var_1507_axis_0 = const()[name = tensor("op_1507_axis_0"), val = tensor(-1)]; + tensor var_1507_0, tensor var_1507_1, tensor var_1507_2 = split(axis = var_1507_axis_0, split_sizes = tile_12, x = var_1506)[name = tensor("op_1507")]; + tensor var_1511 = const()[name = tensor("op_1511"), val = tensor([1, 1804, 4, 128])]; + tensor var_1512 = reshape(shape = var_1511, x = var_1507_0)[name = tensor("op_1512")]; + tensor var_1514 = const()[name = tensor("op_1514"), val = tensor([1, 1804, 4, 128])]; + tensor var_1515 = reshape(shape = var_1514, x = var_1507_1)[name = tensor("op_1515")]; + tensor var_1517 = const()[name = tensor("op_1517"), val = tensor([1, 1804, 4, 128])]; + tensor var_1518 = reshape(shape = var_1517, x = var_1507_2)[name = tensor("op_1518")]; + tensor value_25_perm_0 = const()[name = tensor("value_25_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor inputs_25 = mul(x = var_1507_2, y = mask_7)[name = tensor("inputs_25")]; + tensor input_371_perm_0 = const()[name = tensor("input_371_perm_0"), val = tensor([0, 2, 1])]; + tensor const_153 = const()[name = tensor("const_153"), val = tensor(0x0p+0)]; + tensor input_373_pad_0 = const()[name = tensor("input_373_pad_0"), val = tensor([0, 0, 0, 0, 5, 5])]; + tensor input_373_mode_0 = const()[name = tensor("input_373_mode_0"), val = tensor("constant")]; + tensor input_371 = transpose(perm = input_371_perm_0, x = inputs_25)[name = tensor("transpose_696")]; + tensor input_373 = pad(constant_val = const_153, mode = input_373_mode_0, pad = input_373_pad_0, x = input_371)[name = tensor("input_373")]; + tensor x_125_pad_type_0 = const()[name = tensor("x_125_pad_type_0"), val = tensor("valid")]; + tensor x_125_groups_0 = const()[name = tensor("x_125_groups_0"), val = tensor(512)]; + tensor x_125_strides_0 = const()[name = tensor("x_125_strides_0"), val = tensor([1])]; + tensor x_125_pad_0 = const()[name = tensor("x_125_pad_0"), val = tensor([0, 0])]; + tensor x_125_dilations_0 = const()[name = tensor("x_125_dilations_0"), val = tensor([1])]; + tensor x_125 = conv(dilations = x_125_dilations_0, groups = x_125_groups_0, pad = x_125_pad_0, pad_type = x_125_pad_type_0, strides = x_125_strides_0, weight = encoder_encoders_11_self_attn_fsmn_block_weight, x = input_373)[name = tensor("x_125")]; + tensor x_127_perm_0 = const()[name = tensor("x_127_perm_0"), val = tensor([0, 2, 1])]; + tensor x_127 = transpose(perm = x_127_perm_0, x = x_125)[name = tensor("transpose_695")]; + tensor input_375 = add(x = x_127, y = inputs_25)[name = tensor("input_375")]; + tensor fsmn_memory_25 = mul(x = input_375, y = mask_7)[name = tensor("fsmn_memory_25")]; + tensor var_1537 = const()[name = tensor("op_1537"), val = tensor(0x1.6a09e6p-4)]; + tensor q_h_51 = mul(x = var_1512, y = var_1537)[name = tensor("q_h_51")]; + tensor scores_49_transpose_x_0 = const()[name = tensor("scores_49_transpose_x_0"), val = tensor(false)]; + tensor scores_49_transpose_y_0 = const()[name = tensor("scores_49_transpose_y_0"), val = tensor(false)]; + tensor transpose_234_perm_0 = const()[name = tensor("transpose_234_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_235_perm_0 = const()[name = tensor("transpose_235_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_235 = transpose(perm = transpose_235_perm_0, x = var_1515)[name = tensor("transpose_693")]; + tensor transpose_234 = transpose(perm = transpose_234_perm_0, x = q_h_51)[name = tensor("transpose_694")]; + tensor scores_49 = matmul(transpose_x = scores_49_transpose_x_0, transpose_y = scores_49_transpose_y_0, x = transpose_234, y = transpose_235)[name = tensor("scores_49")]; + tensor scores_51 = select(a = var_48, b = scores_49, cond = mask_9)[name = tensor("scores_51")]; + tensor var_1545 = softmax(axis = var_61, x = scores_51)[name = tensor("op_1545")]; + tensor input_377 = select(a = var_53, b = var_1545, cond = mask_9)[name = tensor("input_377")]; + tensor x_131_transpose_x_0 = const()[name = tensor("x_131_transpose_x_0"), val = tensor(false)]; + tensor x_131_transpose_y_0 = const()[name = tensor("x_131_transpose_y_0"), val = tensor(false)]; + tensor value_25 = transpose(perm = value_25_perm_0, x = var_1518)[name = tensor("transpose_697")]; + tensor x_131 = matmul(transpose_x = x_131_transpose_x_0, transpose_y = x_131_transpose_y_0, x = input_377, y = value_25)[name = tensor("x_131")]; + tensor var_1549_perm_0 = const()[name = tensor("op_1549_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1551 = const()[name = tensor("op_1551"), val = tensor([1, -1, 512])]; + tensor var_1549 = transpose(perm = var_1549_perm_0, x = x_131)[name = tensor("transpose_692")]; + tensor input_379 = reshape(shape = var_1551, x = var_1549)[name = tensor("input_379")]; + tensor att_outs_25 = linear(bias = encoder_encoders_11_self_attn_linear_out_bias, weight = encoder_encoders_11_self_attn_linear_out_weight, x = input_379)[name = tensor("linear_49")]; + tensor input_381 = add(x = att_outs_25, y = fsmn_memory_25)[name = tensor("input_381")]; + tensor input_383 = add(x = input_367, y = input_381)[name = tensor("input_383")]; + tensor const_155 = const()[name = tensor("const_155"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939606784)))]; + tensor const_156 = const()[name = tensor("const_156"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939608896)))]; + tensor output_51_axes_0 = const()[name = tensor("output_51_axes_0"), val = tensor([-1])]; + tensor output_51 = layer_norm(axes = output_51_axes_0, beta = const_156, epsilon = var_46, gamma = const_155, x = input_383)[name = tensor("output_51")]; + tensor input_389 = linear(bias = encoder_encoders_11_feed_forward_w_1_bias, weight = encoder_encoders_11_feed_forward_w_1_weight, x = output_51)[name = tensor("linear_50")]; + tensor input_391 = relu(x = input_389)[name = tensor("input_391")]; + tensor input_395 = linear(bias = encoder_encoders_11_feed_forward_w_2_bias, weight = encoder_encoders_11_feed_forward_w_2_weight, x = input_391)[name = tensor("linear_51")]; + tensor input_397 = add(x = input_383, y = input_395)[name = tensor("input_397")]; + tensor const_157 = const()[name = tensor("const_157"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939611008)))]; + tensor const_158 = const()[name = tensor("const_158"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939613120)))]; + tensor output_53_axes_0 = const()[name = tensor("output_53_axes_0"), val = tensor([-1])]; + tensor output_53 = layer_norm(axes = output_53_axes_0, beta = const_158, epsilon = var_46, gamma = const_157, x = input_397)[name = tensor("output_53")]; + tensor var_1608 = linear(bias = encoder_encoders_12_self_attn_linear_q_k_v_bias, weight = encoder_encoders_12_self_attn_linear_q_k_v_weight, x = output_53)[name = tensor("linear_52")]; + tensor tile_13 = const()[name = tensor("tile_13"), val = tensor([512, 512, 512])]; + tensor var_1609_axis_0 = const()[name = tensor("op_1609_axis_0"), val = tensor(-1)]; + tensor var_1609_0, tensor var_1609_1, tensor var_1609_2 = split(axis = var_1609_axis_0, split_sizes = tile_13, x = var_1608)[name = tensor("op_1609")]; + tensor var_1613 = const()[name = tensor("op_1613"), val = tensor([1, 1804, 4, 128])]; + tensor var_1614 = reshape(shape = var_1613, x = var_1609_0)[name = tensor("op_1614")]; + tensor var_1616 = const()[name = tensor("op_1616"), val = tensor([1, 1804, 4, 128])]; + tensor var_1617 = reshape(shape = var_1616, x = var_1609_1)[name = tensor("op_1617")]; + tensor var_1619 = const()[name = tensor("op_1619"), val = tensor([1, 1804, 4, 128])]; + tensor var_1620 = reshape(shape = var_1619, x = var_1609_2)[name = tensor("op_1620")]; + tensor value_27_perm_0 = const()[name = tensor("value_27_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor inputs_27 = mul(x = var_1609_2, y = mask_7)[name = tensor("inputs_27")]; + tensor input_401_perm_0 = const()[name = tensor("input_401_perm_0"), val = tensor([0, 2, 1])]; + tensor const_164 = const()[name = tensor("const_164"), val = tensor(0x0p+0)]; + tensor input_403_pad_0 = const()[name = tensor("input_403_pad_0"), val = tensor([0, 0, 0, 0, 5, 5])]; + tensor input_403_mode_0 = const()[name = tensor("input_403_mode_0"), val = tensor("constant")]; + tensor input_401 = transpose(perm = input_401_perm_0, x = inputs_27)[name = tensor("transpose_690")]; + tensor input_403 = pad(constant_val = const_164, mode = input_403_mode_0, pad = input_403_pad_0, x = input_401)[name = tensor("input_403")]; + tensor x_135_pad_type_0 = const()[name = tensor("x_135_pad_type_0"), val = tensor("valid")]; + tensor x_135_groups_0 = const()[name = tensor("x_135_groups_0"), val = tensor(512)]; + tensor x_135_strides_0 = const()[name = tensor("x_135_strides_0"), val = tensor([1])]; + tensor x_135_pad_0 = const()[name = tensor("x_135_pad_0"), val = tensor([0, 0])]; + tensor x_135_dilations_0 = const()[name = tensor("x_135_dilations_0"), val = tensor([1])]; + tensor x_135 = conv(dilations = x_135_dilations_0, groups = x_135_groups_0, pad = x_135_pad_0, pad_type = x_135_pad_type_0, strides = x_135_strides_0, weight = encoder_encoders_12_self_attn_fsmn_block_weight, x = input_403)[name = tensor("x_135")]; + tensor x_137_perm_0 = const()[name = tensor("x_137_perm_0"), val = tensor([0, 2, 1])]; + tensor x_137 = transpose(perm = x_137_perm_0, x = x_135)[name = tensor("transpose_689")]; + tensor input_405 = add(x = x_137, y = inputs_27)[name = tensor("input_405")]; + tensor fsmn_memory_27 = mul(x = input_405, y = mask_7)[name = tensor("fsmn_memory_27")]; + tensor var_1639 = const()[name = tensor("op_1639"), val = tensor(0x1.6a09e6p-4)]; + tensor q_h_55 = mul(x = var_1614, y = var_1639)[name = tensor("q_h_55")]; + tensor scores_53_transpose_x_0 = const()[name = tensor("scores_53_transpose_x_0"), val = tensor(false)]; + tensor scores_53_transpose_y_0 = const()[name = tensor("scores_53_transpose_y_0"), val = tensor(false)]; + tensor transpose_236_perm_0 = const()[name = tensor("transpose_236_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_237_perm_0 = const()[name = tensor("transpose_237_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_237 = transpose(perm = transpose_237_perm_0, x = var_1617)[name = tensor("transpose_687")]; + tensor transpose_236 = transpose(perm = transpose_236_perm_0, x = q_h_55)[name = tensor("transpose_688")]; + tensor scores_53 = matmul(transpose_x = scores_53_transpose_x_0, transpose_y = scores_53_transpose_y_0, x = transpose_236, y = transpose_237)[name = tensor("scores_53")]; + tensor scores_55 = select(a = var_48, b = scores_53, cond = mask_9)[name = tensor("scores_55")]; + tensor var_1647 = softmax(axis = var_61, x = scores_55)[name = tensor("op_1647")]; + tensor input_407 = select(a = var_53, b = var_1647, cond = mask_9)[name = tensor("input_407")]; + tensor x_141_transpose_x_0 = const()[name = tensor("x_141_transpose_x_0"), val = tensor(false)]; + tensor x_141_transpose_y_0 = const()[name = tensor("x_141_transpose_y_0"), val = tensor(false)]; + tensor value_27 = transpose(perm = value_27_perm_0, x = var_1620)[name = tensor("transpose_691")]; + tensor x_141 = matmul(transpose_x = x_141_transpose_x_0, transpose_y = x_141_transpose_y_0, x = input_407, y = value_27)[name = tensor("x_141")]; + tensor var_1651_perm_0 = const()[name = tensor("op_1651_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1653 = const()[name = tensor("op_1653"), val = tensor([1, -1, 512])]; + tensor var_1651 = transpose(perm = var_1651_perm_0, x = x_141)[name = tensor("transpose_686")]; + tensor input_409 = reshape(shape = var_1653, x = var_1651)[name = tensor("input_409")]; + tensor att_outs_27 = linear(bias = encoder_encoders_12_self_attn_linear_out_bias, weight = encoder_encoders_12_self_attn_linear_out_weight, x = input_409)[name = tensor("linear_53")]; + tensor input_411 = add(x = att_outs_27, y = fsmn_memory_27)[name = tensor("input_411")]; + tensor input_413 = add(x = input_397, y = input_411)[name = tensor("input_413")]; + tensor const_166 = const()[name = tensor("const_166"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939615232)))]; + tensor const_167 = const()[name = tensor("const_167"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939617344)))]; + tensor output_55_axes_0 = const()[name = tensor("output_55_axes_0"), val = tensor([-1])]; + tensor output_55 = layer_norm(axes = output_55_axes_0, beta = const_167, epsilon = var_46, gamma = const_166, x = input_413)[name = tensor("output_55")]; + tensor input_419 = linear(bias = encoder_encoders_12_feed_forward_w_1_bias, weight = encoder_encoders_12_feed_forward_w_1_weight, x = output_55)[name = tensor("linear_54")]; + tensor input_421 = relu(x = input_419)[name = tensor("input_421")]; + tensor input_425 = linear(bias = encoder_encoders_12_feed_forward_w_2_bias, weight = encoder_encoders_12_feed_forward_w_2_weight, x = input_421)[name = tensor("linear_55")]; + tensor input_427 = add(x = input_413, y = input_425)[name = tensor("input_427")]; + tensor const_168 = const()[name = tensor("const_168"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939619456)))]; + tensor const_169 = const()[name = tensor("const_169"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939621568)))]; + tensor output_57_axes_0 = const()[name = tensor("output_57_axes_0"), val = tensor([-1])]; + tensor output_57 = layer_norm(axes = output_57_axes_0, beta = const_169, epsilon = var_46, gamma = const_168, x = input_427)[name = tensor("output_57")]; + tensor var_1710 = linear(bias = encoder_encoders_13_self_attn_linear_q_k_v_bias, weight = encoder_encoders_13_self_attn_linear_q_k_v_weight, x = output_57)[name = tensor("linear_56")]; + tensor tile_14 = const()[name = tensor("tile_14"), val = tensor([512, 512, 512])]; + tensor var_1711_axis_0 = const()[name = tensor("op_1711_axis_0"), val = tensor(-1)]; + tensor var_1711_0, tensor var_1711_1, tensor var_1711_2 = split(axis = var_1711_axis_0, split_sizes = tile_14, x = var_1710)[name = tensor("op_1711")]; + tensor var_1715 = const()[name = tensor("op_1715"), val = tensor([1, 1804, 4, 128])]; + tensor var_1716 = reshape(shape = var_1715, x = var_1711_0)[name = tensor("op_1716")]; + tensor var_1718 = const()[name = tensor("op_1718"), val = tensor([1, 1804, 4, 128])]; + tensor var_1719 = reshape(shape = var_1718, x = var_1711_1)[name = tensor("op_1719")]; + tensor var_1721 = const()[name = tensor("op_1721"), val = tensor([1, 1804, 4, 128])]; + tensor var_1722 = reshape(shape = var_1721, x = var_1711_2)[name = tensor("op_1722")]; + tensor value_29_perm_0 = const()[name = tensor("value_29_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor inputs_29 = mul(x = var_1711_2, y = mask_7)[name = tensor("inputs_29")]; + tensor input_431_perm_0 = const()[name = tensor("input_431_perm_0"), val = tensor([0, 2, 1])]; + tensor const_175 = const()[name = tensor("const_175"), val = tensor(0x0p+0)]; + tensor input_433_pad_0 = const()[name = tensor("input_433_pad_0"), val = tensor([0, 0, 0, 0, 5, 5])]; + tensor input_433_mode_0 = const()[name = tensor("input_433_mode_0"), val = tensor("constant")]; + tensor input_431 = transpose(perm = input_431_perm_0, x = inputs_29)[name = tensor("transpose_684")]; + tensor input_433 = pad(constant_val = const_175, mode = input_433_mode_0, pad = input_433_pad_0, x = input_431)[name = tensor("input_433")]; + tensor x_145_pad_type_0 = const()[name = tensor("x_145_pad_type_0"), val = tensor("valid")]; + tensor x_145_groups_0 = const()[name = tensor("x_145_groups_0"), val = tensor(512)]; + tensor x_145_strides_0 = const()[name = tensor("x_145_strides_0"), val = tensor([1])]; + tensor x_145_pad_0 = const()[name = tensor("x_145_pad_0"), val = tensor([0, 0])]; + tensor x_145_dilations_0 = const()[name = tensor("x_145_dilations_0"), val = tensor([1])]; + tensor x_145 = conv(dilations = x_145_dilations_0, groups = x_145_groups_0, pad = x_145_pad_0, pad_type = x_145_pad_type_0, strides = x_145_strides_0, weight = encoder_encoders_13_self_attn_fsmn_block_weight, x = input_433)[name = tensor("x_145")]; + tensor x_147_perm_0 = const()[name = tensor("x_147_perm_0"), val = tensor([0, 2, 1])]; + tensor x_147 = transpose(perm = x_147_perm_0, x = x_145)[name = tensor("transpose_683")]; + tensor input_435 = add(x = x_147, y = inputs_29)[name = tensor("input_435")]; + tensor fsmn_memory_29 = mul(x = input_435, y = mask_7)[name = tensor("fsmn_memory_29")]; + tensor var_1741 = const()[name = tensor("op_1741"), val = tensor(0x1.6a09e6p-4)]; + tensor q_h_59 = mul(x = var_1716, y = var_1741)[name = tensor("q_h_59")]; + tensor scores_57_transpose_x_0 = const()[name = tensor("scores_57_transpose_x_0"), val = tensor(false)]; + tensor scores_57_transpose_y_0 = const()[name = tensor("scores_57_transpose_y_0"), val = tensor(false)]; + tensor transpose_238_perm_0 = const()[name = tensor("transpose_238_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_239_perm_0 = const()[name = tensor("transpose_239_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_239 = transpose(perm = transpose_239_perm_0, x = var_1719)[name = tensor("transpose_681")]; + tensor transpose_238 = transpose(perm = transpose_238_perm_0, x = q_h_59)[name = tensor("transpose_682")]; + tensor scores_57 = matmul(transpose_x = scores_57_transpose_x_0, transpose_y = scores_57_transpose_y_0, x = transpose_238, y = transpose_239)[name = tensor("scores_57")]; + tensor scores_59 = select(a = var_48, b = scores_57, cond = mask_9)[name = tensor("scores_59")]; + tensor var_1749 = softmax(axis = var_61, x = scores_59)[name = tensor("op_1749")]; + tensor input_437 = select(a = var_53, b = var_1749, cond = mask_9)[name = tensor("input_437")]; + tensor x_151_transpose_x_0 = const()[name = tensor("x_151_transpose_x_0"), val = tensor(false)]; + tensor x_151_transpose_y_0 = const()[name = tensor("x_151_transpose_y_0"), val = tensor(false)]; + tensor value_29 = transpose(perm = value_29_perm_0, x = var_1722)[name = tensor("transpose_685")]; + tensor x_151 = matmul(transpose_x = x_151_transpose_x_0, transpose_y = x_151_transpose_y_0, x = input_437, y = value_29)[name = tensor("x_151")]; + tensor var_1753_perm_0 = const()[name = tensor("op_1753_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1755 = const()[name = tensor("op_1755"), val = tensor([1, -1, 512])]; + tensor var_1753 = transpose(perm = var_1753_perm_0, x = x_151)[name = tensor("transpose_680")]; + tensor input_439 = reshape(shape = var_1755, x = var_1753)[name = tensor("input_439")]; + tensor att_outs_29 = linear(bias = encoder_encoders_13_self_attn_linear_out_bias, weight = encoder_encoders_13_self_attn_linear_out_weight, x = input_439)[name = tensor("linear_57")]; + tensor input_441 = add(x = att_outs_29, y = fsmn_memory_29)[name = tensor("input_441")]; + tensor input_443 = add(x = input_427, y = input_441)[name = tensor("input_443")]; + tensor const_177 = const()[name = tensor("const_177"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939623680)))]; + tensor const_178 = const()[name = tensor("const_178"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939625792)))]; + tensor output_59_axes_0 = const()[name = tensor("output_59_axes_0"), val = tensor([-1])]; + tensor output_59 = layer_norm(axes = output_59_axes_0, beta = const_178, epsilon = var_46, gamma = const_177, x = input_443)[name = tensor("output_59")]; + tensor input_449 = linear(bias = encoder_encoders_13_feed_forward_w_1_bias, weight = encoder_encoders_13_feed_forward_w_1_weight, x = output_59)[name = tensor("linear_58")]; + tensor input_451 = relu(x = input_449)[name = tensor("input_451")]; + tensor input_455 = linear(bias = encoder_encoders_13_feed_forward_w_2_bias, weight = encoder_encoders_13_feed_forward_w_2_weight, x = input_451)[name = tensor("linear_59")]; + tensor input_457 = add(x = input_443, y = input_455)[name = tensor("input_457")]; + tensor const_179 = const()[name = tensor("const_179"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939627904)))]; + tensor const_180 = const()[name = tensor("const_180"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939630016)))]; + tensor output_61_axes_0 = const()[name = tensor("output_61_axes_0"), val = tensor([-1])]; + tensor output_61 = layer_norm(axes = output_61_axes_0, beta = const_180, epsilon = var_46, gamma = const_179, x = input_457)[name = tensor("output_61")]; + tensor var_1812 = linear(bias = encoder_encoders_14_self_attn_linear_q_k_v_bias, weight = encoder_encoders_14_self_attn_linear_q_k_v_weight, x = output_61)[name = tensor("linear_60")]; + tensor tile_15 = const()[name = tensor("tile_15"), val = tensor([512, 512, 512])]; + tensor var_1813_axis_0 = const()[name = tensor("op_1813_axis_0"), val = tensor(-1)]; + tensor var_1813_0, tensor var_1813_1, tensor var_1813_2 = split(axis = var_1813_axis_0, split_sizes = tile_15, x = var_1812)[name = tensor("op_1813")]; + tensor var_1817 = const()[name = tensor("op_1817"), val = tensor([1, 1804, 4, 128])]; + tensor var_1818 = reshape(shape = var_1817, x = var_1813_0)[name = tensor("op_1818")]; + tensor var_1820 = const()[name = tensor("op_1820"), val = tensor([1, 1804, 4, 128])]; + tensor var_1821 = reshape(shape = var_1820, x = var_1813_1)[name = tensor("op_1821")]; + tensor var_1823 = const()[name = tensor("op_1823"), val = tensor([1, 1804, 4, 128])]; + tensor var_1824 = reshape(shape = var_1823, x = var_1813_2)[name = tensor("op_1824")]; + tensor value_31_perm_0 = const()[name = tensor("value_31_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor inputs_31 = mul(x = var_1813_2, y = mask_7)[name = tensor("inputs_31")]; + tensor input_461_perm_0 = const()[name = tensor("input_461_perm_0"), val = tensor([0, 2, 1])]; + tensor const_186 = const()[name = tensor("const_186"), val = tensor(0x0p+0)]; + tensor input_463_pad_0 = const()[name = tensor("input_463_pad_0"), val = tensor([0, 0, 0, 0, 5, 5])]; + tensor input_463_mode_0 = const()[name = tensor("input_463_mode_0"), val = tensor("constant")]; + tensor input_461 = transpose(perm = input_461_perm_0, x = inputs_31)[name = tensor("transpose_678")]; + tensor input_463 = pad(constant_val = const_186, mode = input_463_mode_0, pad = input_463_pad_0, x = input_461)[name = tensor("input_463")]; + tensor x_155_pad_type_0 = const()[name = tensor("x_155_pad_type_0"), val = tensor("valid")]; + tensor x_155_groups_0 = const()[name = tensor("x_155_groups_0"), val = tensor(512)]; + tensor x_155_strides_0 = const()[name = tensor("x_155_strides_0"), val = tensor([1])]; + tensor x_155_pad_0 = const()[name = tensor("x_155_pad_0"), val = tensor([0, 0])]; + tensor x_155_dilations_0 = const()[name = tensor("x_155_dilations_0"), val = tensor([1])]; + tensor x_155 = conv(dilations = x_155_dilations_0, groups = x_155_groups_0, pad = x_155_pad_0, pad_type = x_155_pad_type_0, strides = x_155_strides_0, weight = encoder_encoders_14_self_attn_fsmn_block_weight, x = input_463)[name = tensor("x_155")]; + tensor x_157_perm_0 = const()[name = tensor("x_157_perm_0"), val = tensor([0, 2, 1])]; + tensor x_157 = transpose(perm = x_157_perm_0, x = x_155)[name = tensor("transpose_677")]; + tensor input_465 = add(x = x_157, y = inputs_31)[name = tensor("input_465")]; + tensor fsmn_memory_31 = mul(x = input_465, y = mask_7)[name = tensor("fsmn_memory_31")]; + tensor var_1843 = const()[name = tensor("op_1843"), val = tensor(0x1.6a09e6p-4)]; + tensor q_h_63 = mul(x = var_1818, y = var_1843)[name = tensor("q_h_63")]; + tensor scores_61_transpose_x_0 = const()[name = tensor("scores_61_transpose_x_0"), val = tensor(false)]; + tensor scores_61_transpose_y_0 = const()[name = tensor("scores_61_transpose_y_0"), val = tensor(false)]; + tensor transpose_240_perm_0 = const()[name = tensor("transpose_240_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_241_perm_0 = const()[name = tensor("transpose_241_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_241 = transpose(perm = transpose_241_perm_0, x = var_1821)[name = tensor("transpose_675")]; + tensor transpose_240 = transpose(perm = transpose_240_perm_0, x = q_h_63)[name = tensor("transpose_676")]; + tensor scores_61 = matmul(transpose_x = scores_61_transpose_x_0, transpose_y = scores_61_transpose_y_0, x = transpose_240, y = transpose_241)[name = tensor("scores_61")]; + tensor scores_63 = select(a = var_48, b = scores_61, cond = mask_9)[name = tensor("scores_63")]; + tensor var_1851 = softmax(axis = var_61, x = scores_63)[name = tensor("op_1851")]; + tensor input_467 = select(a = var_53, b = var_1851, cond = mask_9)[name = tensor("input_467")]; + tensor x_161_transpose_x_0 = const()[name = tensor("x_161_transpose_x_0"), val = tensor(false)]; + tensor x_161_transpose_y_0 = const()[name = tensor("x_161_transpose_y_0"), val = tensor(false)]; + tensor value_31 = transpose(perm = value_31_perm_0, x = var_1824)[name = tensor("transpose_679")]; + tensor x_161 = matmul(transpose_x = x_161_transpose_x_0, transpose_y = x_161_transpose_y_0, x = input_467, y = value_31)[name = tensor("x_161")]; + tensor var_1855_perm_0 = const()[name = tensor("op_1855_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1857 = const()[name = tensor("op_1857"), val = tensor([1, -1, 512])]; + tensor var_1855 = transpose(perm = var_1855_perm_0, x = x_161)[name = tensor("transpose_674")]; + tensor input_469 = reshape(shape = var_1857, x = var_1855)[name = tensor("input_469")]; + tensor att_outs_31 = linear(bias = encoder_encoders_14_self_attn_linear_out_bias, weight = encoder_encoders_14_self_attn_linear_out_weight, x = input_469)[name = tensor("linear_61")]; + tensor input_471 = add(x = att_outs_31, y = fsmn_memory_31)[name = tensor("input_471")]; + tensor input_473 = add(x = input_457, y = input_471)[name = tensor("input_473")]; + tensor const_188 = const()[name = tensor("const_188"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939632128)))]; + tensor const_189 = const()[name = tensor("const_189"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939634240)))]; + tensor output_63_axes_0 = const()[name = tensor("output_63_axes_0"), val = tensor([-1])]; + tensor output_63 = layer_norm(axes = output_63_axes_0, beta = const_189, epsilon = var_46, gamma = const_188, x = input_473)[name = tensor("output_63")]; + tensor input_479 = linear(bias = encoder_encoders_14_feed_forward_w_1_bias, weight = encoder_encoders_14_feed_forward_w_1_weight, x = output_63)[name = tensor("linear_62")]; + tensor input_481 = relu(x = input_479)[name = tensor("input_481")]; + tensor input_485 = linear(bias = encoder_encoders_14_feed_forward_w_2_bias, weight = encoder_encoders_14_feed_forward_w_2_weight, x = input_481)[name = tensor("linear_63")]; + tensor input_487 = add(x = input_473, y = input_485)[name = tensor("input_487")]; + tensor const_190 = const()[name = tensor("const_190"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939636352)))]; + tensor const_191 = const()[name = tensor("const_191"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939638464)))]; + tensor output_65_axes_0 = const()[name = tensor("output_65_axes_0"), val = tensor([-1])]; + tensor output_65 = layer_norm(axes = output_65_axes_0, beta = const_191, epsilon = var_46, gamma = const_190, x = input_487)[name = tensor("output_65")]; + tensor var_1914 = linear(bias = encoder_encoders_15_self_attn_linear_q_k_v_bias, weight = encoder_encoders_15_self_attn_linear_q_k_v_weight, x = output_65)[name = tensor("linear_64")]; + tensor tile_16 = const()[name = tensor("tile_16"), val = tensor([512, 512, 512])]; + tensor var_1915_axis_0 = const()[name = tensor("op_1915_axis_0"), val = tensor(-1)]; + tensor var_1915_0, tensor var_1915_1, tensor var_1915_2 = split(axis = var_1915_axis_0, split_sizes = tile_16, x = var_1914)[name = tensor("op_1915")]; + tensor var_1919 = const()[name = tensor("op_1919"), val = tensor([1, 1804, 4, 128])]; + tensor var_1920 = reshape(shape = var_1919, x = var_1915_0)[name = tensor("op_1920")]; + tensor var_1922 = const()[name = tensor("op_1922"), val = tensor([1, 1804, 4, 128])]; + tensor var_1923 = reshape(shape = var_1922, x = var_1915_1)[name = tensor("op_1923")]; + tensor var_1925 = const()[name = tensor("op_1925"), val = tensor([1, 1804, 4, 128])]; + tensor var_1926 = reshape(shape = var_1925, x = var_1915_2)[name = tensor("op_1926")]; + tensor value_33_perm_0 = const()[name = tensor("value_33_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor inputs_33 = mul(x = var_1915_2, y = mask_7)[name = tensor("inputs_33")]; + tensor input_491_perm_0 = const()[name = tensor("input_491_perm_0"), val = tensor([0, 2, 1])]; + tensor const_197 = const()[name = tensor("const_197"), val = tensor(0x0p+0)]; + tensor input_493_pad_0 = const()[name = tensor("input_493_pad_0"), val = tensor([0, 0, 0, 0, 5, 5])]; + tensor input_493_mode_0 = const()[name = tensor("input_493_mode_0"), val = tensor("constant")]; + tensor input_491 = transpose(perm = input_491_perm_0, x = inputs_33)[name = tensor("transpose_672")]; + tensor input_493 = pad(constant_val = const_197, mode = input_493_mode_0, pad = input_493_pad_0, x = input_491)[name = tensor("input_493")]; + tensor x_165_pad_type_0 = const()[name = tensor("x_165_pad_type_0"), val = tensor("valid")]; + tensor x_165_groups_0 = const()[name = tensor("x_165_groups_0"), val = tensor(512)]; + tensor x_165_strides_0 = const()[name = tensor("x_165_strides_0"), val = tensor([1])]; + tensor x_165_pad_0 = const()[name = tensor("x_165_pad_0"), val = tensor([0, 0])]; + tensor x_165_dilations_0 = const()[name = tensor("x_165_dilations_0"), val = tensor([1])]; + tensor x_165 = conv(dilations = x_165_dilations_0, groups = x_165_groups_0, pad = x_165_pad_0, pad_type = x_165_pad_type_0, strides = x_165_strides_0, weight = encoder_encoders_15_self_attn_fsmn_block_weight, x = input_493)[name = tensor("x_165")]; + tensor x_167_perm_0 = const()[name = tensor("x_167_perm_0"), val = tensor([0, 2, 1])]; + tensor x_167 = transpose(perm = x_167_perm_0, x = x_165)[name = tensor("transpose_671")]; + tensor input_495 = add(x = x_167, y = inputs_33)[name = tensor("input_495")]; + tensor fsmn_memory_33 = mul(x = input_495, y = mask_7)[name = tensor("fsmn_memory_33")]; + tensor var_1945 = const()[name = tensor("op_1945"), val = tensor(0x1.6a09e6p-4)]; + tensor q_h_67 = mul(x = var_1920, y = var_1945)[name = tensor("q_h_67")]; + tensor scores_65_transpose_x_0 = const()[name = tensor("scores_65_transpose_x_0"), val = tensor(false)]; + tensor scores_65_transpose_y_0 = const()[name = tensor("scores_65_transpose_y_0"), val = tensor(false)]; + tensor transpose_242_perm_0 = const()[name = tensor("transpose_242_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_243_perm_0 = const()[name = tensor("transpose_243_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_243 = transpose(perm = transpose_243_perm_0, x = var_1923)[name = tensor("transpose_669")]; + tensor transpose_242 = transpose(perm = transpose_242_perm_0, x = q_h_67)[name = tensor("transpose_670")]; + tensor scores_65 = matmul(transpose_x = scores_65_transpose_x_0, transpose_y = scores_65_transpose_y_0, x = transpose_242, y = transpose_243)[name = tensor("scores_65")]; + tensor scores_67 = select(a = var_48, b = scores_65, cond = mask_9)[name = tensor("scores_67")]; + tensor var_1953 = softmax(axis = var_61, x = scores_67)[name = tensor("op_1953")]; + tensor input_497 = select(a = var_53, b = var_1953, cond = mask_9)[name = tensor("input_497")]; + tensor x_171_transpose_x_0 = const()[name = tensor("x_171_transpose_x_0"), val = tensor(false)]; + tensor x_171_transpose_y_0 = const()[name = tensor("x_171_transpose_y_0"), val = tensor(false)]; + tensor value_33 = transpose(perm = value_33_perm_0, x = var_1926)[name = tensor("transpose_673")]; + tensor x_171 = matmul(transpose_x = x_171_transpose_x_0, transpose_y = x_171_transpose_y_0, x = input_497, y = value_33)[name = tensor("x_171")]; + tensor var_1957_perm_0 = const()[name = tensor("op_1957_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1959 = const()[name = tensor("op_1959"), val = tensor([1, -1, 512])]; + tensor var_1957 = transpose(perm = var_1957_perm_0, x = x_171)[name = tensor("transpose_668")]; + tensor input_499 = reshape(shape = var_1959, x = var_1957)[name = tensor("input_499")]; + tensor att_outs_33 = linear(bias = encoder_encoders_15_self_attn_linear_out_bias, weight = encoder_encoders_15_self_attn_linear_out_weight, x = input_499)[name = tensor("linear_65")]; + tensor input_501 = add(x = att_outs_33, y = fsmn_memory_33)[name = tensor("input_501")]; + tensor input_503 = add(x = input_487, y = input_501)[name = tensor("input_503")]; + tensor const_199 = const()[name = tensor("const_199"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939640576)))]; + tensor const_200 = const()[name = tensor("const_200"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939642688)))]; + tensor output_67_axes_0 = const()[name = tensor("output_67_axes_0"), val = tensor([-1])]; + tensor output_67 = layer_norm(axes = output_67_axes_0, beta = const_200, epsilon = var_46, gamma = const_199, x = input_503)[name = tensor("output_67")]; + tensor input_509 = linear(bias = encoder_encoders_15_feed_forward_w_1_bias, weight = encoder_encoders_15_feed_forward_w_1_weight, x = output_67)[name = tensor("linear_66")]; + tensor input_511 = relu(x = input_509)[name = tensor("input_511")]; + tensor input_515 = linear(bias = encoder_encoders_15_feed_forward_w_2_bias, weight = encoder_encoders_15_feed_forward_w_2_weight, x = input_511)[name = tensor("linear_67")]; + tensor input_517 = add(x = input_503, y = input_515)[name = tensor("input_517")]; + tensor const_201 = const()[name = tensor("const_201"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939644800)))]; + tensor const_202 = const()[name = tensor("const_202"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939646912)))]; + tensor output_69_axes_0 = const()[name = tensor("output_69_axes_0"), val = tensor([-1])]; + tensor output_69 = layer_norm(axes = output_69_axes_0, beta = const_202, epsilon = var_46, gamma = const_201, x = input_517)[name = tensor("output_69")]; + tensor var_2016 = linear(bias = encoder_encoders_16_self_attn_linear_q_k_v_bias, weight = encoder_encoders_16_self_attn_linear_q_k_v_weight, x = output_69)[name = tensor("linear_68")]; + tensor tile_17 = const()[name = tensor("tile_17"), val = tensor([512, 512, 512])]; + tensor var_2017_axis_0 = const()[name = tensor("op_2017_axis_0"), val = tensor(-1)]; + tensor var_2017_0, tensor var_2017_1, tensor var_2017_2 = split(axis = var_2017_axis_0, split_sizes = tile_17, x = var_2016)[name = tensor("op_2017")]; + tensor var_2021 = const()[name = tensor("op_2021"), val = tensor([1, 1804, 4, 128])]; + tensor var_2022 = reshape(shape = var_2021, x = var_2017_0)[name = tensor("op_2022")]; + tensor var_2024 = const()[name = tensor("op_2024"), val = tensor([1, 1804, 4, 128])]; + tensor var_2025 = reshape(shape = var_2024, x = var_2017_1)[name = tensor("op_2025")]; + tensor var_2027 = const()[name = tensor("op_2027"), val = tensor([1, 1804, 4, 128])]; + tensor var_2028 = reshape(shape = var_2027, x = var_2017_2)[name = tensor("op_2028")]; + tensor value_35_perm_0 = const()[name = tensor("value_35_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor inputs_35 = mul(x = var_2017_2, y = mask_7)[name = tensor("inputs_35")]; + tensor input_521_perm_0 = const()[name = tensor("input_521_perm_0"), val = tensor([0, 2, 1])]; + tensor const_208 = const()[name = tensor("const_208"), val = tensor(0x0p+0)]; + tensor input_523_pad_0 = const()[name = tensor("input_523_pad_0"), val = tensor([0, 0, 0, 0, 5, 5])]; + tensor input_523_mode_0 = const()[name = tensor("input_523_mode_0"), val = tensor("constant")]; + tensor input_521 = transpose(perm = input_521_perm_0, x = inputs_35)[name = tensor("transpose_666")]; + tensor input_523 = pad(constant_val = const_208, mode = input_523_mode_0, pad = input_523_pad_0, x = input_521)[name = tensor("input_523")]; + tensor x_175_pad_type_0 = const()[name = tensor("x_175_pad_type_0"), val = tensor("valid")]; + tensor x_175_groups_0 = const()[name = tensor("x_175_groups_0"), val = tensor(512)]; + tensor x_175_strides_0 = const()[name = tensor("x_175_strides_0"), val = tensor([1])]; + tensor x_175_pad_0 = const()[name = tensor("x_175_pad_0"), val = tensor([0, 0])]; + tensor x_175_dilations_0 = const()[name = tensor("x_175_dilations_0"), val = tensor([1])]; + tensor x_175 = conv(dilations = x_175_dilations_0, groups = x_175_groups_0, pad = x_175_pad_0, pad_type = x_175_pad_type_0, strides = x_175_strides_0, weight = encoder_encoders_16_self_attn_fsmn_block_weight, x = input_523)[name = tensor("x_175")]; + tensor x_177_perm_0 = const()[name = tensor("x_177_perm_0"), val = tensor([0, 2, 1])]; + tensor x_177 = transpose(perm = x_177_perm_0, x = x_175)[name = tensor("transpose_665")]; + tensor input_525 = add(x = x_177, y = inputs_35)[name = tensor("input_525")]; + tensor fsmn_memory_35 = mul(x = input_525, y = mask_7)[name = tensor("fsmn_memory_35")]; + tensor var_2047 = const()[name = tensor("op_2047"), val = tensor(0x1.6a09e6p-4)]; + tensor q_h_71 = mul(x = var_2022, y = var_2047)[name = tensor("q_h_71")]; + tensor scores_69_transpose_x_0 = const()[name = tensor("scores_69_transpose_x_0"), val = tensor(false)]; + tensor scores_69_transpose_y_0 = const()[name = tensor("scores_69_transpose_y_0"), val = tensor(false)]; + tensor transpose_244_perm_0 = const()[name = tensor("transpose_244_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_245_perm_0 = const()[name = tensor("transpose_245_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_245 = transpose(perm = transpose_245_perm_0, x = var_2025)[name = tensor("transpose_663")]; + tensor transpose_244 = transpose(perm = transpose_244_perm_0, x = q_h_71)[name = tensor("transpose_664")]; + tensor scores_69 = matmul(transpose_x = scores_69_transpose_x_0, transpose_y = scores_69_transpose_y_0, x = transpose_244, y = transpose_245)[name = tensor("scores_69")]; + tensor scores_71 = select(a = var_48, b = scores_69, cond = mask_9)[name = tensor("scores_71")]; + tensor var_2055 = softmax(axis = var_61, x = scores_71)[name = tensor("op_2055")]; + tensor input_527 = select(a = var_53, b = var_2055, cond = mask_9)[name = tensor("input_527")]; + tensor x_181_transpose_x_0 = const()[name = tensor("x_181_transpose_x_0"), val = tensor(false)]; + tensor x_181_transpose_y_0 = const()[name = tensor("x_181_transpose_y_0"), val = tensor(false)]; + tensor value_35 = transpose(perm = value_35_perm_0, x = var_2028)[name = tensor("transpose_667")]; + tensor x_181 = matmul(transpose_x = x_181_transpose_x_0, transpose_y = x_181_transpose_y_0, x = input_527, y = value_35)[name = tensor("x_181")]; + tensor var_2059_perm_0 = const()[name = tensor("op_2059_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2061 = const()[name = tensor("op_2061"), val = tensor([1, -1, 512])]; + tensor var_2059 = transpose(perm = var_2059_perm_0, x = x_181)[name = tensor("transpose_662")]; + tensor input_529 = reshape(shape = var_2061, x = var_2059)[name = tensor("input_529")]; + tensor att_outs_35 = linear(bias = encoder_encoders_16_self_attn_linear_out_bias, weight = encoder_encoders_16_self_attn_linear_out_weight, x = input_529)[name = tensor("linear_69")]; + tensor input_531 = add(x = att_outs_35, y = fsmn_memory_35)[name = tensor("input_531")]; + tensor input_533 = add(x = input_517, y = input_531)[name = tensor("input_533")]; + tensor const_210 = const()[name = tensor("const_210"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939649024)))]; + tensor const_211 = const()[name = tensor("const_211"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939651136)))]; + tensor output_71_axes_0 = const()[name = tensor("output_71_axes_0"), val = tensor([-1])]; + tensor output_71 = layer_norm(axes = output_71_axes_0, beta = const_211, epsilon = var_46, gamma = const_210, x = input_533)[name = tensor("output_71")]; + tensor input_539 = linear(bias = encoder_encoders_16_feed_forward_w_1_bias, weight = encoder_encoders_16_feed_forward_w_1_weight, x = output_71)[name = tensor("linear_70")]; + tensor input_541 = relu(x = input_539)[name = tensor("input_541")]; + tensor input_545 = linear(bias = encoder_encoders_16_feed_forward_w_2_bias, weight = encoder_encoders_16_feed_forward_w_2_weight, x = input_541)[name = tensor("linear_71")]; + tensor input_547 = add(x = input_533, y = input_545)[name = tensor("input_547")]; + tensor const_212 = const()[name = tensor("const_212"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939653248)))]; + tensor const_213 = const()[name = tensor("const_213"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939655360)))]; + tensor output_73_axes_0 = const()[name = tensor("output_73_axes_0"), val = tensor([-1])]; + tensor output_73 = layer_norm(axes = output_73_axes_0, beta = const_213, epsilon = var_46, gamma = const_212, x = input_547)[name = tensor("output_73")]; + tensor var_2118 = linear(bias = encoder_encoders_17_self_attn_linear_q_k_v_bias, weight = encoder_encoders_17_self_attn_linear_q_k_v_weight, x = output_73)[name = tensor("linear_72")]; + tensor tile_18 = const()[name = tensor("tile_18"), val = tensor([512, 512, 512])]; + tensor var_2119_axis_0 = const()[name = tensor("op_2119_axis_0"), val = tensor(-1)]; + tensor var_2119_0, tensor var_2119_1, tensor var_2119_2 = split(axis = var_2119_axis_0, split_sizes = tile_18, x = var_2118)[name = tensor("op_2119")]; + tensor var_2123 = const()[name = tensor("op_2123"), val = tensor([1, 1804, 4, 128])]; + tensor var_2124 = reshape(shape = var_2123, x = var_2119_0)[name = tensor("op_2124")]; + tensor var_2126 = const()[name = tensor("op_2126"), val = tensor([1, 1804, 4, 128])]; + tensor var_2127 = reshape(shape = var_2126, x = var_2119_1)[name = tensor("op_2127")]; + tensor var_2129 = const()[name = tensor("op_2129"), val = tensor([1, 1804, 4, 128])]; + tensor var_2130 = reshape(shape = var_2129, x = var_2119_2)[name = tensor("op_2130")]; + tensor value_37_perm_0 = const()[name = tensor("value_37_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor inputs_37 = mul(x = var_2119_2, y = mask_7)[name = tensor("inputs_37")]; + tensor input_551_perm_0 = const()[name = tensor("input_551_perm_0"), val = tensor([0, 2, 1])]; + tensor const_219 = const()[name = tensor("const_219"), val = tensor(0x0p+0)]; + tensor input_553_pad_0 = const()[name = tensor("input_553_pad_0"), val = tensor([0, 0, 0, 0, 5, 5])]; + tensor input_553_mode_0 = const()[name = tensor("input_553_mode_0"), val = tensor("constant")]; + tensor input_551 = transpose(perm = input_551_perm_0, x = inputs_37)[name = tensor("transpose_660")]; + tensor input_553 = pad(constant_val = const_219, mode = input_553_mode_0, pad = input_553_pad_0, x = input_551)[name = tensor("input_553")]; + tensor x_185_pad_type_0 = const()[name = tensor("x_185_pad_type_0"), val = tensor("valid")]; + tensor x_185_groups_0 = const()[name = tensor("x_185_groups_0"), val = tensor(512)]; + tensor x_185_strides_0 = const()[name = tensor("x_185_strides_0"), val = tensor([1])]; + tensor x_185_pad_0 = const()[name = tensor("x_185_pad_0"), val = tensor([0, 0])]; + tensor x_185_dilations_0 = const()[name = tensor("x_185_dilations_0"), val = tensor([1])]; + tensor x_185 = conv(dilations = x_185_dilations_0, groups = x_185_groups_0, pad = x_185_pad_0, pad_type = x_185_pad_type_0, strides = x_185_strides_0, weight = encoder_encoders_17_self_attn_fsmn_block_weight, x = input_553)[name = tensor("x_185")]; + tensor x_187_perm_0 = const()[name = tensor("x_187_perm_0"), val = tensor([0, 2, 1])]; + tensor x_187 = transpose(perm = x_187_perm_0, x = x_185)[name = tensor("transpose_659")]; + tensor input_555 = add(x = x_187, y = inputs_37)[name = tensor("input_555")]; + tensor fsmn_memory_37 = mul(x = input_555, y = mask_7)[name = tensor("fsmn_memory_37")]; + tensor var_2149 = const()[name = tensor("op_2149"), val = tensor(0x1.6a09e6p-4)]; + tensor q_h_75 = mul(x = var_2124, y = var_2149)[name = tensor("q_h_75")]; + tensor scores_73_transpose_x_0 = const()[name = tensor("scores_73_transpose_x_0"), val = tensor(false)]; + tensor scores_73_transpose_y_0 = const()[name = tensor("scores_73_transpose_y_0"), val = tensor(false)]; + tensor transpose_246_perm_0 = const()[name = tensor("transpose_246_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_247_perm_0 = const()[name = tensor("transpose_247_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_247 = transpose(perm = transpose_247_perm_0, x = var_2127)[name = tensor("transpose_657")]; + tensor transpose_246 = transpose(perm = transpose_246_perm_0, x = q_h_75)[name = tensor("transpose_658")]; + tensor scores_73 = matmul(transpose_x = scores_73_transpose_x_0, transpose_y = scores_73_transpose_y_0, x = transpose_246, y = transpose_247)[name = tensor("scores_73")]; + tensor scores_75 = select(a = var_48, b = scores_73, cond = mask_9)[name = tensor("scores_75")]; + tensor var_2157 = softmax(axis = var_61, x = scores_75)[name = tensor("op_2157")]; + tensor input_557 = select(a = var_53, b = var_2157, cond = mask_9)[name = tensor("input_557")]; + tensor x_191_transpose_x_0 = const()[name = tensor("x_191_transpose_x_0"), val = tensor(false)]; + tensor x_191_transpose_y_0 = const()[name = tensor("x_191_transpose_y_0"), val = tensor(false)]; + tensor value_37 = transpose(perm = value_37_perm_0, x = var_2130)[name = tensor("transpose_661")]; + tensor x_191 = matmul(transpose_x = x_191_transpose_x_0, transpose_y = x_191_transpose_y_0, x = input_557, y = value_37)[name = tensor("x_191")]; + tensor var_2161_perm_0 = const()[name = tensor("op_2161_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2163 = const()[name = tensor("op_2163"), val = tensor([1, -1, 512])]; + tensor var_2161 = transpose(perm = var_2161_perm_0, x = x_191)[name = tensor("transpose_656")]; + tensor input_559 = reshape(shape = var_2163, x = var_2161)[name = tensor("input_559")]; + tensor att_outs_37 = linear(bias = encoder_encoders_17_self_attn_linear_out_bias, weight = encoder_encoders_17_self_attn_linear_out_weight, x = input_559)[name = tensor("linear_73")]; + tensor input_561 = add(x = att_outs_37, y = fsmn_memory_37)[name = tensor("input_561")]; + tensor input_563 = add(x = input_547, y = input_561)[name = tensor("input_563")]; + tensor const_221 = const()[name = tensor("const_221"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939657472)))]; + tensor const_222 = const()[name = tensor("const_222"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939659584)))]; + tensor output_75_axes_0 = const()[name = tensor("output_75_axes_0"), val = tensor([-1])]; + tensor output_75 = layer_norm(axes = output_75_axes_0, beta = const_222, epsilon = var_46, gamma = const_221, x = input_563)[name = tensor("output_75")]; + tensor input_569 = linear(bias = encoder_encoders_17_feed_forward_w_1_bias, weight = encoder_encoders_17_feed_forward_w_1_weight, x = output_75)[name = tensor("linear_74")]; + tensor input_571 = relu(x = input_569)[name = tensor("input_571")]; + tensor input_575 = linear(bias = encoder_encoders_17_feed_forward_w_2_bias, weight = encoder_encoders_17_feed_forward_w_2_weight, x = input_571)[name = tensor("linear_75")]; + tensor input_577 = add(x = input_563, y = input_575)[name = tensor("input_577")]; + tensor const_223 = const()[name = tensor("const_223"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939661696)))]; + tensor const_224 = const()[name = tensor("const_224"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939663808)))]; + tensor output_77_axes_0 = const()[name = tensor("output_77_axes_0"), val = tensor([-1])]; + tensor output_77 = layer_norm(axes = output_77_axes_0, beta = const_224, epsilon = var_46, gamma = const_223, x = input_577)[name = tensor("output_77")]; + tensor var_2220 = linear(bias = encoder_encoders_18_self_attn_linear_q_k_v_bias, weight = encoder_encoders_18_self_attn_linear_q_k_v_weight, x = output_77)[name = tensor("linear_76")]; + tensor tile_19 = const()[name = tensor("tile_19"), val = tensor([512, 512, 512])]; + tensor var_2221_axis_0 = const()[name = tensor("op_2221_axis_0"), val = tensor(-1)]; + tensor var_2221_0, tensor var_2221_1, tensor var_2221_2 = split(axis = var_2221_axis_0, split_sizes = tile_19, x = var_2220)[name = tensor("op_2221")]; + tensor var_2225 = const()[name = tensor("op_2225"), val = tensor([1, 1804, 4, 128])]; + tensor var_2226 = reshape(shape = var_2225, x = var_2221_0)[name = tensor("op_2226")]; + tensor var_2228 = const()[name = tensor("op_2228"), val = tensor([1, 1804, 4, 128])]; + tensor var_2229 = reshape(shape = var_2228, x = var_2221_1)[name = tensor("op_2229")]; + tensor var_2231 = const()[name = tensor("op_2231"), val = tensor([1, 1804, 4, 128])]; + tensor var_2232 = reshape(shape = var_2231, x = var_2221_2)[name = tensor("op_2232")]; + tensor value_39_perm_0 = const()[name = tensor("value_39_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor inputs_39 = mul(x = var_2221_2, y = mask_7)[name = tensor("inputs_39")]; + tensor input_581_perm_0 = const()[name = tensor("input_581_perm_0"), val = tensor([0, 2, 1])]; + tensor const_230 = const()[name = tensor("const_230"), val = tensor(0x0p+0)]; + tensor input_583_pad_0 = const()[name = tensor("input_583_pad_0"), val = tensor([0, 0, 0, 0, 5, 5])]; + tensor input_583_mode_0 = const()[name = tensor("input_583_mode_0"), val = tensor("constant")]; + tensor input_581 = transpose(perm = input_581_perm_0, x = inputs_39)[name = tensor("transpose_654")]; + tensor input_583 = pad(constant_val = const_230, mode = input_583_mode_0, pad = input_583_pad_0, x = input_581)[name = tensor("input_583")]; + tensor x_195_pad_type_0 = const()[name = tensor("x_195_pad_type_0"), val = tensor("valid")]; + tensor x_195_groups_0 = const()[name = tensor("x_195_groups_0"), val = tensor(512)]; + tensor x_195_strides_0 = const()[name = tensor("x_195_strides_0"), val = tensor([1])]; + tensor x_195_pad_0 = const()[name = tensor("x_195_pad_0"), val = tensor([0, 0])]; + tensor x_195_dilations_0 = const()[name = tensor("x_195_dilations_0"), val = tensor([1])]; + tensor x_195 = conv(dilations = x_195_dilations_0, groups = x_195_groups_0, pad = x_195_pad_0, pad_type = x_195_pad_type_0, strides = x_195_strides_0, weight = encoder_encoders_18_self_attn_fsmn_block_weight, x = input_583)[name = tensor("x_195")]; + tensor x_197_perm_0 = const()[name = tensor("x_197_perm_0"), val = tensor([0, 2, 1])]; + tensor x_197 = transpose(perm = x_197_perm_0, x = x_195)[name = tensor("transpose_653")]; + tensor input_585 = add(x = x_197, y = inputs_39)[name = tensor("input_585")]; + tensor fsmn_memory_39 = mul(x = input_585, y = mask_7)[name = tensor("fsmn_memory_39")]; + tensor var_2251 = const()[name = tensor("op_2251"), val = tensor(0x1.6a09e6p-4)]; + tensor q_h_79 = mul(x = var_2226, y = var_2251)[name = tensor("q_h_79")]; + tensor scores_77_transpose_x_0 = const()[name = tensor("scores_77_transpose_x_0"), val = tensor(false)]; + tensor scores_77_transpose_y_0 = const()[name = tensor("scores_77_transpose_y_0"), val = tensor(false)]; + tensor transpose_248_perm_0 = const()[name = tensor("transpose_248_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_249_perm_0 = const()[name = tensor("transpose_249_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_249 = transpose(perm = transpose_249_perm_0, x = var_2229)[name = tensor("transpose_651")]; + tensor transpose_248 = transpose(perm = transpose_248_perm_0, x = q_h_79)[name = tensor("transpose_652")]; + tensor scores_77 = matmul(transpose_x = scores_77_transpose_x_0, transpose_y = scores_77_transpose_y_0, x = transpose_248, y = transpose_249)[name = tensor("scores_77")]; + tensor scores_79 = select(a = var_48, b = scores_77, cond = mask_9)[name = tensor("scores_79")]; + tensor var_2259 = softmax(axis = var_61, x = scores_79)[name = tensor("op_2259")]; + tensor input_587 = select(a = var_53, b = var_2259, cond = mask_9)[name = tensor("input_587")]; + tensor x_201_transpose_x_0 = const()[name = tensor("x_201_transpose_x_0"), val = tensor(false)]; + tensor x_201_transpose_y_0 = const()[name = tensor("x_201_transpose_y_0"), val = tensor(false)]; + tensor value_39 = transpose(perm = value_39_perm_0, x = var_2232)[name = tensor("transpose_655")]; + tensor x_201 = matmul(transpose_x = x_201_transpose_x_0, transpose_y = x_201_transpose_y_0, x = input_587, y = value_39)[name = tensor("x_201")]; + tensor var_2263_perm_0 = const()[name = tensor("op_2263_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2265 = const()[name = tensor("op_2265"), val = tensor([1, -1, 512])]; + tensor var_2263 = transpose(perm = var_2263_perm_0, x = x_201)[name = tensor("transpose_650")]; + tensor input_589 = reshape(shape = var_2265, x = var_2263)[name = tensor("input_589")]; + tensor att_outs_39 = linear(bias = encoder_encoders_18_self_attn_linear_out_bias, weight = encoder_encoders_18_self_attn_linear_out_weight, x = input_589)[name = tensor("linear_77")]; + tensor input_591 = add(x = att_outs_39, y = fsmn_memory_39)[name = tensor("input_591")]; + tensor input_593 = add(x = input_577, y = input_591)[name = tensor("input_593")]; + tensor const_232 = const()[name = tensor("const_232"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939665920)))]; + tensor const_233 = const()[name = tensor("const_233"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939668032)))]; + tensor output_79_axes_0 = const()[name = tensor("output_79_axes_0"), val = tensor([-1])]; + tensor output_79 = layer_norm(axes = output_79_axes_0, beta = const_233, epsilon = var_46, gamma = const_232, x = input_593)[name = tensor("output_79")]; + tensor input_599 = linear(bias = encoder_encoders_18_feed_forward_w_1_bias, weight = encoder_encoders_18_feed_forward_w_1_weight, x = output_79)[name = tensor("linear_78")]; + tensor input_601 = relu(x = input_599)[name = tensor("input_601")]; + tensor input_605 = linear(bias = encoder_encoders_18_feed_forward_w_2_bias, weight = encoder_encoders_18_feed_forward_w_2_weight, x = input_601)[name = tensor("linear_79")]; + tensor input_607 = add(x = input_593, y = input_605)[name = tensor("input_607")]; + tensor const_234 = const()[name = tensor("const_234"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939670144)))]; + tensor const_235 = const()[name = tensor("const_235"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939672256)))]; + tensor output_81_axes_0 = const()[name = tensor("output_81_axes_0"), val = tensor([-1])]; + tensor output_81 = layer_norm(axes = output_81_axes_0, beta = const_235, epsilon = var_46, gamma = const_234, x = input_607)[name = tensor("output_81")]; + tensor var_2322 = linear(bias = encoder_encoders_19_self_attn_linear_q_k_v_bias, weight = encoder_encoders_19_self_attn_linear_q_k_v_weight, x = output_81)[name = tensor("linear_80")]; + tensor tile_20 = const()[name = tensor("tile_20"), val = tensor([512, 512, 512])]; + tensor var_2323_axis_0 = const()[name = tensor("op_2323_axis_0"), val = tensor(-1)]; + tensor var_2323_0, tensor var_2323_1, tensor var_2323_2 = split(axis = var_2323_axis_0, split_sizes = tile_20, x = var_2322)[name = tensor("op_2323")]; + tensor var_2327 = const()[name = tensor("op_2327"), val = tensor([1, 1804, 4, 128])]; + tensor var_2328 = reshape(shape = var_2327, x = var_2323_0)[name = tensor("op_2328")]; + tensor var_2330 = const()[name = tensor("op_2330"), val = tensor([1, 1804, 4, 128])]; + tensor var_2331 = reshape(shape = var_2330, x = var_2323_1)[name = tensor("op_2331")]; + tensor var_2333 = const()[name = tensor("op_2333"), val = tensor([1, 1804, 4, 128])]; + tensor var_2334 = reshape(shape = var_2333, x = var_2323_2)[name = tensor("op_2334")]; + tensor value_41_perm_0 = const()[name = tensor("value_41_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor inputs_41 = mul(x = var_2323_2, y = mask_7)[name = tensor("inputs_41")]; + tensor input_611_perm_0 = const()[name = tensor("input_611_perm_0"), val = tensor([0, 2, 1])]; + tensor const_241 = const()[name = tensor("const_241"), val = tensor(0x0p+0)]; + tensor input_613_pad_0 = const()[name = tensor("input_613_pad_0"), val = tensor([0, 0, 0, 0, 5, 5])]; + tensor input_613_mode_0 = const()[name = tensor("input_613_mode_0"), val = tensor("constant")]; + tensor input_611 = transpose(perm = input_611_perm_0, x = inputs_41)[name = tensor("transpose_648")]; + tensor input_613 = pad(constant_val = const_241, mode = input_613_mode_0, pad = input_613_pad_0, x = input_611)[name = tensor("input_613")]; + tensor x_205_pad_type_0 = const()[name = tensor("x_205_pad_type_0"), val = tensor("valid")]; + tensor x_205_groups_0 = const()[name = tensor("x_205_groups_0"), val = tensor(512)]; + tensor x_205_strides_0 = const()[name = tensor("x_205_strides_0"), val = tensor([1])]; + tensor x_205_pad_0 = const()[name = tensor("x_205_pad_0"), val = tensor([0, 0])]; + tensor x_205_dilations_0 = const()[name = tensor("x_205_dilations_0"), val = tensor([1])]; + tensor x_205 = conv(dilations = x_205_dilations_0, groups = x_205_groups_0, pad = x_205_pad_0, pad_type = x_205_pad_type_0, strides = x_205_strides_0, weight = encoder_encoders_19_self_attn_fsmn_block_weight, x = input_613)[name = tensor("x_205")]; + tensor x_207_perm_0 = const()[name = tensor("x_207_perm_0"), val = tensor([0, 2, 1])]; + tensor x_207 = transpose(perm = x_207_perm_0, x = x_205)[name = tensor("transpose_647")]; + tensor input_615 = add(x = x_207, y = inputs_41)[name = tensor("input_615")]; + tensor fsmn_memory_41 = mul(x = input_615, y = mask_7)[name = tensor("fsmn_memory_41")]; + tensor var_2353 = const()[name = tensor("op_2353"), val = tensor(0x1.6a09e6p-4)]; + tensor q_h_83 = mul(x = var_2328, y = var_2353)[name = tensor("q_h_83")]; + tensor scores_81_transpose_x_0 = const()[name = tensor("scores_81_transpose_x_0"), val = tensor(false)]; + tensor scores_81_transpose_y_0 = const()[name = tensor("scores_81_transpose_y_0"), val = tensor(false)]; + tensor transpose_250_perm_0 = const()[name = tensor("transpose_250_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_251_perm_0 = const()[name = tensor("transpose_251_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_251 = transpose(perm = transpose_251_perm_0, x = var_2331)[name = tensor("transpose_645")]; + tensor transpose_250 = transpose(perm = transpose_250_perm_0, x = q_h_83)[name = tensor("transpose_646")]; + tensor scores_81 = matmul(transpose_x = scores_81_transpose_x_0, transpose_y = scores_81_transpose_y_0, x = transpose_250, y = transpose_251)[name = tensor("scores_81")]; + tensor scores_83 = select(a = var_48, b = scores_81, cond = mask_9)[name = tensor("scores_83")]; + tensor var_2361 = softmax(axis = var_61, x = scores_83)[name = tensor("op_2361")]; + tensor input_617 = select(a = var_53, b = var_2361, cond = mask_9)[name = tensor("input_617")]; + tensor x_211_transpose_x_0 = const()[name = tensor("x_211_transpose_x_0"), val = tensor(false)]; + tensor x_211_transpose_y_0 = const()[name = tensor("x_211_transpose_y_0"), val = tensor(false)]; + tensor value_41 = transpose(perm = value_41_perm_0, x = var_2334)[name = tensor("transpose_649")]; + tensor x_211 = matmul(transpose_x = x_211_transpose_x_0, transpose_y = x_211_transpose_y_0, x = input_617, y = value_41)[name = tensor("x_211")]; + tensor var_2365_perm_0 = const()[name = tensor("op_2365_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2367 = const()[name = tensor("op_2367"), val = tensor([1, -1, 512])]; + tensor var_2365 = transpose(perm = var_2365_perm_0, x = x_211)[name = tensor("transpose_644")]; + tensor input_619 = reshape(shape = var_2367, x = var_2365)[name = tensor("input_619")]; + tensor att_outs_41 = linear(bias = encoder_encoders_19_self_attn_linear_out_bias, weight = encoder_encoders_19_self_attn_linear_out_weight, x = input_619)[name = tensor("linear_81")]; + tensor input_621 = add(x = att_outs_41, y = fsmn_memory_41)[name = tensor("input_621")]; + tensor input_623 = add(x = input_607, y = input_621)[name = tensor("input_623")]; + tensor const_243 = const()[name = tensor("const_243"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939674368)))]; + tensor const_244 = const()[name = tensor("const_244"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939676480)))]; + tensor output_83_axes_0 = const()[name = tensor("output_83_axes_0"), val = tensor([-1])]; + tensor output_83 = layer_norm(axes = output_83_axes_0, beta = const_244, epsilon = var_46, gamma = const_243, x = input_623)[name = tensor("output_83")]; + tensor input_629 = linear(bias = encoder_encoders_19_feed_forward_w_1_bias, weight = encoder_encoders_19_feed_forward_w_1_weight, x = output_83)[name = tensor("linear_82")]; + tensor input_631 = relu(x = input_629)[name = tensor("input_631")]; + tensor input_635 = linear(bias = encoder_encoders_19_feed_forward_w_2_bias, weight = encoder_encoders_19_feed_forward_w_2_weight, x = input_631)[name = tensor("linear_83")]; + tensor input_637 = add(x = input_623, y = input_635)[name = tensor("input_637")]; + tensor const_245 = const()[name = tensor("const_245"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939678592)))]; + tensor const_246 = const()[name = tensor("const_246"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939680704)))]; + tensor output_85_axes_0 = const()[name = tensor("output_85_axes_0"), val = tensor([-1])]; + tensor output_85 = layer_norm(axes = output_85_axes_0, beta = const_246, epsilon = var_46, gamma = const_245, x = input_637)[name = tensor("output_85")]; + tensor var_2424 = linear(bias = encoder_encoders_20_self_attn_linear_q_k_v_bias, weight = encoder_encoders_20_self_attn_linear_q_k_v_weight, x = output_85)[name = tensor("linear_84")]; + tensor tile_21 = const()[name = tensor("tile_21"), val = tensor([512, 512, 512])]; + tensor var_2425_axis_0 = const()[name = tensor("op_2425_axis_0"), val = tensor(-1)]; + tensor var_2425_0, tensor var_2425_1, tensor var_2425_2 = split(axis = var_2425_axis_0, split_sizes = tile_21, x = var_2424)[name = tensor("op_2425")]; + tensor var_2429 = const()[name = tensor("op_2429"), val = tensor([1, 1804, 4, 128])]; + tensor var_2430 = reshape(shape = var_2429, x = var_2425_0)[name = tensor("op_2430")]; + tensor var_2432 = const()[name = tensor("op_2432"), val = tensor([1, 1804, 4, 128])]; + tensor var_2433 = reshape(shape = var_2432, x = var_2425_1)[name = tensor("op_2433")]; + tensor var_2435 = const()[name = tensor("op_2435"), val = tensor([1, 1804, 4, 128])]; + tensor var_2436 = reshape(shape = var_2435, x = var_2425_2)[name = tensor("op_2436")]; + tensor value_43_perm_0 = const()[name = tensor("value_43_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor inputs_43 = mul(x = var_2425_2, y = mask_7)[name = tensor("inputs_43")]; + tensor input_641_perm_0 = const()[name = tensor("input_641_perm_0"), val = tensor([0, 2, 1])]; + tensor const_252 = const()[name = tensor("const_252"), val = tensor(0x0p+0)]; + tensor input_643_pad_0 = const()[name = tensor("input_643_pad_0"), val = tensor([0, 0, 0, 0, 5, 5])]; + tensor input_643_mode_0 = const()[name = tensor("input_643_mode_0"), val = tensor("constant")]; + tensor input_641 = transpose(perm = input_641_perm_0, x = inputs_43)[name = tensor("transpose_642")]; + tensor input_643 = pad(constant_val = const_252, mode = input_643_mode_0, pad = input_643_pad_0, x = input_641)[name = tensor("input_643")]; + tensor x_215_pad_type_0 = const()[name = tensor("x_215_pad_type_0"), val = tensor("valid")]; + tensor x_215_groups_0 = const()[name = tensor("x_215_groups_0"), val = tensor(512)]; + tensor x_215_strides_0 = const()[name = tensor("x_215_strides_0"), val = tensor([1])]; + tensor x_215_pad_0 = const()[name = tensor("x_215_pad_0"), val = tensor([0, 0])]; + tensor x_215_dilations_0 = const()[name = tensor("x_215_dilations_0"), val = tensor([1])]; + tensor x_215 = conv(dilations = x_215_dilations_0, groups = x_215_groups_0, pad = x_215_pad_0, pad_type = x_215_pad_type_0, strides = x_215_strides_0, weight = encoder_encoders_20_self_attn_fsmn_block_weight, x = input_643)[name = tensor("x_215")]; + tensor x_217_perm_0 = const()[name = tensor("x_217_perm_0"), val = tensor([0, 2, 1])]; + tensor x_217 = transpose(perm = x_217_perm_0, x = x_215)[name = tensor("transpose_641")]; + tensor input_645 = add(x = x_217, y = inputs_43)[name = tensor("input_645")]; + tensor fsmn_memory_43 = mul(x = input_645, y = mask_7)[name = tensor("fsmn_memory_43")]; + tensor var_2455 = const()[name = tensor("op_2455"), val = tensor(0x1.6a09e6p-4)]; + tensor q_h_87 = mul(x = var_2430, y = var_2455)[name = tensor("q_h_87")]; + tensor scores_85_transpose_x_0 = const()[name = tensor("scores_85_transpose_x_0"), val = tensor(false)]; + tensor scores_85_transpose_y_0 = const()[name = tensor("scores_85_transpose_y_0"), val = tensor(false)]; + tensor transpose_252_perm_0 = const()[name = tensor("transpose_252_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_253_perm_0 = const()[name = tensor("transpose_253_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_253 = transpose(perm = transpose_253_perm_0, x = var_2433)[name = tensor("transpose_639")]; + tensor transpose_252 = transpose(perm = transpose_252_perm_0, x = q_h_87)[name = tensor("transpose_640")]; + tensor scores_85 = matmul(transpose_x = scores_85_transpose_x_0, transpose_y = scores_85_transpose_y_0, x = transpose_252, y = transpose_253)[name = tensor("scores_85")]; + tensor scores_87 = select(a = var_48, b = scores_85, cond = mask_9)[name = tensor("scores_87")]; + tensor var_2463 = softmax(axis = var_61, x = scores_87)[name = tensor("op_2463")]; + tensor input_647 = select(a = var_53, b = var_2463, cond = mask_9)[name = tensor("input_647")]; + tensor x_221_transpose_x_0 = const()[name = tensor("x_221_transpose_x_0"), val = tensor(false)]; + tensor x_221_transpose_y_0 = const()[name = tensor("x_221_transpose_y_0"), val = tensor(false)]; + tensor value_43 = transpose(perm = value_43_perm_0, x = var_2436)[name = tensor("transpose_643")]; + tensor x_221 = matmul(transpose_x = x_221_transpose_x_0, transpose_y = x_221_transpose_y_0, x = input_647, y = value_43)[name = tensor("x_221")]; + tensor var_2467_perm_0 = const()[name = tensor("op_2467_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2469 = const()[name = tensor("op_2469"), val = tensor([1, -1, 512])]; + tensor var_2467 = transpose(perm = var_2467_perm_0, x = x_221)[name = tensor("transpose_638")]; + tensor input_649 = reshape(shape = var_2469, x = var_2467)[name = tensor("input_649")]; + tensor att_outs_43 = linear(bias = encoder_encoders_20_self_attn_linear_out_bias, weight = encoder_encoders_20_self_attn_linear_out_weight, x = input_649)[name = tensor("linear_85")]; + tensor input_651 = add(x = att_outs_43, y = fsmn_memory_43)[name = tensor("input_651")]; + tensor input_653 = add(x = input_637, y = input_651)[name = tensor("input_653")]; + tensor const_254 = const()[name = tensor("const_254"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939682816)))]; + tensor const_255 = const()[name = tensor("const_255"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939684928)))]; + tensor output_87_axes_0 = const()[name = tensor("output_87_axes_0"), val = tensor([-1])]; + tensor output_87 = layer_norm(axes = output_87_axes_0, beta = const_255, epsilon = var_46, gamma = const_254, x = input_653)[name = tensor("output_87")]; + tensor input_659 = linear(bias = encoder_encoders_20_feed_forward_w_1_bias, weight = encoder_encoders_20_feed_forward_w_1_weight, x = output_87)[name = tensor("linear_86")]; + tensor input_661 = relu(x = input_659)[name = tensor("input_661")]; + tensor input_665 = linear(bias = encoder_encoders_20_feed_forward_w_2_bias, weight = encoder_encoders_20_feed_forward_w_2_weight, x = input_661)[name = tensor("linear_87")]; + tensor input_667 = add(x = input_653, y = input_665)[name = tensor("input_667")]; + tensor const_256 = const()[name = tensor("const_256"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939687040)))]; + tensor const_257 = const()[name = tensor("const_257"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939689152)))]; + tensor output_89_axes_0 = const()[name = tensor("output_89_axes_0"), val = tensor([-1])]; + tensor output_89 = layer_norm(axes = output_89_axes_0, beta = const_257, epsilon = var_46, gamma = const_256, x = input_667)[name = tensor("output_89")]; + tensor var_2526 = linear(bias = encoder_encoders_21_self_attn_linear_q_k_v_bias, weight = encoder_encoders_21_self_attn_linear_q_k_v_weight, x = output_89)[name = tensor("linear_88")]; + tensor tile_22 = const()[name = tensor("tile_22"), val = tensor([512, 512, 512])]; + tensor var_2527_axis_0 = const()[name = tensor("op_2527_axis_0"), val = tensor(-1)]; + tensor var_2527_0, tensor var_2527_1, tensor var_2527_2 = split(axis = var_2527_axis_0, split_sizes = tile_22, x = var_2526)[name = tensor("op_2527")]; + tensor var_2531 = const()[name = tensor("op_2531"), val = tensor([1, 1804, 4, 128])]; + tensor var_2532 = reshape(shape = var_2531, x = var_2527_0)[name = tensor("op_2532")]; + tensor var_2534 = const()[name = tensor("op_2534"), val = tensor([1, 1804, 4, 128])]; + tensor var_2535 = reshape(shape = var_2534, x = var_2527_1)[name = tensor("op_2535")]; + tensor var_2537 = const()[name = tensor("op_2537"), val = tensor([1, 1804, 4, 128])]; + tensor var_2538 = reshape(shape = var_2537, x = var_2527_2)[name = tensor("op_2538")]; + tensor value_45_perm_0 = const()[name = tensor("value_45_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor inputs_45 = mul(x = var_2527_2, y = mask_7)[name = tensor("inputs_45")]; + tensor input_671_perm_0 = const()[name = tensor("input_671_perm_0"), val = tensor([0, 2, 1])]; + tensor const_263 = const()[name = tensor("const_263"), val = tensor(0x0p+0)]; + tensor input_673_pad_0 = const()[name = tensor("input_673_pad_0"), val = tensor([0, 0, 0, 0, 5, 5])]; + tensor input_673_mode_0 = const()[name = tensor("input_673_mode_0"), val = tensor("constant")]; + tensor input_671 = transpose(perm = input_671_perm_0, x = inputs_45)[name = tensor("transpose_636")]; + tensor input_673 = pad(constant_val = const_263, mode = input_673_mode_0, pad = input_673_pad_0, x = input_671)[name = tensor("input_673")]; + tensor x_225_pad_type_0 = const()[name = tensor("x_225_pad_type_0"), val = tensor("valid")]; + tensor x_225_groups_0 = const()[name = tensor("x_225_groups_0"), val = tensor(512)]; + tensor x_225_strides_0 = const()[name = tensor("x_225_strides_0"), val = tensor([1])]; + tensor x_225_pad_0 = const()[name = tensor("x_225_pad_0"), val = tensor([0, 0])]; + tensor x_225_dilations_0 = const()[name = tensor("x_225_dilations_0"), val = tensor([1])]; + tensor x_225 = conv(dilations = x_225_dilations_0, groups = x_225_groups_0, pad = x_225_pad_0, pad_type = x_225_pad_type_0, strides = x_225_strides_0, weight = encoder_encoders_21_self_attn_fsmn_block_weight, x = input_673)[name = tensor("x_225")]; + tensor x_227_perm_0 = const()[name = tensor("x_227_perm_0"), val = tensor([0, 2, 1])]; + tensor x_227 = transpose(perm = x_227_perm_0, x = x_225)[name = tensor("transpose_635")]; + tensor input_675 = add(x = x_227, y = inputs_45)[name = tensor("input_675")]; + tensor fsmn_memory_45 = mul(x = input_675, y = mask_7)[name = tensor("fsmn_memory_45")]; + tensor var_2557 = const()[name = tensor("op_2557"), val = tensor(0x1.6a09e6p-4)]; + tensor q_h_91 = mul(x = var_2532, y = var_2557)[name = tensor("q_h_91")]; + tensor scores_89_transpose_x_0 = const()[name = tensor("scores_89_transpose_x_0"), val = tensor(false)]; + tensor scores_89_transpose_y_0 = const()[name = tensor("scores_89_transpose_y_0"), val = tensor(false)]; + tensor transpose_254_perm_0 = const()[name = tensor("transpose_254_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_255_perm_0 = const()[name = tensor("transpose_255_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_255 = transpose(perm = transpose_255_perm_0, x = var_2535)[name = tensor("transpose_633")]; + tensor transpose_254 = transpose(perm = transpose_254_perm_0, x = q_h_91)[name = tensor("transpose_634")]; + tensor scores_89 = matmul(transpose_x = scores_89_transpose_x_0, transpose_y = scores_89_transpose_y_0, x = transpose_254, y = transpose_255)[name = tensor("scores_89")]; + tensor scores_91 = select(a = var_48, b = scores_89, cond = mask_9)[name = tensor("scores_91")]; + tensor var_2565 = softmax(axis = var_61, x = scores_91)[name = tensor("op_2565")]; + tensor input_677 = select(a = var_53, b = var_2565, cond = mask_9)[name = tensor("input_677")]; + tensor x_231_transpose_x_0 = const()[name = tensor("x_231_transpose_x_0"), val = tensor(false)]; + tensor x_231_transpose_y_0 = const()[name = tensor("x_231_transpose_y_0"), val = tensor(false)]; + tensor value_45 = transpose(perm = value_45_perm_0, x = var_2538)[name = tensor("transpose_637")]; + tensor x_231 = matmul(transpose_x = x_231_transpose_x_0, transpose_y = x_231_transpose_y_0, x = input_677, y = value_45)[name = tensor("x_231")]; + tensor var_2569_perm_0 = const()[name = tensor("op_2569_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2571 = const()[name = tensor("op_2571"), val = tensor([1, -1, 512])]; + tensor var_2569 = transpose(perm = var_2569_perm_0, x = x_231)[name = tensor("transpose_632")]; + tensor input_679 = reshape(shape = var_2571, x = var_2569)[name = tensor("input_679")]; + tensor att_outs_45 = linear(bias = encoder_encoders_21_self_attn_linear_out_bias, weight = encoder_encoders_21_self_attn_linear_out_weight, x = input_679)[name = tensor("linear_89")]; + tensor input_681 = add(x = att_outs_45, y = fsmn_memory_45)[name = tensor("input_681")]; + tensor input_683 = add(x = input_667, y = input_681)[name = tensor("input_683")]; + tensor const_265 = const()[name = tensor("const_265"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939691264)))]; + tensor const_266 = const()[name = tensor("const_266"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939693376)))]; + tensor output_91_axes_0 = const()[name = tensor("output_91_axes_0"), val = tensor([-1])]; + tensor output_91 = layer_norm(axes = output_91_axes_0, beta = const_266, epsilon = var_46, gamma = const_265, x = input_683)[name = tensor("output_91")]; + tensor input_689 = linear(bias = encoder_encoders_21_feed_forward_w_1_bias, weight = encoder_encoders_21_feed_forward_w_1_weight, x = output_91)[name = tensor("linear_90")]; + tensor input_691 = relu(x = input_689)[name = tensor("input_691")]; + tensor input_695 = linear(bias = encoder_encoders_21_feed_forward_w_2_bias, weight = encoder_encoders_21_feed_forward_w_2_weight, x = input_691)[name = tensor("linear_91")]; + tensor input_697 = add(x = input_683, y = input_695)[name = tensor("input_697")]; + tensor const_267 = const()[name = tensor("const_267"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939695488)))]; + tensor const_268 = const()[name = tensor("const_268"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939697600)))]; + tensor output_93_axes_0 = const()[name = tensor("output_93_axes_0"), val = tensor([-1])]; + tensor output_93 = layer_norm(axes = output_93_axes_0, beta = const_268, epsilon = var_46, gamma = const_267, x = input_697)[name = tensor("output_93")]; + tensor var_2628 = linear(bias = encoder_encoders_22_self_attn_linear_q_k_v_bias, weight = encoder_encoders_22_self_attn_linear_q_k_v_weight, x = output_93)[name = tensor("linear_92")]; + tensor tile_23 = const()[name = tensor("tile_23"), val = tensor([512, 512, 512])]; + tensor var_2629_axis_0 = const()[name = tensor("op_2629_axis_0"), val = tensor(-1)]; + tensor var_2629_0, tensor var_2629_1, tensor var_2629_2 = split(axis = var_2629_axis_0, split_sizes = tile_23, x = var_2628)[name = tensor("op_2629")]; + tensor var_2633 = const()[name = tensor("op_2633"), val = tensor([1, 1804, 4, 128])]; + tensor var_2634 = reshape(shape = var_2633, x = var_2629_0)[name = tensor("op_2634")]; + tensor var_2636 = const()[name = tensor("op_2636"), val = tensor([1, 1804, 4, 128])]; + tensor var_2637 = reshape(shape = var_2636, x = var_2629_1)[name = tensor("op_2637")]; + tensor var_2639 = const()[name = tensor("op_2639"), val = tensor([1, 1804, 4, 128])]; + tensor var_2640 = reshape(shape = var_2639, x = var_2629_2)[name = tensor("op_2640")]; + tensor value_47_perm_0 = const()[name = tensor("value_47_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor inputs_47 = mul(x = var_2629_2, y = mask_7)[name = tensor("inputs_47")]; + tensor input_701_perm_0 = const()[name = tensor("input_701_perm_0"), val = tensor([0, 2, 1])]; + tensor const_274 = const()[name = tensor("const_274"), val = tensor(0x0p+0)]; + tensor input_703_pad_0 = const()[name = tensor("input_703_pad_0"), val = tensor([0, 0, 0, 0, 5, 5])]; + tensor input_703_mode_0 = const()[name = tensor("input_703_mode_0"), val = tensor("constant")]; + tensor input_701 = transpose(perm = input_701_perm_0, x = inputs_47)[name = tensor("transpose_630")]; + tensor input_703 = pad(constant_val = const_274, mode = input_703_mode_0, pad = input_703_pad_0, x = input_701)[name = tensor("input_703")]; + tensor x_235_pad_type_0 = const()[name = tensor("x_235_pad_type_0"), val = tensor("valid")]; + tensor x_235_groups_0 = const()[name = tensor("x_235_groups_0"), val = tensor(512)]; + tensor x_235_strides_0 = const()[name = tensor("x_235_strides_0"), val = tensor([1])]; + tensor x_235_pad_0 = const()[name = tensor("x_235_pad_0"), val = tensor([0, 0])]; + tensor x_235_dilations_0 = const()[name = tensor("x_235_dilations_0"), val = tensor([1])]; + tensor x_235 = conv(dilations = x_235_dilations_0, groups = x_235_groups_0, pad = x_235_pad_0, pad_type = x_235_pad_type_0, strides = x_235_strides_0, weight = encoder_encoders_22_self_attn_fsmn_block_weight, x = input_703)[name = tensor("x_235")]; + tensor x_237_perm_0 = const()[name = tensor("x_237_perm_0"), val = tensor([0, 2, 1])]; + tensor x_237 = transpose(perm = x_237_perm_0, x = x_235)[name = tensor("transpose_629")]; + tensor input_705 = add(x = x_237, y = inputs_47)[name = tensor("input_705")]; + tensor fsmn_memory_47 = mul(x = input_705, y = mask_7)[name = tensor("fsmn_memory_47")]; + tensor var_2659 = const()[name = tensor("op_2659"), val = tensor(0x1.6a09e6p-4)]; + tensor q_h_95 = mul(x = var_2634, y = var_2659)[name = tensor("q_h_95")]; + tensor scores_93_transpose_x_0 = const()[name = tensor("scores_93_transpose_x_0"), val = tensor(false)]; + tensor scores_93_transpose_y_0 = const()[name = tensor("scores_93_transpose_y_0"), val = tensor(false)]; + tensor transpose_256_perm_0 = const()[name = tensor("transpose_256_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_257_perm_0 = const()[name = tensor("transpose_257_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_257 = transpose(perm = transpose_257_perm_0, x = var_2637)[name = tensor("transpose_627")]; + tensor transpose_256 = transpose(perm = transpose_256_perm_0, x = q_h_95)[name = tensor("transpose_628")]; + tensor scores_93 = matmul(transpose_x = scores_93_transpose_x_0, transpose_y = scores_93_transpose_y_0, x = transpose_256, y = transpose_257)[name = tensor("scores_93")]; + tensor scores_95 = select(a = var_48, b = scores_93, cond = mask_9)[name = tensor("scores_95")]; + tensor var_2667 = softmax(axis = var_61, x = scores_95)[name = tensor("op_2667")]; + tensor input_707 = select(a = var_53, b = var_2667, cond = mask_9)[name = tensor("input_707")]; + tensor x_241_transpose_x_0 = const()[name = tensor("x_241_transpose_x_0"), val = tensor(false)]; + tensor x_241_transpose_y_0 = const()[name = tensor("x_241_transpose_y_0"), val = tensor(false)]; + tensor value_47 = transpose(perm = value_47_perm_0, x = var_2640)[name = tensor("transpose_631")]; + tensor x_241 = matmul(transpose_x = x_241_transpose_x_0, transpose_y = x_241_transpose_y_0, x = input_707, y = value_47)[name = tensor("x_241")]; + tensor var_2671_perm_0 = const()[name = tensor("op_2671_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2673 = const()[name = tensor("op_2673"), val = tensor([1, -1, 512])]; + tensor var_2671 = transpose(perm = var_2671_perm_0, x = x_241)[name = tensor("transpose_626")]; + tensor input_709 = reshape(shape = var_2673, x = var_2671)[name = tensor("input_709")]; + tensor att_outs_47 = linear(bias = encoder_encoders_22_self_attn_linear_out_bias, weight = encoder_encoders_22_self_attn_linear_out_weight, x = input_709)[name = tensor("linear_93")]; + tensor input_711 = add(x = att_outs_47, y = fsmn_memory_47)[name = tensor("input_711")]; + tensor input_713 = add(x = input_697, y = input_711)[name = tensor("input_713")]; + tensor const_276 = const()[name = tensor("const_276"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939699712)))]; + tensor const_277 = const()[name = tensor("const_277"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939701824)))]; + tensor output_95_axes_0 = const()[name = tensor("output_95_axes_0"), val = tensor([-1])]; + tensor output_95 = layer_norm(axes = output_95_axes_0, beta = const_277, epsilon = var_46, gamma = const_276, x = input_713)[name = tensor("output_95")]; + tensor input_719 = linear(bias = encoder_encoders_22_feed_forward_w_1_bias, weight = encoder_encoders_22_feed_forward_w_1_weight, x = output_95)[name = tensor("linear_94")]; + tensor input_721 = relu(x = input_719)[name = tensor("input_721")]; + tensor input_725 = linear(bias = encoder_encoders_22_feed_forward_w_2_bias, weight = encoder_encoders_22_feed_forward_w_2_weight, x = input_721)[name = tensor("linear_95")]; + tensor input_727 = add(x = input_713, y = input_725)[name = tensor("input_727")]; + tensor const_278 = const()[name = tensor("const_278"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939703936)))]; + tensor const_279 = const()[name = tensor("const_279"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939706048)))]; + tensor output_97_axes_0 = const()[name = tensor("output_97_axes_0"), val = tensor([-1])]; + tensor output_97 = layer_norm(axes = output_97_axes_0, beta = const_279, epsilon = var_46, gamma = const_278, x = input_727)[name = tensor("output_97")]; + tensor var_2730 = linear(bias = encoder_encoders_23_self_attn_linear_q_k_v_bias, weight = encoder_encoders_23_self_attn_linear_q_k_v_weight, x = output_97)[name = tensor("linear_96")]; + tensor tile_24 = const()[name = tensor("tile_24"), val = tensor([512, 512, 512])]; + tensor var_2731_axis_0 = const()[name = tensor("op_2731_axis_0"), val = tensor(-1)]; + tensor var_2731_0, tensor var_2731_1, tensor var_2731_2 = split(axis = var_2731_axis_0, split_sizes = tile_24, x = var_2730)[name = tensor("op_2731")]; + tensor var_2735 = const()[name = tensor("op_2735"), val = tensor([1, 1804, 4, 128])]; + tensor var_2736 = reshape(shape = var_2735, x = var_2731_0)[name = tensor("op_2736")]; + tensor var_2738 = const()[name = tensor("op_2738"), val = tensor([1, 1804, 4, 128])]; + tensor var_2739 = reshape(shape = var_2738, x = var_2731_1)[name = tensor("op_2739")]; + tensor var_2741 = const()[name = tensor("op_2741"), val = tensor([1, 1804, 4, 128])]; + tensor var_2742 = reshape(shape = var_2741, x = var_2731_2)[name = tensor("op_2742")]; + tensor value_49_perm_0 = const()[name = tensor("value_49_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor inputs_49 = mul(x = var_2731_2, y = mask_7)[name = tensor("inputs_49")]; + tensor input_731_perm_0 = const()[name = tensor("input_731_perm_0"), val = tensor([0, 2, 1])]; + tensor const_285 = const()[name = tensor("const_285"), val = tensor(0x0p+0)]; + tensor input_733_pad_0 = const()[name = tensor("input_733_pad_0"), val = tensor([0, 0, 0, 0, 5, 5])]; + tensor input_733_mode_0 = const()[name = tensor("input_733_mode_0"), val = tensor("constant")]; + tensor input_731 = transpose(perm = input_731_perm_0, x = inputs_49)[name = tensor("transpose_624")]; + tensor input_733 = pad(constant_val = const_285, mode = input_733_mode_0, pad = input_733_pad_0, x = input_731)[name = tensor("input_733")]; + tensor x_245_pad_type_0 = const()[name = tensor("x_245_pad_type_0"), val = tensor("valid")]; + tensor x_245_groups_0 = const()[name = tensor("x_245_groups_0"), val = tensor(512)]; + tensor x_245_strides_0 = const()[name = tensor("x_245_strides_0"), val = tensor([1])]; + tensor x_245_pad_0 = const()[name = tensor("x_245_pad_0"), val = tensor([0, 0])]; + tensor x_245_dilations_0 = const()[name = tensor("x_245_dilations_0"), val = tensor([1])]; + tensor x_245 = conv(dilations = x_245_dilations_0, groups = x_245_groups_0, pad = x_245_pad_0, pad_type = x_245_pad_type_0, strides = x_245_strides_0, weight = encoder_encoders_23_self_attn_fsmn_block_weight, x = input_733)[name = tensor("x_245")]; + tensor x_247_perm_0 = const()[name = tensor("x_247_perm_0"), val = tensor([0, 2, 1])]; + tensor x_247 = transpose(perm = x_247_perm_0, x = x_245)[name = tensor("transpose_623")]; + tensor input_735 = add(x = x_247, y = inputs_49)[name = tensor("input_735")]; + tensor fsmn_memory_49 = mul(x = input_735, y = mask_7)[name = tensor("fsmn_memory_49")]; + tensor var_2761 = const()[name = tensor("op_2761"), val = tensor(0x1.6a09e6p-4)]; + tensor q_h_99 = mul(x = var_2736, y = var_2761)[name = tensor("q_h_99")]; + tensor scores_97_transpose_x_0 = const()[name = tensor("scores_97_transpose_x_0"), val = tensor(false)]; + tensor scores_97_transpose_y_0 = const()[name = tensor("scores_97_transpose_y_0"), val = tensor(false)]; + tensor transpose_258_perm_0 = const()[name = tensor("transpose_258_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_259_perm_0 = const()[name = tensor("transpose_259_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_259 = transpose(perm = transpose_259_perm_0, x = var_2739)[name = tensor("transpose_621")]; + tensor transpose_258 = transpose(perm = transpose_258_perm_0, x = q_h_99)[name = tensor("transpose_622")]; + tensor scores_97 = matmul(transpose_x = scores_97_transpose_x_0, transpose_y = scores_97_transpose_y_0, x = transpose_258, y = transpose_259)[name = tensor("scores_97")]; + tensor scores_99 = select(a = var_48, b = scores_97, cond = mask_9)[name = tensor("scores_99")]; + tensor var_2769 = softmax(axis = var_61, x = scores_99)[name = tensor("op_2769")]; + tensor input_737 = select(a = var_53, b = var_2769, cond = mask_9)[name = tensor("input_737")]; + tensor x_251_transpose_x_0 = const()[name = tensor("x_251_transpose_x_0"), val = tensor(false)]; + tensor x_251_transpose_y_0 = const()[name = tensor("x_251_transpose_y_0"), val = tensor(false)]; + tensor value_49 = transpose(perm = value_49_perm_0, x = var_2742)[name = tensor("transpose_625")]; + tensor x_251 = matmul(transpose_x = x_251_transpose_x_0, transpose_y = x_251_transpose_y_0, x = input_737, y = value_49)[name = tensor("x_251")]; + tensor var_2773_perm_0 = const()[name = tensor("op_2773_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2775 = const()[name = tensor("op_2775"), val = tensor([1, -1, 512])]; + tensor var_2773 = transpose(perm = var_2773_perm_0, x = x_251)[name = tensor("transpose_620")]; + tensor input_739 = reshape(shape = var_2775, x = var_2773)[name = tensor("input_739")]; + tensor att_outs_49 = linear(bias = encoder_encoders_23_self_attn_linear_out_bias, weight = encoder_encoders_23_self_attn_linear_out_weight, x = input_739)[name = tensor("linear_97")]; + tensor input_741 = add(x = att_outs_49, y = fsmn_memory_49)[name = tensor("input_741")]; + tensor input_743 = add(x = input_727, y = input_741)[name = tensor("input_743")]; + tensor const_287 = const()[name = tensor("const_287"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939708160)))]; + tensor const_288 = const()[name = tensor("const_288"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939710272)))]; + tensor output_99_axes_0 = const()[name = tensor("output_99_axes_0"), val = tensor([-1])]; + tensor output_99 = layer_norm(axes = output_99_axes_0, beta = const_288, epsilon = var_46, gamma = const_287, x = input_743)[name = tensor("output_99")]; + tensor input_749 = linear(bias = encoder_encoders_23_feed_forward_w_1_bias, weight = encoder_encoders_23_feed_forward_w_1_weight, x = output_99)[name = tensor("linear_98")]; + tensor input_751 = relu(x = input_749)[name = tensor("input_751")]; + tensor input_755 = linear(bias = encoder_encoders_23_feed_forward_w_2_bias, weight = encoder_encoders_23_feed_forward_w_2_weight, x = input_751)[name = tensor("linear_99")]; + tensor input_757 = add(x = input_743, y = input_755)[name = tensor("input_757")]; + tensor const_289 = const()[name = tensor("const_289"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939712384)))]; + tensor const_290 = const()[name = tensor("const_290"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939714496)))]; + tensor output_101_axes_0 = const()[name = tensor("output_101_axes_0"), val = tensor([-1])]; + tensor output_101 = layer_norm(axes = output_101_axes_0, beta = const_290, epsilon = var_46, gamma = const_289, x = input_757)[name = tensor("output_101")]; + tensor var_2832 = linear(bias = encoder_encoders_24_self_attn_linear_q_k_v_bias, weight = encoder_encoders_24_self_attn_linear_q_k_v_weight, x = output_101)[name = tensor("linear_100")]; + tensor tile_25 = const()[name = tensor("tile_25"), val = tensor([512, 512, 512])]; + tensor var_2833_axis_0 = const()[name = tensor("op_2833_axis_0"), val = tensor(-1)]; + tensor var_2833_0, tensor var_2833_1, tensor var_2833_2 = split(axis = var_2833_axis_0, split_sizes = tile_25, x = var_2832)[name = tensor("op_2833")]; + tensor var_2837 = const()[name = tensor("op_2837"), val = tensor([1, 1804, 4, 128])]; + tensor var_2838 = reshape(shape = var_2837, x = var_2833_0)[name = tensor("op_2838")]; + tensor var_2840 = const()[name = tensor("op_2840"), val = tensor([1, 1804, 4, 128])]; + tensor var_2841 = reshape(shape = var_2840, x = var_2833_1)[name = tensor("op_2841")]; + tensor var_2843 = const()[name = tensor("op_2843"), val = tensor([1, 1804, 4, 128])]; + tensor var_2844 = reshape(shape = var_2843, x = var_2833_2)[name = tensor("op_2844")]; + tensor value_51_perm_0 = const()[name = tensor("value_51_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor inputs_51 = mul(x = var_2833_2, y = mask_7)[name = tensor("inputs_51")]; + tensor input_761_perm_0 = const()[name = tensor("input_761_perm_0"), val = tensor([0, 2, 1])]; + tensor const_296 = const()[name = tensor("const_296"), val = tensor(0x0p+0)]; + tensor input_763_pad_0 = const()[name = tensor("input_763_pad_0"), val = tensor([0, 0, 0, 0, 5, 5])]; + tensor input_763_mode_0 = const()[name = tensor("input_763_mode_0"), val = tensor("constant")]; + tensor input_761 = transpose(perm = input_761_perm_0, x = inputs_51)[name = tensor("transpose_618")]; + tensor input_763 = pad(constant_val = const_296, mode = input_763_mode_0, pad = input_763_pad_0, x = input_761)[name = tensor("input_763")]; + tensor x_255_pad_type_0 = const()[name = tensor("x_255_pad_type_0"), val = tensor("valid")]; + tensor x_255_groups_0 = const()[name = tensor("x_255_groups_0"), val = tensor(512)]; + tensor x_255_strides_0 = const()[name = tensor("x_255_strides_0"), val = tensor([1])]; + tensor x_255_pad_0 = const()[name = tensor("x_255_pad_0"), val = tensor([0, 0])]; + tensor x_255_dilations_0 = const()[name = tensor("x_255_dilations_0"), val = tensor([1])]; + tensor x_255 = conv(dilations = x_255_dilations_0, groups = x_255_groups_0, pad = x_255_pad_0, pad_type = x_255_pad_type_0, strides = x_255_strides_0, weight = encoder_encoders_24_self_attn_fsmn_block_weight, x = input_763)[name = tensor("x_255")]; + tensor x_257_perm_0 = const()[name = tensor("x_257_perm_0"), val = tensor([0, 2, 1])]; + tensor x_257 = transpose(perm = x_257_perm_0, x = x_255)[name = tensor("transpose_617")]; + tensor input_765 = add(x = x_257, y = inputs_51)[name = tensor("input_765")]; + tensor fsmn_memory_51 = mul(x = input_765, y = mask_7)[name = tensor("fsmn_memory_51")]; + tensor var_2863 = const()[name = tensor("op_2863"), val = tensor(0x1.6a09e6p-4)]; + tensor q_h_103 = mul(x = var_2838, y = var_2863)[name = tensor("q_h_103")]; + tensor scores_101_transpose_x_0 = const()[name = tensor("scores_101_transpose_x_0"), val = tensor(false)]; + tensor scores_101_transpose_y_0 = const()[name = tensor("scores_101_transpose_y_0"), val = tensor(false)]; + tensor transpose_260_perm_0 = const()[name = tensor("transpose_260_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_261_perm_0 = const()[name = tensor("transpose_261_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_261 = transpose(perm = transpose_261_perm_0, x = var_2841)[name = tensor("transpose_615")]; + tensor transpose_260 = transpose(perm = transpose_260_perm_0, x = q_h_103)[name = tensor("transpose_616")]; + tensor scores_101 = matmul(transpose_x = scores_101_transpose_x_0, transpose_y = scores_101_transpose_y_0, x = transpose_260, y = transpose_261)[name = tensor("scores_101")]; + tensor scores_103 = select(a = var_48, b = scores_101, cond = mask_9)[name = tensor("scores_103")]; + tensor var_2871 = softmax(axis = var_61, x = scores_103)[name = tensor("op_2871")]; + tensor input_767 = select(a = var_53, b = var_2871, cond = mask_9)[name = tensor("input_767")]; + tensor x_261_transpose_x_0 = const()[name = tensor("x_261_transpose_x_0"), val = tensor(false)]; + tensor x_261_transpose_y_0 = const()[name = tensor("x_261_transpose_y_0"), val = tensor(false)]; + tensor value_51 = transpose(perm = value_51_perm_0, x = var_2844)[name = tensor("transpose_619")]; + tensor x_261 = matmul(transpose_x = x_261_transpose_x_0, transpose_y = x_261_transpose_y_0, x = input_767, y = value_51)[name = tensor("x_261")]; + tensor var_2875_perm_0 = const()[name = tensor("op_2875_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2877 = const()[name = tensor("op_2877"), val = tensor([1, -1, 512])]; + tensor var_2875 = transpose(perm = var_2875_perm_0, x = x_261)[name = tensor("transpose_614")]; + tensor input_769 = reshape(shape = var_2877, x = var_2875)[name = tensor("input_769")]; + tensor att_outs_51 = linear(bias = encoder_encoders_24_self_attn_linear_out_bias, weight = encoder_encoders_24_self_attn_linear_out_weight, x = input_769)[name = tensor("linear_101")]; + tensor input_771 = add(x = att_outs_51, y = fsmn_memory_51)[name = tensor("input_771")]; + tensor input_773 = add(x = input_757, y = input_771)[name = tensor("input_773")]; + tensor const_298 = const()[name = tensor("const_298"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939716608)))]; + tensor const_299 = const()[name = tensor("const_299"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939718720)))]; + tensor output_103_axes_0 = const()[name = tensor("output_103_axes_0"), val = tensor([-1])]; + tensor output_103 = layer_norm(axes = output_103_axes_0, beta = const_299, epsilon = var_46, gamma = const_298, x = input_773)[name = tensor("output_103")]; + tensor input_779 = linear(bias = encoder_encoders_24_feed_forward_w_1_bias, weight = encoder_encoders_24_feed_forward_w_1_weight, x = output_103)[name = tensor("linear_102")]; + tensor input_781 = relu(x = input_779)[name = tensor("input_781")]; + tensor input_785 = linear(bias = encoder_encoders_24_feed_forward_w_2_bias, weight = encoder_encoders_24_feed_forward_w_2_weight, x = input_781)[name = tensor("linear_103")]; + tensor input_787 = add(x = input_773, y = input_785)[name = tensor("input_787")]; + tensor const_300 = const()[name = tensor("const_300"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939720832)))]; + tensor const_301 = const()[name = tensor("const_301"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939722944)))]; + tensor output_105_axes_0 = const()[name = tensor("output_105_axes_0"), val = tensor([-1])]; + tensor output_105 = layer_norm(axes = output_105_axes_0, beta = const_301, epsilon = var_46, gamma = const_300, x = input_787)[name = tensor("output_105")]; + tensor var_2934 = linear(bias = encoder_encoders_25_self_attn_linear_q_k_v_bias, weight = encoder_encoders_25_self_attn_linear_q_k_v_weight, x = output_105)[name = tensor("linear_104")]; + tensor tile_26 = const()[name = tensor("tile_26"), val = tensor([512, 512, 512])]; + tensor var_2935_axis_0 = const()[name = tensor("op_2935_axis_0"), val = tensor(-1)]; + tensor var_2935_0, tensor var_2935_1, tensor var_2935_2 = split(axis = var_2935_axis_0, split_sizes = tile_26, x = var_2934)[name = tensor("op_2935")]; + tensor var_2939 = const()[name = tensor("op_2939"), val = tensor([1, 1804, 4, 128])]; + tensor var_2940 = reshape(shape = var_2939, x = var_2935_0)[name = tensor("op_2940")]; + tensor var_2942 = const()[name = tensor("op_2942"), val = tensor([1, 1804, 4, 128])]; + tensor var_2943 = reshape(shape = var_2942, x = var_2935_1)[name = tensor("op_2943")]; + tensor var_2945 = const()[name = tensor("op_2945"), val = tensor([1, 1804, 4, 128])]; + tensor var_2946 = reshape(shape = var_2945, x = var_2935_2)[name = tensor("op_2946")]; + tensor value_53_perm_0 = const()[name = tensor("value_53_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor inputs_53 = mul(x = var_2935_2, y = mask_7)[name = tensor("inputs_53")]; + tensor input_791_perm_0 = const()[name = tensor("input_791_perm_0"), val = tensor([0, 2, 1])]; + tensor const_307 = const()[name = tensor("const_307"), val = tensor(0x0p+0)]; + tensor input_793_pad_0 = const()[name = tensor("input_793_pad_0"), val = tensor([0, 0, 0, 0, 5, 5])]; + tensor input_793_mode_0 = const()[name = tensor("input_793_mode_0"), val = tensor("constant")]; + tensor input_791 = transpose(perm = input_791_perm_0, x = inputs_53)[name = tensor("transpose_612")]; + tensor input_793 = pad(constant_val = const_307, mode = input_793_mode_0, pad = input_793_pad_0, x = input_791)[name = tensor("input_793")]; + tensor x_265_pad_type_0 = const()[name = tensor("x_265_pad_type_0"), val = tensor("valid")]; + tensor x_265_groups_0 = const()[name = tensor("x_265_groups_0"), val = tensor(512)]; + tensor x_265_strides_0 = const()[name = tensor("x_265_strides_0"), val = tensor([1])]; + tensor x_265_pad_0 = const()[name = tensor("x_265_pad_0"), val = tensor([0, 0])]; + tensor x_265_dilations_0 = const()[name = tensor("x_265_dilations_0"), val = tensor([1])]; + tensor x_265 = conv(dilations = x_265_dilations_0, groups = x_265_groups_0, pad = x_265_pad_0, pad_type = x_265_pad_type_0, strides = x_265_strides_0, weight = encoder_encoders_25_self_attn_fsmn_block_weight, x = input_793)[name = tensor("x_265")]; + tensor x_267_perm_0 = const()[name = tensor("x_267_perm_0"), val = tensor([0, 2, 1])]; + tensor x_267 = transpose(perm = x_267_perm_0, x = x_265)[name = tensor("transpose_611")]; + tensor input_795 = add(x = x_267, y = inputs_53)[name = tensor("input_795")]; + tensor fsmn_memory_53 = mul(x = input_795, y = mask_7)[name = tensor("fsmn_memory_53")]; + tensor var_2965 = const()[name = tensor("op_2965"), val = tensor(0x1.6a09e6p-4)]; + tensor q_h_107 = mul(x = var_2940, y = var_2965)[name = tensor("q_h_107")]; + tensor scores_105_transpose_x_0 = const()[name = tensor("scores_105_transpose_x_0"), val = tensor(false)]; + tensor scores_105_transpose_y_0 = const()[name = tensor("scores_105_transpose_y_0"), val = tensor(false)]; + tensor transpose_262_perm_0 = const()[name = tensor("transpose_262_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_263_perm_0 = const()[name = tensor("transpose_263_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_263 = transpose(perm = transpose_263_perm_0, x = var_2943)[name = tensor("transpose_609")]; + tensor transpose_262 = transpose(perm = transpose_262_perm_0, x = q_h_107)[name = tensor("transpose_610")]; + tensor scores_105 = matmul(transpose_x = scores_105_transpose_x_0, transpose_y = scores_105_transpose_y_0, x = transpose_262, y = transpose_263)[name = tensor("scores_105")]; + tensor scores_107 = select(a = var_48, b = scores_105, cond = mask_9)[name = tensor("scores_107")]; + tensor var_2973 = softmax(axis = var_61, x = scores_107)[name = tensor("op_2973")]; + tensor input_797 = select(a = var_53, b = var_2973, cond = mask_9)[name = tensor("input_797")]; + tensor x_271_transpose_x_0 = const()[name = tensor("x_271_transpose_x_0"), val = tensor(false)]; + tensor x_271_transpose_y_0 = const()[name = tensor("x_271_transpose_y_0"), val = tensor(false)]; + tensor value_53 = transpose(perm = value_53_perm_0, x = var_2946)[name = tensor("transpose_613")]; + tensor x_271 = matmul(transpose_x = x_271_transpose_x_0, transpose_y = x_271_transpose_y_0, x = input_797, y = value_53)[name = tensor("x_271")]; + tensor var_2977_perm_0 = const()[name = tensor("op_2977_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2979 = const()[name = tensor("op_2979"), val = tensor([1, -1, 512])]; + tensor var_2977 = transpose(perm = var_2977_perm_0, x = x_271)[name = tensor("transpose_608")]; + tensor input_799 = reshape(shape = var_2979, x = var_2977)[name = tensor("input_799")]; + tensor att_outs_53 = linear(bias = encoder_encoders_25_self_attn_linear_out_bias, weight = encoder_encoders_25_self_attn_linear_out_weight, x = input_799)[name = tensor("linear_105")]; + tensor input_801 = add(x = att_outs_53, y = fsmn_memory_53)[name = tensor("input_801")]; + tensor input_803 = add(x = input_787, y = input_801)[name = tensor("input_803")]; + tensor const_309 = const()[name = tensor("const_309"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939725056)))]; + tensor const_310 = const()[name = tensor("const_310"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939727168)))]; + tensor output_107_axes_0 = const()[name = tensor("output_107_axes_0"), val = tensor([-1])]; + tensor output_107 = layer_norm(axes = output_107_axes_0, beta = const_310, epsilon = var_46, gamma = const_309, x = input_803)[name = tensor("output_107")]; + tensor input_809 = linear(bias = encoder_encoders_25_feed_forward_w_1_bias, weight = encoder_encoders_25_feed_forward_w_1_weight, x = output_107)[name = tensor("linear_106")]; + tensor input_811 = relu(x = input_809)[name = tensor("input_811")]; + tensor input_815 = linear(bias = encoder_encoders_25_feed_forward_w_2_bias, weight = encoder_encoders_25_feed_forward_w_2_weight, x = input_811)[name = tensor("linear_107")]; + tensor input_817 = add(x = input_803, y = input_815)[name = tensor("input_817")]; + tensor const_311 = const()[name = tensor("const_311"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939729280)))]; + tensor const_312 = const()[name = tensor("const_312"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939731392)))]; + tensor output_109_axes_0 = const()[name = tensor("output_109_axes_0"), val = tensor([-1])]; + tensor output_109 = layer_norm(axes = output_109_axes_0, beta = const_312, epsilon = var_46, gamma = const_311, x = input_817)[name = tensor("output_109")]; + tensor var_3036 = linear(bias = encoder_encoders_26_self_attn_linear_q_k_v_bias, weight = encoder_encoders_26_self_attn_linear_q_k_v_weight, x = output_109)[name = tensor("linear_108")]; + tensor tile_27 = const()[name = tensor("tile_27"), val = tensor([512, 512, 512])]; + tensor var_3037_axis_0 = const()[name = tensor("op_3037_axis_0"), val = tensor(-1)]; + tensor var_3037_0, tensor var_3037_1, tensor var_3037_2 = split(axis = var_3037_axis_0, split_sizes = tile_27, x = var_3036)[name = tensor("op_3037")]; + tensor var_3041 = const()[name = tensor("op_3041"), val = tensor([1, 1804, 4, 128])]; + tensor var_3042 = reshape(shape = var_3041, x = var_3037_0)[name = tensor("op_3042")]; + tensor var_3044 = const()[name = tensor("op_3044"), val = tensor([1, 1804, 4, 128])]; + tensor var_3045 = reshape(shape = var_3044, x = var_3037_1)[name = tensor("op_3045")]; + tensor var_3047 = const()[name = tensor("op_3047"), val = tensor([1, 1804, 4, 128])]; + tensor var_3048 = reshape(shape = var_3047, x = var_3037_2)[name = tensor("op_3048")]; + tensor value_55_perm_0 = const()[name = tensor("value_55_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor inputs_55 = mul(x = var_3037_2, y = mask_7)[name = tensor("inputs_55")]; + tensor input_821_perm_0 = const()[name = tensor("input_821_perm_0"), val = tensor([0, 2, 1])]; + tensor const_318 = const()[name = tensor("const_318"), val = tensor(0x0p+0)]; + tensor input_823_pad_0 = const()[name = tensor("input_823_pad_0"), val = tensor([0, 0, 0, 0, 5, 5])]; + tensor input_823_mode_0 = const()[name = tensor("input_823_mode_0"), val = tensor("constant")]; + tensor input_821 = transpose(perm = input_821_perm_0, x = inputs_55)[name = tensor("transpose_606")]; + tensor input_823 = pad(constant_val = const_318, mode = input_823_mode_0, pad = input_823_pad_0, x = input_821)[name = tensor("input_823")]; + tensor x_275_pad_type_0 = const()[name = tensor("x_275_pad_type_0"), val = tensor("valid")]; + tensor x_275_groups_0 = const()[name = tensor("x_275_groups_0"), val = tensor(512)]; + tensor x_275_strides_0 = const()[name = tensor("x_275_strides_0"), val = tensor([1])]; + tensor x_275_pad_0 = const()[name = tensor("x_275_pad_0"), val = tensor([0, 0])]; + tensor x_275_dilations_0 = const()[name = tensor("x_275_dilations_0"), val = tensor([1])]; + tensor x_275 = conv(dilations = x_275_dilations_0, groups = x_275_groups_0, pad = x_275_pad_0, pad_type = x_275_pad_type_0, strides = x_275_strides_0, weight = encoder_encoders_26_self_attn_fsmn_block_weight, x = input_823)[name = tensor("x_275")]; + tensor x_277_perm_0 = const()[name = tensor("x_277_perm_0"), val = tensor([0, 2, 1])]; + tensor x_277 = transpose(perm = x_277_perm_0, x = x_275)[name = tensor("transpose_605")]; + tensor input_825 = add(x = x_277, y = inputs_55)[name = tensor("input_825")]; + tensor fsmn_memory_55 = mul(x = input_825, y = mask_7)[name = tensor("fsmn_memory_55")]; + tensor var_3067 = const()[name = tensor("op_3067"), val = tensor(0x1.6a09e6p-4)]; + tensor q_h_111 = mul(x = var_3042, y = var_3067)[name = tensor("q_h_111")]; + tensor scores_109_transpose_x_0 = const()[name = tensor("scores_109_transpose_x_0"), val = tensor(false)]; + tensor scores_109_transpose_y_0 = const()[name = tensor("scores_109_transpose_y_0"), val = tensor(false)]; + tensor transpose_264_perm_0 = const()[name = tensor("transpose_264_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_265_perm_0 = const()[name = tensor("transpose_265_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_265 = transpose(perm = transpose_265_perm_0, x = var_3045)[name = tensor("transpose_603")]; + tensor transpose_264 = transpose(perm = transpose_264_perm_0, x = q_h_111)[name = tensor("transpose_604")]; + tensor scores_109 = matmul(transpose_x = scores_109_transpose_x_0, transpose_y = scores_109_transpose_y_0, x = transpose_264, y = transpose_265)[name = tensor("scores_109")]; + tensor scores_111 = select(a = var_48, b = scores_109, cond = mask_9)[name = tensor("scores_111")]; + tensor var_3075 = softmax(axis = var_61, x = scores_111)[name = tensor("op_3075")]; + tensor input_827 = select(a = var_53, b = var_3075, cond = mask_9)[name = tensor("input_827")]; + tensor x_281_transpose_x_0 = const()[name = tensor("x_281_transpose_x_0"), val = tensor(false)]; + tensor x_281_transpose_y_0 = const()[name = tensor("x_281_transpose_y_0"), val = tensor(false)]; + tensor value_55 = transpose(perm = value_55_perm_0, x = var_3048)[name = tensor("transpose_607")]; + tensor x_281 = matmul(transpose_x = x_281_transpose_x_0, transpose_y = x_281_transpose_y_0, x = input_827, y = value_55)[name = tensor("x_281")]; + tensor var_3079_perm_0 = const()[name = tensor("op_3079_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_3081 = const()[name = tensor("op_3081"), val = tensor([1, -1, 512])]; + tensor var_3079 = transpose(perm = var_3079_perm_0, x = x_281)[name = tensor("transpose_602")]; + tensor input_829 = reshape(shape = var_3081, x = var_3079)[name = tensor("input_829")]; + tensor att_outs_55 = linear(bias = encoder_encoders_26_self_attn_linear_out_bias, weight = encoder_encoders_26_self_attn_linear_out_weight, x = input_829)[name = tensor("linear_109")]; + tensor input_831 = add(x = att_outs_55, y = fsmn_memory_55)[name = tensor("input_831")]; + tensor input_833 = add(x = input_817, y = input_831)[name = tensor("input_833")]; + tensor const_320 = const()[name = tensor("const_320"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939733504)))]; + tensor const_321 = const()[name = tensor("const_321"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939735616)))]; + tensor output_111_axes_0 = const()[name = tensor("output_111_axes_0"), val = tensor([-1])]; + tensor output_111 = layer_norm(axes = output_111_axes_0, beta = const_321, epsilon = var_46, gamma = const_320, x = input_833)[name = tensor("output_111")]; + tensor input_839 = linear(bias = encoder_encoders_26_feed_forward_w_1_bias, weight = encoder_encoders_26_feed_forward_w_1_weight, x = output_111)[name = tensor("linear_110")]; + tensor input_841 = relu(x = input_839)[name = tensor("input_841")]; + tensor input_845 = linear(bias = encoder_encoders_26_feed_forward_w_2_bias, weight = encoder_encoders_26_feed_forward_w_2_weight, x = input_841)[name = tensor("linear_111")]; + tensor input_847 = add(x = input_833, y = input_845)[name = tensor("input_847")]; + tensor const_322 = const()[name = tensor("const_322"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939737728)))]; + tensor const_323 = const()[name = tensor("const_323"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939739840)))]; + tensor output_113_axes_0 = const()[name = tensor("output_113_axes_0"), val = tensor([-1])]; + tensor output_113 = layer_norm(axes = output_113_axes_0, beta = const_323, epsilon = var_46, gamma = const_322, x = input_847)[name = tensor("output_113")]; + tensor var_3138 = linear(bias = encoder_encoders_27_self_attn_linear_q_k_v_bias, weight = encoder_encoders_27_self_attn_linear_q_k_v_weight, x = output_113)[name = tensor("linear_112")]; + tensor tile_28 = const()[name = tensor("tile_28"), val = tensor([512, 512, 512])]; + tensor var_3139_axis_0 = const()[name = tensor("op_3139_axis_0"), val = tensor(-1)]; + tensor var_3139_0, tensor var_3139_1, tensor var_3139_2 = split(axis = var_3139_axis_0, split_sizes = tile_28, x = var_3138)[name = tensor("op_3139")]; + tensor var_3143 = const()[name = tensor("op_3143"), val = tensor([1, 1804, 4, 128])]; + tensor var_3144 = reshape(shape = var_3143, x = var_3139_0)[name = tensor("op_3144")]; + tensor var_3146 = const()[name = tensor("op_3146"), val = tensor([1, 1804, 4, 128])]; + tensor var_3147 = reshape(shape = var_3146, x = var_3139_1)[name = tensor("op_3147")]; + tensor var_3149 = const()[name = tensor("op_3149"), val = tensor([1, 1804, 4, 128])]; + tensor var_3150 = reshape(shape = var_3149, x = var_3139_2)[name = tensor("op_3150")]; + tensor value_57_perm_0 = const()[name = tensor("value_57_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor inputs_57 = mul(x = var_3139_2, y = mask_7)[name = tensor("inputs_57")]; + tensor input_851_perm_0 = const()[name = tensor("input_851_perm_0"), val = tensor([0, 2, 1])]; + tensor const_329 = const()[name = tensor("const_329"), val = tensor(0x0p+0)]; + tensor input_853_pad_0 = const()[name = tensor("input_853_pad_0"), val = tensor([0, 0, 0, 0, 5, 5])]; + tensor input_853_mode_0 = const()[name = tensor("input_853_mode_0"), val = tensor("constant")]; + tensor input_851 = transpose(perm = input_851_perm_0, x = inputs_57)[name = tensor("transpose_600")]; + tensor input_853 = pad(constant_val = const_329, mode = input_853_mode_0, pad = input_853_pad_0, x = input_851)[name = tensor("input_853")]; + tensor x_285_pad_type_0 = const()[name = tensor("x_285_pad_type_0"), val = tensor("valid")]; + tensor x_285_groups_0 = const()[name = tensor("x_285_groups_0"), val = tensor(512)]; + tensor x_285_strides_0 = const()[name = tensor("x_285_strides_0"), val = tensor([1])]; + tensor x_285_pad_0 = const()[name = tensor("x_285_pad_0"), val = tensor([0, 0])]; + tensor x_285_dilations_0 = const()[name = tensor("x_285_dilations_0"), val = tensor([1])]; + tensor x_285 = conv(dilations = x_285_dilations_0, groups = x_285_groups_0, pad = x_285_pad_0, pad_type = x_285_pad_type_0, strides = x_285_strides_0, weight = encoder_encoders_27_self_attn_fsmn_block_weight, x = input_853)[name = tensor("x_285")]; + tensor x_287_perm_0 = const()[name = tensor("x_287_perm_0"), val = tensor([0, 2, 1])]; + tensor x_287 = transpose(perm = x_287_perm_0, x = x_285)[name = tensor("transpose_599")]; + tensor input_855 = add(x = x_287, y = inputs_57)[name = tensor("input_855")]; + tensor fsmn_memory_57 = mul(x = input_855, y = mask_7)[name = tensor("fsmn_memory_57")]; + tensor var_3169 = const()[name = tensor("op_3169"), val = tensor(0x1.6a09e6p-4)]; + tensor q_h_115 = mul(x = var_3144, y = var_3169)[name = tensor("q_h_115")]; + tensor scores_113_transpose_x_0 = const()[name = tensor("scores_113_transpose_x_0"), val = tensor(false)]; + tensor scores_113_transpose_y_0 = const()[name = tensor("scores_113_transpose_y_0"), val = tensor(false)]; + tensor transpose_266_perm_0 = const()[name = tensor("transpose_266_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_267_perm_0 = const()[name = tensor("transpose_267_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_267 = transpose(perm = transpose_267_perm_0, x = var_3147)[name = tensor("transpose_597")]; + tensor transpose_266 = transpose(perm = transpose_266_perm_0, x = q_h_115)[name = tensor("transpose_598")]; + tensor scores_113 = matmul(transpose_x = scores_113_transpose_x_0, transpose_y = scores_113_transpose_y_0, x = transpose_266, y = transpose_267)[name = tensor("scores_113")]; + tensor scores_115 = select(a = var_48, b = scores_113, cond = mask_9)[name = tensor("scores_115")]; + tensor var_3177 = softmax(axis = var_61, x = scores_115)[name = tensor("op_3177")]; + tensor input_857 = select(a = var_53, b = var_3177, cond = mask_9)[name = tensor("input_857")]; + tensor x_291_transpose_x_0 = const()[name = tensor("x_291_transpose_x_0"), val = tensor(false)]; + tensor x_291_transpose_y_0 = const()[name = tensor("x_291_transpose_y_0"), val = tensor(false)]; + tensor value_57 = transpose(perm = value_57_perm_0, x = var_3150)[name = tensor("transpose_601")]; + tensor x_291 = matmul(transpose_x = x_291_transpose_x_0, transpose_y = x_291_transpose_y_0, x = input_857, y = value_57)[name = tensor("x_291")]; + tensor var_3181_perm_0 = const()[name = tensor("op_3181_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_3183 = const()[name = tensor("op_3183"), val = tensor([1, -1, 512])]; + tensor var_3181 = transpose(perm = var_3181_perm_0, x = x_291)[name = tensor("transpose_596")]; + tensor input_859 = reshape(shape = var_3183, x = var_3181)[name = tensor("input_859")]; + tensor att_outs_57 = linear(bias = encoder_encoders_27_self_attn_linear_out_bias, weight = encoder_encoders_27_self_attn_linear_out_weight, x = input_859)[name = tensor("linear_113")]; + tensor input_861 = add(x = att_outs_57, y = fsmn_memory_57)[name = tensor("input_861")]; + tensor input_863 = add(x = input_847, y = input_861)[name = tensor("input_863")]; + tensor const_331 = const()[name = tensor("const_331"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939741952)))]; + tensor const_332 = const()[name = tensor("const_332"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939744064)))]; + tensor output_115_axes_0 = const()[name = tensor("output_115_axes_0"), val = tensor([-1])]; + tensor output_115 = layer_norm(axes = output_115_axes_0, beta = const_332, epsilon = var_46, gamma = const_331, x = input_863)[name = tensor("output_115")]; + tensor input_869 = linear(bias = encoder_encoders_27_feed_forward_w_1_bias, weight = encoder_encoders_27_feed_forward_w_1_weight, x = output_115)[name = tensor("linear_114")]; + tensor input_871 = relu(x = input_869)[name = tensor("input_871")]; + tensor input_875 = linear(bias = encoder_encoders_27_feed_forward_w_2_bias, weight = encoder_encoders_27_feed_forward_w_2_weight, x = input_871)[name = tensor("linear_115")]; + tensor input_877 = add(x = input_863, y = input_875)[name = tensor("input_877")]; + tensor const_333 = const()[name = tensor("const_333"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939746176)))]; + tensor const_334 = const()[name = tensor("const_334"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939748288)))]; + tensor output_117_axes_0 = const()[name = tensor("output_117_axes_0"), val = tensor([-1])]; + tensor output_117 = layer_norm(axes = output_117_axes_0, beta = const_334, epsilon = var_46, gamma = const_333, x = input_877)[name = tensor("output_117")]; + tensor var_3240 = linear(bias = encoder_encoders_28_self_attn_linear_q_k_v_bias, weight = encoder_encoders_28_self_attn_linear_q_k_v_weight, x = output_117)[name = tensor("linear_116")]; + tensor tile_29 = const()[name = tensor("tile_29"), val = tensor([512, 512, 512])]; + tensor var_3241_axis_0 = const()[name = tensor("op_3241_axis_0"), val = tensor(-1)]; + tensor var_3241_0, tensor var_3241_1, tensor var_3241_2 = split(axis = var_3241_axis_0, split_sizes = tile_29, x = var_3240)[name = tensor("op_3241")]; + tensor var_3245 = const()[name = tensor("op_3245"), val = tensor([1, 1804, 4, 128])]; + tensor var_3246 = reshape(shape = var_3245, x = var_3241_0)[name = tensor("op_3246")]; + tensor var_3248 = const()[name = tensor("op_3248"), val = tensor([1, 1804, 4, 128])]; + tensor var_3249 = reshape(shape = var_3248, x = var_3241_1)[name = tensor("op_3249")]; + tensor var_3251 = const()[name = tensor("op_3251"), val = tensor([1, 1804, 4, 128])]; + tensor var_3252 = reshape(shape = var_3251, x = var_3241_2)[name = tensor("op_3252")]; + tensor value_59_perm_0 = const()[name = tensor("value_59_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor inputs_59 = mul(x = var_3241_2, y = mask_7)[name = tensor("inputs_59")]; + tensor input_881_perm_0 = const()[name = tensor("input_881_perm_0"), val = tensor([0, 2, 1])]; + tensor const_340 = const()[name = tensor("const_340"), val = tensor(0x0p+0)]; + tensor input_883_pad_0 = const()[name = tensor("input_883_pad_0"), val = tensor([0, 0, 0, 0, 5, 5])]; + tensor input_883_mode_0 = const()[name = tensor("input_883_mode_0"), val = tensor("constant")]; + tensor input_881 = transpose(perm = input_881_perm_0, x = inputs_59)[name = tensor("transpose_594")]; + tensor input_883 = pad(constant_val = const_340, mode = input_883_mode_0, pad = input_883_pad_0, x = input_881)[name = tensor("input_883")]; + tensor x_295_pad_type_0 = const()[name = tensor("x_295_pad_type_0"), val = tensor("valid")]; + tensor x_295_groups_0 = const()[name = tensor("x_295_groups_0"), val = tensor(512)]; + tensor x_295_strides_0 = const()[name = tensor("x_295_strides_0"), val = tensor([1])]; + tensor x_295_pad_0 = const()[name = tensor("x_295_pad_0"), val = tensor([0, 0])]; + tensor x_295_dilations_0 = const()[name = tensor("x_295_dilations_0"), val = tensor([1])]; + tensor x_295 = conv(dilations = x_295_dilations_0, groups = x_295_groups_0, pad = x_295_pad_0, pad_type = x_295_pad_type_0, strides = x_295_strides_0, weight = encoder_encoders_28_self_attn_fsmn_block_weight, x = input_883)[name = tensor("x_295")]; + tensor x_297_perm_0 = const()[name = tensor("x_297_perm_0"), val = tensor([0, 2, 1])]; + tensor x_297 = transpose(perm = x_297_perm_0, x = x_295)[name = tensor("transpose_593")]; + tensor input_885 = add(x = x_297, y = inputs_59)[name = tensor("input_885")]; + tensor fsmn_memory_59 = mul(x = input_885, y = mask_7)[name = tensor("fsmn_memory_59")]; + tensor var_3271 = const()[name = tensor("op_3271"), val = tensor(0x1.6a09e6p-4)]; + tensor q_h_119 = mul(x = var_3246, y = var_3271)[name = tensor("q_h_119")]; + tensor scores_117_transpose_x_0 = const()[name = tensor("scores_117_transpose_x_0"), val = tensor(false)]; + tensor scores_117_transpose_y_0 = const()[name = tensor("scores_117_transpose_y_0"), val = tensor(false)]; + tensor transpose_268_perm_0 = const()[name = tensor("transpose_268_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_269_perm_0 = const()[name = tensor("transpose_269_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_269 = transpose(perm = transpose_269_perm_0, x = var_3249)[name = tensor("transpose_591")]; + tensor transpose_268 = transpose(perm = transpose_268_perm_0, x = q_h_119)[name = tensor("transpose_592")]; + tensor scores_117 = matmul(transpose_x = scores_117_transpose_x_0, transpose_y = scores_117_transpose_y_0, x = transpose_268, y = transpose_269)[name = tensor("scores_117")]; + tensor scores_119 = select(a = var_48, b = scores_117, cond = mask_9)[name = tensor("scores_119")]; + tensor var_3279 = softmax(axis = var_61, x = scores_119)[name = tensor("op_3279")]; + tensor input_887 = select(a = var_53, b = var_3279, cond = mask_9)[name = tensor("input_887")]; + tensor x_301_transpose_x_0 = const()[name = tensor("x_301_transpose_x_0"), val = tensor(false)]; + tensor x_301_transpose_y_0 = const()[name = tensor("x_301_transpose_y_0"), val = tensor(false)]; + tensor value_59 = transpose(perm = value_59_perm_0, x = var_3252)[name = tensor("transpose_595")]; + tensor x_301 = matmul(transpose_x = x_301_transpose_x_0, transpose_y = x_301_transpose_y_0, x = input_887, y = value_59)[name = tensor("x_301")]; + tensor var_3283_perm_0 = const()[name = tensor("op_3283_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_3285 = const()[name = tensor("op_3285"), val = tensor([1, -1, 512])]; + tensor var_3283 = transpose(perm = var_3283_perm_0, x = x_301)[name = tensor("transpose_590")]; + tensor input_889 = reshape(shape = var_3285, x = var_3283)[name = tensor("input_889")]; + tensor att_outs_59 = linear(bias = encoder_encoders_28_self_attn_linear_out_bias, weight = encoder_encoders_28_self_attn_linear_out_weight, x = input_889)[name = tensor("linear_117")]; + tensor input_891 = add(x = att_outs_59, y = fsmn_memory_59)[name = tensor("input_891")]; + tensor input_893 = add(x = input_877, y = input_891)[name = tensor("input_893")]; + tensor const_342 = const()[name = tensor("const_342"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939750400)))]; + tensor const_343 = const()[name = tensor("const_343"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939752512)))]; + tensor output_119_axes_0 = const()[name = tensor("output_119_axes_0"), val = tensor([-1])]; + tensor output_119 = layer_norm(axes = output_119_axes_0, beta = const_343, epsilon = var_46, gamma = const_342, x = input_893)[name = tensor("output_119")]; + tensor input_899 = linear(bias = encoder_encoders_28_feed_forward_w_1_bias, weight = encoder_encoders_28_feed_forward_w_1_weight, x = output_119)[name = tensor("linear_118")]; + tensor input_901 = relu(x = input_899)[name = tensor("input_901")]; + tensor input_905 = linear(bias = encoder_encoders_28_feed_forward_w_2_bias, weight = encoder_encoders_28_feed_forward_w_2_weight, x = input_901)[name = tensor("linear_119")]; + tensor input_907 = add(x = input_893, y = input_905)[name = tensor("input_907")]; + tensor const_344 = const()[name = tensor("const_344"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939754624)))]; + tensor const_345 = const()[name = tensor("const_345"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939756736)))]; + tensor output_121_axes_0 = const()[name = tensor("output_121_axes_0"), val = tensor([-1])]; + tensor output_121 = layer_norm(axes = output_121_axes_0, beta = const_345, epsilon = var_46, gamma = const_344, x = input_907)[name = tensor("output_121")]; + tensor var_3342 = linear(bias = encoder_encoders_29_self_attn_linear_q_k_v_bias, weight = encoder_encoders_29_self_attn_linear_q_k_v_weight, x = output_121)[name = tensor("linear_120")]; + tensor tile_30 = const()[name = tensor("tile_30"), val = tensor([512, 512, 512])]; + tensor var_3343_axis_0 = const()[name = tensor("op_3343_axis_0"), val = tensor(-1)]; + tensor var_3343_0, tensor var_3343_1, tensor var_3343_2 = split(axis = var_3343_axis_0, split_sizes = tile_30, x = var_3342)[name = tensor("op_3343")]; + tensor var_3347 = const()[name = tensor("op_3347"), val = tensor([1, 1804, 4, 128])]; + tensor var_3348 = reshape(shape = var_3347, x = var_3343_0)[name = tensor("op_3348")]; + tensor var_3350 = const()[name = tensor("op_3350"), val = tensor([1, 1804, 4, 128])]; + tensor var_3351 = reshape(shape = var_3350, x = var_3343_1)[name = tensor("op_3351")]; + tensor var_3353 = const()[name = tensor("op_3353"), val = tensor([1, 1804, 4, 128])]; + tensor var_3354 = reshape(shape = var_3353, x = var_3343_2)[name = tensor("op_3354")]; + tensor value_61_perm_0 = const()[name = tensor("value_61_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor inputs_61 = mul(x = var_3343_2, y = mask_7)[name = tensor("inputs_61")]; + tensor input_911_perm_0 = const()[name = tensor("input_911_perm_0"), val = tensor([0, 2, 1])]; + tensor const_351 = const()[name = tensor("const_351"), val = tensor(0x0p+0)]; + tensor input_913_pad_0 = const()[name = tensor("input_913_pad_0"), val = tensor([0, 0, 0, 0, 5, 5])]; + tensor input_913_mode_0 = const()[name = tensor("input_913_mode_0"), val = tensor("constant")]; + tensor input_911 = transpose(perm = input_911_perm_0, x = inputs_61)[name = tensor("transpose_588")]; + tensor input_913 = pad(constant_val = const_351, mode = input_913_mode_0, pad = input_913_pad_0, x = input_911)[name = tensor("input_913")]; + tensor x_305_pad_type_0 = const()[name = tensor("x_305_pad_type_0"), val = tensor("valid")]; + tensor x_305_groups_0 = const()[name = tensor("x_305_groups_0"), val = tensor(512)]; + tensor x_305_strides_0 = const()[name = tensor("x_305_strides_0"), val = tensor([1])]; + tensor x_305_pad_0 = const()[name = tensor("x_305_pad_0"), val = tensor([0, 0])]; + tensor x_305_dilations_0 = const()[name = tensor("x_305_dilations_0"), val = tensor([1])]; + tensor x_305 = conv(dilations = x_305_dilations_0, groups = x_305_groups_0, pad = x_305_pad_0, pad_type = x_305_pad_type_0, strides = x_305_strides_0, weight = encoder_encoders_29_self_attn_fsmn_block_weight, x = input_913)[name = tensor("x_305")]; + tensor x_307_perm_0 = const()[name = tensor("x_307_perm_0"), val = tensor([0, 2, 1])]; + tensor x_307 = transpose(perm = x_307_perm_0, x = x_305)[name = tensor("transpose_587")]; + tensor input_915 = add(x = x_307, y = inputs_61)[name = tensor("input_915")]; + tensor fsmn_memory_61 = mul(x = input_915, y = mask_7)[name = tensor("fsmn_memory_61")]; + tensor var_3373 = const()[name = tensor("op_3373"), val = tensor(0x1.6a09e6p-4)]; + tensor q_h_123 = mul(x = var_3348, y = var_3373)[name = tensor("q_h_123")]; + tensor scores_121_transpose_x_0 = const()[name = tensor("scores_121_transpose_x_0"), val = tensor(false)]; + tensor scores_121_transpose_y_0 = const()[name = tensor("scores_121_transpose_y_0"), val = tensor(false)]; + tensor transpose_270_perm_0 = const()[name = tensor("transpose_270_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_271_perm_0 = const()[name = tensor("transpose_271_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_271 = transpose(perm = transpose_271_perm_0, x = var_3351)[name = tensor("transpose_585")]; + tensor transpose_270 = transpose(perm = transpose_270_perm_0, x = q_h_123)[name = tensor("transpose_586")]; + tensor scores_121 = matmul(transpose_x = scores_121_transpose_x_0, transpose_y = scores_121_transpose_y_0, x = transpose_270, y = transpose_271)[name = tensor("scores_121")]; + tensor scores_123 = select(a = var_48, b = scores_121, cond = mask_9)[name = tensor("scores_123")]; + tensor var_3381 = softmax(axis = var_61, x = scores_123)[name = tensor("op_3381")]; + tensor input_917 = select(a = var_53, b = var_3381, cond = mask_9)[name = tensor("input_917")]; + tensor x_311_transpose_x_0 = const()[name = tensor("x_311_transpose_x_0"), val = tensor(false)]; + tensor x_311_transpose_y_0 = const()[name = tensor("x_311_transpose_y_0"), val = tensor(false)]; + tensor value_61 = transpose(perm = value_61_perm_0, x = var_3354)[name = tensor("transpose_589")]; + tensor x_311 = matmul(transpose_x = x_311_transpose_x_0, transpose_y = x_311_transpose_y_0, x = input_917, y = value_61)[name = tensor("x_311")]; + tensor var_3385_perm_0 = const()[name = tensor("op_3385_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_3387 = const()[name = tensor("op_3387"), val = tensor([1, -1, 512])]; + tensor var_3385 = transpose(perm = var_3385_perm_0, x = x_311)[name = tensor("transpose_584")]; + tensor input_919 = reshape(shape = var_3387, x = var_3385)[name = tensor("input_919")]; + tensor att_outs_61 = linear(bias = encoder_encoders_29_self_attn_linear_out_bias, weight = encoder_encoders_29_self_attn_linear_out_weight, x = input_919)[name = tensor("linear_121")]; + tensor input_921 = add(x = att_outs_61, y = fsmn_memory_61)[name = tensor("input_921")]; + tensor input_923 = add(x = input_907, y = input_921)[name = tensor("input_923")]; + tensor const_353 = const()[name = tensor("const_353"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939758848)))]; + tensor const_354 = const()[name = tensor("const_354"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939760960)))]; + tensor output_123_axes_0 = const()[name = tensor("output_123_axes_0"), val = tensor([-1])]; + tensor output_123 = layer_norm(axes = output_123_axes_0, beta = const_354, epsilon = var_46, gamma = const_353, x = input_923)[name = tensor("output_123")]; + tensor input_929 = linear(bias = encoder_encoders_29_feed_forward_w_1_bias, weight = encoder_encoders_29_feed_forward_w_1_weight, x = output_123)[name = tensor("linear_122")]; + tensor input_931 = relu(x = input_929)[name = tensor("input_931")]; + tensor input_935 = linear(bias = encoder_encoders_29_feed_forward_w_2_bias, weight = encoder_encoders_29_feed_forward_w_2_weight, x = input_931)[name = tensor("linear_123")]; + tensor input_937 = add(x = input_923, y = input_935)[name = tensor("input_937")]; + tensor const_355 = const()[name = tensor("const_355"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939763072)))]; + tensor const_356 = const()[name = tensor("const_356"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939765184)))]; + tensor output_125_axes_0 = const()[name = tensor("output_125_axes_0"), val = tensor([-1])]; + tensor output_125 = layer_norm(axes = output_125_axes_0, beta = const_356, epsilon = var_46, gamma = const_355, x = input_937)[name = tensor("output_125")]; + tensor var_3444 = linear(bias = encoder_encoders_30_self_attn_linear_q_k_v_bias, weight = encoder_encoders_30_self_attn_linear_q_k_v_weight, x = output_125)[name = tensor("linear_124")]; + tensor tile_31 = const()[name = tensor("tile_31"), val = tensor([512, 512, 512])]; + tensor var_3445_axis_0 = const()[name = tensor("op_3445_axis_0"), val = tensor(-1)]; + tensor var_3445_0, tensor var_3445_1, tensor var_3445_2 = split(axis = var_3445_axis_0, split_sizes = tile_31, x = var_3444)[name = tensor("op_3445")]; + tensor var_3449 = const()[name = tensor("op_3449"), val = tensor([1, 1804, 4, 128])]; + tensor var_3450 = reshape(shape = var_3449, x = var_3445_0)[name = tensor("op_3450")]; + tensor var_3452 = const()[name = tensor("op_3452"), val = tensor([1, 1804, 4, 128])]; + tensor var_3453 = reshape(shape = var_3452, x = var_3445_1)[name = tensor("op_3453")]; + tensor var_3455 = const()[name = tensor("op_3455"), val = tensor([1, 1804, 4, 128])]; + tensor var_3456 = reshape(shape = var_3455, x = var_3445_2)[name = tensor("op_3456")]; + tensor value_63_perm_0 = const()[name = tensor("value_63_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor inputs_63 = mul(x = var_3445_2, y = mask_7)[name = tensor("inputs_63")]; + tensor input_941_perm_0 = const()[name = tensor("input_941_perm_0"), val = tensor([0, 2, 1])]; + tensor const_362 = const()[name = tensor("const_362"), val = tensor(0x0p+0)]; + tensor input_943_pad_0 = const()[name = tensor("input_943_pad_0"), val = tensor([0, 0, 0, 0, 5, 5])]; + tensor input_943_mode_0 = const()[name = tensor("input_943_mode_0"), val = tensor("constant")]; + tensor input_941 = transpose(perm = input_941_perm_0, x = inputs_63)[name = tensor("transpose_582")]; + tensor input_943 = pad(constant_val = const_362, mode = input_943_mode_0, pad = input_943_pad_0, x = input_941)[name = tensor("input_943")]; + tensor x_315_pad_type_0 = const()[name = tensor("x_315_pad_type_0"), val = tensor("valid")]; + tensor x_315_groups_0 = const()[name = tensor("x_315_groups_0"), val = tensor(512)]; + tensor x_315_strides_0 = const()[name = tensor("x_315_strides_0"), val = tensor([1])]; + tensor x_315_pad_0 = const()[name = tensor("x_315_pad_0"), val = tensor([0, 0])]; + tensor x_315_dilations_0 = const()[name = tensor("x_315_dilations_0"), val = tensor([1])]; + tensor x_315 = conv(dilations = x_315_dilations_0, groups = x_315_groups_0, pad = x_315_pad_0, pad_type = x_315_pad_type_0, strides = x_315_strides_0, weight = encoder_encoders_30_self_attn_fsmn_block_weight, x = input_943)[name = tensor("x_315")]; + tensor x_317_perm_0 = const()[name = tensor("x_317_perm_0"), val = tensor([0, 2, 1])]; + tensor x_317 = transpose(perm = x_317_perm_0, x = x_315)[name = tensor("transpose_581")]; + tensor input_945 = add(x = x_317, y = inputs_63)[name = tensor("input_945")]; + tensor fsmn_memory_63 = mul(x = input_945, y = mask_7)[name = tensor("fsmn_memory_63")]; + tensor var_3475 = const()[name = tensor("op_3475"), val = tensor(0x1.6a09e6p-4)]; + tensor q_h_127 = mul(x = var_3450, y = var_3475)[name = tensor("q_h_127")]; + tensor scores_125_transpose_x_0 = const()[name = tensor("scores_125_transpose_x_0"), val = tensor(false)]; + tensor scores_125_transpose_y_0 = const()[name = tensor("scores_125_transpose_y_0"), val = tensor(false)]; + tensor transpose_272_perm_0 = const()[name = tensor("transpose_272_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_273_perm_0 = const()[name = tensor("transpose_273_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_273 = transpose(perm = transpose_273_perm_0, x = var_3453)[name = tensor("transpose_579")]; + tensor transpose_272 = transpose(perm = transpose_272_perm_0, x = q_h_127)[name = tensor("transpose_580")]; + tensor scores_125 = matmul(transpose_x = scores_125_transpose_x_0, transpose_y = scores_125_transpose_y_0, x = transpose_272, y = transpose_273)[name = tensor("scores_125")]; + tensor scores_127 = select(a = var_48, b = scores_125, cond = mask_9)[name = tensor("scores_127")]; + tensor var_3483 = softmax(axis = var_61, x = scores_127)[name = tensor("op_3483")]; + tensor input_947 = select(a = var_53, b = var_3483, cond = mask_9)[name = tensor("input_947")]; + tensor x_321_transpose_x_0 = const()[name = tensor("x_321_transpose_x_0"), val = tensor(false)]; + tensor x_321_transpose_y_0 = const()[name = tensor("x_321_transpose_y_0"), val = tensor(false)]; + tensor value_63 = transpose(perm = value_63_perm_0, x = var_3456)[name = tensor("transpose_583")]; + tensor x_321 = matmul(transpose_x = x_321_transpose_x_0, transpose_y = x_321_transpose_y_0, x = input_947, y = value_63)[name = tensor("x_321")]; + tensor var_3487_perm_0 = const()[name = tensor("op_3487_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_3489 = const()[name = tensor("op_3489"), val = tensor([1, -1, 512])]; + tensor var_3487 = transpose(perm = var_3487_perm_0, x = x_321)[name = tensor("transpose_578")]; + tensor input_949 = reshape(shape = var_3489, x = var_3487)[name = tensor("input_949")]; + tensor att_outs_63 = linear(bias = encoder_encoders_30_self_attn_linear_out_bias, weight = encoder_encoders_30_self_attn_linear_out_weight, x = input_949)[name = tensor("linear_125")]; + tensor input_951 = add(x = att_outs_63, y = fsmn_memory_63)[name = tensor("input_951")]; + tensor input_953 = add(x = input_937, y = input_951)[name = tensor("input_953")]; + tensor const_364 = const()[name = tensor("const_364"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939767296)))]; + tensor const_365 = const()[name = tensor("const_365"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939769408)))]; + tensor output_127_axes_0 = const()[name = tensor("output_127_axes_0"), val = tensor([-1])]; + tensor output_127 = layer_norm(axes = output_127_axes_0, beta = const_365, epsilon = var_46, gamma = const_364, x = input_953)[name = tensor("output_127")]; + tensor input_959 = linear(bias = encoder_encoders_30_feed_forward_w_1_bias, weight = encoder_encoders_30_feed_forward_w_1_weight, x = output_127)[name = tensor("linear_126")]; + tensor input_961 = relu(x = input_959)[name = tensor("input_961")]; + tensor input_965 = linear(bias = encoder_encoders_30_feed_forward_w_2_bias, weight = encoder_encoders_30_feed_forward_w_2_weight, x = input_961)[name = tensor("linear_127")]; + tensor input_967 = add(x = input_953, y = input_965)[name = tensor("input_967")]; + tensor const_366 = const()[name = tensor("const_366"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939771520)))]; + tensor const_367 = const()[name = tensor("const_367"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939773632)))]; + tensor output_129_axes_0 = const()[name = tensor("output_129_axes_0"), val = tensor([-1])]; + tensor output_129 = layer_norm(axes = output_129_axes_0, beta = const_367, epsilon = var_46, gamma = const_366, x = input_967)[name = tensor("output_129")]; + tensor var_3546 = linear(bias = encoder_encoders_31_self_attn_linear_q_k_v_bias, weight = encoder_encoders_31_self_attn_linear_q_k_v_weight, x = output_129)[name = tensor("linear_128")]; + tensor tile_32 = const()[name = tensor("tile_32"), val = tensor([512, 512, 512])]; + tensor var_3547_axis_0 = const()[name = tensor("op_3547_axis_0"), val = tensor(-1)]; + tensor var_3547_0, tensor var_3547_1, tensor var_3547_2 = split(axis = var_3547_axis_0, split_sizes = tile_32, x = var_3546)[name = tensor("op_3547")]; + tensor var_3551 = const()[name = tensor("op_3551"), val = tensor([1, 1804, 4, 128])]; + tensor var_3552 = reshape(shape = var_3551, x = var_3547_0)[name = tensor("op_3552")]; + tensor var_3554 = const()[name = tensor("op_3554"), val = tensor([1, 1804, 4, 128])]; + tensor var_3555 = reshape(shape = var_3554, x = var_3547_1)[name = tensor("op_3555")]; + tensor var_3557 = const()[name = tensor("op_3557"), val = tensor([1, 1804, 4, 128])]; + tensor var_3558 = reshape(shape = var_3557, x = var_3547_2)[name = tensor("op_3558")]; + tensor value_65_perm_0 = const()[name = tensor("value_65_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor inputs_65 = mul(x = var_3547_2, y = mask_7)[name = tensor("inputs_65")]; + tensor input_971_perm_0 = const()[name = tensor("input_971_perm_0"), val = tensor([0, 2, 1])]; + tensor const_373 = const()[name = tensor("const_373"), val = tensor(0x0p+0)]; + tensor input_973_pad_0 = const()[name = tensor("input_973_pad_0"), val = tensor([0, 0, 0, 0, 5, 5])]; + tensor input_973_mode_0 = const()[name = tensor("input_973_mode_0"), val = tensor("constant")]; + tensor input_971 = transpose(perm = input_971_perm_0, x = inputs_65)[name = tensor("transpose_576")]; + tensor input_973 = pad(constant_val = const_373, mode = input_973_mode_0, pad = input_973_pad_0, x = input_971)[name = tensor("input_973")]; + tensor x_325_pad_type_0 = const()[name = tensor("x_325_pad_type_0"), val = tensor("valid")]; + tensor x_325_groups_0 = const()[name = tensor("x_325_groups_0"), val = tensor(512)]; + tensor x_325_strides_0 = const()[name = tensor("x_325_strides_0"), val = tensor([1])]; + tensor x_325_pad_0 = const()[name = tensor("x_325_pad_0"), val = tensor([0, 0])]; + tensor x_325_dilations_0 = const()[name = tensor("x_325_dilations_0"), val = tensor([1])]; + tensor x_325 = conv(dilations = x_325_dilations_0, groups = x_325_groups_0, pad = x_325_pad_0, pad_type = x_325_pad_type_0, strides = x_325_strides_0, weight = encoder_encoders_31_self_attn_fsmn_block_weight, x = input_973)[name = tensor("x_325")]; + tensor x_327_perm_0 = const()[name = tensor("x_327_perm_0"), val = tensor([0, 2, 1])]; + tensor x_327 = transpose(perm = x_327_perm_0, x = x_325)[name = tensor("transpose_575")]; + tensor input_975 = add(x = x_327, y = inputs_65)[name = tensor("input_975")]; + tensor fsmn_memory_65 = mul(x = input_975, y = mask_7)[name = tensor("fsmn_memory_65")]; + tensor var_3577 = const()[name = tensor("op_3577"), val = tensor(0x1.6a09e6p-4)]; + tensor q_h_131 = mul(x = var_3552, y = var_3577)[name = tensor("q_h_131")]; + tensor scores_129_transpose_x_0 = const()[name = tensor("scores_129_transpose_x_0"), val = tensor(false)]; + tensor scores_129_transpose_y_0 = const()[name = tensor("scores_129_transpose_y_0"), val = tensor(false)]; + tensor transpose_274_perm_0 = const()[name = tensor("transpose_274_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_275_perm_0 = const()[name = tensor("transpose_275_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_275 = transpose(perm = transpose_275_perm_0, x = var_3555)[name = tensor("transpose_573")]; + tensor transpose_274 = transpose(perm = transpose_274_perm_0, x = q_h_131)[name = tensor("transpose_574")]; + tensor scores_129 = matmul(transpose_x = scores_129_transpose_x_0, transpose_y = scores_129_transpose_y_0, x = transpose_274, y = transpose_275)[name = tensor("scores_129")]; + tensor scores_131 = select(a = var_48, b = scores_129, cond = mask_9)[name = tensor("scores_131")]; + tensor var_3585 = softmax(axis = var_61, x = scores_131)[name = tensor("op_3585")]; + tensor input_977 = select(a = var_53, b = var_3585, cond = mask_9)[name = tensor("input_977")]; + tensor x_331_transpose_x_0 = const()[name = tensor("x_331_transpose_x_0"), val = tensor(false)]; + tensor x_331_transpose_y_0 = const()[name = tensor("x_331_transpose_y_0"), val = tensor(false)]; + tensor value_65 = transpose(perm = value_65_perm_0, x = var_3558)[name = tensor("transpose_577")]; + tensor x_331 = matmul(transpose_x = x_331_transpose_x_0, transpose_y = x_331_transpose_y_0, x = input_977, y = value_65)[name = tensor("x_331")]; + tensor var_3589_perm_0 = const()[name = tensor("op_3589_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_3591 = const()[name = tensor("op_3591"), val = tensor([1, -1, 512])]; + tensor var_3589 = transpose(perm = var_3589_perm_0, x = x_331)[name = tensor("transpose_572")]; + tensor input_979 = reshape(shape = var_3591, x = var_3589)[name = tensor("input_979")]; + tensor att_outs_65 = linear(bias = encoder_encoders_31_self_attn_linear_out_bias, weight = encoder_encoders_31_self_attn_linear_out_weight, x = input_979)[name = tensor("linear_129")]; + tensor input_981 = add(x = att_outs_65, y = fsmn_memory_65)[name = tensor("input_981")]; + tensor input_983 = add(x = input_967, y = input_981)[name = tensor("input_983")]; + tensor const_375 = const()[name = tensor("const_375"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939775744)))]; + tensor const_376 = const()[name = tensor("const_376"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939777856)))]; + tensor output_131_axes_0 = const()[name = tensor("output_131_axes_0"), val = tensor([-1])]; + tensor output_131 = layer_norm(axes = output_131_axes_0, beta = const_376, epsilon = var_46, gamma = const_375, x = input_983)[name = tensor("output_131")]; + tensor input_989 = linear(bias = encoder_encoders_31_feed_forward_w_1_bias, weight = encoder_encoders_31_feed_forward_w_1_weight, x = output_131)[name = tensor("linear_130")]; + tensor input_991 = relu(x = input_989)[name = tensor("input_991")]; + tensor input_995 = linear(bias = encoder_encoders_31_feed_forward_w_2_bias, weight = encoder_encoders_31_feed_forward_w_2_weight, x = input_991)[name = tensor("linear_131")]; + tensor input_997 = add(x = input_983, y = input_995)[name = tensor("input_997")]; + tensor const_377 = const()[name = tensor("const_377"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939779968)))]; + tensor const_378 = const()[name = tensor("const_378"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939782080)))]; + tensor output_133_axes_0 = const()[name = tensor("output_133_axes_0"), val = tensor([-1])]; + tensor output_133 = layer_norm(axes = output_133_axes_0, beta = const_378, epsilon = var_46, gamma = const_377, x = input_997)[name = tensor("output_133")]; + tensor var_3648 = linear(bias = encoder_encoders_32_self_attn_linear_q_k_v_bias, weight = encoder_encoders_32_self_attn_linear_q_k_v_weight, x = output_133)[name = tensor("linear_132")]; + tensor tile_33 = const()[name = tensor("tile_33"), val = tensor([512, 512, 512])]; + tensor var_3649_axis_0 = const()[name = tensor("op_3649_axis_0"), val = tensor(-1)]; + tensor var_3649_0, tensor var_3649_1, tensor var_3649_2 = split(axis = var_3649_axis_0, split_sizes = tile_33, x = var_3648)[name = tensor("op_3649")]; + tensor var_3653 = const()[name = tensor("op_3653"), val = tensor([1, 1804, 4, 128])]; + tensor var_3654 = reshape(shape = var_3653, x = var_3649_0)[name = tensor("op_3654")]; + tensor var_3656 = const()[name = tensor("op_3656"), val = tensor([1, 1804, 4, 128])]; + tensor var_3657 = reshape(shape = var_3656, x = var_3649_1)[name = tensor("op_3657")]; + tensor var_3659 = const()[name = tensor("op_3659"), val = tensor([1, 1804, 4, 128])]; + tensor var_3660 = reshape(shape = var_3659, x = var_3649_2)[name = tensor("op_3660")]; + tensor value_67_perm_0 = const()[name = tensor("value_67_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor inputs_67 = mul(x = var_3649_2, y = mask_7)[name = tensor("inputs_67")]; + tensor input_1001_perm_0 = const()[name = tensor("input_1001_perm_0"), val = tensor([0, 2, 1])]; + tensor const_384 = const()[name = tensor("const_384"), val = tensor(0x0p+0)]; + tensor input_1003_pad_0 = const()[name = tensor("input_1003_pad_0"), val = tensor([0, 0, 0, 0, 5, 5])]; + tensor input_1003_mode_0 = const()[name = tensor("input_1003_mode_0"), val = tensor("constant")]; + tensor input_1001 = transpose(perm = input_1001_perm_0, x = inputs_67)[name = tensor("transpose_570")]; + tensor input_1003 = pad(constant_val = const_384, mode = input_1003_mode_0, pad = input_1003_pad_0, x = input_1001)[name = tensor("input_1003")]; + tensor x_335_pad_type_0 = const()[name = tensor("x_335_pad_type_0"), val = tensor("valid")]; + tensor x_335_groups_0 = const()[name = tensor("x_335_groups_0"), val = tensor(512)]; + tensor x_335_strides_0 = const()[name = tensor("x_335_strides_0"), val = tensor([1])]; + tensor x_335_pad_0 = const()[name = tensor("x_335_pad_0"), val = tensor([0, 0])]; + tensor x_335_dilations_0 = const()[name = tensor("x_335_dilations_0"), val = tensor([1])]; + tensor x_335 = conv(dilations = x_335_dilations_0, groups = x_335_groups_0, pad = x_335_pad_0, pad_type = x_335_pad_type_0, strides = x_335_strides_0, weight = encoder_encoders_32_self_attn_fsmn_block_weight, x = input_1003)[name = tensor("x_335")]; + tensor x_337_perm_0 = const()[name = tensor("x_337_perm_0"), val = tensor([0, 2, 1])]; + tensor x_337 = transpose(perm = x_337_perm_0, x = x_335)[name = tensor("transpose_569")]; + tensor input_1005 = add(x = x_337, y = inputs_67)[name = tensor("input_1005")]; + tensor fsmn_memory_67 = mul(x = input_1005, y = mask_7)[name = tensor("fsmn_memory_67")]; + tensor var_3679 = const()[name = tensor("op_3679"), val = tensor(0x1.6a09e6p-4)]; + tensor q_h_135 = mul(x = var_3654, y = var_3679)[name = tensor("q_h_135")]; + tensor scores_133_transpose_x_0 = const()[name = tensor("scores_133_transpose_x_0"), val = tensor(false)]; + tensor scores_133_transpose_y_0 = const()[name = tensor("scores_133_transpose_y_0"), val = tensor(false)]; + tensor transpose_276_perm_0 = const()[name = tensor("transpose_276_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_277_perm_0 = const()[name = tensor("transpose_277_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_277 = transpose(perm = transpose_277_perm_0, x = var_3657)[name = tensor("transpose_567")]; + tensor transpose_276 = transpose(perm = transpose_276_perm_0, x = q_h_135)[name = tensor("transpose_568")]; + tensor scores_133 = matmul(transpose_x = scores_133_transpose_x_0, transpose_y = scores_133_transpose_y_0, x = transpose_276, y = transpose_277)[name = tensor("scores_133")]; + tensor scores_135 = select(a = var_48, b = scores_133, cond = mask_9)[name = tensor("scores_135")]; + tensor var_3687 = softmax(axis = var_61, x = scores_135)[name = tensor("op_3687")]; + tensor input_1007 = select(a = var_53, b = var_3687, cond = mask_9)[name = tensor("input_1007")]; + tensor x_341_transpose_x_0 = const()[name = tensor("x_341_transpose_x_0"), val = tensor(false)]; + tensor x_341_transpose_y_0 = const()[name = tensor("x_341_transpose_y_0"), val = tensor(false)]; + tensor value_67 = transpose(perm = value_67_perm_0, x = var_3660)[name = tensor("transpose_571")]; + tensor x_341 = matmul(transpose_x = x_341_transpose_x_0, transpose_y = x_341_transpose_y_0, x = input_1007, y = value_67)[name = tensor("x_341")]; + tensor var_3691_perm_0 = const()[name = tensor("op_3691_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_3693 = const()[name = tensor("op_3693"), val = tensor([1, -1, 512])]; + tensor var_3691 = transpose(perm = var_3691_perm_0, x = x_341)[name = tensor("transpose_566")]; + tensor input_1009 = reshape(shape = var_3693, x = var_3691)[name = tensor("input_1009")]; + tensor att_outs_67 = linear(bias = encoder_encoders_32_self_attn_linear_out_bias, weight = encoder_encoders_32_self_attn_linear_out_weight, x = input_1009)[name = tensor("linear_133")]; + tensor input_1011 = add(x = att_outs_67, y = fsmn_memory_67)[name = tensor("input_1011")]; + tensor input_1013 = add(x = input_997, y = input_1011)[name = tensor("input_1013")]; + tensor const_386 = const()[name = tensor("const_386"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939784192)))]; + tensor const_387 = const()[name = tensor("const_387"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939786304)))]; + tensor output_135_axes_0 = const()[name = tensor("output_135_axes_0"), val = tensor([-1])]; + tensor output_135 = layer_norm(axes = output_135_axes_0, beta = const_387, epsilon = var_46, gamma = const_386, x = input_1013)[name = tensor("output_135")]; + tensor input_1019 = linear(bias = encoder_encoders_32_feed_forward_w_1_bias, weight = encoder_encoders_32_feed_forward_w_1_weight, x = output_135)[name = tensor("linear_134")]; + tensor input_1021 = relu(x = input_1019)[name = tensor("input_1021")]; + tensor input_1025 = linear(bias = encoder_encoders_32_feed_forward_w_2_bias, weight = encoder_encoders_32_feed_forward_w_2_weight, x = input_1021)[name = tensor("linear_135")]; + tensor input_1027 = add(x = input_1013, y = input_1025)[name = tensor("input_1027")]; + tensor const_388 = const()[name = tensor("const_388"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939788416)))]; + tensor const_389 = const()[name = tensor("const_389"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939790528)))]; + tensor output_137_axes_0 = const()[name = tensor("output_137_axes_0"), val = tensor([-1])]; + tensor output_137 = layer_norm(axes = output_137_axes_0, beta = const_389, epsilon = var_46, gamma = const_388, x = input_1027)[name = tensor("output_137")]; + tensor var_3750 = linear(bias = encoder_encoders_33_self_attn_linear_q_k_v_bias, weight = encoder_encoders_33_self_attn_linear_q_k_v_weight, x = output_137)[name = tensor("linear_136")]; + tensor tile_34 = const()[name = tensor("tile_34"), val = tensor([512, 512, 512])]; + tensor var_3751_axis_0 = const()[name = tensor("op_3751_axis_0"), val = tensor(-1)]; + tensor var_3751_0, tensor var_3751_1, tensor var_3751_2 = split(axis = var_3751_axis_0, split_sizes = tile_34, x = var_3750)[name = tensor("op_3751")]; + tensor var_3755 = const()[name = tensor("op_3755"), val = tensor([1, 1804, 4, 128])]; + tensor var_3756 = reshape(shape = var_3755, x = var_3751_0)[name = tensor("op_3756")]; + tensor var_3758 = const()[name = tensor("op_3758"), val = tensor([1, 1804, 4, 128])]; + tensor var_3759 = reshape(shape = var_3758, x = var_3751_1)[name = tensor("op_3759")]; + tensor var_3761 = const()[name = tensor("op_3761"), val = tensor([1, 1804, 4, 128])]; + tensor var_3762 = reshape(shape = var_3761, x = var_3751_2)[name = tensor("op_3762")]; + tensor value_69_perm_0 = const()[name = tensor("value_69_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor inputs_69 = mul(x = var_3751_2, y = mask_7)[name = tensor("inputs_69")]; + tensor input_1031_perm_0 = const()[name = tensor("input_1031_perm_0"), val = tensor([0, 2, 1])]; + tensor const_395 = const()[name = tensor("const_395"), val = tensor(0x0p+0)]; + tensor input_1033_pad_0 = const()[name = tensor("input_1033_pad_0"), val = tensor([0, 0, 0, 0, 5, 5])]; + tensor input_1033_mode_0 = const()[name = tensor("input_1033_mode_0"), val = tensor("constant")]; + tensor input_1031 = transpose(perm = input_1031_perm_0, x = inputs_69)[name = tensor("transpose_564")]; + tensor input_1033 = pad(constant_val = const_395, mode = input_1033_mode_0, pad = input_1033_pad_0, x = input_1031)[name = tensor("input_1033")]; + tensor x_345_pad_type_0 = const()[name = tensor("x_345_pad_type_0"), val = tensor("valid")]; + tensor x_345_groups_0 = const()[name = tensor("x_345_groups_0"), val = tensor(512)]; + tensor x_345_strides_0 = const()[name = tensor("x_345_strides_0"), val = tensor([1])]; + tensor x_345_pad_0 = const()[name = tensor("x_345_pad_0"), val = tensor([0, 0])]; + tensor x_345_dilations_0 = const()[name = tensor("x_345_dilations_0"), val = tensor([1])]; + tensor x_345 = conv(dilations = x_345_dilations_0, groups = x_345_groups_0, pad = x_345_pad_0, pad_type = x_345_pad_type_0, strides = x_345_strides_0, weight = encoder_encoders_33_self_attn_fsmn_block_weight, x = input_1033)[name = tensor("x_345")]; + tensor x_347_perm_0 = const()[name = tensor("x_347_perm_0"), val = tensor([0, 2, 1])]; + tensor x_347 = transpose(perm = x_347_perm_0, x = x_345)[name = tensor("transpose_563")]; + tensor input_1035 = add(x = x_347, y = inputs_69)[name = tensor("input_1035")]; + tensor fsmn_memory_69 = mul(x = input_1035, y = mask_7)[name = tensor("fsmn_memory_69")]; + tensor var_3781 = const()[name = tensor("op_3781"), val = tensor(0x1.6a09e6p-4)]; + tensor q_h_139 = mul(x = var_3756, y = var_3781)[name = tensor("q_h_139")]; + tensor scores_137_transpose_x_0 = const()[name = tensor("scores_137_transpose_x_0"), val = tensor(false)]; + tensor scores_137_transpose_y_0 = const()[name = tensor("scores_137_transpose_y_0"), val = tensor(false)]; + tensor transpose_278_perm_0 = const()[name = tensor("transpose_278_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_279_perm_0 = const()[name = tensor("transpose_279_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_279 = transpose(perm = transpose_279_perm_0, x = var_3759)[name = tensor("transpose_561")]; + tensor transpose_278 = transpose(perm = transpose_278_perm_0, x = q_h_139)[name = tensor("transpose_562")]; + tensor scores_137 = matmul(transpose_x = scores_137_transpose_x_0, transpose_y = scores_137_transpose_y_0, x = transpose_278, y = transpose_279)[name = tensor("scores_137")]; + tensor scores_139 = select(a = var_48, b = scores_137, cond = mask_9)[name = tensor("scores_139")]; + tensor var_3789 = softmax(axis = var_61, x = scores_139)[name = tensor("op_3789")]; + tensor input_1037 = select(a = var_53, b = var_3789, cond = mask_9)[name = tensor("input_1037")]; + tensor x_351_transpose_x_0 = const()[name = tensor("x_351_transpose_x_0"), val = tensor(false)]; + tensor x_351_transpose_y_0 = const()[name = tensor("x_351_transpose_y_0"), val = tensor(false)]; + tensor value_69 = transpose(perm = value_69_perm_0, x = var_3762)[name = tensor("transpose_565")]; + tensor x_351 = matmul(transpose_x = x_351_transpose_x_0, transpose_y = x_351_transpose_y_0, x = input_1037, y = value_69)[name = tensor("x_351")]; + tensor var_3793_perm_0 = const()[name = tensor("op_3793_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_3795 = const()[name = tensor("op_3795"), val = tensor([1, -1, 512])]; + tensor var_3793 = transpose(perm = var_3793_perm_0, x = x_351)[name = tensor("transpose_560")]; + tensor input_1039 = reshape(shape = var_3795, x = var_3793)[name = tensor("input_1039")]; + tensor att_outs_69 = linear(bias = encoder_encoders_33_self_attn_linear_out_bias, weight = encoder_encoders_33_self_attn_linear_out_weight, x = input_1039)[name = tensor("linear_137")]; + tensor input_1041 = add(x = att_outs_69, y = fsmn_memory_69)[name = tensor("input_1041")]; + tensor input_1043 = add(x = input_1027, y = input_1041)[name = tensor("input_1043")]; + tensor const_397 = const()[name = tensor("const_397"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939792640)))]; + tensor const_398 = const()[name = tensor("const_398"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939794752)))]; + tensor output_139_axes_0 = const()[name = tensor("output_139_axes_0"), val = tensor([-1])]; + tensor output_139 = layer_norm(axes = output_139_axes_0, beta = const_398, epsilon = var_46, gamma = const_397, x = input_1043)[name = tensor("output_139")]; + tensor input_1049 = linear(bias = encoder_encoders_33_feed_forward_w_1_bias, weight = encoder_encoders_33_feed_forward_w_1_weight, x = output_139)[name = tensor("linear_138")]; + tensor input_1051 = relu(x = input_1049)[name = tensor("input_1051")]; + tensor input_1055 = linear(bias = encoder_encoders_33_feed_forward_w_2_bias, weight = encoder_encoders_33_feed_forward_w_2_weight, x = input_1051)[name = tensor("linear_139")]; + tensor input_1057 = add(x = input_1043, y = input_1055)[name = tensor("input_1057")]; + tensor const_399 = const()[name = tensor("const_399"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939796864)))]; + tensor const_400 = const()[name = tensor("const_400"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939798976)))]; + tensor output_141_axes_0 = const()[name = tensor("output_141_axes_0"), val = tensor([-1])]; + tensor output_141 = layer_norm(axes = output_141_axes_0, beta = const_400, epsilon = var_46, gamma = const_399, x = input_1057)[name = tensor("output_141")]; + tensor var_3852 = linear(bias = encoder_encoders_34_self_attn_linear_q_k_v_bias, weight = encoder_encoders_34_self_attn_linear_q_k_v_weight, x = output_141)[name = tensor("linear_140")]; + tensor tile_35 = const()[name = tensor("tile_35"), val = tensor([512, 512, 512])]; + tensor var_3853_axis_0 = const()[name = tensor("op_3853_axis_0"), val = tensor(-1)]; + tensor var_3853_0, tensor var_3853_1, tensor var_3853_2 = split(axis = var_3853_axis_0, split_sizes = tile_35, x = var_3852)[name = tensor("op_3853")]; + tensor var_3857 = const()[name = tensor("op_3857"), val = tensor([1, 1804, 4, 128])]; + tensor var_3858 = reshape(shape = var_3857, x = var_3853_0)[name = tensor("op_3858")]; + tensor var_3860 = const()[name = tensor("op_3860"), val = tensor([1, 1804, 4, 128])]; + tensor var_3861 = reshape(shape = var_3860, x = var_3853_1)[name = tensor("op_3861")]; + tensor var_3863 = const()[name = tensor("op_3863"), val = tensor([1, 1804, 4, 128])]; + tensor var_3864 = reshape(shape = var_3863, x = var_3853_2)[name = tensor("op_3864")]; + tensor value_71_perm_0 = const()[name = tensor("value_71_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor inputs_71 = mul(x = var_3853_2, y = mask_7)[name = tensor("inputs_71")]; + tensor input_1061_perm_0 = const()[name = tensor("input_1061_perm_0"), val = tensor([0, 2, 1])]; + tensor const_406 = const()[name = tensor("const_406"), val = tensor(0x0p+0)]; + tensor input_1063_pad_0 = const()[name = tensor("input_1063_pad_0"), val = tensor([0, 0, 0, 0, 5, 5])]; + tensor input_1063_mode_0 = const()[name = tensor("input_1063_mode_0"), val = tensor("constant")]; + tensor input_1061 = transpose(perm = input_1061_perm_0, x = inputs_71)[name = tensor("transpose_558")]; + tensor input_1063 = pad(constant_val = const_406, mode = input_1063_mode_0, pad = input_1063_pad_0, x = input_1061)[name = tensor("input_1063")]; + tensor x_355_pad_type_0 = const()[name = tensor("x_355_pad_type_0"), val = tensor("valid")]; + tensor x_355_groups_0 = const()[name = tensor("x_355_groups_0"), val = tensor(512)]; + tensor x_355_strides_0 = const()[name = tensor("x_355_strides_0"), val = tensor([1])]; + tensor x_355_pad_0 = const()[name = tensor("x_355_pad_0"), val = tensor([0, 0])]; + tensor x_355_dilations_0 = const()[name = tensor("x_355_dilations_0"), val = tensor([1])]; + tensor x_355 = conv(dilations = x_355_dilations_0, groups = x_355_groups_0, pad = x_355_pad_0, pad_type = x_355_pad_type_0, strides = x_355_strides_0, weight = encoder_encoders_34_self_attn_fsmn_block_weight, x = input_1063)[name = tensor("x_355")]; + tensor x_357_perm_0 = const()[name = tensor("x_357_perm_0"), val = tensor([0, 2, 1])]; + tensor x_357 = transpose(perm = x_357_perm_0, x = x_355)[name = tensor("transpose_557")]; + tensor input_1065 = add(x = x_357, y = inputs_71)[name = tensor("input_1065")]; + tensor fsmn_memory_71 = mul(x = input_1065, y = mask_7)[name = tensor("fsmn_memory_71")]; + tensor var_3883 = const()[name = tensor("op_3883"), val = tensor(0x1.6a09e6p-4)]; + tensor q_h_143 = mul(x = var_3858, y = var_3883)[name = tensor("q_h_143")]; + tensor scores_141_transpose_x_0 = const()[name = tensor("scores_141_transpose_x_0"), val = tensor(false)]; + tensor scores_141_transpose_y_0 = const()[name = tensor("scores_141_transpose_y_0"), val = tensor(false)]; + tensor transpose_280_perm_0 = const()[name = tensor("transpose_280_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_281_perm_0 = const()[name = tensor("transpose_281_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_281 = transpose(perm = transpose_281_perm_0, x = var_3861)[name = tensor("transpose_555")]; + tensor transpose_280 = transpose(perm = transpose_280_perm_0, x = q_h_143)[name = tensor("transpose_556")]; + tensor scores_141 = matmul(transpose_x = scores_141_transpose_x_0, transpose_y = scores_141_transpose_y_0, x = transpose_280, y = transpose_281)[name = tensor("scores_141")]; + tensor scores_143 = select(a = var_48, b = scores_141, cond = mask_9)[name = tensor("scores_143")]; + tensor var_3891 = softmax(axis = var_61, x = scores_143)[name = tensor("op_3891")]; + tensor input_1067 = select(a = var_53, b = var_3891, cond = mask_9)[name = tensor("input_1067")]; + tensor x_361_transpose_x_0 = const()[name = tensor("x_361_transpose_x_0"), val = tensor(false)]; + tensor x_361_transpose_y_0 = const()[name = tensor("x_361_transpose_y_0"), val = tensor(false)]; + tensor value_71 = transpose(perm = value_71_perm_0, x = var_3864)[name = tensor("transpose_559")]; + tensor x_361 = matmul(transpose_x = x_361_transpose_x_0, transpose_y = x_361_transpose_y_0, x = input_1067, y = value_71)[name = tensor("x_361")]; + tensor var_3895_perm_0 = const()[name = tensor("op_3895_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_3897 = const()[name = tensor("op_3897"), val = tensor([1, -1, 512])]; + tensor var_3895 = transpose(perm = var_3895_perm_0, x = x_361)[name = tensor("transpose_554")]; + tensor input_1069 = reshape(shape = var_3897, x = var_3895)[name = tensor("input_1069")]; + tensor att_outs_71 = linear(bias = encoder_encoders_34_self_attn_linear_out_bias, weight = encoder_encoders_34_self_attn_linear_out_weight, x = input_1069)[name = tensor("linear_141")]; + tensor input_1071 = add(x = att_outs_71, y = fsmn_memory_71)[name = tensor("input_1071")]; + tensor input_1073 = add(x = input_1057, y = input_1071)[name = tensor("input_1073")]; + tensor const_408 = const()[name = tensor("const_408"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939801088)))]; + tensor const_409 = const()[name = tensor("const_409"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939803200)))]; + tensor output_143_axes_0 = const()[name = tensor("output_143_axes_0"), val = tensor([-1])]; + tensor output_143 = layer_norm(axes = output_143_axes_0, beta = const_409, epsilon = var_46, gamma = const_408, x = input_1073)[name = tensor("output_143")]; + tensor input_1079 = linear(bias = encoder_encoders_34_feed_forward_w_1_bias, weight = encoder_encoders_34_feed_forward_w_1_weight, x = output_143)[name = tensor("linear_142")]; + tensor input_1081 = relu(x = input_1079)[name = tensor("input_1081")]; + tensor input_1085 = linear(bias = encoder_encoders_34_feed_forward_w_2_bias, weight = encoder_encoders_34_feed_forward_w_2_weight, x = input_1081)[name = tensor("linear_143")]; + tensor input_1087 = add(x = input_1073, y = input_1085)[name = tensor("input_1087")]; + tensor const_410 = const()[name = tensor("const_410"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939805312)))]; + tensor const_411 = const()[name = tensor("const_411"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939807424)))]; + tensor output_145_axes_0 = const()[name = tensor("output_145_axes_0"), val = tensor([-1])]; + tensor output_145 = layer_norm(axes = output_145_axes_0, beta = const_411, epsilon = var_46, gamma = const_410, x = input_1087)[name = tensor("output_145")]; + tensor var_3954 = linear(bias = encoder_encoders_35_self_attn_linear_q_k_v_bias, weight = encoder_encoders_35_self_attn_linear_q_k_v_weight, x = output_145)[name = tensor("linear_144")]; + tensor tile_36 = const()[name = tensor("tile_36"), val = tensor([512, 512, 512])]; + tensor var_3955_axis_0 = const()[name = tensor("op_3955_axis_0"), val = tensor(-1)]; + tensor var_3955_0, tensor var_3955_1, tensor var_3955_2 = split(axis = var_3955_axis_0, split_sizes = tile_36, x = var_3954)[name = tensor("op_3955")]; + tensor var_3959 = const()[name = tensor("op_3959"), val = tensor([1, 1804, 4, 128])]; + tensor var_3960 = reshape(shape = var_3959, x = var_3955_0)[name = tensor("op_3960")]; + tensor var_3962 = const()[name = tensor("op_3962"), val = tensor([1, 1804, 4, 128])]; + tensor var_3963 = reshape(shape = var_3962, x = var_3955_1)[name = tensor("op_3963")]; + tensor var_3965 = const()[name = tensor("op_3965"), val = tensor([1, 1804, 4, 128])]; + tensor var_3966 = reshape(shape = var_3965, x = var_3955_2)[name = tensor("op_3966")]; + tensor value_73_perm_0 = const()[name = tensor("value_73_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor inputs_73 = mul(x = var_3955_2, y = mask_7)[name = tensor("inputs_73")]; + tensor input_1091_perm_0 = const()[name = tensor("input_1091_perm_0"), val = tensor([0, 2, 1])]; + tensor const_417 = const()[name = tensor("const_417"), val = tensor(0x0p+0)]; + tensor input_1093_pad_0 = const()[name = tensor("input_1093_pad_0"), val = tensor([0, 0, 0, 0, 5, 5])]; + tensor input_1093_mode_0 = const()[name = tensor("input_1093_mode_0"), val = tensor("constant")]; + tensor input_1091 = transpose(perm = input_1091_perm_0, x = inputs_73)[name = tensor("transpose_552")]; + tensor input_1093 = pad(constant_val = const_417, mode = input_1093_mode_0, pad = input_1093_pad_0, x = input_1091)[name = tensor("input_1093")]; + tensor x_365_pad_type_0 = const()[name = tensor("x_365_pad_type_0"), val = tensor("valid")]; + tensor x_365_groups_0 = const()[name = tensor("x_365_groups_0"), val = tensor(512)]; + tensor x_365_strides_0 = const()[name = tensor("x_365_strides_0"), val = tensor([1])]; + tensor x_365_pad_0 = const()[name = tensor("x_365_pad_0"), val = tensor([0, 0])]; + tensor x_365_dilations_0 = const()[name = tensor("x_365_dilations_0"), val = tensor([1])]; + tensor x_365 = conv(dilations = x_365_dilations_0, groups = x_365_groups_0, pad = x_365_pad_0, pad_type = x_365_pad_type_0, strides = x_365_strides_0, weight = encoder_encoders_35_self_attn_fsmn_block_weight, x = input_1093)[name = tensor("x_365")]; + tensor x_367_perm_0 = const()[name = tensor("x_367_perm_0"), val = tensor([0, 2, 1])]; + tensor x_367 = transpose(perm = x_367_perm_0, x = x_365)[name = tensor("transpose_551")]; + tensor input_1095 = add(x = x_367, y = inputs_73)[name = tensor("input_1095")]; + tensor fsmn_memory_73 = mul(x = input_1095, y = mask_7)[name = tensor("fsmn_memory_73")]; + tensor var_3985 = const()[name = tensor("op_3985"), val = tensor(0x1.6a09e6p-4)]; + tensor q_h_147 = mul(x = var_3960, y = var_3985)[name = tensor("q_h_147")]; + tensor scores_145_transpose_x_0 = const()[name = tensor("scores_145_transpose_x_0"), val = tensor(false)]; + tensor scores_145_transpose_y_0 = const()[name = tensor("scores_145_transpose_y_0"), val = tensor(false)]; + tensor transpose_282_perm_0 = const()[name = tensor("transpose_282_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_283_perm_0 = const()[name = tensor("transpose_283_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_283 = transpose(perm = transpose_283_perm_0, x = var_3963)[name = tensor("transpose_549")]; + tensor transpose_282 = transpose(perm = transpose_282_perm_0, x = q_h_147)[name = tensor("transpose_550")]; + tensor scores_145 = matmul(transpose_x = scores_145_transpose_x_0, transpose_y = scores_145_transpose_y_0, x = transpose_282, y = transpose_283)[name = tensor("scores_145")]; + tensor scores_147 = select(a = var_48, b = scores_145, cond = mask_9)[name = tensor("scores_147")]; + tensor var_3993 = softmax(axis = var_61, x = scores_147)[name = tensor("op_3993")]; + tensor input_1097 = select(a = var_53, b = var_3993, cond = mask_9)[name = tensor("input_1097")]; + tensor x_371_transpose_x_0 = const()[name = tensor("x_371_transpose_x_0"), val = tensor(false)]; + tensor x_371_transpose_y_0 = const()[name = tensor("x_371_transpose_y_0"), val = tensor(false)]; + tensor value_73 = transpose(perm = value_73_perm_0, x = var_3966)[name = tensor("transpose_553")]; + tensor x_371 = matmul(transpose_x = x_371_transpose_x_0, transpose_y = x_371_transpose_y_0, x = input_1097, y = value_73)[name = tensor("x_371")]; + tensor var_3997_perm_0 = const()[name = tensor("op_3997_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_3999 = const()[name = tensor("op_3999"), val = tensor([1, -1, 512])]; + tensor var_3997 = transpose(perm = var_3997_perm_0, x = x_371)[name = tensor("transpose_548")]; + tensor input_1099 = reshape(shape = var_3999, x = var_3997)[name = tensor("input_1099")]; + tensor att_outs_73 = linear(bias = encoder_encoders_35_self_attn_linear_out_bias, weight = encoder_encoders_35_self_attn_linear_out_weight, x = input_1099)[name = tensor("linear_145")]; + tensor input_1101 = add(x = att_outs_73, y = fsmn_memory_73)[name = tensor("input_1101")]; + tensor input_1103 = add(x = input_1087, y = input_1101)[name = tensor("input_1103")]; + tensor const_419 = const()[name = tensor("const_419"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939809536)))]; + tensor const_420 = const()[name = tensor("const_420"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939811648)))]; + tensor output_147_axes_0 = const()[name = tensor("output_147_axes_0"), val = tensor([-1])]; + tensor output_147 = layer_norm(axes = output_147_axes_0, beta = const_420, epsilon = var_46, gamma = const_419, x = input_1103)[name = tensor("output_147")]; + tensor input_1109 = linear(bias = encoder_encoders_35_feed_forward_w_1_bias, weight = encoder_encoders_35_feed_forward_w_1_weight, x = output_147)[name = tensor("linear_146")]; + tensor input_1111 = relu(x = input_1109)[name = tensor("input_1111")]; + tensor input_1115 = linear(bias = encoder_encoders_35_feed_forward_w_2_bias, weight = encoder_encoders_35_feed_forward_w_2_weight, x = input_1111)[name = tensor("linear_147")]; + tensor input_1117 = add(x = input_1103, y = input_1115)[name = tensor("input_1117")]; + tensor const_421 = const()[name = tensor("const_421"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939813760)))]; + tensor const_422 = const()[name = tensor("const_422"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939815872)))]; + tensor output_149_axes_0 = const()[name = tensor("output_149_axes_0"), val = tensor([-1])]; + tensor output_149 = layer_norm(axes = output_149_axes_0, beta = const_422, epsilon = var_46, gamma = const_421, x = input_1117)[name = tensor("output_149")]; + tensor var_4056 = linear(bias = encoder_encoders_36_self_attn_linear_q_k_v_bias, weight = encoder_encoders_36_self_attn_linear_q_k_v_weight, x = output_149)[name = tensor("linear_148")]; + tensor tile_37 = const()[name = tensor("tile_37"), val = tensor([512, 512, 512])]; + tensor var_4057_axis_0 = const()[name = tensor("op_4057_axis_0"), val = tensor(-1)]; + tensor var_4057_0, tensor var_4057_1, tensor var_4057_2 = split(axis = var_4057_axis_0, split_sizes = tile_37, x = var_4056)[name = tensor("op_4057")]; + tensor var_4061 = const()[name = tensor("op_4061"), val = tensor([1, 1804, 4, 128])]; + tensor var_4062 = reshape(shape = var_4061, x = var_4057_0)[name = tensor("op_4062")]; + tensor var_4064 = const()[name = tensor("op_4064"), val = tensor([1, 1804, 4, 128])]; + tensor var_4065 = reshape(shape = var_4064, x = var_4057_1)[name = tensor("op_4065")]; + tensor var_4067 = const()[name = tensor("op_4067"), val = tensor([1, 1804, 4, 128])]; + tensor var_4068 = reshape(shape = var_4067, x = var_4057_2)[name = tensor("op_4068")]; + tensor value_75_perm_0 = const()[name = tensor("value_75_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor inputs_75 = mul(x = var_4057_2, y = mask_7)[name = tensor("inputs_75")]; + tensor input_1121_perm_0 = const()[name = tensor("input_1121_perm_0"), val = tensor([0, 2, 1])]; + tensor const_428 = const()[name = tensor("const_428"), val = tensor(0x0p+0)]; + tensor input_1123_pad_0 = const()[name = tensor("input_1123_pad_0"), val = tensor([0, 0, 0, 0, 5, 5])]; + tensor input_1123_mode_0 = const()[name = tensor("input_1123_mode_0"), val = tensor("constant")]; + tensor input_1121 = transpose(perm = input_1121_perm_0, x = inputs_75)[name = tensor("transpose_546")]; + tensor input_1123 = pad(constant_val = const_428, mode = input_1123_mode_0, pad = input_1123_pad_0, x = input_1121)[name = tensor("input_1123")]; + tensor x_375_pad_type_0 = const()[name = tensor("x_375_pad_type_0"), val = tensor("valid")]; + tensor x_375_groups_0 = const()[name = tensor("x_375_groups_0"), val = tensor(512)]; + tensor x_375_strides_0 = const()[name = tensor("x_375_strides_0"), val = tensor([1])]; + tensor x_375_pad_0 = const()[name = tensor("x_375_pad_0"), val = tensor([0, 0])]; + tensor x_375_dilations_0 = const()[name = tensor("x_375_dilations_0"), val = tensor([1])]; + tensor x_375 = conv(dilations = x_375_dilations_0, groups = x_375_groups_0, pad = x_375_pad_0, pad_type = x_375_pad_type_0, strides = x_375_strides_0, weight = encoder_encoders_36_self_attn_fsmn_block_weight, x = input_1123)[name = tensor("x_375")]; + tensor x_377_perm_0 = const()[name = tensor("x_377_perm_0"), val = tensor([0, 2, 1])]; + tensor x_377 = transpose(perm = x_377_perm_0, x = x_375)[name = tensor("transpose_545")]; + tensor input_1125 = add(x = x_377, y = inputs_75)[name = tensor("input_1125")]; + tensor fsmn_memory_75 = mul(x = input_1125, y = mask_7)[name = tensor("fsmn_memory_75")]; + tensor var_4087 = const()[name = tensor("op_4087"), val = tensor(0x1.6a09e6p-4)]; + tensor q_h_151 = mul(x = var_4062, y = var_4087)[name = tensor("q_h_151")]; + tensor scores_149_transpose_x_0 = const()[name = tensor("scores_149_transpose_x_0"), val = tensor(false)]; + tensor scores_149_transpose_y_0 = const()[name = tensor("scores_149_transpose_y_0"), val = tensor(false)]; + tensor transpose_284_perm_0 = const()[name = tensor("transpose_284_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_285_perm_0 = const()[name = tensor("transpose_285_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_285 = transpose(perm = transpose_285_perm_0, x = var_4065)[name = tensor("transpose_543")]; + tensor transpose_284 = transpose(perm = transpose_284_perm_0, x = q_h_151)[name = tensor("transpose_544")]; + tensor scores_149 = matmul(transpose_x = scores_149_transpose_x_0, transpose_y = scores_149_transpose_y_0, x = transpose_284, y = transpose_285)[name = tensor("scores_149")]; + tensor scores_151 = select(a = var_48, b = scores_149, cond = mask_9)[name = tensor("scores_151")]; + tensor var_4095 = softmax(axis = var_61, x = scores_151)[name = tensor("op_4095")]; + tensor input_1127 = select(a = var_53, b = var_4095, cond = mask_9)[name = tensor("input_1127")]; + tensor x_381_transpose_x_0 = const()[name = tensor("x_381_transpose_x_0"), val = tensor(false)]; + tensor x_381_transpose_y_0 = const()[name = tensor("x_381_transpose_y_0"), val = tensor(false)]; + tensor value_75 = transpose(perm = value_75_perm_0, x = var_4068)[name = tensor("transpose_547")]; + tensor x_381 = matmul(transpose_x = x_381_transpose_x_0, transpose_y = x_381_transpose_y_0, x = input_1127, y = value_75)[name = tensor("x_381")]; + tensor var_4099_perm_0 = const()[name = tensor("op_4099_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_4101 = const()[name = tensor("op_4101"), val = tensor([1, -1, 512])]; + tensor var_4099 = transpose(perm = var_4099_perm_0, x = x_381)[name = tensor("transpose_542")]; + tensor input_1129 = reshape(shape = var_4101, x = var_4099)[name = tensor("input_1129")]; + tensor att_outs_75 = linear(bias = encoder_encoders_36_self_attn_linear_out_bias, weight = encoder_encoders_36_self_attn_linear_out_weight, x = input_1129)[name = tensor("linear_149")]; + tensor input_1131 = add(x = att_outs_75, y = fsmn_memory_75)[name = tensor("input_1131")]; + tensor input_1133 = add(x = input_1117, y = input_1131)[name = tensor("input_1133")]; + tensor const_430 = const()[name = tensor("const_430"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939817984)))]; + tensor const_431 = const()[name = tensor("const_431"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939820096)))]; + tensor output_151_axes_0 = const()[name = tensor("output_151_axes_0"), val = tensor([-1])]; + tensor output_151 = layer_norm(axes = output_151_axes_0, beta = const_431, epsilon = var_46, gamma = const_430, x = input_1133)[name = tensor("output_151")]; + tensor input_1139 = linear(bias = encoder_encoders_36_feed_forward_w_1_bias, weight = encoder_encoders_36_feed_forward_w_1_weight, x = output_151)[name = tensor("linear_150")]; + tensor input_1141 = relu(x = input_1139)[name = tensor("input_1141")]; + tensor input_1145 = linear(bias = encoder_encoders_36_feed_forward_w_2_bias, weight = encoder_encoders_36_feed_forward_w_2_weight, x = input_1141)[name = tensor("linear_151")]; + tensor input_1147 = add(x = input_1133, y = input_1145)[name = tensor("input_1147")]; + tensor const_432 = const()[name = tensor("const_432"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939822208)))]; + tensor const_433 = const()[name = tensor("const_433"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939824320)))]; + tensor output_153_axes_0 = const()[name = tensor("output_153_axes_0"), val = tensor([-1])]; + tensor output_153 = layer_norm(axes = output_153_axes_0, beta = const_433, epsilon = var_46, gamma = const_432, x = input_1147)[name = tensor("output_153")]; + tensor var_4158 = linear(bias = encoder_encoders_37_self_attn_linear_q_k_v_bias, weight = encoder_encoders_37_self_attn_linear_q_k_v_weight, x = output_153)[name = tensor("linear_152")]; + tensor tile_38 = const()[name = tensor("tile_38"), val = tensor([512, 512, 512])]; + tensor var_4159_axis_0 = const()[name = tensor("op_4159_axis_0"), val = tensor(-1)]; + tensor var_4159_0, tensor var_4159_1, tensor var_4159_2 = split(axis = var_4159_axis_0, split_sizes = tile_38, x = var_4158)[name = tensor("op_4159")]; + tensor var_4163 = const()[name = tensor("op_4163"), val = tensor([1, 1804, 4, 128])]; + tensor var_4164 = reshape(shape = var_4163, x = var_4159_0)[name = tensor("op_4164")]; + tensor var_4166 = const()[name = tensor("op_4166"), val = tensor([1, 1804, 4, 128])]; + tensor var_4167 = reshape(shape = var_4166, x = var_4159_1)[name = tensor("op_4167")]; + tensor var_4169 = const()[name = tensor("op_4169"), val = tensor([1, 1804, 4, 128])]; + tensor var_4170 = reshape(shape = var_4169, x = var_4159_2)[name = tensor("op_4170")]; + tensor value_77_perm_0 = const()[name = tensor("value_77_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor inputs_77 = mul(x = var_4159_2, y = mask_7)[name = tensor("inputs_77")]; + tensor input_1151_perm_0 = const()[name = tensor("input_1151_perm_0"), val = tensor([0, 2, 1])]; + tensor const_439 = const()[name = tensor("const_439"), val = tensor(0x0p+0)]; + tensor input_1153_pad_0 = const()[name = tensor("input_1153_pad_0"), val = tensor([0, 0, 0, 0, 5, 5])]; + tensor input_1153_mode_0 = const()[name = tensor("input_1153_mode_0"), val = tensor("constant")]; + tensor input_1151 = transpose(perm = input_1151_perm_0, x = inputs_77)[name = tensor("transpose_540")]; + tensor input_1153 = pad(constant_val = const_439, mode = input_1153_mode_0, pad = input_1153_pad_0, x = input_1151)[name = tensor("input_1153")]; + tensor x_385_pad_type_0 = const()[name = tensor("x_385_pad_type_0"), val = tensor("valid")]; + tensor x_385_groups_0 = const()[name = tensor("x_385_groups_0"), val = tensor(512)]; + tensor x_385_strides_0 = const()[name = tensor("x_385_strides_0"), val = tensor([1])]; + tensor x_385_pad_0 = const()[name = tensor("x_385_pad_0"), val = tensor([0, 0])]; + tensor x_385_dilations_0 = const()[name = tensor("x_385_dilations_0"), val = tensor([1])]; + tensor x_385 = conv(dilations = x_385_dilations_0, groups = x_385_groups_0, pad = x_385_pad_0, pad_type = x_385_pad_type_0, strides = x_385_strides_0, weight = encoder_encoders_37_self_attn_fsmn_block_weight, x = input_1153)[name = tensor("x_385")]; + tensor x_387_perm_0 = const()[name = tensor("x_387_perm_0"), val = tensor([0, 2, 1])]; + tensor x_387 = transpose(perm = x_387_perm_0, x = x_385)[name = tensor("transpose_539")]; + tensor input_1155 = add(x = x_387, y = inputs_77)[name = tensor("input_1155")]; + tensor fsmn_memory_77 = mul(x = input_1155, y = mask_7)[name = tensor("fsmn_memory_77")]; + tensor var_4189 = const()[name = tensor("op_4189"), val = tensor(0x1.6a09e6p-4)]; + tensor q_h_155 = mul(x = var_4164, y = var_4189)[name = tensor("q_h_155")]; + tensor scores_153_transpose_x_0 = const()[name = tensor("scores_153_transpose_x_0"), val = tensor(false)]; + tensor scores_153_transpose_y_0 = const()[name = tensor("scores_153_transpose_y_0"), val = tensor(false)]; + tensor transpose_286_perm_0 = const()[name = tensor("transpose_286_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_287_perm_0 = const()[name = tensor("transpose_287_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_287 = transpose(perm = transpose_287_perm_0, x = var_4167)[name = tensor("transpose_537")]; + tensor transpose_286 = transpose(perm = transpose_286_perm_0, x = q_h_155)[name = tensor("transpose_538")]; + tensor scores_153 = matmul(transpose_x = scores_153_transpose_x_0, transpose_y = scores_153_transpose_y_0, x = transpose_286, y = transpose_287)[name = tensor("scores_153")]; + tensor scores_155 = select(a = var_48, b = scores_153, cond = mask_9)[name = tensor("scores_155")]; + tensor var_4197 = softmax(axis = var_61, x = scores_155)[name = tensor("op_4197")]; + tensor input_1157 = select(a = var_53, b = var_4197, cond = mask_9)[name = tensor("input_1157")]; + tensor x_391_transpose_x_0 = const()[name = tensor("x_391_transpose_x_0"), val = tensor(false)]; + tensor x_391_transpose_y_0 = const()[name = tensor("x_391_transpose_y_0"), val = tensor(false)]; + tensor value_77 = transpose(perm = value_77_perm_0, x = var_4170)[name = tensor("transpose_541")]; + tensor x_391 = matmul(transpose_x = x_391_transpose_x_0, transpose_y = x_391_transpose_y_0, x = input_1157, y = value_77)[name = tensor("x_391")]; + tensor var_4201_perm_0 = const()[name = tensor("op_4201_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_4203 = const()[name = tensor("op_4203"), val = tensor([1, -1, 512])]; + tensor var_4201 = transpose(perm = var_4201_perm_0, x = x_391)[name = tensor("transpose_536")]; + tensor input_1159 = reshape(shape = var_4203, x = var_4201)[name = tensor("input_1159")]; + tensor att_outs_77 = linear(bias = encoder_encoders_37_self_attn_linear_out_bias, weight = encoder_encoders_37_self_attn_linear_out_weight, x = input_1159)[name = tensor("linear_153")]; + tensor input_1161 = add(x = att_outs_77, y = fsmn_memory_77)[name = tensor("input_1161")]; + tensor input_1163 = add(x = input_1147, y = input_1161)[name = tensor("input_1163")]; + tensor const_441 = const()[name = tensor("const_441"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939826432)))]; + tensor const_442 = const()[name = tensor("const_442"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939828544)))]; + tensor output_155_axes_0 = const()[name = tensor("output_155_axes_0"), val = tensor([-1])]; + tensor output_155 = layer_norm(axes = output_155_axes_0, beta = const_442, epsilon = var_46, gamma = const_441, x = input_1163)[name = tensor("output_155")]; + tensor input_1169 = linear(bias = encoder_encoders_37_feed_forward_w_1_bias, weight = encoder_encoders_37_feed_forward_w_1_weight, x = output_155)[name = tensor("linear_154")]; + tensor input_1171 = relu(x = input_1169)[name = tensor("input_1171")]; + tensor input_1175 = linear(bias = encoder_encoders_37_feed_forward_w_2_bias, weight = encoder_encoders_37_feed_forward_w_2_weight, x = input_1171)[name = tensor("linear_155")]; + tensor input_1177 = add(x = input_1163, y = input_1175)[name = tensor("input_1177")]; + tensor const_443 = const()[name = tensor("const_443"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939830656)))]; + tensor const_444 = const()[name = tensor("const_444"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939832768)))]; + tensor output_157_axes_0 = const()[name = tensor("output_157_axes_0"), val = tensor([-1])]; + tensor output_157 = layer_norm(axes = output_157_axes_0, beta = const_444, epsilon = var_46, gamma = const_443, x = input_1177)[name = tensor("output_157")]; + tensor var_4260 = linear(bias = encoder_encoders_38_self_attn_linear_q_k_v_bias, weight = encoder_encoders_38_self_attn_linear_q_k_v_weight, x = output_157)[name = tensor("linear_156")]; + tensor tile_39 = const()[name = tensor("tile_39"), val = tensor([512, 512, 512])]; + tensor var_4261_axis_0 = const()[name = tensor("op_4261_axis_0"), val = tensor(-1)]; + tensor var_4261_0, tensor var_4261_1, tensor var_4261_2 = split(axis = var_4261_axis_0, split_sizes = tile_39, x = var_4260)[name = tensor("op_4261")]; + tensor var_4265 = const()[name = tensor("op_4265"), val = tensor([1, 1804, 4, 128])]; + tensor var_4266 = reshape(shape = var_4265, x = var_4261_0)[name = tensor("op_4266")]; + tensor var_4268 = const()[name = tensor("op_4268"), val = tensor([1, 1804, 4, 128])]; + tensor var_4269 = reshape(shape = var_4268, x = var_4261_1)[name = tensor("op_4269")]; + tensor var_4271 = const()[name = tensor("op_4271"), val = tensor([1, 1804, 4, 128])]; + tensor var_4272 = reshape(shape = var_4271, x = var_4261_2)[name = tensor("op_4272")]; + tensor value_79_perm_0 = const()[name = tensor("value_79_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor inputs_79 = mul(x = var_4261_2, y = mask_7)[name = tensor("inputs_79")]; + tensor input_1181_perm_0 = const()[name = tensor("input_1181_perm_0"), val = tensor([0, 2, 1])]; + tensor const_450 = const()[name = tensor("const_450"), val = tensor(0x0p+0)]; + tensor input_1183_pad_0 = const()[name = tensor("input_1183_pad_0"), val = tensor([0, 0, 0, 0, 5, 5])]; + tensor input_1183_mode_0 = const()[name = tensor("input_1183_mode_0"), val = tensor("constant")]; + tensor input_1181 = transpose(perm = input_1181_perm_0, x = inputs_79)[name = tensor("transpose_534")]; + tensor input_1183 = pad(constant_val = const_450, mode = input_1183_mode_0, pad = input_1183_pad_0, x = input_1181)[name = tensor("input_1183")]; + tensor x_395_pad_type_0 = const()[name = tensor("x_395_pad_type_0"), val = tensor("valid")]; + tensor x_395_groups_0 = const()[name = tensor("x_395_groups_0"), val = tensor(512)]; + tensor x_395_strides_0 = const()[name = tensor("x_395_strides_0"), val = tensor([1])]; + tensor x_395_pad_0 = const()[name = tensor("x_395_pad_0"), val = tensor([0, 0])]; + tensor x_395_dilations_0 = const()[name = tensor("x_395_dilations_0"), val = tensor([1])]; + tensor x_395 = conv(dilations = x_395_dilations_0, groups = x_395_groups_0, pad = x_395_pad_0, pad_type = x_395_pad_type_0, strides = x_395_strides_0, weight = encoder_encoders_38_self_attn_fsmn_block_weight, x = input_1183)[name = tensor("x_395")]; + tensor x_397_perm_0 = const()[name = tensor("x_397_perm_0"), val = tensor([0, 2, 1])]; + tensor x_397 = transpose(perm = x_397_perm_0, x = x_395)[name = tensor("transpose_533")]; + tensor input_1185 = add(x = x_397, y = inputs_79)[name = tensor("input_1185")]; + tensor fsmn_memory_79 = mul(x = input_1185, y = mask_7)[name = tensor("fsmn_memory_79")]; + tensor var_4291 = const()[name = tensor("op_4291"), val = tensor(0x1.6a09e6p-4)]; + tensor q_h_159 = mul(x = var_4266, y = var_4291)[name = tensor("q_h_159")]; + tensor scores_157_transpose_x_0 = const()[name = tensor("scores_157_transpose_x_0"), val = tensor(false)]; + tensor scores_157_transpose_y_0 = const()[name = tensor("scores_157_transpose_y_0"), val = tensor(false)]; + tensor transpose_288_perm_0 = const()[name = tensor("transpose_288_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_289_perm_0 = const()[name = tensor("transpose_289_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_289 = transpose(perm = transpose_289_perm_0, x = var_4269)[name = tensor("transpose_531")]; + tensor transpose_288 = transpose(perm = transpose_288_perm_0, x = q_h_159)[name = tensor("transpose_532")]; + tensor scores_157 = matmul(transpose_x = scores_157_transpose_x_0, transpose_y = scores_157_transpose_y_0, x = transpose_288, y = transpose_289)[name = tensor("scores_157")]; + tensor scores_159 = select(a = var_48, b = scores_157, cond = mask_9)[name = tensor("scores_159")]; + tensor var_4299 = softmax(axis = var_61, x = scores_159)[name = tensor("op_4299")]; + tensor input_1187 = select(a = var_53, b = var_4299, cond = mask_9)[name = tensor("input_1187")]; + tensor x_401_transpose_x_0 = const()[name = tensor("x_401_transpose_x_0"), val = tensor(false)]; + tensor x_401_transpose_y_0 = const()[name = tensor("x_401_transpose_y_0"), val = tensor(false)]; + tensor value_79 = transpose(perm = value_79_perm_0, x = var_4272)[name = tensor("transpose_535")]; + tensor x_401 = matmul(transpose_x = x_401_transpose_x_0, transpose_y = x_401_transpose_y_0, x = input_1187, y = value_79)[name = tensor("x_401")]; + tensor var_4303_perm_0 = const()[name = tensor("op_4303_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_4305 = const()[name = tensor("op_4305"), val = tensor([1, -1, 512])]; + tensor var_4303 = transpose(perm = var_4303_perm_0, x = x_401)[name = tensor("transpose_530")]; + tensor input_1189 = reshape(shape = var_4305, x = var_4303)[name = tensor("input_1189")]; + tensor att_outs_79 = linear(bias = encoder_encoders_38_self_attn_linear_out_bias, weight = encoder_encoders_38_self_attn_linear_out_weight, x = input_1189)[name = tensor("linear_157")]; + tensor input_1191 = add(x = att_outs_79, y = fsmn_memory_79)[name = tensor("input_1191")]; + tensor input_1193 = add(x = input_1177, y = input_1191)[name = tensor("input_1193")]; + tensor const_452 = const()[name = tensor("const_452"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939834880)))]; + tensor const_453 = const()[name = tensor("const_453"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939836992)))]; + tensor output_159_axes_0 = const()[name = tensor("output_159_axes_0"), val = tensor([-1])]; + tensor output_159 = layer_norm(axes = output_159_axes_0, beta = const_453, epsilon = var_46, gamma = const_452, x = input_1193)[name = tensor("output_159")]; + tensor input_1199 = linear(bias = encoder_encoders_38_feed_forward_w_1_bias, weight = encoder_encoders_38_feed_forward_w_1_weight, x = output_159)[name = tensor("linear_158")]; + tensor input_1201 = relu(x = input_1199)[name = tensor("input_1201")]; + tensor input_1205 = linear(bias = encoder_encoders_38_feed_forward_w_2_bias, weight = encoder_encoders_38_feed_forward_w_2_weight, x = input_1201)[name = tensor("linear_159")]; + tensor input_1207 = add(x = input_1193, y = input_1205)[name = tensor("input_1207")]; + tensor const_454 = const()[name = tensor("const_454"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939839104)))]; + tensor const_455 = const()[name = tensor("const_455"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939841216)))]; + tensor output_161_axes_0 = const()[name = tensor("output_161_axes_0"), val = tensor([-1])]; + tensor output_161 = layer_norm(axes = output_161_axes_0, beta = const_455, epsilon = var_46, gamma = const_454, x = input_1207)[name = tensor("output_161")]; + tensor var_4362 = linear(bias = encoder_encoders_39_self_attn_linear_q_k_v_bias, weight = encoder_encoders_39_self_attn_linear_q_k_v_weight, x = output_161)[name = tensor("linear_160")]; + tensor tile_40 = const()[name = tensor("tile_40"), val = tensor([512, 512, 512])]; + tensor var_4363_axis_0 = const()[name = tensor("op_4363_axis_0"), val = tensor(-1)]; + tensor var_4363_0, tensor var_4363_1, tensor var_4363_2 = split(axis = var_4363_axis_0, split_sizes = tile_40, x = var_4362)[name = tensor("op_4363")]; + tensor var_4367 = const()[name = tensor("op_4367"), val = tensor([1, 1804, 4, 128])]; + tensor var_4368 = reshape(shape = var_4367, x = var_4363_0)[name = tensor("op_4368")]; + tensor var_4370 = const()[name = tensor("op_4370"), val = tensor([1, 1804, 4, 128])]; + tensor var_4371 = reshape(shape = var_4370, x = var_4363_1)[name = tensor("op_4371")]; + tensor var_4373 = const()[name = tensor("op_4373"), val = tensor([1, 1804, 4, 128])]; + tensor var_4374 = reshape(shape = var_4373, x = var_4363_2)[name = tensor("op_4374")]; + tensor value_81_perm_0 = const()[name = tensor("value_81_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor inputs_81 = mul(x = var_4363_2, y = mask_7)[name = tensor("inputs_81")]; + tensor input_1211_perm_0 = const()[name = tensor("input_1211_perm_0"), val = tensor([0, 2, 1])]; + tensor const_461 = const()[name = tensor("const_461"), val = tensor(0x0p+0)]; + tensor input_1213_pad_0 = const()[name = tensor("input_1213_pad_0"), val = tensor([0, 0, 0, 0, 5, 5])]; + tensor input_1213_mode_0 = const()[name = tensor("input_1213_mode_0"), val = tensor("constant")]; + tensor input_1211 = transpose(perm = input_1211_perm_0, x = inputs_81)[name = tensor("transpose_528")]; + tensor input_1213 = pad(constant_val = const_461, mode = input_1213_mode_0, pad = input_1213_pad_0, x = input_1211)[name = tensor("input_1213")]; + tensor x_405_pad_type_0 = const()[name = tensor("x_405_pad_type_0"), val = tensor("valid")]; + tensor x_405_groups_0 = const()[name = tensor("x_405_groups_0"), val = tensor(512)]; + tensor x_405_strides_0 = const()[name = tensor("x_405_strides_0"), val = tensor([1])]; + tensor x_405_pad_0 = const()[name = tensor("x_405_pad_0"), val = tensor([0, 0])]; + tensor x_405_dilations_0 = const()[name = tensor("x_405_dilations_0"), val = tensor([1])]; + tensor x_405 = conv(dilations = x_405_dilations_0, groups = x_405_groups_0, pad = x_405_pad_0, pad_type = x_405_pad_type_0, strides = x_405_strides_0, weight = encoder_encoders_39_self_attn_fsmn_block_weight, x = input_1213)[name = tensor("x_405")]; + tensor x_407_perm_0 = const()[name = tensor("x_407_perm_0"), val = tensor([0, 2, 1])]; + tensor x_407 = transpose(perm = x_407_perm_0, x = x_405)[name = tensor("transpose_527")]; + tensor input_1215 = add(x = x_407, y = inputs_81)[name = tensor("input_1215")]; + tensor fsmn_memory_81 = mul(x = input_1215, y = mask_7)[name = tensor("fsmn_memory_81")]; + tensor var_4393 = const()[name = tensor("op_4393"), val = tensor(0x1.6a09e6p-4)]; + tensor q_h_163 = mul(x = var_4368, y = var_4393)[name = tensor("q_h_163")]; + tensor scores_161_transpose_x_0 = const()[name = tensor("scores_161_transpose_x_0"), val = tensor(false)]; + tensor scores_161_transpose_y_0 = const()[name = tensor("scores_161_transpose_y_0"), val = tensor(false)]; + tensor transpose_290_perm_0 = const()[name = tensor("transpose_290_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_291_perm_0 = const()[name = tensor("transpose_291_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_291 = transpose(perm = transpose_291_perm_0, x = var_4371)[name = tensor("transpose_525")]; + tensor transpose_290 = transpose(perm = transpose_290_perm_0, x = q_h_163)[name = tensor("transpose_526")]; + tensor scores_161 = matmul(transpose_x = scores_161_transpose_x_0, transpose_y = scores_161_transpose_y_0, x = transpose_290, y = transpose_291)[name = tensor("scores_161")]; + tensor scores_163 = select(a = var_48, b = scores_161, cond = mask_9)[name = tensor("scores_163")]; + tensor var_4401 = softmax(axis = var_61, x = scores_163)[name = tensor("op_4401")]; + tensor input_1217 = select(a = var_53, b = var_4401, cond = mask_9)[name = tensor("input_1217")]; + tensor x_411_transpose_x_0 = const()[name = tensor("x_411_transpose_x_0"), val = tensor(false)]; + tensor x_411_transpose_y_0 = const()[name = tensor("x_411_transpose_y_0"), val = tensor(false)]; + tensor value_81 = transpose(perm = value_81_perm_0, x = var_4374)[name = tensor("transpose_529")]; + tensor x_411 = matmul(transpose_x = x_411_transpose_x_0, transpose_y = x_411_transpose_y_0, x = input_1217, y = value_81)[name = tensor("x_411")]; + tensor var_4405_perm_0 = const()[name = tensor("op_4405_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_4407 = const()[name = tensor("op_4407"), val = tensor([1, -1, 512])]; + tensor var_4405 = transpose(perm = var_4405_perm_0, x = x_411)[name = tensor("transpose_524")]; + tensor input_1219 = reshape(shape = var_4407, x = var_4405)[name = tensor("input_1219")]; + tensor att_outs_81 = linear(bias = encoder_encoders_39_self_attn_linear_out_bias, weight = encoder_encoders_39_self_attn_linear_out_weight, x = input_1219)[name = tensor("linear_161")]; + tensor input_1221 = add(x = att_outs_81, y = fsmn_memory_81)[name = tensor("input_1221")]; + tensor input_1223 = add(x = input_1207, y = input_1221)[name = tensor("input_1223")]; + tensor const_463 = const()[name = tensor("const_463"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939843328)))]; + tensor const_464 = const()[name = tensor("const_464"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939845440)))]; + tensor output_163_axes_0 = const()[name = tensor("output_163_axes_0"), val = tensor([-1])]; + tensor output_163 = layer_norm(axes = output_163_axes_0, beta = const_464, epsilon = var_46, gamma = const_463, x = input_1223)[name = tensor("output_163")]; + tensor input_1229 = linear(bias = encoder_encoders_39_feed_forward_w_1_bias, weight = encoder_encoders_39_feed_forward_w_1_weight, x = output_163)[name = tensor("linear_162")]; + tensor input_1231 = relu(x = input_1229)[name = tensor("input_1231")]; + tensor input_1235 = linear(bias = encoder_encoders_39_feed_forward_w_2_bias, weight = encoder_encoders_39_feed_forward_w_2_weight, x = input_1231)[name = tensor("linear_163")]; + tensor input_1237 = add(x = input_1223, y = input_1235)[name = tensor("input_1237")]; + tensor const_465 = const()[name = tensor("const_465"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939847552)))]; + tensor const_466 = const()[name = tensor("const_466"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939849664)))]; + tensor output_165_axes_0 = const()[name = tensor("output_165_axes_0"), val = tensor([-1])]; + tensor output_165 = layer_norm(axes = output_165_axes_0, beta = const_466, epsilon = var_46, gamma = const_465, x = input_1237)[name = tensor("output_165")]; + tensor var_4464 = linear(bias = encoder_encoders_40_self_attn_linear_q_k_v_bias, weight = encoder_encoders_40_self_attn_linear_q_k_v_weight, x = output_165)[name = tensor("linear_164")]; + tensor tile_41 = const()[name = tensor("tile_41"), val = tensor([512, 512, 512])]; + tensor var_4465_axis_0 = const()[name = tensor("op_4465_axis_0"), val = tensor(-1)]; + tensor var_4465_0, tensor var_4465_1, tensor var_4465_2 = split(axis = var_4465_axis_0, split_sizes = tile_41, x = var_4464)[name = tensor("op_4465")]; + tensor var_4469 = const()[name = tensor("op_4469"), val = tensor([1, 1804, 4, 128])]; + tensor var_4470 = reshape(shape = var_4469, x = var_4465_0)[name = tensor("op_4470")]; + tensor var_4472 = const()[name = tensor("op_4472"), val = tensor([1, 1804, 4, 128])]; + tensor var_4473 = reshape(shape = var_4472, x = var_4465_1)[name = tensor("op_4473")]; + tensor var_4475 = const()[name = tensor("op_4475"), val = tensor([1, 1804, 4, 128])]; + tensor var_4476 = reshape(shape = var_4475, x = var_4465_2)[name = tensor("op_4476")]; + tensor value_83_perm_0 = const()[name = tensor("value_83_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor inputs_83 = mul(x = var_4465_2, y = mask_7)[name = tensor("inputs_83")]; + tensor input_1241_perm_0 = const()[name = tensor("input_1241_perm_0"), val = tensor([0, 2, 1])]; + tensor const_472 = const()[name = tensor("const_472"), val = tensor(0x0p+0)]; + tensor input_1243_pad_0 = const()[name = tensor("input_1243_pad_0"), val = tensor([0, 0, 0, 0, 5, 5])]; + tensor input_1243_mode_0 = const()[name = tensor("input_1243_mode_0"), val = tensor("constant")]; + tensor input_1241 = transpose(perm = input_1241_perm_0, x = inputs_83)[name = tensor("transpose_522")]; + tensor input_1243 = pad(constant_val = const_472, mode = input_1243_mode_0, pad = input_1243_pad_0, x = input_1241)[name = tensor("input_1243")]; + tensor x_415_pad_type_0 = const()[name = tensor("x_415_pad_type_0"), val = tensor("valid")]; + tensor x_415_groups_0 = const()[name = tensor("x_415_groups_0"), val = tensor(512)]; + tensor x_415_strides_0 = const()[name = tensor("x_415_strides_0"), val = tensor([1])]; + tensor x_415_pad_0 = const()[name = tensor("x_415_pad_0"), val = tensor([0, 0])]; + tensor x_415_dilations_0 = const()[name = tensor("x_415_dilations_0"), val = tensor([1])]; + tensor x_415 = conv(dilations = x_415_dilations_0, groups = x_415_groups_0, pad = x_415_pad_0, pad_type = x_415_pad_type_0, strides = x_415_strides_0, weight = encoder_encoders_40_self_attn_fsmn_block_weight, x = input_1243)[name = tensor("x_415")]; + tensor x_417_perm_0 = const()[name = tensor("x_417_perm_0"), val = tensor([0, 2, 1])]; + tensor x_417 = transpose(perm = x_417_perm_0, x = x_415)[name = tensor("transpose_521")]; + tensor input_1245 = add(x = x_417, y = inputs_83)[name = tensor("input_1245")]; + tensor fsmn_memory_83 = mul(x = input_1245, y = mask_7)[name = tensor("fsmn_memory_83")]; + tensor var_4495 = const()[name = tensor("op_4495"), val = tensor(0x1.6a09e6p-4)]; + tensor q_h_167 = mul(x = var_4470, y = var_4495)[name = tensor("q_h_167")]; + tensor scores_165_transpose_x_0 = const()[name = tensor("scores_165_transpose_x_0"), val = tensor(false)]; + tensor scores_165_transpose_y_0 = const()[name = tensor("scores_165_transpose_y_0"), val = tensor(false)]; + tensor transpose_292_perm_0 = const()[name = tensor("transpose_292_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_293_perm_0 = const()[name = tensor("transpose_293_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_293 = transpose(perm = transpose_293_perm_0, x = var_4473)[name = tensor("transpose_519")]; + tensor transpose_292 = transpose(perm = transpose_292_perm_0, x = q_h_167)[name = tensor("transpose_520")]; + tensor scores_165 = matmul(transpose_x = scores_165_transpose_x_0, transpose_y = scores_165_transpose_y_0, x = transpose_292, y = transpose_293)[name = tensor("scores_165")]; + tensor scores_167 = select(a = var_48, b = scores_165, cond = mask_9)[name = tensor("scores_167")]; + tensor var_4503 = softmax(axis = var_61, x = scores_167)[name = tensor("op_4503")]; + tensor input_1247 = select(a = var_53, b = var_4503, cond = mask_9)[name = tensor("input_1247")]; + tensor x_421_transpose_x_0 = const()[name = tensor("x_421_transpose_x_0"), val = tensor(false)]; + tensor x_421_transpose_y_0 = const()[name = tensor("x_421_transpose_y_0"), val = tensor(false)]; + tensor value_83 = transpose(perm = value_83_perm_0, x = var_4476)[name = tensor("transpose_523")]; + tensor x_421 = matmul(transpose_x = x_421_transpose_x_0, transpose_y = x_421_transpose_y_0, x = input_1247, y = value_83)[name = tensor("x_421")]; + tensor var_4507_perm_0 = const()[name = tensor("op_4507_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_4509 = const()[name = tensor("op_4509"), val = tensor([1, -1, 512])]; + tensor var_4507 = transpose(perm = var_4507_perm_0, x = x_421)[name = tensor("transpose_518")]; + tensor input_1249 = reshape(shape = var_4509, x = var_4507)[name = tensor("input_1249")]; + tensor att_outs_83 = linear(bias = encoder_encoders_40_self_attn_linear_out_bias, weight = encoder_encoders_40_self_attn_linear_out_weight, x = input_1249)[name = tensor("linear_165")]; + tensor input_1251 = add(x = att_outs_83, y = fsmn_memory_83)[name = tensor("input_1251")]; + tensor input_1253 = add(x = input_1237, y = input_1251)[name = tensor("input_1253")]; + tensor const_474 = const()[name = tensor("const_474"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939851776)))]; + tensor const_475 = const()[name = tensor("const_475"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939853888)))]; + tensor output_167_axes_0 = const()[name = tensor("output_167_axes_0"), val = tensor([-1])]; + tensor output_167 = layer_norm(axes = output_167_axes_0, beta = const_475, epsilon = var_46, gamma = const_474, x = input_1253)[name = tensor("output_167")]; + tensor input_1259 = linear(bias = encoder_encoders_40_feed_forward_w_1_bias, weight = encoder_encoders_40_feed_forward_w_1_weight, x = output_167)[name = tensor("linear_166")]; + tensor input_1261 = relu(x = input_1259)[name = tensor("input_1261")]; + tensor input_1265 = linear(bias = encoder_encoders_40_feed_forward_w_2_bias, weight = encoder_encoders_40_feed_forward_w_2_weight, x = input_1261)[name = tensor("linear_167")]; + tensor input_1267 = add(x = input_1253, y = input_1265)[name = tensor("input_1267")]; + tensor const_476 = const()[name = tensor("const_476"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939856000)))]; + tensor const_477 = const()[name = tensor("const_477"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939858112)))]; + tensor output_169_axes_0 = const()[name = tensor("output_169_axes_0"), val = tensor([-1])]; + tensor output_169 = layer_norm(axes = output_169_axes_0, beta = const_477, epsilon = var_46, gamma = const_476, x = input_1267)[name = tensor("output_169")]; + tensor var_4566 = linear(bias = encoder_encoders_41_self_attn_linear_q_k_v_bias, weight = encoder_encoders_41_self_attn_linear_q_k_v_weight, x = output_169)[name = tensor("linear_168")]; + tensor tile_42 = const()[name = tensor("tile_42"), val = tensor([512, 512, 512])]; + tensor var_4567_axis_0 = const()[name = tensor("op_4567_axis_0"), val = tensor(-1)]; + tensor var_4567_0, tensor var_4567_1, tensor var_4567_2 = split(axis = var_4567_axis_0, split_sizes = tile_42, x = var_4566)[name = tensor("op_4567")]; + tensor var_4571 = const()[name = tensor("op_4571"), val = tensor([1, 1804, 4, 128])]; + tensor var_4572 = reshape(shape = var_4571, x = var_4567_0)[name = tensor("op_4572")]; + tensor var_4574 = const()[name = tensor("op_4574"), val = tensor([1, 1804, 4, 128])]; + tensor var_4575 = reshape(shape = var_4574, x = var_4567_1)[name = tensor("op_4575")]; + tensor var_4577 = const()[name = tensor("op_4577"), val = tensor([1, 1804, 4, 128])]; + tensor var_4578 = reshape(shape = var_4577, x = var_4567_2)[name = tensor("op_4578")]; + tensor value_85_perm_0 = const()[name = tensor("value_85_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor inputs_85 = mul(x = var_4567_2, y = mask_7)[name = tensor("inputs_85")]; + tensor input_1271_perm_0 = const()[name = tensor("input_1271_perm_0"), val = tensor([0, 2, 1])]; + tensor const_483 = const()[name = tensor("const_483"), val = tensor(0x0p+0)]; + tensor input_1273_pad_0 = const()[name = tensor("input_1273_pad_0"), val = tensor([0, 0, 0, 0, 5, 5])]; + tensor input_1273_mode_0 = const()[name = tensor("input_1273_mode_0"), val = tensor("constant")]; + tensor input_1271 = transpose(perm = input_1271_perm_0, x = inputs_85)[name = tensor("transpose_516")]; + tensor input_1273 = pad(constant_val = const_483, mode = input_1273_mode_0, pad = input_1273_pad_0, x = input_1271)[name = tensor("input_1273")]; + tensor x_425_pad_type_0 = const()[name = tensor("x_425_pad_type_0"), val = tensor("valid")]; + tensor x_425_groups_0 = const()[name = tensor("x_425_groups_0"), val = tensor(512)]; + tensor x_425_strides_0 = const()[name = tensor("x_425_strides_0"), val = tensor([1])]; + tensor x_425_pad_0 = const()[name = tensor("x_425_pad_0"), val = tensor([0, 0])]; + tensor x_425_dilations_0 = const()[name = tensor("x_425_dilations_0"), val = tensor([1])]; + tensor x_425 = conv(dilations = x_425_dilations_0, groups = x_425_groups_0, pad = x_425_pad_0, pad_type = x_425_pad_type_0, strides = x_425_strides_0, weight = encoder_encoders_41_self_attn_fsmn_block_weight, x = input_1273)[name = tensor("x_425")]; + tensor x_427_perm_0 = const()[name = tensor("x_427_perm_0"), val = tensor([0, 2, 1])]; + tensor x_427 = transpose(perm = x_427_perm_0, x = x_425)[name = tensor("transpose_515")]; + tensor input_1275 = add(x = x_427, y = inputs_85)[name = tensor("input_1275")]; + tensor fsmn_memory_85 = mul(x = input_1275, y = mask_7)[name = tensor("fsmn_memory_85")]; + tensor var_4597 = const()[name = tensor("op_4597"), val = tensor(0x1.6a09e6p-4)]; + tensor q_h_171 = mul(x = var_4572, y = var_4597)[name = tensor("q_h_171")]; + tensor scores_169_transpose_x_0 = const()[name = tensor("scores_169_transpose_x_0"), val = tensor(false)]; + tensor scores_169_transpose_y_0 = const()[name = tensor("scores_169_transpose_y_0"), val = tensor(false)]; + tensor transpose_294_perm_0 = const()[name = tensor("transpose_294_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_295_perm_0 = const()[name = tensor("transpose_295_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_295 = transpose(perm = transpose_295_perm_0, x = var_4575)[name = tensor("transpose_513")]; + tensor transpose_294 = transpose(perm = transpose_294_perm_0, x = q_h_171)[name = tensor("transpose_514")]; + tensor scores_169 = matmul(transpose_x = scores_169_transpose_x_0, transpose_y = scores_169_transpose_y_0, x = transpose_294, y = transpose_295)[name = tensor("scores_169")]; + tensor scores_171 = select(a = var_48, b = scores_169, cond = mask_9)[name = tensor("scores_171")]; + tensor var_4605 = softmax(axis = var_61, x = scores_171)[name = tensor("op_4605")]; + tensor input_1277 = select(a = var_53, b = var_4605, cond = mask_9)[name = tensor("input_1277")]; + tensor x_431_transpose_x_0 = const()[name = tensor("x_431_transpose_x_0"), val = tensor(false)]; + tensor x_431_transpose_y_0 = const()[name = tensor("x_431_transpose_y_0"), val = tensor(false)]; + tensor value_85 = transpose(perm = value_85_perm_0, x = var_4578)[name = tensor("transpose_517")]; + tensor x_431 = matmul(transpose_x = x_431_transpose_x_0, transpose_y = x_431_transpose_y_0, x = input_1277, y = value_85)[name = tensor("x_431")]; + tensor var_4609_perm_0 = const()[name = tensor("op_4609_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_4611 = const()[name = tensor("op_4611"), val = tensor([1, -1, 512])]; + tensor var_4609 = transpose(perm = var_4609_perm_0, x = x_431)[name = tensor("transpose_512")]; + tensor input_1279 = reshape(shape = var_4611, x = var_4609)[name = tensor("input_1279")]; + tensor att_outs_85 = linear(bias = encoder_encoders_41_self_attn_linear_out_bias, weight = encoder_encoders_41_self_attn_linear_out_weight, x = input_1279)[name = tensor("linear_169")]; + tensor input_1281 = add(x = att_outs_85, y = fsmn_memory_85)[name = tensor("input_1281")]; + tensor input_1283 = add(x = input_1267, y = input_1281)[name = tensor("input_1283")]; + tensor const_485 = const()[name = tensor("const_485"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939860224)))]; + tensor const_486 = const()[name = tensor("const_486"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939862336)))]; + tensor output_171_axes_0 = const()[name = tensor("output_171_axes_0"), val = tensor([-1])]; + tensor output_171 = layer_norm(axes = output_171_axes_0, beta = const_486, epsilon = var_46, gamma = const_485, x = input_1283)[name = tensor("output_171")]; + tensor input_1289 = linear(bias = encoder_encoders_41_feed_forward_w_1_bias, weight = encoder_encoders_41_feed_forward_w_1_weight, x = output_171)[name = tensor("linear_170")]; + tensor input_1291 = relu(x = input_1289)[name = tensor("input_1291")]; + tensor input_1295 = linear(bias = encoder_encoders_41_feed_forward_w_2_bias, weight = encoder_encoders_41_feed_forward_w_2_weight, x = input_1291)[name = tensor("linear_171")]; + tensor input_1297 = add(x = input_1283, y = input_1295)[name = tensor("input_1297")]; + tensor const_487 = const()[name = tensor("const_487"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939864448)))]; + tensor const_488 = const()[name = tensor("const_488"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939866560)))]; + tensor output_173_axes_0 = const()[name = tensor("output_173_axes_0"), val = tensor([-1])]; + tensor output_173 = layer_norm(axes = output_173_axes_0, beta = const_488, epsilon = var_46, gamma = const_487, x = input_1297)[name = tensor("output_173")]; + tensor var_4668 = linear(bias = encoder_encoders_42_self_attn_linear_q_k_v_bias, weight = encoder_encoders_42_self_attn_linear_q_k_v_weight, x = output_173)[name = tensor("linear_172")]; + tensor tile_43 = const()[name = tensor("tile_43"), val = tensor([512, 512, 512])]; + tensor var_4669_axis_0 = const()[name = tensor("op_4669_axis_0"), val = tensor(-1)]; + tensor var_4669_0, tensor var_4669_1, tensor var_4669_2 = split(axis = var_4669_axis_0, split_sizes = tile_43, x = var_4668)[name = tensor("op_4669")]; + tensor var_4673 = const()[name = tensor("op_4673"), val = tensor([1, 1804, 4, 128])]; + tensor var_4674 = reshape(shape = var_4673, x = var_4669_0)[name = tensor("op_4674")]; + tensor var_4676 = const()[name = tensor("op_4676"), val = tensor([1, 1804, 4, 128])]; + tensor var_4677 = reshape(shape = var_4676, x = var_4669_1)[name = tensor("op_4677")]; + tensor var_4679 = const()[name = tensor("op_4679"), val = tensor([1, 1804, 4, 128])]; + tensor var_4680 = reshape(shape = var_4679, x = var_4669_2)[name = tensor("op_4680")]; + tensor value_87_perm_0 = const()[name = tensor("value_87_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor inputs_87 = mul(x = var_4669_2, y = mask_7)[name = tensor("inputs_87")]; + tensor input_1301_perm_0 = const()[name = tensor("input_1301_perm_0"), val = tensor([0, 2, 1])]; + tensor const_494 = const()[name = tensor("const_494"), val = tensor(0x0p+0)]; + tensor input_1303_pad_0 = const()[name = tensor("input_1303_pad_0"), val = tensor([0, 0, 0, 0, 5, 5])]; + tensor input_1303_mode_0 = const()[name = tensor("input_1303_mode_0"), val = tensor("constant")]; + tensor input_1301 = transpose(perm = input_1301_perm_0, x = inputs_87)[name = tensor("transpose_510")]; + tensor input_1303 = pad(constant_val = const_494, mode = input_1303_mode_0, pad = input_1303_pad_0, x = input_1301)[name = tensor("input_1303")]; + tensor x_435_pad_type_0 = const()[name = tensor("x_435_pad_type_0"), val = tensor("valid")]; + tensor x_435_groups_0 = const()[name = tensor("x_435_groups_0"), val = tensor(512)]; + tensor x_435_strides_0 = const()[name = tensor("x_435_strides_0"), val = tensor([1])]; + tensor x_435_pad_0 = const()[name = tensor("x_435_pad_0"), val = tensor([0, 0])]; + tensor x_435_dilations_0 = const()[name = tensor("x_435_dilations_0"), val = tensor([1])]; + tensor x_435 = conv(dilations = x_435_dilations_0, groups = x_435_groups_0, pad = x_435_pad_0, pad_type = x_435_pad_type_0, strides = x_435_strides_0, weight = encoder_encoders_42_self_attn_fsmn_block_weight, x = input_1303)[name = tensor("x_435")]; + tensor x_437_perm_0 = const()[name = tensor("x_437_perm_0"), val = tensor([0, 2, 1])]; + tensor x_437 = transpose(perm = x_437_perm_0, x = x_435)[name = tensor("transpose_509")]; + tensor input_1305 = add(x = x_437, y = inputs_87)[name = tensor("input_1305")]; + tensor fsmn_memory_87 = mul(x = input_1305, y = mask_7)[name = tensor("fsmn_memory_87")]; + tensor var_4699 = const()[name = tensor("op_4699"), val = tensor(0x1.6a09e6p-4)]; + tensor q_h_175 = mul(x = var_4674, y = var_4699)[name = tensor("q_h_175")]; + tensor scores_173_transpose_x_0 = const()[name = tensor("scores_173_transpose_x_0"), val = tensor(false)]; + tensor scores_173_transpose_y_0 = const()[name = tensor("scores_173_transpose_y_0"), val = tensor(false)]; + tensor transpose_296_perm_0 = const()[name = tensor("transpose_296_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_297_perm_0 = const()[name = tensor("transpose_297_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_297 = transpose(perm = transpose_297_perm_0, x = var_4677)[name = tensor("transpose_507")]; + tensor transpose_296 = transpose(perm = transpose_296_perm_0, x = q_h_175)[name = tensor("transpose_508")]; + tensor scores_173 = matmul(transpose_x = scores_173_transpose_x_0, transpose_y = scores_173_transpose_y_0, x = transpose_296, y = transpose_297)[name = tensor("scores_173")]; + tensor scores_175 = select(a = var_48, b = scores_173, cond = mask_9)[name = tensor("scores_175")]; + tensor var_4707 = softmax(axis = var_61, x = scores_175)[name = tensor("op_4707")]; + tensor input_1307 = select(a = var_53, b = var_4707, cond = mask_9)[name = tensor("input_1307")]; + tensor x_441_transpose_x_0 = const()[name = tensor("x_441_transpose_x_0"), val = tensor(false)]; + tensor x_441_transpose_y_0 = const()[name = tensor("x_441_transpose_y_0"), val = tensor(false)]; + tensor value_87 = transpose(perm = value_87_perm_0, x = var_4680)[name = tensor("transpose_511")]; + tensor x_441 = matmul(transpose_x = x_441_transpose_x_0, transpose_y = x_441_transpose_y_0, x = input_1307, y = value_87)[name = tensor("x_441")]; + tensor var_4711_perm_0 = const()[name = tensor("op_4711_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_4713 = const()[name = tensor("op_4713"), val = tensor([1, -1, 512])]; + tensor var_4711 = transpose(perm = var_4711_perm_0, x = x_441)[name = tensor("transpose_506")]; + tensor input_1309 = reshape(shape = var_4713, x = var_4711)[name = tensor("input_1309")]; + tensor att_outs_87 = linear(bias = encoder_encoders_42_self_attn_linear_out_bias, weight = encoder_encoders_42_self_attn_linear_out_weight, x = input_1309)[name = tensor("linear_173")]; + tensor input_1311 = add(x = att_outs_87, y = fsmn_memory_87)[name = tensor("input_1311")]; + tensor input_1313 = add(x = input_1297, y = input_1311)[name = tensor("input_1313")]; + tensor const_496 = const()[name = tensor("const_496"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939868672)))]; + tensor const_497 = const()[name = tensor("const_497"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939870784)))]; + tensor output_175_axes_0 = const()[name = tensor("output_175_axes_0"), val = tensor([-1])]; + tensor output_175 = layer_norm(axes = output_175_axes_0, beta = const_497, epsilon = var_46, gamma = const_496, x = input_1313)[name = tensor("output_175")]; + tensor input_1319 = linear(bias = encoder_encoders_42_feed_forward_w_1_bias, weight = encoder_encoders_42_feed_forward_w_1_weight, x = output_175)[name = tensor("linear_174")]; + tensor input_1321 = relu(x = input_1319)[name = tensor("input_1321")]; + tensor input_1325 = linear(bias = encoder_encoders_42_feed_forward_w_2_bias, weight = encoder_encoders_42_feed_forward_w_2_weight, x = input_1321)[name = tensor("linear_175")]; + tensor input_1327 = add(x = input_1313, y = input_1325)[name = tensor("input_1327")]; + tensor const_498 = const()[name = tensor("const_498"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939872896)))]; + tensor const_499 = const()[name = tensor("const_499"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939875008)))]; + tensor output_177_axes_0 = const()[name = tensor("output_177_axes_0"), val = tensor([-1])]; + tensor output_177 = layer_norm(axes = output_177_axes_0, beta = const_499, epsilon = var_46, gamma = const_498, x = input_1327)[name = tensor("output_177")]; + tensor var_4770 = linear(bias = encoder_encoders_43_self_attn_linear_q_k_v_bias, weight = encoder_encoders_43_self_attn_linear_q_k_v_weight, x = output_177)[name = tensor("linear_176")]; + tensor tile_44 = const()[name = tensor("tile_44"), val = tensor([512, 512, 512])]; + tensor var_4771_axis_0 = const()[name = tensor("op_4771_axis_0"), val = tensor(-1)]; + tensor var_4771_0, tensor var_4771_1, tensor var_4771_2 = split(axis = var_4771_axis_0, split_sizes = tile_44, x = var_4770)[name = tensor("op_4771")]; + tensor var_4775 = const()[name = tensor("op_4775"), val = tensor([1, 1804, 4, 128])]; + tensor var_4776 = reshape(shape = var_4775, x = var_4771_0)[name = tensor("op_4776")]; + tensor var_4778 = const()[name = tensor("op_4778"), val = tensor([1, 1804, 4, 128])]; + tensor var_4779 = reshape(shape = var_4778, x = var_4771_1)[name = tensor("op_4779")]; + tensor var_4781 = const()[name = tensor("op_4781"), val = tensor([1, 1804, 4, 128])]; + tensor var_4782 = reshape(shape = var_4781, x = var_4771_2)[name = tensor("op_4782")]; + tensor value_89_perm_0 = const()[name = tensor("value_89_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor inputs_89 = mul(x = var_4771_2, y = mask_7)[name = tensor("inputs_89")]; + tensor input_1331_perm_0 = const()[name = tensor("input_1331_perm_0"), val = tensor([0, 2, 1])]; + tensor const_505 = const()[name = tensor("const_505"), val = tensor(0x0p+0)]; + tensor input_1333_pad_0 = const()[name = tensor("input_1333_pad_0"), val = tensor([0, 0, 0, 0, 5, 5])]; + tensor input_1333_mode_0 = const()[name = tensor("input_1333_mode_0"), val = tensor("constant")]; + tensor input_1331 = transpose(perm = input_1331_perm_0, x = inputs_89)[name = tensor("transpose_504")]; + tensor input_1333 = pad(constant_val = const_505, mode = input_1333_mode_0, pad = input_1333_pad_0, x = input_1331)[name = tensor("input_1333")]; + tensor x_445_pad_type_0 = const()[name = tensor("x_445_pad_type_0"), val = tensor("valid")]; + tensor x_445_groups_0 = const()[name = tensor("x_445_groups_0"), val = tensor(512)]; + tensor x_445_strides_0 = const()[name = tensor("x_445_strides_0"), val = tensor([1])]; + tensor x_445_pad_0 = const()[name = tensor("x_445_pad_0"), val = tensor([0, 0])]; + tensor x_445_dilations_0 = const()[name = tensor("x_445_dilations_0"), val = tensor([1])]; + tensor x_445 = conv(dilations = x_445_dilations_0, groups = x_445_groups_0, pad = x_445_pad_0, pad_type = x_445_pad_type_0, strides = x_445_strides_0, weight = encoder_encoders_43_self_attn_fsmn_block_weight, x = input_1333)[name = tensor("x_445")]; + tensor x_447_perm_0 = const()[name = tensor("x_447_perm_0"), val = tensor([0, 2, 1])]; + tensor x_447 = transpose(perm = x_447_perm_0, x = x_445)[name = tensor("transpose_503")]; + tensor input_1335 = add(x = x_447, y = inputs_89)[name = tensor("input_1335")]; + tensor fsmn_memory_89 = mul(x = input_1335, y = mask_7)[name = tensor("fsmn_memory_89")]; + tensor var_4801 = const()[name = tensor("op_4801"), val = tensor(0x1.6a09e6p-4)]; + tensor q_h_179 = mul(x = var_4776, y = var_4801)[name = tensor("q_h_179")]; + tensor scores_177_transpose_x_0 = const()[name = tensor("scores_177_transpose_x_0"), val = tensor(false)]; + tensor scores_177_transpose_y_0 = const()[name = tensor("scores_177_transpose_y_0"), val = tensor(false)]; + tensor transpose_298_perm_0 = const()[name = tensor("transpose_298_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_299_perm_0 = const()[name = tensor("transpose_299_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_299 = transpose(perm = transpose_299_perm_0, x = var_4779)[name = tensor("transpose_501")]; + tensor transpose_298 = transpose(perm = transpose_298_perm_0, x = q_h_179)[name = tensor("transpose_502")]; + tensor scores_177 = matmul(transpose_x = scores_177_transpose_x_0, transpose_y = scores_177_transpose_y_0, x = transpose_298, y = transpose_299)[name = tensor("scores_177")]; + tensor scores_179 = select(a = var_48, b = scores_177, cond = mask_9)[name = tensor("scores_179")]; + tensor var_4809 = softmax(axis = var_61, x = scores_179)[name = tensor("op_4809")]; + tensor input_1337 = select(a = var_53, b = var_4809, cond = mask_9)[name = tensor("input_1337")]; + tensor x_451_transpose_x_0 = const()[name = tensor("x_451_transpose_x_0"), val = tensor(false)]; + tensor x_451_transpose_y_0 = const()[name = tensor("x_451_transpose_y_0"), val = tensor(false)]; + tensor value_89 = transpose(perm = value_89_perm_0, x = var_4782)[name = tensor("transpose_505")]; + tensor x_451 = matmul(transpose_x = x_451_transpose_x_0, transpose_y = x_451_transpose_y_0, x = input_1337, y = value_89)[name = tensor("x_451")]; + tensor var_4813_perm_0 = const()[name = tensor("op_4813_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_4815 = const()[name = tensor("op_4815"), val = tensor([1, -1, 512])]; + tensor var_4813 = transpose(perm = var_4813_perm_0, x = x_451)[name = tensor("transpose_500")]; + tensor input_1339 = reshape(shape = var_4815, x = var_4813)[name = tensor("input_1339")]; + tensor att_outs_89 = linear(bias = encoder_encoders_43_self_attn_linear_out_bias, weight = encoder_encoders_43_self_attn_linear_out_weight, x = input_1339)[name = tensor("linear_177")]; + tensor input_1341 = add(x = att_outs_89, y = fsmn_memory_89)[name = tensor("input_1341")]; + tensor input_1343 = add(x = input_1327, y = input_1341)[name = tensor("input_1343")]; + tensor const_507 = const()[name = tensor("const_507"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939877120)))]; + tensor const_508 = const()[name = tensor("const_508"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939879232)))]; + tensor output_179_axes_0 = const()[name = tensor("output_179_axes_0"), val = tensor([-1])]; + tensor output_179 = layer_norm(axes = output_179_axes_0, beta = const_508, epsilon = var_46, gamma = const_507, x = input_1343)[name = tensor("output_179")]; + tensor input_1349 = linear(bias = encoder_encoders_43_feed_forward_w_1_bias, weight = encoder_encoders_43_feed_forward_w_1_weight, x = output_179)[name = tensor("linear_178")]; + tensor input_1351 = relu(x = input_1349)[name = tensor("input_1351")]; + tensor input_1355 = linear(bias = encoder_encoders_43_feed_forward_w_2_bias, weight = encoder_encoders_43_feed_forward_w_2_weight, x = input_1351)[name = tensor("linear_179")]; + tensor input_1357 = add(x = input_1343, y = input_1355)[name = tensor("input_1357")]; + tensor const_509 = const()[name = tensor("const_509"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939881344)))]; + tensor const_510 = const()[name = tensor("const_510"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939883456)))]; + tensor output_181_axes_0 = const()[name = tensor("output_181_axes_0"), val = tensor([-1])]; + tensor output_181 = layer_norm(axes = output_181_axes_0, beta = const_510, epsilon = var_46, gamma = const_509, x = input_1357)[name = tensor("output_181")]; + tensor var_4872 = linear(bias = encoder_encoders_44_self_attn_linear_q_k_v_bias, weight = encoder_encoders_44_self_attn_linear_q_k_v_weight, x = output_181)[name = tensor("linear_180")]; + tensor tile_45 = const()[name = tensor("tile_45"), val = tensor([512, 512, 512])]; + tensor var_4873_axis_0 = const()[name = tensor("op_4873_axis_0"), val = tensor(-1)]; + tensor var_4873_0, tensor var_4873_1, tensor var_4873_2 = split(axis = var_4873_axis_0, split_sizes = tile_45, x = var_4872)[name = tensor("op_4873")]; + tensor var_4877 = const()[name = tensor("op_4877"), val = tensor([1, 1804, 4, 128])]; + tensor var_4878 = reshape(shape = var_4877, x = var_4873_0)[name = tensor("op_4878")]; + tensor var_4880 = const()[name = tensor("op_4880"), val = tensor([1, 1804, 4, 128])]; + tensor var_4881 = reshape(shape = var_4880, x = var_4873_1)[name = tensor("op_4881")]; + tensor var_4883 = const()[name = tensor("op_4883"), val = tensor([1, 1804, 4, 128])]; + tensor var_4884 = reshape(shape = var_4883, x = var_4873_2)[name = tensor("op_4884")]; + tensor value_91_perm_0 = const()[name = tensor("value_91_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor inputs_91 = mul(x = var_4873_2, y = mask_7)[name = tensor("inputs_91")]; + tensor input_1361_perm_0 = const()[name = tensor("input_1361_perm_0"), val = tensor([0, 2, 1])]; + tensor const_516 = const()[name = tensor("const_516"), val = tensor(0x0p+0)]; + tensor input_1363_pad_0 = const()[name = tensor("input_1363_pad_0"), val = tensor([0, 0, 0, 0, 5, 5])]; + tensor input_1363_mode_0 = const()[name = tensor("input_1363_mode_0"), val = tensor("constant")]; + tensor input_1361 = transpose(perm = input_1361_perm_0, x = inputs_91)[name = tensor("transpose_498")]; + tensor input_1363 = pad(constant_val = const_516, mode = input_1363_mode_0, pad = input_1363_pad_0, x = input_1361)[name = tensor("input_1363")]; + tensor x_455_pad_type_0 = const()[name = tensor("x_455_pad_type_0"), val = tensor("valid")]; + tensor x_455_groups_0 = const()[name = tensor("x_455_groups_0"), val = tensor(512)]; + tensor x_455_strides_0 = const()[name = tensor("x_455_strides_0"), val = tensor([1])]; + tensor x_455_pad_0 = const()[name = tensor("x_455_pad_0"), val = tensor([0, 0])]; + tensor x_455_dilations_0 = const()[name = tensor("x_455_dilations_0"), val = tensor([1])]; + tensor x_455 = conv(dilations = x_455_dilations_0, groups = x_455_groups_0, pad = x_455_pad_0, pad_type = x_455_pad_type_0, strides = x_455_strides_0, weight = encoder_encoders_44_self_attn_fsmn_block_weight, x = input_1363)[name = tensor("x_455")]; + tensor x_457_perm_0 = const()[name = tensor("x_457_perm_0"), val = tensor([0, 2, 1])]; + tensor x_457 = transpose(perm = x_457_perm_0, x = x_455)[name = tensor("transpose_497")]; + tensor input_1365 = add(x = x_457, y = inputs_91)[name = tensor("input_1365")]; + tensor fsmn_memory_91 = mul(x = input_1365, y = mask_7)[name = tensor("fsmn_memory_91")]; + tensor var_4903 = const()[name = tensor("op_4903"), val = tensor(0x1.6a09e6p-4)]; + tensor q_h_183 = mul(x = var_4878, y = var_4903)[name = tensor("q_h_183")]; + tensor scores_181_transpose_x_0 = const()[name = tensor("scores_181_transpose_x_0"), val = tensor(false)]; + tensor scores_181_transpose_y_0 = const()[name = tensor("scores_181_transpose_y_0"), val = tensor(false)]; + tensor transpose_300_perm_0 = const()[name = tensor("transpose_300_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_301_perm_0 = const()[name = tensor("transpose_301_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_301 = transpose(perm = transpose_301_perm_0, x = var_4881)[name = tensor("transpose_495")]; + tensor transpose_300 = transpose(perm = transpose_300_perm_0, x = q_h_183)[name = tensor("transpose_496")]; + tensor scores_181 = matmul(transpose_x = scores_181_transpose_x_0, transpose_y = scores_181_transpose_y_0, x = transpose_300, y = transpose_301)[name = tensor("scores_181")]; + tensor scores_183 = select(a = var_48, b = scores_181, cond = mask_9)[name = tensor("scores_183")]; + tensor var_4911 = softmax(axis = var_61, x = scores_183)[name = tensor("op_4911")]; + tensor input_1367 = select(a = var_53, b = var_4911, cond = mask_9)[name = tensor("input_1367")]; + tensor x_461_transpose_x_0 = const()[name = tensor("x_461_transpose_x_0"), val = tensor(false)]; + tensor x_461_transpose_y_0 = const()[name = tensor("x_461_transpose_y_0"), val = tensor(false)]; + tensor value_91 = transpose(perm = value_91_perm_0, x = var_4884)[name = tensor("transpose_499")]; + tensor x_461 = matmul(transpose_x = x_461_transpose_x_0, transpose_y = x_461_transpose_y_0, x = input_1367, y = value_91)[name = tensor("x_461")]; + tensor var_4915_perm_0 = const()[name = tensor("op_4915_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_4917 = const()[name = tensor("op_4917"), val = tensor([1, -1, 512])]; + tensor var_4915 = transpose(perm = var_4915_perm_0, x = x_461)[name = tensor("transpose_494")]; + tensor input_1369 = reshape(shape = var_4917, x = var_4915)[name = tensor("input_1369")]; + tensor att_outs_91 = linear(bias = encoder_encoders_44_self_attn_linear_out_bias, weight = encoder_encoders_44_self_attn_linear_out_weight, x = input_1369)[name = tensor("linear_181")]; + tensor input_1371 = add(x = att_outs_91, y = fsmn_memory_91)[name = tensor("input_1371")]; + tensor input_1373 = add(x = input_1357, y = input_1371)[name = tensor("input_1373")]; + tensor const_518 = const()[name = tensor("const_518"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939885568)))]; + tensor const_519 = const()[name = tensor("const_519"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939887680)))]; + tensor output_183_axes_0 = const()[name = tensor("output_183_axes_0"), val = tensor([-1])]; + tensor output_183 = layer_norm(axes = output_183_axes_0, beta = const_519, epsilon = var_46, gamma = const_518, x = input_1373)[name = tensor("output_183")]; + tensor input_1379 = linear(bias = encoder_encoders_44_feed_forward_w_1_bias, weight = encoder_encoders_44_feed_forward_w_1_weight, x = output_183)[name = tensor("linear_182")]; + tensor input_1381 = relu(x = input_1379)[name = tensor("input_1381")]; + tensor input_1385 = linear(bias = encoder_encoders_44_feed_forward_w_2_bias, weight = encoder_encoders_44_feed_forward_w_2_weight, x = input_1381)[name = tensor("linear_183")]; + tensor input_1387 = add(x = input_1373, y = input_1385)[name = tensor("input_1387")]; + tensor const_520 = const()[name = tensor("const_520"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939889792)))]; + tensor const_521 = const()[name = tensor("const_521"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939891904)))]; + tensor output_185_axes_0 = const()[name = tensor("output_185_axes_0"), val = tensor([-1])]; + tensor output_185 = layer_norm(axes = output_185_axes_0, beta = const_521, epsilon = var_46, gamma = const_520, x = input_1387)[name = tensor("output_185")]; + tensor var_4974 = linear(bias = encoder_encoders_45_self_attn_linear_q_k_v_bias, weight = encoder_encoders_45_self_attn_linear_q_k_v_weight, x = output_185)[name = tensor("linear_184")]; + tensor tile_46 = const()[name = tensor("tile_46"), val = tensor([512, 512, 512])]; + tensor var_4975_axis_0 = const()[name = tensor("op_4975_axis_0"), val = tensor(-1)]; + tensor var_4975_0, tensor var_4975_1, tensor var_4975_2 = split(axis = var_4975_axis_0, split_sizes = tile_46, x = var_4974)[name = tensor("op_4975")]; + tensor var_4979 = const()[name = tensor("op_4979"), val = tensor([1, 1804, 4, 128])]; + tensor var_4980 = reshape(shape = var_4979, x = var_4975_0)[name = tensor("op_4980")]; + tensor var_4982 = const()[name = tensor("op_4982"), val = tensor([1, 1804, 4, 128])]; + tensor var_4983 = reshape(shape = var_4982, x = var_4975_1)[name = tensor("op_4983")]; + tensor var_4985 = const()[name = tensor("op_4985"), val = tensor([1, 1804, 4, 128])]; + tensor var_4986 = reshape(shape = var_4985, x = var_4975_2)[name = tensor("op_4986")]; + tensor value_93_perm_0 = const()[name = tensor("value_93_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor inputs_93 = mul(x = var_4975_2, y = mask_7)[name = tensor("inputs_93")]; + tensor input_1391_perm_0 = const()[name = tensor("input_1391_perm_0"), val = tensor([0, 2, 1])]; + tensor const_527 = const()[name = tensor("const_527"), val = tensor(0x0p+0)]; + tensor input_1393_pad_0 = const()[name = tensor("input_1393_pad_0"), val = tensor([0, 0, 0, 0, 5, 5])]; + tensor input_1393_mode_0 = const()[name = tensor("input_1393_mode_0"), val = tensor("constant")]; + tensor input_1391 = transpose(perm = input_1391_perm_0, x = inputs_93)[name = tensor("transpose_492")]; + tensor input_1393 = pad(constant_val = const_527, mode = input_1393_mode_0, pad = input_1393_pad_0, x = input_1391)[name = tensor("input_1393")]; + tensor x_465_pad_type_0 = const()[name = tensor("x_465_pad_type_0"), val = tensor("valid")]; + tensor x_465_groups_0 = const()[name = tensor("x_465_groups_0"), val = tensor(512)]; + tensor x_465_strides_0 = const()[name = tensor("x_465_strides_0"), val = tensor([1])]; + tensor x_465_pad_0 = const()[name = tensor("x_465_pad_0"), val = tensor([0, 0])]; + tensor x_465_dilations_0 = const()[name = tensor("x_465_dilations_0"), val = tensor([1])]; + tensor x_465 = conv(dilations = x_465_dilations_0, groups = x_465_groups_0, pad = x_465_pad_0, pad_type = x_465_pad_type_0, strides = x_465_strides_0, weight = encoder_encoders_45_self_attn_fsmn_block_weight, x = input_1393)[name = tensor("x_465")]; + tensor x_467_perm_0 = const()[name = tensor("x_467_perm_0"), val = tensor([0, 2, 1])]; + tensor x_467 = transpose(perm = x_467_perm_0, x = x_465)[name = tensor("transpose_491")]; + tensor input_1395 = add(x = x_467, y = inputs_93)[name = tensor("input_1395")]; + tensor fsmn_memory_93 = mul(x = input_1395, y = mask_7)[name = tensor("fsmn_memory_93")]; + tensor var_5005 = const()[name = tensor("op_5005"), val = tensor(0x1.6a09e6p-4)]; + tensor q_h_187 = mul(x = var_4980, y = var_5005)[name = tensor("q_h_187")]; + tensor scores_185_transpose_x_0 = const()[name = tensor("scores_185_transpose_x_0"), val = tensor(false)]; + tensor scores_185_transpose_y_0 = const()[name = tensor("scores_185_transpose_y_0"), val = tensor(false)]; + tensor transpose_302_perm_0 = const()[name = tensor("transpose_302_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_303_perm_0 = const()[name = tensor("transpose_303_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_303 = transpose(perm = transpose_303_perm_0, x = var_4983)[name = tensor("transpose_489")]; + tensor transpose_302 = transpose(perm = transpose_302_perm_0, x = q_h_187)[name = tensor("transpose_490")]; + tensor scores_185 = matmul(transpose_x = scores_185_transpose_x_0, transpose_y = scores_185_transpose_y_0, x = transpose_302, y = transpose_303)[name = tensor("scores_185")]; + tensor scores_187 = select(a = var_48, b = scores_185, cond = mask_9)[name = tensor("scores_187")]; + tensor var_5013 = softmax(axis = var_61, x = scores_187)[name = tensor("op_5013")]; + tensor input_1397 = select(a = var_53, b = var_5013, cond = mask_9)[name = tensor("input_1397")]; + tensor x_471_transpose_x_0 = const()[name = tensor("x_471_transpose_x_0"), val = tensor(false)]; + tensor x_471_transpose_y_0 = const()[name = tensor("x_471_transpose_y_0"), val = tensor(false)]; + tensor value_93 = transpose(perm = value_93_perm_0, x = var_4986)[name = tensor("transpose_493")]; + tensor x_471 = matmul(transpose_x = x_471_transpose_x_0, transpose_y = x_471_transpose_y_0, x = input_1397, y = value_93)[name = tensor("x_471")]; + tensor var_5017_perm_0 = const()[name = tensor("op_5017_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_5019 = const()[name = tensor("op_5019"), val = tensor([1, -1, 512])]; + tensor var_5017 = transpose(perm = var_5017_perm_0, x = x_471)[name = tensor("transpose_488")]; + tensor input_1399 = reshape(shape = var_5019, x = var_5017)[name = tensor("input_1399")]; + tensor att_outs_93 = linear(bias = encoder_encoders_45_self_attn_linear_out_bias, weight = encoder_encoders_45_self_attn_linear_out_weight, x = input_1399)[name = tensor("linear_185")]; + tensor input_1401 = add(x = att_outs_93, y = fsmn_memory_93)[name = tensor("input_1401")]; + tensor input_1403 = add(x = input_1387, y = input_1401)[name = tensor("input_1403")]; + tensor const_529 = const()[name = tensor("const_529"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939894016)))]; + tensor const_530 = const()[name = tensor("const_530"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939896128)))]; + tensor output_187_axes_0 = const()[name = tensor("output_187_axes_0"), val = tensor([-1])]; + tensor output_187 = layer_norm(axes = output_187_axes_0, beta = const_530, epsilon = var_46, gamma = const_529, x = input_1403)[name = tensor("output_187")]; + tensor input_1409 = linear(bias = encoder_encoders_45_feed_forward_w_1_bias, weight = encoder_encoders_45_feed_forward_w_1_weight, x = output_187)[name = tensor("linear_186")]; + tensor input_1411 = relu(x = input_1409)[name = tensor("input_1411")]; + tensor input_1415 = linear(bias = encoder_encoders_45_feed_forward_w_2_bias, weight = encoder_encoders_45_feed_forward_w_2_weight, x = input_1411)[name = tensor("linear_187")]; + tensor input_1417 = add(x = input_1403, y = input_1415)[name = tensor("input_1417")]; + tensor const_531 = const()[name = tensor("const_531"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939898240)))]; + tensor const_532 = const()[name = tensor("const_532"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939900352)))]; + tensor output_189_axes_0 = const()[name = tensor("output_189_axes_0"), val = tensor([-1])]; + tensor output_189 = layer_norm(axes = output_189_axes_0, beta = const_532, epsilon = var_46, gamma = const_531, x = input_1417)[name = tensor("output_189")]; + tensor var_5076 = linear(bias = encoder_encoders_46_self_attn_linear_q_k_v_bias, weight = encoder_encoders_46_self_attn_linear_q_k_v_weight, x = output_189)[name = tensor("linear_188")]; + tensor tile_47 = const()[name = tensor("tile_47"), val = tensor([512, 512, 512])]; + tensor var_5077_axis_0 = const()[name = tensor("op_5077_axis_0"), val = tensor(-1)]; + tensor var_5077_0, tensor var_5077_1, tensor var_5077_2 = split(axis = var_5077_axis_0, split_sizes = tile_47, x = var_5076)[name = tensor("op_5077")]; + tensor var_5081 = const()[name = tensor("op_5081"), val = tensor([1, 1804, 4, 128])]; + tensor var_5082 = reshape(shape = var_5081, x = var_5077_0)[name = tensor("op_5082")]; + tensor var_5084 = const()[name = tensor("op_5084"), val = tensor([1, 1804, 4, 128])]; + tensor var_5085 = reshape(shape = var_5084, x = var_5077_1)[name = tensor("op_5085")]; + tensor var_5087 = const()[name = tensor("op_5087"), val = tensor([1, 1804, 4, 128])]; + tensor var_5088 = reshape(shape = var_5087, x = var_5077_2)[name = tensor("op_5088")]; + tensor value_95_perm_0 = const()[name = tensor("value_95_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor inputs_95 = mul(x = var_5077_2, y = mask_7)[name = tensor("inputs_95")]; + tensor input_1421_perm_0 = const()[name = tensor("input_1421_perm_0"), val = tensor([0, 2, 1])]; + tensor const_538 = const()[name = tensor("const_538"), val = tensor(0x0p+0)]; + tensor input_1423_pad_0 = const()[name = tensor("input_1423_pad_0"), val = tensor([0, 0, 0, 0, 5, 5])]; + tensor input_1423_mode_0 = const()[name = tensor("input_1423_mode_0"), val = tensor("constant")]; + tensor input_1421 = transpose(perm = input_1421_perm_0, x = inputs_95)[name = tensor("transpose_486")]; + tensor input_1423 = pad(constant_val = const_538, mode = input_1423_mode_0, pad = input_1423_pad_0, x = input_1421)[name = tensor("input_1423")]; + tensor x_475_pad_type_0 = const()[name = tensor("x_475_pad_type_0"), val = tensor("valid")]; + tensor x_475_groups_0 = const()[name = tensor("x_475_groups_0"), val = tensor(512)]; + tensor x_475_strides_0 = const()[name = tensor("x_475_strides_0"), val = tensor([1])]; + tensor x_475_pad_0 = const()[name = tensor("x_475_pad_0"), val = tensor([0, 0])]; + tensor x_475_dilations_0 = const()[name = tensor("x_475_dilations_0"), val = tensor([1])]; + tensor x_475 = conv(dilations = x_475_dilations_0, groups = x_475_groups_0, pad = x_475_pad_0, pad_type = x_475_pad_type_0, strides = x_475_strides_0, weight = encoder_encoders_46_self_attn_fsmn_block_weight, x = input_1423)[name = tensor("x_475")]; + tensor x_477_perm_0 = const()[name = tensor("x_477_perm_0"), val = tensor([0, 2, 1])]; + tensor x_477 = transpose(perm = x_477_perm_0, x = x_475)[name = tensor("transpose_485")]; + tensor input_1425 = add(x = x_477, y = inputs_95)[name = tensor("input_1425")]; + tensor fsmn_memory_95 = mul(x = input_1425, y = mask_7)[name = tensor("fsmn_memory_95")]; + tensor var_5107 = const()[name = tensor("op_5107"), val = tensor(0x1.6a09e6p-4)]; + tensor q_h_191 = mul(x = var_5082, y = var_5107)[name = tensor("q_h_191")]; + tensor scores_189_transpose_x_0 = const()[name = tensor("scores_189_transpose_x_0"), val = tensor(false)]; + tensor scores_189_transpose_y_0 = const()[name = tensor("scores_189_transpose_y_0"), val = tensor(false)]; + tensor transpose_304_perm_0 = const()[name = tensor("transpose_304_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_305_perm_0 = const()[name = tensor("transpose_305_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_305 = transpose(perm = transpose_305_perm_0, x = var_5085)[name = tensor("transpose_483")]; + tensor transpose_304 = transpose(perm = transpose_304_perm_0, x = q_h_191)[name = tensor("transpose_484")]; + tensor scores_189 = matmul(transpose_x = scores_189_transpose_x_0, transpose_y = scores_189_transpose_y_0, x = transpose_304, y = transpose_305)[name = tensor("scores_189")]; + tensor scores_191 = select(a = var_48, b = scores_189, cond = mask_9)[name = tensor("scores_191")]; + tensor var_5115 = softmax(axis = var_61, x = scores_191)[name = tensor("op_5115")]; + tensor input_1427 = select(a = var_53, b = var_5115, cond = mask_9)[name = tensor("input_1427")]; + tensor x_481_transpose_x_0 = const()[name = tensor("x_481_transpose_x_0"), val = tensor(false)]; + tensor x_481_transpose_y_0 = const()[name = tensor("x_481_transpose_y_0"), val = tensor(false)]; + tensor value_95 = transpose(perm = value_95_perm_0, x = var_5088)[name = tensor("transpose_487")]; + tensor x_481 = matmul(transpose_x = x_481_transpose_x_0, transpose_y = x_481_transpose_y_0, x = input_1427, y = value_95)[name = tensor("x_481")]; + tensor var_5119_perm_0 = const()[name = tensor("op_5119_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_5121 = const()[name = tensor("op_5121"), val = tensor([1, -1, 512])]; + tensor var_5119 = transpose(perm = var_5119_perm_0, x = x_481)[name = tensor("transpose_482")]; + tensor input_1429 = reshape(shape = var_5121, x = var_5119)[name = tensor("input_1429")]; + tensor att_outs_95 = linear(bias = encoder_encoders_46_self_attn_linear_out_bias, weight = encoder_encoders_46_self_attn_linear_out_weight, x = input_1429)[name = tensor("linear_189")]; + tensor input_1431 = add(x = att_outs_95, y = fsmn_memory_95)[name = tensor("input_1431")]; + tensor input_1433 = add(x = input_1417, y = input_1431)[name = tensor("input_1433")]; + tensor const_540 = const()[name = tensor("const_540"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939902464)))]; + tensor const_541 = const()[name = tensor("const_541"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939904576)))]; + tensor output_191_axes_0 = const()[name = tensor("output_191_axes_0"), val = tensor([-1])]; + tensor output_191 = layer_norm(axes = output_191_axes_0, beta = const_541, epsilon = var_46, gamma = const_540, x = input_1433)[name = tensor("output_191")]; + tensor input_1439 = linear(bias = encoder_encoders_46_feed_forward_w_1_bias, weight = encoder_encoders_46_feed_forward_w_1_weight, x = output_191)[name = tensor("linear_190")]; + tensor input_1441 = relu(x = input_1439)[name = tensor("input_1441")]; + tensor input_1445 = linear(bias = encoder_encoders_46_feed_forward_w_2_bias, weight = encoder_encoders_46_feed_forward_w_2_weight, x = input_1441)[name = tensor("linear_191")]; + tensor input_1447 = add(x = input_1433, y = input_1445)[name = tensor("input_1447")]; + tensor const_542 = const()[name = tensor("const_542"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939906688)))]; + tensor const_543 = const()[name = tensor("const_543"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939908800)))]; + tensor output_193_axes_0 = const()[name = tensor("output_193_axes_0"), val = tensor([-1])]; + tensor output_193 = layer_norm(axes = output_193_axes_0, beta = const_543, epsilon = var_46, gamma = const_542, x = input_1447)[name = tensor("output_193")]; + tensor var_5178 = linear(bias = encoder_encoders_47_self_attn_linear_q_k_v_bias, weight = encoder_encoders_47_self_attn_linear_q_k_v_weight, x = output_193)[name = tensor("linear_192")]; + tensor tile_48 = const()[name = tensor("tile_48"), val = tensor([512, 512, 512])]; + tensor var_5179_axis_0 = const()[name = tensor("op_5179_axis_0"), val = tensor(-1)]; + tensor var_5179_0, tensor var_5179_1, tensor var_5179_2 = split(axis = var_5179_axis_0, split_sizes = tile_48, x = var_5178)[name = tensor("op_5179")]; + tensor var_5183 = const()[name = tensor("op_5183"), val = tensor([1, 1804, 4, 128])]; + tensor var_5184 = reshape(shape = var_5183, x = var_5179_0)[name = tensor("op_5184")]; + tensor var_5186 = const()[name = tensor("op_5186"), val = tensor([1, 1804, 4, 128])]; + tensor var_5187 = reshape(shape = var_5186, x = var_5179_1)[name = tensor("op_5187")]; + tensor var_5189 = const()[name = tensor("op_5189"), val = tensor([1, 1804, 4, 128])]; + tensor var_5190 = reshape(shape = var_5189, x = var_5179_2)[name = tensor("op_5190")]; + tensor value_97_perm_0 = const()[name = tensor("value_97_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor inputs_97 = mul(x = var_5179_2, y = mask_7)[name = tensor("inputs_97")]; + tensor input_1451_perm_0 = const()[name = tensor("input_1451_perm_0"), val = tensor([0, 2, 1])]; + tensor const_549 = const()[name = tensor("const_549"), val = tensor(0x0p+0)]; + tensor input_1453_pad_0 = const()[name = tensor("input_1453_pad_0"), val = tensor([0, 0, 0, 0, 5, 5])]; + tensor input_1453_mode_0 = const()[name = tensor("input_1453_mode_0"), val = tensor("constant")]; + tensor input_1451 = transpose(perm = input_1451_perm_0, x = inputs_97)[name = tensor("transpose_480")]; + tensor input_1453 = pad(constant_val = const_549, mode = input_1453_mode_0, pad = input_1453_pad_0, x = input_1451)[name = tensor("input_1453")]; + tensor x_485_pad_type_0 = const()[name = tensor("x_485_pad_type_0"), val = tensor("valid")]; + tensor x_485_groups_0 = const()[name = tensor("x_485_groups_0"), val = tensor(512)]; + tensor x_485_strides_0 = const()[name = tensor("x_485_strides_0"), val = tensor([1])]; + tensor x_485_pad_0 = const()[name = tensor("x_485_pad_0"), val = tensor([0, 0])]; + tensor x_485_dilations_0 = const()[name = tensor("x_485_dilations_0"), val = tensor([1])]; + tensor x_485 = conv(dilations = x_485_dilations_0, groups = x_485_groups_0, pad = x_485_pad_0, pad_type = x_485_pad_type_0, strides = x_485_strides_0, weight = encoder_encoders_47_self_attn_fsmn_block_weight, x = input_1453)[name = tensor("x_485")]; + tensor x_487_perm_0 = const()[name = tensor("x_487_perm_0"), val = tensor([0, 2, 1])]; + tensor x_487 = transpose(perm = x_487_perm_0, x = x_485)[name = tensor("transpose_479")]; + tensor input_1455 = add(x = x_487, y = inputs_97)[name = tensor("input_1455")]; + tensor fsmn_memory_97 = mul(x = input_1455, y = mask_7)[name = tensor("fsmn_memory_97")]; + tensor var_5209 = const()[name = tensor("op_5209"), val = tensor(0x1.6a09e6p-4)]; + tensor q_h_195 = mul(x = var_5184, y = var_5209)[name = tensor("q_h_195")]; + tensor scores_193_transpose_x_0 = const()[name = tensor("scores_193_transpose_x_0"), val = tensor(false)]; + tensor scores_193_transpose_y_0 = const()[name = tensor("scores_193_transpose_y_0"), val = tensor(false)]; + tensor transpose_306_perm_0 = const()[name = tensor("transpose_306_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_307_perm_0 = const()[name = tensor("transpose_307_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_307 = transpose(perm = transpose_307_perm_0, x = var_5187)[name = tensor("transpose_477")]; + tensor transpose_306 = transpose(perm = transpose_306_perm_0, x = q_h_195)[name = tensor("transpose_478")]; + tensor scores_193 = matmul(transpose_x = scores_193_transpose_x_0, transpose_y = scores_193_transpose_y_0, x = transpose_306, y = transpose_307)[name = tensor("scores_193")]; + tensor scores_195 = select(a = var_48, b = scores_193, cond = mask_9)[name = tensor("scores_195")]; + tensor var_5217 = softmax(axis = var_61, x = scores_195)[name = tensor("op_5217")]; + tensor input_1457 = select(a = var_53, b = var_5217, cond = mask_9)[name = tensor("input_1457")]; + tensor x_491_transpose_x_0 = const()[name = tensor("x_491_transpose_x_0"), val = tensor(false)]; + tensor x_491_transpose_y_0 = const()[name = tensor("x_491_transpose_y_0"), val = tensor(false)]; + tensor value_97 = transpose(perm = value_97_perm_0, x = var_5190)[name = tensor("transpose_481")]; + tensor x_491 = matmul(transpose_x = x_491_transpose_x_0, transpose_y = x_491_transpose_y_0, x = input_1457, y = value_97)[name = tensor("x_491")]; + tensor var_5221_perm_0 = const()[name = tensor("op_5221_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_5223 = const()[name = tensor("op_5223"), val = tensor([1, -1, 512])]; + tensor var_5221 = transpose(perm = var_5221_perm_0, x = x_491)[name = tensor("transpose_476")]; + tensor input_1459 = reshape(shape = var_5223, x = var_5221)[name = tensor("input_1459")]; + tensor att_outs_97 = linear(bias = encoder_encoders_47_self_attn_linear_out_bias, weight = encoder_encoders_47_self_attn_linear_out_weight, x = input_1459)[name = tensor("linear_193")]; + tensor input_1461 = add(x = att_outs_97, y = fsmn_memory_97)[name = tensor("input_1461")]; + tensor input_1463 = add(x = input_1447, y = input_1461)[name = tensor("input_1463")]; + tensor const_551 = const()[name = tensor("const_551"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939910912)))]; + tensor const_552 = const()[name = tensor("const_552"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939913024)))]; + tensor output_195_axes_0 = const()[name = tensor("output_195_axes_0"), val = tensor([-1])]; + tensor output_195 = layer_norm(axes = output_195_axes_0, beta = const_552, epsilon = var_46, gamma = const_551, x = input_1463)[name = tensor("output_195")]; + tensor input_1469 = linear(bias = encoder_encoders_47_feed_forward_w_1_bias, weight = encoder_encoders_47_feed_forward_w_1_weight, x = output_195)[name = tensor("linear_194")]; + tensor input_1471 = relu(x = input_1469)[name = tensor("input_1471")]; + tensor input_1475 = linear(bias = encoder_encoders_47_feed_forward_w_2_bias, weight = encoder_encoders_47_feed_forward_w_2_weight, x = input_1471)[name = tensor("linear_195")]; + tensor input_1477 = add(x = input_1463, y = input_1475)[name = tensor("input_1477")]; + tensor const_553 = const()[name = tensor("const_553"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939915136)))]; + tensor const_554 = const()[name = tensor("const_554"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939917248)))]; + tensor output_197_axes_0 = const()[name = tensor("output_197_axes_0"), val = tensor([-1])]; + tensor output_197 = layer_norm(axes = output_197_axes_0, beta = const_554, epsilon = var_46, gamma = const_553, x = input_1477)[name = tensor("output_197")]; + tensor var_5280 = linear(bias = encoder_encoders_48_self_attn_linear_q_k_v_bias, weight = encoder_encoders_48_self_attn_linear_q_k_v_weight, x = output_197)[name = tensor("linear_196")]; + tensor tile_49 = const()[name = tensor("tile_49"), val = tensor([512, 512, 512])]; + tensor var_5281_axis_0 = const()[name = tensor("op_5281_axis_0"), val = tensor(-1)]; + tensor var_5281_0, tensor var_5281_1, tensor var_5281_2 = split(axis = var_5281_axis_0, split_sizes = tile_49, x = var_5280)[name = tensor("op_5281")]; + tensor var_5285 = const()[name = tensor("op_5285"), val = tensor([1, 1804, 4, 128])]; + tensor var_5286 = reshape(shape = var_5285, x = var_5281_0)[name = tensor("op_5286")]; + tensor var_5288 = const()[name = tensor("op_5288"), val = tensor([1, 1804, 4, 128])]; + tensor var_5289 = reshape(shape = var_5288, x = var_5281_1)[name = tensor("op_5289")]; + tensor var_5291 = const()[name = tensor("op_5291"), val = tensor([1, 1804, 4, 128])]; + tensor var_5292 = reshape(shape = var_5291, x = var_5281_2)[name = tensor("op_5292")]; + tensor value_99_perm_0 = const()[name = tensor("value_99_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor inputs_99 = mul(x = var_5281_2, y = mask_7)[name = tensor("inputs_99")]; + tensor input_1481_perm_0 = const()[name = tensor("input_1481_perm_0"), val = tensor([0, 2, 1])]; + tensor const_560 = const()[name = tensor("const_560"), val = tensor(0x0p+0)]; + tensor input_1483_pad_0 = const()[name = tensor("input_1483_pad_0"), val = tensor([0, 0, 0, 0, 5, 5])]; + tensor input_1483_mode_0 = const()[name = tensor("input_1483_mode_0"), val = tensor("constant")]; + tensor input_1481 = transpose(perm = input_1481_perm_0, x = inputs_99)[name = tensor("transpose_474")]; + tensor input_1483 = pad(constant_val = const_560, mode = input_1483_mode_0, pad = input_1483_pad_0, x = input_1481)[name = tensor("input_1483")]; + tensor x_495_pad_type_0 = const()[name = tensor("x_495_pad_type_0"), val = tensor("valid")]; + tensor x_495_groups_0 = const()[name = tensor("x_495_groups_0"), val = tensor(512)]; + tensor x_495_strides_0 = const()[name = tensor("x_495_strides_0"), val = tensor([1])]; + tensor x_495_pad_0 = const()[name = tensor("x_495_pad_0"), val = tensor([0, 0])]; + tensor x_495_dilations_0 = const()[name = tensor("x_495_dilations_0"), val = tensor([1])]; + tensor x_495 = conv(dilations = x_495_dilations_0, groups = x_495_groups_0, pad = x_495_pad_0, pad_type = x_495_pad_type_0, strides = x_495_strides_0, weight = encoder_encoders_48_self_attn_fsmn_block_weight, x = input_1483)[name = tensor("x_495")]; + tensor x_497_perm_0 = const()[name = tensor("x_497_perm_0"), val = tensor([0, 2, 1])]; + tensor x_497 = transpose(perm = x_497_perm_0, x = x_495)[name = tensor("transpose_473")]; + tensor input_1485 = add(x = x_497, y = inputs_99)[name = tensor("input_1485")]; + tensor fsmn_memory_99 = mul(x = input_1485, y = mask_7)[name = tensor("fsmn_memory_99")]; + tensor var_5311 = const()[name = tensor("op_5311"), val = tensor(0x1.6a09e6p-4)]; + tensor q_h_199 = mul(x = var_5286, y = var_5311)[name = tensor("q_h_199")]; + tensor scores_197_transpose_x_0 = const()[name = tensor("scores_197_transpose_x_0"), val = tensor(false)]; + tensor scores_197_transpose_y_0 = const()[name = tensor("scores_197_transpose_y_0"), val = tensor(false)]; + tensor transpose_308_perm_0 = const()[name = tensor("transpose_308_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_309_perm_0 = const()[name = tensor("transpose_309_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_309 = transpose(perm = transpose_309_perm_0, x = var_5289)[name = tensor("transpose_471")]; + tensor transpose_308 = transpose(perm = transpose_308_perm_0, x = q_h_199)[name = tensor("transpose_472")]; + tensor scores_197 = matmul(transpose_x = scores_197_transpose_x_0, transpose_y = scores_197_transpose_y_0, x = transpose_308, y = transpose_309)[name = tensor("scores_197")]; + tensor scores_199 = select(a = var_48, b = scores_197, cond = mask_9)[name = tensor("scores_199")]; + tensor var_5319 = softmax(axis = var_61, x = scores_199)[name = tensor("op_5319")]; + tensor input_1487 = select(a = var_53, b = var_5319, cond = mask_9)[name = tensor("input_1487")]; + tensor x_501_transpose_x_0 = const()[name = tensor("x_501_transpose_x_0"), val = tensor(false)]; + tensor x_501_transpose_y_0 = const()[name = tensor("x_501_transpose_y_0"), val = tensor(false)]; + tensor value_99 = transpose(perm = value_99_perm_0, x = var_5292)[name = tensor("transpose_475")]; + tensor x_501 = matmul(transpose_x = x_501_transpose_x_0, transpose_y = x_501_transpose_y_0, x = input_1487, y = value_99)[name = tensor("x_501")]; + tensor var_5323_perm_0 = const()[name = tensor("op_5323_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_5325 = const()[name = tensor("op_5325"), val = tensor([1, -1, 512])]; + tensor var_5323 = transpose(perm = var_5323_perm_0, x = x_501)[name = tensor("transpose_470")]; + tensor input_1489 = reshape(shape = var_5325, x = var_5323)[name = tensor("input_1489")]; + tensor att_outs_99 = linear(bias = encoder_encoders_48_self_attn_linear_out_bias, weight = encoder_encoders_48_self_attn_linear_out_weight, x = input_1489)[name = tensor("linear_197")]; + tensor input_1491 = add(x = att_outs_99, y = fsmn_memory_99)[name = tensor("input_1491")]; + tensor input_1493 = add(x = input_1477, y = input_1491)[name = tensor("input_1493")]; + tensor const_562 = const()[name = tensor("const_562"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939919360)))]; + tensor const_563 = const()[name = tensor("const_563"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939921472)))]; + tensor output_199_axes_0 = const()[name = tensor("output_199_axes_0"), val = tensor([-1])]; + tensor output_199 = layer_norm(axes = output_199_axes_0, beta = const_563, epsilon = var_46, gamma = const_562, x = input_1493)[name = tensor("output_199")]; + tensor input_1499 = linear(bias = encoder_encoders_48_feed_forward_w_1_bias, weight = encoder_encoders_48_feed_forward_w_1_weight, x = output_199)[name = tensor("linear_198")]; + tensor input_1501 = relu(x = input_1499)[name = tensor("input_1501")]; + tensor input_1505 = linear(bias = encoder_encoders_48_feed_forward_w_2_bias, weight = encoder_encoders_48_feed_forward_w_2_weight, x = input_1501)[name = tensor("linear_199")]; + tensor input_1507 = add(x = input_1493, y = input_1505)[name = tensor("input_1507")]; + tensor const_564 = const()[name = tensor("const_564"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939923584)))]; + tensor const_565 = const()[name = tensor("const_565"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939925696)))]; + tensor output_201_axes_0 = const()[name = tensor("output_201_axes_0"), val = tensor([-1])]; + tensor output_201 = layer_norm(axes = output_201_axes_0, beta = const_565, epsilon = var_46, gamma = const_564, x = input_1507)[name = tensor("output_201")]; + tensor const_566 = const()[name = tensor("const_566"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939927808)))]; + tensor const_567 = const()[name = tensor("const_567"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939929920)))]; + tensor output_203_axes_0 = const()[name = tensor("output_203_axes_0"), val = tensor([-1])]; + tensor output_203 = layer_norm(axes = output_203_axes_0, beta = const_567, epsilon = var_46, gamma = const_566, x = output_201)[name = tensor("output_203")]; + tensor var_5390 = linear(bias = encoder_tp_encoders_0_self_attn_linear_q_k_v_bias, weight = encoder_tp_encoders_0_self_attn_linear_q_k_v_weight, x = output_203)[name = tensor("linear_200")]; + tensor tile_50 = const()[name = tensor("tile_50"), val = tensor([512, 512, 512])]; + tensor var_5391_axis_0 = const()[name = tensor("op_5391_axis_0"), val = tensor(-1)]; + tensor var_5391_0, tensor var_5391_1, tensor var_5391_2 = split(axis = var_5391_axis_0, split_sizes = tile_50, x = var_5390)[name = tensor("op_5391")]; + tensor var_5395 = const()[name = tensor("op_5395"), val = tensor([1, 1804, 4, 128])]; + tensor var_5396 = reshape(shape = var_5395, x = var_5391_0)[name = tensor("op_5396")]; + tensor var_5398 = const()[name = tensor("op_5398"), val = tensor([1, 1804, 4, 128])]; + tensor var_5399 = reshape(shape = var_5398, x = var_5391_1)[name = tensor("op_5399")]; + tensor var_5401 = const()[name = tensor("op_5401"), val = tensor([1, 1804, 4, 128])]; + tensor var_5402 = reshape(shape = var_5401, x = var_5391_2)[name = tensor("op_5402")]; + tensor value_101_perm_0 = const()[name = tensor("value_101_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor inputs_101 = mul(x = var_5391_2, y = mask_7)[name = tensor("inputs_101")]; + tensor input_1515_perm_0 = const()[name = tensor("input_1515_perm_0"), val = tensor([0, 2, 1])]; + tensor const_573 = const()[name = tensor("const_573"), val = tensor(0x0p+0)]; + tensor input_1517_pad_0 = const()[name = tensor("input_1517_pad_0"), val = tensor([0, 0, 0, 0, 5, 5])]; + tensor input_1517_mode_0 = const()[name = tensor("input_1517_mode_0"), val = tensor("constant")]; + tensor input_1515 = transpose(perm = input_1515_perm_0, x = inputs_101)[name = tensor("transpose_468")]; + tensor input_1517 = pad(constant_val = const_573, mode = input_1517_mode_0, pad = input_1517_pad_0, x = input_1515)[name = tensor("input_1517")]; + tensor x_505_pad_type_0 = const()[name = tensor("x_505_pad_type_0"), val = tensor("valid")]; + tensor x_505_groups_0 = const()[name = tensor("x_505_groups_0"), val = tensor(512)]; + tensor x_505_strides_0 = const()[name = tensor("x_505_strides_0"), val = tensor([1])]; + tensor x_505_pad_0 = const()[name = tensor("x_505_pad_0"), val = tensor([0, 0])]; + tensor x_505_dilations_0 = const()[name = tensor("x_505_dilations_0"), val = tensor([1])]; + tensor x_505 = conv(dilations = x_505_dilations_0, groups = x_505_groups_0, pad = x_505_pad_0, pad_type = x_505_pad_type_0, strides = x_505_strides_0, weight = encoder_tp_encoders_0_self_attn_fsmn_block_weight, x = input_1517)[name = tensor("x_505")]; + tensor x_507_perm_0 = const()[name = tensor("x_507_perm_0"), val = tensor([0, 2, 1])]; + tensor x_507 = transpose(perm = x_507_perm_0, x = x_505)[name = tensor("transpose_467")]; + tensor input_1519 = add(x = x_507, y = inputs_101)[name = tensor("input_1519")]; + tensor fsmn_memory_101 = mul(x = input_1519, y = mask_7)[name = tensor("fsmn_memory_101")]; + tensor var_5421 = const()[name = tensor("op_5421"), val = tensor(0x1.6a09e6p-4)]; + tensor q_h_203 = mul(x = var_5396, y = var_5421)[name = tensor("q_h_203")]; + tensor scores_201_transpose_x_0 = const()[name = tensor("scores_201_transpose_x_0"), val = tensor(false)]; + tensor scores_201_transpose_y_0 = const()[name = tensor("scores_201_transpose_y_0"), val = tensor(false)]; + tensor transpose_310_perm_0 = const()[name = tensor("transpose_310_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_311_perm_0 = const()[name = tensor("transpose_311_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_311 = transpose(perm = transpose_311_perm_0, x = var_5399)[name = tensor("transpose_465")]; + tensor transpose_310 = transpose(perm = transpose_310_perm_0, x = q_h_203)[name = tensor("transpose_466")]; + tensor scores_201 = matmul(transpose_x = scores_201_transpose_x_0, transpose_y = scores_201_transpose_y_0, x = transpose_310, y = transpose_311)[name = tensor("scores_201")]; + tensor scores_203 = select(a = var_48, b = scores_201, cond = mask_9)[name = tensor("scores_203")]; + tensor var_5429 = softmax(axis = var_61, x = scores_203)[name = tensor("op_5429")]; + tensor input_1521 = select(a = var_53, b = var_5429, cond = mask_9)[name = tensor("input_1521")]; + tensor x_511_transpose_x_0 = const()[name = tensor("x_511_transpose_x_0"), val = tensor(false)]; + tensor x_511_transpose_y_0 = const()[name = tensor("x_511_transpose_y_0"), val = tensor(false)]; + tensor value_101 = transpose(perm = value_101_perm_0, x = var_5402)[name = tensor("transpose_469")]; + tensor x_511 = matmul(transpose_x = x_511_transpose_x_0, transpose_y = x_511_transpose_y_0, x = input_1521, y = value_101)[name = tensor("x_511")]; + tensor var_5433_perm_0 = const()[name = tensor("op_5433_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_5435 = const()[name = tensor("op_5435"), val = tensor([1, -1, 512])]; + tensor var_5433 = transpose(perm = var_5433_perm_0, x = x_511)[name = tensor("transpose_464")]; + tensor input_1523 = reshape(shape = var_5435, x = var_5433)[name = tensor("input_1523")]; + tensor att_outs_101 = linear(bias = encoder_tp_encoders_0_self_attn_linear_out_bias, weight = encoder_tp_encoders_0_self_attn_linear_out_weight, x = input_1523)[name = tensor("linear_201")]; + tensor input_1525 = add(x = att_outs_101, y = fsmn_memory_101)[name = tensor("input_1525")]; + tensor input_1527 = add(x = output_201, y = input_1525)[name = tensor("input_1527")]; + tensor const_575 = const()[name = tensor("const_575"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939932032)))]; + tensor const_576 = const()[name = tensor("const_576"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939934144)))]; + tensor output_205_axes_0 = const()[name = tensor("output_205_axes_0"), val = tensor([-1])]; + tensor output_205 = layer_norm(axes = output_205_axes_0, beta = const_576, epsilon = var_46, gamma = const_575, x = input_1527)[name = tensor("output_205")]; + tensor input_1533 = linear(bias = encoder_tp_encoders_0_feed_forward_w_1_bias, weight = encoder_tp_encoders_0_feed_forward_w_1_weight, x = output_205)[name = tensor("linear_202")]; + tensor input_1535 = relu(x = input_1533)[name = tensor("input_1535")]; + tensor input_1539 = linear(bias = encoder_tp_encoders_0_feed_forward_w_2_bias, weight = encoder_tp_encoders_0_feed_forward_w_2_weight, x = input_1535)[name = tensor("linear_203")]; + tensor input_1541 = add(x = input_1527, y = input_1539)[name = tensor("input_1541")]; + tensor const_577 = const()[name = tensor("const_577"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939936256)))]; + tensor const_578 = const()[name = tensor("const_578"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939938368)))]; + tensor output_207_axes_0 = const()[name = tensor("output_207_axes_0"), val = tensor([-1])]; + tensor output_207 = layer_norm(axes = output_207_axes_0, beta = const_578, epsilon = var_46, gamma = const_577, x = input_1541)[name = tensor("output_207")]; + tensor var_5492 = linear(bias = encoder_tp_encoders_1_self_attn_linear_q_k_v_bias, weight = encoder_tp_encoders_1_self_attn_linear_q_k_v_weight, x = output_207)[name = tensor("linear_204")]; + tensor tile_51 = const()[name = tensor("tile_51"), val = tensor([512, 512, 512])]; + tensor var_5493_axis_0 = const()[name = tensor("op_5493_axis_0"), val = tensor(-1)]; + tensor var_5493_0, tensor var_5493_1, tensor var_5493_2 = split(axis = var_5493_axis_0, split_sizes = tile_51, x = var_5492)[name = tensor("op_5493")]; + tensor var_5497 = const()[name = tensor("op_5497"), val = tensor([1, 1804, 4, 128])]; + tensor var_5498 = reshape(shape = var_5497, x = var_5493_0)[name = tensor("op_5498")]; + tensor var_5500 = const()[name = tensor("op_5500"), val = tensor([1, 1804, 4, 128])]; + tensor var_5501 = reshape(shape = var_5500, x = var_5493_1)[name = tensor("op_5501")]; + tensor var_5503 = const()[name = tensor("op_5503"), val = tensor([1, 1804, 4, 128])]; + tensor var_5504 = reshape(shape = var_5503, x = var_5493_2)[name = tensor("op_5504")]; + tensor value_103_perm_0 = const()[name = tensor("value_103_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor inputs_103 = mul(x = var_5493_2, y = mask_7)[name = tensor("inputs_103")]; + tensor input_1545_perm_0 = const()[name = tensor("input_1545_perm_0"), val = tensor([0, 2, 1])]; + tensor const_584 = const()[name = tensor("const_584"), val = tensor(0x0p+0)]; + tensor input_1547_pad_0 = const()[name = tensor("input_1547_pad_0"), val = tensor([0, 0, 0, 0, 5, 5])]; + tensor input_1547_mode_0 = const()[name = tensor("input_1547_mode_0"), val = tensor("constant")]; + tensor input_1545 = transpose(perm = input_1545_perm_0, x = inputs_103)[name = tensor("transpose_462")]; + tensor input_1547 = pad(constant_val = const_584, mode = input_1547_mode_0, pad = input_1547_pad_0, x = input_1545)[name = tensor("input_1547")]; + tensor x_515_pad_type_0 = const()[name = tensor("x_515_pad_type_0"), val = tensor("valid")]; + tensor x_515_groups_0 = const()[name = tensor("x_515_groups_0"), val = tensor(512)]; + tensor x_515_strides_0 = const()[name = tensor("x_515_strides_0"), val = tensor([1])]; + tensor x_515_pad_0 = const()[name = tensor("x_515_pad_0"), val = tensor([0, 0])]; + tensor x_515_dilations_0 = const()[name = tensor("x_515_dilations_0"), val = tensor([1])]; + tensor x_515 = conv(dilations = x_515_dilations_0, groups = x_515_groups_0, pad = x_515_pad_0, pad_type = x_515_pad_type_0, strides = x_515_strides_0, weight = encoder_tp_encoders_1_self_attn_fsmn_block_weight, x = input_1547)[name = tensor("x_515")]; + tensor x_517_perm_0 = const()[name = tensor("x_517_perm_0"), val = tensor([0, 2, 1])]; + tensor x_517 = transpose(perm = x_517_perm_0, x = x_515)[name = tensor("transpose_461")]; + tensor input_1549 = add(x = x_517, y = inputs_103)[name = tensor("input_1549")]; + tensor fsmn_memory_103 = mul(x = input_1549, y = mask_7)[name = tensor("fsmn_memory_103")]; + tensor var_5523 = const()[name = tensor("op_5523"), val = tensor(0x1.6a09e6p-4)]; + tensor q_h_207 = mul(x = var_5498, y = var_5523)[name = tensor("q_h_207")]; + tensor scores_205_transpose_x_0 = const()[name = tensor("scores_205_transpose_x_0"), val = tensor(false)]; + tensor scores_205_transpose_y_0 = const()[name = tensor("scores_205_transpose_y_0"), val = tensor(false)]; + tensor transpose_312_perm_0 = const()[name = tensor("transpose_312_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_313_perm_0 = const()[name = tensor("transpose_313_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_313 = transpose(perm = transpose_313_perm_0, x = var_5501)[name = tensor("transpose_459")]; + tensor transpose_312 = transpose(perm = transpose_312_perm_0, x = q_h_207)[name = tensor("transpose_460")]; + tensor scores_205 = matmul(transpose_x = scores_205_transpose_x_0, transpose_y = scores_205_transpose_y_0, x = transpose_312, y = transpose_313)[name = tensor("scores_205")]; + tensor scores_207 = select(a = var_48, b = scores_205, cond = mask_9)[name = tensor("scores_207")]; + tensor var_5531 = softmax(axis = var_61, x = scores_207)[name = tensor("op_5531")]; + tensor input_1551 = select(a = var_53, b = var_5531, cond = mask_9)[name = tensor("input_1551")]; + tensor x_521_transpose_x_0 = const()[name = tensor("x_521_transpose_x_0"), val = tensor(false)]; + tensor x_521_transpose_y_0 = const()[name = tensor("x_521_transpose_y_0"), val = tensor(false)]; + tensor value_103 = transpose(perm = value_103_perm_0, x = var_5504)[name = tensor("transpose_463")]; + tensor x_521 = matmul(transpose_x = x_521_transpose_x_0, transpose_y = x_521_transpose_y_0, x = input_1551, y = value_103)[name = tensor("x_521")]; + tensor var_5535_perm_0 = const()[name = tensor("op_5535_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_5537 = const()[name = tensor("op_5537"), val = tensor([1, -1, 512])]; + tensor var_5535 = transpose(perm = var_5535_perm_0, x = x_521)[name = tensor("transpose_458")]; + tensor input_1553 = reshape(shape = var_5537, x = var_5535)[name = tensor("input_1553")]; + tensor att_outs_103 = linear(bias = encoder_tp_encoders_1_self_attn_linear_out_bias, weight = encoder_tp_encoders_1_self_attn_linear_out_weight, x = input_1553)[name = tensor("linear_205")]; + tensor input_1555 = add(x = att_outs_103, y = fsmn_memory_103)[name = tensor("input_1555")]; + tensor input_1557 = add(x = input_1541, y = input_1555)[name = tensor("input_1557")]; + tensor const_586 = const()[name = tensor("const_586"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939940480)))]; + tensor const_587 = const()[name = tensor("const_587"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939942592)))]; + tensor output_209_axes_0 = const()[name = tensor("output_209_axes_0"), val = tensor([-1])]; + tensor output_209 = layer_norm(axes = output_209_axes_0, beta = const_587, epsilon = var_46, gamma = const_586, x = input_1557)[name = tensor("output_209")]; + tensor input_1563 = linear(bias = encoder_tp_encoders_1_feed_forward_w_1_bias, weight = encoder_tp_encoders_1_feed_forward_w_1_weight, x = output_209)[name = tensor("linear_206")]; + tensor input_1565 = relu(x = input_1563)[name = tensor("input_1565")]; + tensor input_1569 = linear(bias = encoder_tp_encoders_1_feed_forward_w_2_bias, weight = encoder_tp_encoders_1_feed_forward_w_2_weight, x = input_1565)[name = tensor("linear_207")]; + tensor input_1571 = add(x = input_1557, y = input_1569)[name = tensor("input_1571")]; + tensor const_588 = const()[name = tensor("const_588"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939944704)))]; + tensor const_589 = const()[name = tensor("const_589"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939946816)))]; + tensor output_211_axes_0 = const()[name = tensor("output_211_axes_0"), val = tensor([-1])]; + tensor output_211 = layer_norm(axes = output_211_axes_0, beta = const_589, epsilon = var_46, gamma = const_588, x = input_1571)[name = tensor("output_211")]; + tensor var_5594 = linear(bias = encoder_tp_encoders_2_self_attn_linear_q_k_v_bias, weight = encoder_tp_encoders_2_self_attn_linear_q_k_v_weight, x = output_211)[name = tensor("linear_208")]; + tensor tile_52 = const()[name = tensor("tile_52"), val = tensor([512, 512, 512])]; + tensor var_5595_axis_0 = const()[name = tensor("op_5595_axis_0"), val = tensor(-1)]; + tensor var_5595_0, tensor var_5595_1, tensor var_5595_2 = split(axis = var_5595_axis_0, split_sizes = tile_52, x = var_5594)[name = tensor("op_5595")]; + tensor var_5599 = const()[name = tensor("op_5599"), val = tensor([1, 1804, 4, 128])]; + tensor var_5600 = reshape(shape = var_5599, x = var_5595_0)[name = tensor("op_5600")]; + tensor var_5602 = const()[name = tensor("op_5602"), val = tensor([1, 1804, 4, 128])]; + tensor var_5603 = reshape(shape = var_5602, x = var_5595_1)[name = tensor("op_5603")]; + tensor var_5605 = const()[name = tensor("op_5605"), val = tensor([1, 1804, 4, 128])]; + tensor var_5606 = reshape(shape = var_5605, x = var_5595_2)[name = tensor("op_5606")]; + tensor value_105_perm_0 = const()[name = tensor("value_105_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor inputs_105 = mul(x = var_5595_2, y = mask_7)[name = tensor("inputs_105")]; + tensor input_1575_perm_0 = const()[name = tensor("input_1575_perm_0"), val = tensor([0, 2, 1])]; + tensor const_595 = const()[name = tensor("const_595"), val = tensor(0x0p+0)]; + tensor input_1577_pad_0 = const()[name = tensor("input_1577_pad_0"), val = tensor([0, 0, 0, 0, 5, 5])]; + tensor input_1577_mode_0 = const()[name = tensor("input_1577_mode_0"), val = tensor("constant")]; + tensor input_1575 = transpose(perm = input_1575_perm_0, x = inputs_105)[name = tensor("transpose_456")]; + tensor input_1577 = pad(constant_val = const_595, mode = input_1577_mode_0, pad = input_1577_pad_0, x = input_1575)[name = tensor("input_1577")]; + tensor x_525_pad_type_0 = const()[name = tensor("x_525_pad_type_0"), val = tensor("valid")]; + tensor x_525_groups_0 = const()[name = tensor("x_525_groups_0"), val = tensor(512)]; + tensor x_525_strides_0 = const()[name = tensor("x_525_strides_0"), val = tensor([1])]; + tensor x_525_pad_0 = const()[name = tensor("x_525_pad_0"), val = tensor([0, 0])]; + tensor x_525_dilations_0 = const()[name = tensor("x_525_dilations_0"), val = tensor([1])]; + tensor x_525 = conv(dilations = x_525_dilations_0, groups = x_525_groups_0, pad = x_525_pad_0, pad_type = x_525_pad_type_0, strides = x_525_strides_0, weight = encoder_tp_encoders_2_self_attn_fsmn_block_weight, x = input_1577)[name = tensor("x_525")]; + tensor x_527_perm_0 = const()[name = tensor("x_527_perm_0"), val = tensor([0, 2, 1])]; + tensor x_527 = transpose(perm = x_527_perm_0, x = x_525)[name = tensor("transpose_455")]; + tensor input_1579 = add(x = x_527, y = inputs_105)[name = tensor("input_1579")]; + tensor fsmn_memory_105 = mul(x = input_1579, y = mask_7)[name = tensor("fsmn_memory_105")]; + tensor var_5625 = const()[name = tensor("op_5625"), val = tensor(0x1.6a09e6p-4)]; + tensor q_h_211 = mul(x = var_5600, y = var_5625)[name = tensor("q_h_211")]; + tensor scores_209_transpose_x_0 = const()[name = tensor("scores_209_transpose_x_0"), val = tensor(false)]; + tensor scores_209_transpose_y_0 = const()[name = tensor("scores_209_transpose_y_0"), val = tensor(false)]; + tensor transpose_314_perm_0 = const()[name = tensor("transpose_314_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_315_perm_0 = const()[name = tensor("transpose_315_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_315 = transpose(perm = transpose_315_perm_0, x = var_5603)[name = tensor("transpose_453")]; + tensor transpose_314 = transpose(perm = transpose_314_perm_0, x = q_h_211)[name = tensor("transpose_454")]; + tensor scores_209 = matmul(transpose_x = scores_209_transpose_x_0, transpose_y = scores_209_transpose_y_0, x = transpose_314, y = transpose_315)[name = tensor("scores_209")]; + tensor scores_211 = select(a = var_48, b = scores_209, cond = mask_9)[name = tensor("scores_211")]; + tensor var_5633 = softmax(axis = var_61, x = scores_211)[name = tensor("op_5633")]; + tensor input_1581 = select(a = var_53, b = var_5633, cond = mask_9)[name = tensor("input_1581")]; + tensor x_531_transpose_x_0 = const()[name = tensor("x_531_transpose_x_0"), val = tensor(false)]; + tensor x_531_transpose_y_0 = const()[name = tensor("x_531_transpose_y_0"), val = tensor(false)]; + tensor value_105 = transpose(perm = value_105_perm_0, x = var_5606)[name = tensor("transpose_457")]; + tensor x_531 = matmul(transpose_x = x_531_transpose_x_0, transpose_y = x_531_transpose_y_0, x = input_1581, y = value_105)[name = tensor("x_531")]; + tensor var_5637_perm_0 = const()[name = tensor("op_5637_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_5639 = const()[name = tensor("op_5639"), val = tensor([1, -1, 512])]; + tensor var_5637 = transpose(perm = var_5637_perm_0, x = x_531)[name = tensor("transpose_452")]; + tensor input_1583 = reshape(shape = var_5639, x = var_5637)[name = tensor("input_1583")]; + tensor att_outs_105 = linear(bias = encoder_tp_encoders_2_self_attn_linear_out_bias, weight = encoder_tp_encoders_2_self_attn_linear_out_weight, x = input_1583)[name = tensor("linear_209")]; + tensor input_1585 = add(x = att_outs_105, y = fsmn_memory_105)[name = tensor("input_1585")]; + tensor input_1587 = add(x = input_1571, y = input_1585)[name = tensor("input_1587")]; + tensor const_597 = const()[name = tensor("const_597"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939948928)))]; + tensor const_598 = const()[name = tensor("const_598"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939951040)))]; + tensor output_213_axes_0 = const()[name = tensor("output_213_axes_0"), val = tensor([-1])]; + tensor output_213 = layer_norm(axes = output_213_axes_0, beta = const_598, epsilon = var_46, gamma = const_597, x = input_1587)[name = tensor("output_213")]; + tensor input_1593 = linear(bias = encoder_tp_encoders_2_feed_forward_w_1_bias, weight = encoder_tp_encoders_2_feed_forward_w_1_weight, x = output_213)[name = tensor("linear_210")]; + tensor input_1595 = relu(x = input_1593)[name = tensor("input_1595")]; + tensor input_1599 = linear(bias = encoder_tp_encoders_2_feed_forward_w_2_bias, weight = encoder_tp_encoders_2_feed_forward_w_2_weight, x = input_1595)[name = tensor("linear_211")]; + tensor input_1601 = add(x = input_1587, y = input_1599)[name = tensor("input_1601")]; + tensor const_599 = const()[name = tensor("const_599"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939953152)))]; + tensor const_600 = const()[name = tensor("const_600"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939955264)))]; + tensor output_215_axes_0 = const()[name = tensor("output_215_axes_0"), val = tensor([-1])]; + tensor output_215 = layer_norm(axes = output_215_axes_0, beta = const_600, epsilon = var_46, gamma = const_599, x = input_1601)[name = tensor("output_215")]; + tensor var_5696 = linear(bias = encoder_tp_encoders_3_self_attn_linear_q_k_v_bias, weight = encoder_tp_encoders_3_self_attn_linear_q_k_v_weight, x = output_215)[name = tensor("linear_212")]; + tensor tile_53 = const()[name = tensor("tile_53"), val = tensor([512, 512, 512])]; + tensor var_5697_axis_0 = const()[name = tensor("op_5697_axis_0"), val = tensor(-1)]; + tensor var_5697_0, tensor var_5697_1, tensor var_5697_2 = split(axis = var_5697_axis_0, split_sizes = tile_53, x = var_5696)[name = tensor("op_5697")]; + tensor var_5701 = const()[name = tensor("op_5701"), val = tensor([1, 1804, 4, 128])]; + tensor var_5702 = reshape(shape = var_5701, x = var_5697_0)[name = tensor("op_5702")]; + tensor var_5704 = const()[name = tensor("op_5704"), val = tensor([1, 1804, 4, 128])]; + tensor var_5705 = reshape(shape = var_5704, x = var_5697_1)[name = tensor("op_5705")]; + tensor var_5707 = const()[name = tensor("op_5707"), val = tensor([1, 1804, 4, 128])]; + tensor var_5708 = reshape(shape = var_5707, x = var_5697_2)[name = tensor("op_5708")]; + tensor value_107_perm_0 = const()[name = tensor("value_107_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor inputs_107 = mul(x = var_5697_2, y = mask_7)[name = tensor("inputs_107")]; + tensor input_1605_perm_0 = const()[name = tensor("input_1605_perm_0"), val = tensor([0, 2, 1])]; + tensor const_606 = const()[name = tensor("const_606"), val = tensor(0x0p+0)]; + tensor input_1607_pad_0 = const()[name = tensor("input_1607_pad_0"), val = tensor([0, 0, 0, 0, 5, 5])]; + tensor input_1607_mode_0 = const()[name = tensor("input_1607_mode_0"), val = tensor("constant")]; + tensor input_1605 = transpose(perm = input_1605_perm_0, x = inputs_107)[name = tensor("transpose_450")]; + tensor input_1607 = pad(constant_val = const_606, mode = input_1607_mode_0, pad = input_1607_pad_0, x = input_1605)[name = tensor("input_1607")]; + tensor x_535_pad_type_0 = const()[name = tensor("x_535_pad_type_0"), val = tensor("valid")]; + tensor x_535_groups_0 = const()[name = tensor("x_535_groups_0"), val = tensor(512)]; + tensor x_535_strides_0 = const()[name = tensor("x_535_strides_0"), val = tensor([1])]; + tensor x_535_pad_0 = const()[name = tensor("x_535_pad_0"), val = tensor([0, 0])]; + tensor x_535_dilations_0 = const()[name = tensor("x_535_dilations_0"), val = tensor([1])]; + tensor x_535 = conv(dilations = x_535_dilations_0, groups = x_535_groups_0, pad = x_535_pad_0, pad_type = x_535_pad_type_0, strides = x_535_strides_0, weight = encoder_tp_encoders_3_self_attn_fsmn_block_weight, x = input_1607)[name = tensor("x_535")]; + tensor x_537_perm_0 = const()[name = tensor("x_537_perm_0"), val = tensor([0, 2, 1])]; + tensor x_537 = transpose(perm = x_537_perm_0, x = x_535)[name = tensor("transpose_449")]; + tensor input_1609 = add(x = x_537, y = inputs_107)[name = tensor("input_1609")]; + tensor fsmn_memory_107 = mul(x = input_1609, y = mask_7)[name = tensor("fsmn_memory_107")]; + tensor var_5727 = const()[name = tensor("op_5727"), val = tensor(0x1.6a09e6p-4)]; + tensor q_h_215 = mul(x = var_5702, y = var_5727)[name = tensor("q_h_215")]; + tensor scores_213_transpose_x_0 = const()[name = tensor("scores_213_transpose_x_0"), val = tensor(false)]; + tensor scores_213_transpose_y_0 = const()[name = tensor("scores_213_transpose_y_0"), val = tensor(false)]; + tensor transpose_316_perm_0 = const()[name = tensor("transpose_316_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_317_perm_0 = const()[name = tensor("transpose_317_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_317 = transpose(perm = transpose_317_perm_0, x = var_5705)[name = tensor("transpose_447")]; + tensor transpose_316 = transpose(perm = transpose_316_perm_0, x = q_h_215)[name = tensor("transpose_448")]; + tensor scores_213 = matmul(transpose_x = scores_213_transpose_x_0, transpose_y = scores_213_transpose_y_0, x = transpose_316, y = transpose_317)[name = tensor("scores_213")]; + tensor scores_215 = select(a = var_48, b = scores_213, cond = mask_9)[name = tensor("scores_215")]; + tensor var_5735 = softmax(axis = var_61, x = scores_215)[name = tensor("op_5735")]; + tensor input_1611 = select(a = var_53, b = var_5735, cond = mask_9)[name = tensor("input_1611")]; + tensor x_541_transpose_x_0 = const()[name = tensor("x_541_transpose_x_0"), val = tensor(false)]; + tensor x_541_transpose_y_0 = const()[name = tensor("x_541_transpose_y_0"), val = tensor(false)]; + tensor value_107 = transpose(perm = value_107_perm_0, x = var_5708)[name = tensor("transpose_451")]; + tensor x_541 = matmul(transpose_x = x_541_transpose_x_0, transpose_y = x_541_transpose_y_0, x = input_1611, y = value_107)[name = tensor("x_541")]; + tensor var_5739_perm_0 = const()[name = tensor("op_5739_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_5741 = const()[name = tensor("op_5741"), val = tensor([1, -1, 512])]; + tensor var_5739 = transpose(perm = var_5739_perm_0, x = x_541)[name = tensor("transpose_446")]; + tensor input_1613 = reshape(shape = var_5741, x = var_5739)[name = tensor("input_1613")]; + tensor att_outs_107 = linear(bias = encoder_tp_encoders_3_self_attn_linear_out_bias, weight = encoder_tp_encoders_3_self_attn_linear_out_weight, x = input_1613)[name = tensor("linear_213")]; + tensor input_1615 = add(x = att_outs_107, y = fsmn_memory_107)[name = tensor("input_1615")]; + tensor input_1617 = add(x = input_1601, y = input_1615)[name = tensor("input_1617")]; + tensor const_608 = const()[name = tensor("const_608"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939957376)))]; + tensor const_609 = const()[name = tensor("const_609"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939959488)))]; + tensor output_217_axes_0 = const()[name = tensor("output_217_axes_0"), val = tensor([-1])]; + tensor output_217 = layer_norm(axes = output_217_axes_0, beta = const_609, epsilon = var_46, gamma = const_608, x = input_1617)[name = tensor("output_217")]; + tensor input_1623 = linear(bias = encoder_tp_encoders_3_feed_forward_w_1_bias, weight = encoder_tp_encoders_3_feed_forward_w_1_weight, x = output_217)[name = tensor("linear_214")]; + tensor input_1625 = relu(x = input_1623)[name = tensor("input_1625")]; + tensor input_1629 = linear(bias = encoder_tp_encoders_3_feed_forward_w_2_bias, weight = encoder_tp_encoders_3_feed_forward_w_2_weight, x = input_1625)[name = tensor("linear_215")]; + tensor input_1631 = add(x = input_1617, y = input_1629)[name = tensor("input_1631")]; + tensor const_610 = const()[name = tensor("const_610"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939961600)))]; + tensor const_611 = const()[name = tensor("const_611"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939963712)))]; + tensor output_219_axes_0 = const()[name = tensor("output_219_axes_0"), val = tensor([-1])]; + tensor output_219 = layer_norm(axes = output_219_axes_0, beta = const_611, epsilon = var_46, gamma = const_610, x = input_1631)[name = tensor("output_219")]; + tensor var_5798 = linear(bias = encoder_tp_encoders_4_self_attn_linear_q_k_v_bias, weight = encoder_tp_encoders_4_self_attn_linear_q_k_v_weight, x = output_219)[name = tensor("linear_216")]; + tensor tile_54 = const()[name = tensor("tile_54"), val = tensor([512, 512, 512])]; + tensor var_5799_axis_0 = const()[name = tensor("op_5799_axis_0"), val = tensor(-1)]; + tensor var_5799_0, tensor var_5799_1, tensor var_5799_2 = split(axis = var_5799_axis_0, split_sizes = tile_54, x = var_5798)[name = tensor("op_5799")]; + tensor var_5803 = const()[name = tensor("op_5803"), val = tensor([1, 1804, 4, 128])]; + tensor var_5804 = reshape(shape = var_5803, x = var_5799_0)[name = tensor("op_5804")]; + tensor var_5806 = const()[name = tensor("op_5806"), val = tensor([1, 1804, 4, 128])]; + tensor var_5807 = reshape(shape = var_5806, x = var_5799_1)[name = tensor("op_5807")]; + tensor var_5809 = const()[name = tensor("op_5809"), val = tensor([1, 1804, 4, 128])]; + tensor var_5810 = reshape(shape = var_5809, x = var_5799_2)[name = tensor("op_5810")]; + tensor value_109_perm_0 = const()[name = tensor("value_109_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor inputs_109 = mul(x = var_5799_2, y = mask_7)[name = tensor("inputs_109")]; + tensor input_1635_perm_0 = const()[name = tensor("input_1635_perm_0"), val = tensor([0, 2, 1])]; + tensor const_617 = const()[name = tensor("const_617"), val = tensor(0x0p+0)]; + tensor input_1637_pad_0 = const()[name = tensor("input_1637_pad_0"), val = tensor([0, 0, 0, 0, 5, 5])]; + tensor input_1637_mode_0 = const()[name = tensor("input_1637_mode_0"), val = tensor("constant")]; + tensor input_1635 = transpose(perm = input_1635_perm_0, x = inputs_109)[name = tensor("transpose_444")]; + tensor input_1637 = pad(constant_val = const_617, mode = input_1637_mode_0, pad = input_1637_pad_0, x = input_1635)[name = tensor("input_1637")]; + tensor x_545_pad_type_0 = const()[name = tensor("x_545_pad_type_0"), val = tensor("valid")]; + tensor x_545_groups_0 = const()[name = tensor("x_545_groups_0"), val = tensor(512)]; + tensor x_545_strides_0 = const()[name = tensor("x_545_strides_0"), val = tensor([1])]; + tensor x_545_pad_0 = const()[name = tensor("x_545_pad_0"), val = tensor([0, 0])]; + tensor x_545_dilations_0 = const()[name = tensor("x_545_dilations_0"), val = tensor([1])]; + tensor x_545 = conv(dilations = x_545_dilations_0, groups = x_545_groups_0, pad = x_545_pad_0, pad_type = x_545_pad_type_0, strides = x_545_strides_0, weight = encoder_tp_encoders_4_self_attn_fsmn_block_weight, x = input_1637)[name = tensor("x_545")]; + tensor x_547_perm_0 = const()[name = tensor("x_547_perm_0"), val = tensor([0, 2, 1])]; + tensor x_547 = transpose(perm = x_547_perm_0, x = x_545)[name = tensor("transpose_443")]; + tensor input_1639 = add(x = x_547, y = inputs_109)[name = tensor("input_1639")]; + tensor fsmn_memory_109 = mul(x = input_1639, y = mask_7)[name = tensor("fsmn_memory_109")]; + tensor var_5829 = const()[name = tensor("op_5829"), val = tensor(0x1.6a09e6p-4)]; + tensor q_h_219 = mul(x = var_5804, y = var_5829)[name = tensor("q_h_219")]; + tensor scores_217_transpose_x_0 = const()[name = tensor("scores_217_transpose_x_0"), val = tensor(false)]; + tensor scores_217_transpose_y_0 = const()[name = tensor("scores_217_transpose_y_0"), val = tensor(false)]; + tensor transpose_318_perm_0 = const()[name = tensor("transpose_318_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_319_perm_0 = const()[name = tensor("transpose_319_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_319 = transpose(perm = transpose_319_perm_0, x = var_5807)[name = tensor("transpose_441")]; + tensor transpose_318 = transpose(perm = transpose_318_perm_0, x = q_h_219)[name = tensor("transpose_442")]; + tensor scores_217 = matmul(transpose_x = scores_217_transpose_x_0, transpose_y = scores_217_transpose_y_0, x = transpose_318, y = transpose_319)[name = tensor("scores_217")]; + tensor scores_219 = select(a = var_48, b = scores_217, cond = mask_9)[name = tensor("scores_219")]; + tensor var_5837 = softmax(axis = var_61, x = scores_219)[name = tensor("op_5837")]; + tensor input_1641 = select(a = var_53, b = var_5837, cond = mask_9)[name = tensor("input_1641")]; + tensor x_551_transpose_x_0 = const()[name = tensor("x_551_transpose_x_0"), val = tensor(false)]; + tensor x_551_transpose_y_0 = const()[name = tensor("x_551_transpose_y_0"), val = tensor(false)]; + tensor value_109 = transpose(perm = value_109_perm_0, x = var_5810)[name = tensor("transpose_445")]; + tensor x_551 = matmul(transpose_x = x_551_transpose_x_0, transpose_y = x_551_transpose_y_0, x = input_1641, y = value_109)[name = tensor("x_551")]; + tensor var_5841_perm_0 = const()[name = tensor("op_5841_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_5843 = const()[name = tensor("op_5843"), val = tensor([1, -1, 512])]; + tensor var_5841 = transpose(perm = var_5841_perm_0, x = x_551)[name = tensor("transpose_440")]; + tensor input_1643 = reshape(shape = var_5843, x = var_5841)[name = tensor("input_1643")]; + tensor att_outs_109 = linear(bias = encoder_tp_encoders_4_self_attn_linear_out_bias, weight = encoder_tp_encoders_4_self_attn_linear_out_weight, x = input_1643)[name = tensor("linear_217")]; + tensor input_1645 = add(x = att_outs_109, y = fsmn_memory_109)[name = tensor("input_1645")]; + tensor input_1647 = add(x = input_1631, y = input_1645)[name = tensor("input_1647")]; + tensor const_619 = const()[name = tensor("const_619"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939965824)))]; + tensor const_620 = const()[name = tensor("const_620"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939967936)))]; + tensor output_221_axes_0 = const()[name = tensor("output_221_axes_0"), val = tensor([-1])]; + tensor output_221 = layer_norm(axes = output_221_axes_0, beta = const_620, epsilon = var_46, gamma = const_619, x = input_1647)[name = tensor("output_221")]; + tensor input_1653 = linear(bias = encoder_tp_encoders_4_feed_forward_w_1_bias, weight = encoder_tp_encoders_4_feed_forward_w_1_weight, x = output_221)[name = tensor("linear_218")]; + tensor input_1655 = relu(x = input_1653)[name = tensor("input_1655")]; + tensor input_1659 = linear(bias = encoder_tp_encoders_4_feed_forward_w_2_bias, weight = encoder_tp_encoders_4_feed_forward_w_2_weight, x = input_1655)[name = tensor("linear_219")]; + tensor input_1661 = add(x = input_1647, y = input_1659)[name = tensor("input_1661")]; + tensor const_621 = const()[name = tensor("const_621"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939970048)))]; + tensor const_622 = const()[name = tensor("const_622"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939972160)))]; + tensor output_223_axes_0 = const()[name = tensor("output_223_axes_0"), val = tensor([-1])]; + tensor output_223 = layer_norm(axes = output_223_axes_0, beta = const_622, epsilon = var_46, gamma = const_621, x = input_1661)[name = tensor("output_223")]; + tensor var_5900 = linear(bias = encoder_tp_encoders_5_self_attn_linear_q_k_v_bias, weight = encoder_tp_encoders_5_self_attn_linear_q_k_v_weight, x = output_223)[name = tensor("linear_220")]; + tensor tile_55 = const()[name = tensor("tile_55"), val = tensor([512, 512, 512])]; + tensor var_5901_axis_0 = const()[name = tensor("op_5901_axis_0"), val = tensor(-1)]; + tensor var_5901_0, tensor var_5901_1, tensor var_5901_2 = split(axis = var_5901_axis_0, split_sizes = tile_55, x = var_5900)[name = tensor("op_5901")]; + tensor var_5905 = const()[name = tensor("op_5905"), val = tensor([1, 1804, 4, 128])]; + tensor var_5906 = reshape(shape = var_5905, x = var_5901_0)[name = tensor("op_5906")]; + tensor var_5908 = const()[name = tensor("op_5908"), val = tensor([1, 1804, 4, 128])]; + tensor var_5909 = reshape(shape = var_5908, x = var_5901_1)[name = tensor("op_5909")]; + tensor var_5911 = const()[name = tensor("op_5911"), val = tensor([1, 1804, 4, 128])]; + tensor var_5912 = reshape(shape = var_5911, x = var_5901_2)[name = tensor("op_5912")]; + tensor value_111_perm_0 = const()[name = tensor("value_111_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor inputs_111 = mul(x = var_5901_2, y = mask_7)[name = tensor("inputs_111")]; + tensor input_1665_perm_0 = const()[name = tensor("input_1665_perm_0"), val = tensor([0, 2, 1])]; + tensor const_628 = const()[name = tensor("const_628"), val = tensor(0x0p+0)]; + tensor input_1667_pad_0 = const()[name = tensor("input_1667_pad_0"), val = tensor([0, 0, 0, 0, 5, 5])]; + tensor input_1667_mode_0 = const()[name = tensor("input_1667_mode_0"), val = tensor("constant")]; + tensor input_1665 = transpose(perm = input_1665_perm_0, x = inputs_111)[name = tensor("transpose_438")]; + tensor input_1667 = pad(constant_val = const_628, mode = input_1667_mode_0, pad = input_1667_pad_0, x = input_1665)[name = tensor("input_1667")]; + tensor x_555_pad_type_0 = const()[name = tensor("x_555_pad_type_0"), val = tensor("valid")]; + tensor x_555_groups_0 = const()[name = tensor("x_555_groups_0"), val = tensor(512)]; + tensor x_555_strides_0 = const()[name = tensor("x_555_strides_0"), val = tensor([1])]; + tensor x_555_pad_0 = const()[name = tensor("x_555_pad_0"), val = tensor([0, 0])]; + tensor x_555_dilations_0 = const()[name = tensor("x_555_dilations_0"), val = tensor([1])]; + tensor x_555 = conv(dilations = x_555_dilations_0, groups = x_555_groups_0, pad = x_555_pad_0, pad_type = x_555_pad_type_0, strides = x_555_strides_0, weight = encoder_tp_encoders_5_self_attn_fsmn_block_weight, x = input_1667)[name = tensor("x_555")]; + tensor x_557_perm_0 = const()[name = tensor("x_557_perm_0"), val = tensor([0, 2, 1])]; + tensor x_557 = transpose(perm = x_557_perm_0, x = x_555)[name = tensor("transpose_437")]; + tensor input_1669 = add(x = x_557, y = inputs_111)[name = tensor("input_1669")]; + tensor fsmn_memory_111 = mul(x = input_1669, y = mask_7)[name = tensor("fsmn_memory_111")]; + tensor var_5931 = const()[name = tensor("op_5931"), val = tensor(0x1.6a09e6p-4)]; + tensor q_h_223 = mul(x = var_5906, y = var_5931)[name = tensor("q_h_223")]; + tensor scores_221_transpose_x_0 = const()[name = tensor("scores_221_transpose_x_0"), val = tensor(false)]; + tensor scores_221_transpose_y_0 = const()[name = tensor("scores_221_transpose_y_0"), val = tensor(false)]; + tensor transpose_320_perm_0 = const()[name = tensor("transpose_320_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_321_perm_0 = const()[name = tensor("transpose_321_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_321 = transpose(perm = transpose_321_perm_0, x = var_5909)[name = tensor("transpose_435")]; + tensor transpose_320 = transpose(perm = transpose_320_perm_0, x = q_h_223)[name = tensor("transpose_436")]; + tensor scores_221 = matmul(transpose_x = scores_221_transpose_x_0, transpose_y = scores_221_transpose_y_0, x = transpose_320, y = transpose_321)[name = tensor("scores_221")]; + tensor scores_223 = select(a = var_48, b = scores_221, cond = mask_9)[name = tensor("scores_223")]; + tensor var_5939 = softmax(axis = var_61, x = scores_223)[name = tensor("op_5939")]; + tensor input_1671 = select(a = var_53, b = var_5939, cond = mask_9)[name = tensor("input_1671")]; + tensor x_561_transpose_x_0 = const()[name = tensor("x_561_transpose_x_0"), val = tensor(false)]; + tensor x_561_transpose_y_0 = const()[name = tensor("x_561_transpose_y_0"), val = tensor(false)]; + tensor value_111 = transpose(perm = value_111_perm_0, x = var_5912)[name = tensor("transpose_439")]; + tensor x_561 = matmul(transpose_x = x_561_transpose_x_0, transpose_y = x_561_transpose_y_0, x = input_1671, y = value_111)[name = tensor("x_561")]; + tensor var_5943_perm_0 = const()[name = tensor("op_5943_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_5945 = const()[name = tensor("op_5945"), val = tensor([1, -1, 512])]; + tensor var_5943 = transpose(perm = var_5943_perm_0, x = x_561)[name = tensor("transpose_434")]; + tensor input_1673 = reshape(shape = var_5945, x = var_5943)[name = tensor("input_1673")]; + tensor att_outs_111 = linear(bias = encoder_tp_encoders_5_self_attn_linear_out_bias, weight = encoder_tp_encoders_5_self_attn_linear_out_weight, x = input_1673)[name = tensor("linear_221")]; + tensor input_1675 = add(x = att_outs_111, y = fsmn_memory_111)[name = tensor("input_1675")]; + tensor input_1677 = add(x = input_1661, y = input_1675)[name = tensor("input_1677")]; + tensor const_630 = const()[name = tensor("const_630"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939974272)))]; + tensor const_631 = const()[name = tensor("const_631"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939976384)))]; + tensor output_225_axes_0 = const()[name = tensor("output_225_axes_0"), val = tensor([-1])]; + tensor output_225 = layer_norm(axes = output_225_axes_0, beta = const_631, epsilon = var_46, gamma = const_630, x = input_1677)[name = tensor("output_225")]; + tensor input_1683 = linear(bias = encoder_tp_encoders_5_feed_forward_w_1_bias, weight = encoder_tp_encoders_5_feed_forward_w_1_weight, x = output_225)[name = tensor("linear_222")]; + tensor input_1685 = relu(x = input_1683)[name = tensor("input_1685")]; + tensor input_1689 = linear(bias = encoder_tp_encoders_5_feed_forward_w_2_bias, weight = encoder_tp_encoders_5_feed_forward_w_2_weight, x = input_1685)[name = tensor("linear_223")]; + tensor input_1691 = add(x = input_1677, y = input_1689)[name = tensor("input_1691")]; + tensor const_632 = const()[name = tensor("const_632"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939978496)))]; + tensor const_633 = const()[name = tensor("const_633"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939980608)))]; + tensor output_227_axes_0 = const()[name = tensor("output_227_axes_0"), val = tensor([-1])]; + tensor output_227 = layer_norm(axes = output_227_axes_0, beta = const_633, epsilon = var_46, gamma = const_632, x = input_1691)[name = tensor("output_227")]; + tensor var_6002 = linear(bias = encoder_tp_encoders_6_self_attn_linear_q_k_v_bias, weight = encoder_tp_encoders_6_self_attn_linear_q_k_v_weight, x = output_227)[name = tensor("linear_224")]; + tensor tile_56 = const()[name = tensor("tile_56"), val = tensor([512, 512, 512])]; + tensor var_6003_axis_0 = const()[name = tensor("op_6003_axis_0"), val = tensor(-1)]; + tensor var_6003_0, tensor var_6003_1, tensor var_6003_2 = split(axis = var_6003_axis_0, split_sizes = tile_56, x = var_6002)[name = tensor("op_6003")]; + tensor var_6007 = const()[name = tensor("op_6007"), val = tensor([1, 1804, 4, 128])]; + tensor var_6008 = reshape(shape = var_6007, x = var_6003_0)[name = tensor("op_6008")]; + tensor var_6010 = const()[name = tensor("op_6010"), val = tensor([1, 1804, 4, 128])]; + tensor var_6011 = reshape(shape = var_6010, x = var_6003_1)[name = tensor("op_6011")]; + tensor var_6013 = const()[name = tensor("op_6013"), val = tensor([1, 1804, 4, 128])]; + tensor var_6014 = reshape(shape = var_6013, x = var_6003_2)[name = tensor("op_6014")]; + tensor value_113_perm_0 = const()[name = tensor("value_113_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor inputs_113 = mul(x = var_6003_2, y = mask_7)[name = tensor("inputs_113")]; + tensor input_1695_perm_0 = const()[name = tensor("input_1695_perm_0"), val = tensor([0, 2, 1])]; + tensor const_639 = const()[name = tensor("const_639"), val = tensor(0x0p+0)]; + tensor input_1697_pad_0 = const()[name = tensor("input_1697_pad_0"), val = tensor([0, 0, 0, 0, 5, 5])]; + tensor input_1697_mode_0 = const()[name = tensor("input_1697_mode_0"), val = tensor("constant")]; + tensor input_1695 = transpose(perm = input_1695_perm_0, x = inputs_113)[name = tensor("transpose_432")]; + tensor input_1697 = pad(constant_val = const_639, mode = input_1697_mode_0, pad = input_1697_pad_0, x = input_1695)[name = tensor("input_1697")]; + tensor x_565_pad_type_0 = const()[name = tensor("x_565_pad_type_0"), val = tensor("valid")]; + tensor x_565_groups_0 = const()[name = tensor("x_565_groups_0"), val = tensor(512)]; + tensor x_565_strides_0 = const()[name = tensor("x_565_strides_0"), val = tensor([1])]; + tensor x_565_pad_0 = const()[name = tensor("x_565_pad_0"), val = tensor([0, 0])]; + tensor x_565_dilations_0 = const()[name = tensor("x_565_dilations_0"), val = tensor([1])]; + tensor x_565 = conv(dilations = x_565_dilations_0, groups = x_565_groups_0, pad = x_565_pad_0, pad_type = x_565_pad_type_0, strides = x_565_strides_0, weight = encoder_tp_encoders_6_self_attn_fsmn_block_weight, x = input_1697)[name = tensor("x_565")]; + tensor x_567_perm_0 = const()[name = tensor("x_567_perm_0"), val = tensor([0, 2, 1])]; + tensor x_567 = transpose(perm = x_567_perm_0, x = x_565)[name = tensor("transpose_431")]; + tensor input_1699 = add(x = x_567, y = inputs_113)[name = tensor("input_1699")]; + tensor fsmn_memory_113 = mul(x = input_1699, y = mask_7)[name = tensor("fsmn_memory_113")]; + tensor var_6033 = const()[name = tensor("op_6033"), val = tensor(0x1.6a09e6p-4)]; + tensor q_h_227 = mul(x = var_6008, y = var_6033)[name = tensor("q_h_227")]; + tensor scores_225_transpose_x_0 = const()[name = tensor("scores_225_transpose_x_0"), val = tensor(false)]; + tensor scores_225_transpose_y_0 = const()[name = tensor("scores_225_transpose_y_0"), val = tensor(false)]; + tensor transpose_322_perm_0 = const()[name = tensor("transpose_322_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_323_perm_0 = const()[name = tensor("transpose_323_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_323 = transpose(perm = transpose_323_perm_0, x = var_6011)[name = tensor("transpose_429")]; + tensor transpose_322 = transpose(perm = transpose_322_perm_0, x = q_h_227)[name = tensor("transpose_430")]; + tensor scores_225 = matmul(transpose_x = scores_225_transpose_x_0, transpose_y = scores_225_transpose_y_0, x = transpose_322, y = transpose_323)[name = tensor("scores_225")]; + tensor scores_227 = select(a = var_48, b = scores_225, cond = mask_9)[name = tensor("scores_227")]; + tensor var_6041 = softmax(axis = var_61, x = scores_227)[name = tensor("op_6041")]; + tensor input_1701 = select(a = var_53, b = var_6041, cond = mask_9)[name = tensor("input_1701")]; + tensor x_571_transpose_x_0 = const()[name = tensor("x_571_transpose_x_0"), val = tensor(false)]; + tensor x_571_transpose_y_0 = const()[name = tensor("x_571_transpose_y_0"), val = tensor(false)]; + tensor value_113 = transpose(perm = value_113_perm_0, x = var_6014)[name = tensor("transpose_433")]; + tensor x_571 = matmul(transpose_x = x_571_transpose_x_0, transpose_y = x_571_transpose_y_0, x = input_1701, y = value_113)[name = tensor("x_571")]; + tensor var_6045_perm_0 = const()[name = tensor("op_6045_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_6047 = const()[name = tensor("op_6047"), val = tensor([1, -1, 512])]; + tensor var_6045 = transpose(perm = var_6045_perm_0, x = x_571)[name = tensor("transpose_428")]; + tensor input_1703 = reshape(shape = var_6047, x = var_6045)[name = tensor("input_1703")]; + tensor att_outs_113 = linear(bias = encoder_tp_encoders_6_self_attn_linear_out_bias, weight = encoder_tp_encoders_6_self_attn_linear_out_weight, x = input_1703)[name = tensor("linear_225")]; + tensor input_1705 = add(x = att_outs_113, y = fsmn_memory_113)[name = tensor("input_1705")]; + tensor input_1707 = add(x = input_1691, y = input_1705)[name = tensor("input_1707")]; + tensor const_641 = const()[name = tensor("const_641"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939982720)))]; + tensor const_642 = const()[name = tensor("const_642"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939984832)))]; + tensor output_229_axes_0 = const()[name = tensor("output_229_axes_0"), val = tensor([-1])]; + tensor output_229 = layer_norm(axes = output_229_axes_0, beta = const_642, epsilon = var_46, gamma = const_641, x = input_1707)[name = tensor("output_229")]; + tensor input_1713 = linear(bias = encoder_tp_encoders_6_feed_forward_w_1_bias, weight = encoder_tp_encoders_6_feed_forward_w_1_weight, x = output_229)[name = tensor("linear_226")]; + tensor input_1715 = relu(x = input_1713)[name = tensor("input_1715")]; + tensor input_1719 = linear(bias = encoder_tp_encoders_6_feed_forward_w_2_bias, weight = encoder_tp_encoders_6_feed_forward_w_2_weight, x = input_1715)[name = tensor("linear_227")]; + tensor input_1721 = add(x = input_1707, y = input_1719)[name = tensor("input_1721")]; + tensor const_643 = const()[name = tensor("const_643"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939986944)))]; + tensor const_644 = const()[name = tensor("const_644"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939989056)))]; + tensor output_231_axes_0 = const()[name = tensor("output_231_axes_0"), val = tensor([-1])]; + tensor output_231 = layer_norm(axes = output_231_axes_0, beta = const_644, epsilon = var_46, gamma = const_643, x = input_1721)[name = tensor("output_231")]; + tensor var_6104 = linear(bias = encoder_tp_encoders_7_self_attn_linear_q_k_v_bias, weight = encoder_tp_encoders_7_self_attn_linear_q_k_v_weight, x = output_231)[name = tensor("linear_228")]; + tensor tile_57 = const()[name = tensor("tile_57"), val = tensor([512, 512, 512])]; + tensor var_6105_axis_0 = const()[name = tensor("op_6105_axis_0"), val = tensor(-1)]; + tensor var_6105_0, tensor var_6105_1, tensor var_6105_2 = split(axis = var_6105_axis_0, split_sizes = tile_57, x = var_6104)[name = tensor("op_6105")]; + tensor var_6109 = const()[name = tensor("op_6109"), val = tensor([1, 1804, 4, 128])]; + tensor var_6110 = reshape(shape = var_6109, x = var_6105_0)[name = tensor("op_6110")]; + tensor var_6112 = const()[name = tensor("op_6112"), val = tensor([1, 1804, 4, 128])]; + tensor var_6113 = reshape(shape = var_6112, x = var_6105_1)[name = tensor("op_6113")]; + tensor var_6115 = const()[name = tensor("op_6115"), val = tensor([1, 1804, 4, 128])]; + tensor var_6116 = reshape(shape = var_6115, x = var_6105_2)[name = tensor("op_6116")]; + tensor value_115_perm_0 = const()[name = tensor("value_115_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor inputs_115 = mul(x = var_6105_2, y = mask_7)[name = tensor("inputs_115")]; + tensor input_1725_perm_0 = const()[name = tensor("input_1725_perm_0"), val = tensor([0, 2, 1])]; + tensor const_650 = const()[name = tensor("const_650"), val = tensor(0x0p+0)]; + tensor input_1727_pad_0 = const()[name = tensor("input_1727_pad_0"), val = tensor([0, 0, 0, 0, 5, 5])]; + tensor input_1727_mode_0 = const()[name = tensor("input_1727_mode_0"), val = tensor("constant")]; + tensor input_1725 = transpose(perm = input_1725_perm_0, x = inputs_115)[name = tensor("transpose_426")]; + tensor input_1727 = pad(constant_val = const_650, mode = input_1727_mode_0, pad = input_1727_pad_0, x = input_1725)[name = tensor("input_1727")]; + tensor x_575_pad_type_0 = const()[name = tensor("x_575_pad_type_0"), val = tensor("valid")]; + tensor x_575_groups_0 = const()[name = tensor("x_575_groups_0"), val = tensor(512)]; + tensor x_575_strides_0 = const()[name = tensor("x_575_strides_0"), val = tensor([1])]; + tensor x_575_pad_0 = const()[name = tensor("x_575_pad_0"), val = tensor([0, 0])]; + tensor x_575_dilations_0 = const()[name = tensor("x_575_dilations_0"), val = tensor([1])]; + tensor x_575 = conv(dilations = x_575_dilations_0, groups = x_575_groups_0, pad = x_575_pad_0, pad_type = x_575_pad_type_0, strides = x_575_strides_0, weight = encoder_tp_encoders_7_self_attn_fsmn_block_weight, x = input_1727)[name = tensor("x_575")]; + tensor x_577_perm_0 = const()[name = tensor("x_577_perm_0"), val = tensor([0, 2, 1])]; + tensor x_577 = transpose(perm = x_577_perm_0, x = x_575)[name = tensor("transpose_425")]; + tensor input_1729 = add(x = x_577, y = inputs_115)[name = tensor("input_1729")]; + tensor fsmn_memory_115 = mul(x = input_1729, y = mask_7)[name = tensor("fsmn_memory_115")]; + tensor var_6135 = const()[name = tensor("op_6135"), val = tensor(0x1.6a09e6p-4)]; + tensor q_h_231 = mul(x = var_6110, y = var_6135)[name = tensor("q_h_231")]; + tensor scores_229_transpose_x_0 = const()[name = tensor("scores_229_transpose_x_0"), val = tensor(false)]; + tensor scores_229_transpose_y_0 = const()[name = tensor("scores_229_transpose_y_0"), val = tensor(false)]; + tensor transpose_324_perm_0 = const()[name = tensor("transpose_324_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_325_perm_0 = const()[name = tensor("transpose_325_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_325 = transpose(perm = transpose_325_perm_0, x = var_6113)[name = tensor("transpose_423")]; + tensor transpose_324 = transpose(perm = transpose_324_perm_0, x = q_h_231)[name = tensor("transpose_424")]; + tensor scores_229 = matmul(transpose_x = scores_229_transpose_x_0, transpose_y = scores_229_transpose_y_0, x = transpose_324, y = transpose_325)[name = tensor("scores_229")]; + tensor scores_231 = select(a = var_48, b = scores_229, cond = mask_9)[name = tensor("scores_231")]; + tensor var_6143 = softmax(axis = var_61, x = scores_231)[name = tensor("op_6143")]; + tensor input_1731 = select(a = var_53, b = var_6143, cond = mask_9)[name = tensor("input_1731")]; + tensor x_581_transpose_x_0 = const()[name = tensor("x_581_transpose_x_0"), val = tensor(false)]; + tensor x_581_transpose_y_0 = const()[name = tensor("x_581_transpose_y_0"), val = tensor(false)]; + tensor value_115 = transpose(perm = value_115_perm_0, x = var_6116)[name = tensor("transpose_427")]; + tensor x_581 = matmul(transpose_x = x_581_transpose_x_0, transpose_y = x_581_transpose_y_0, x = input_1731, y = value_115)[name = tensor("x_581")]; + tensor var_6147_perm_0 = const()[name = tensor("op_6147_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_6149 = const()[name = tensor("op_6149"), val = tensor([1, -1, 512])]; + tensor var_6147 = transpose(perm = var_6147_perm_0, x = x_581)[name = tensor("transpose_422")]; + tensor input_1733 = reshape(shape = var_6149, x = var_6147)[name = tensor("input_1733")]; + tensor att_outs_115 = linear(bias = encoder_tp_encoders_7_self_attn_linear_out_bias, weight = encoder_tp_encoders_7_self_attn_linear_out_weight, x = input_1733)[name = tensor("linear_229")]; + tensor input_1735 = add(x = att_outs_115, y = fsmn_memory_115)[name = tensor("input_1735")]; + tensor input_1737 = add(x = input_1721, y = input_1735)[name = tensor("input_1737")]; + tensor const_652 = const()[name = tensor("const_652"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939991168)))]; + tensor const_653 = const()[name = tensor("const_653"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939993280)))]; + tensor output_233_axes_0 = const()[name = tensor("output_233_axes_0"), val = tensor([-1])]; + tensor output_233 = layer_norm(axes = output_233_axes_0, beta = const_653, epsilon = var_46, gamma = const_652, x = input_1737)[name = tensor("output_233")]; + tensor input_1743 = linear(bias = encoder_tp_encoders_7_feed_forward_w_1_bias, weight = encoder_tp_encoders_7_feed_forward_w_1_weight, x = output_233)[name = tensor("linear_230")]; + tensor input_1745 = relu(x = input_1743)[name = tensor("input_1745")]; + tensor input_1749 = linear(bias = encoder_tp_encoders_7_feed_forward_w_2_bias, weight = encoder_tp_encoders_7_feed_forward_w_2_weight, x = input_1745)[name = tensor("linear_231")]; + tensor input_1751 = add(x = input_1737, y = input_1749)[name = tensor("input_1751")]; + tensor const_654 = const()[name = tensor("const_654"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939995392)))]; + tensor const_655 = const()[name = tensor("const_655"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939997504)))]; + tensor output_235_axes_0 = const()[name = tensor("output_235_axes_0"), val = tensor([-1])]; + tensor output_235 = layer_norm(axes = output_235_axes_0, beta = const_655, epsilon = var_46, gamma = const_654, x = input_1751)[name = tensor("output_235")]; + tensor var_6206 = linear(bias = encoder_tp_encoders_8_self_attn_linear_q_k_v_bias, weight = encoder_tp_encoders_8_self_attn_linear_q_k_v_weight, x = output_235)[name = tensor("linear_232")]; + tensor tile_58 = const()[name = tensor("tile_58"), val = tensor([512, 512, 512])]; + tensor var_6207_axis_0 = const()[name = tensor("op_6207_axis_0"), val = tensor(-1)]; + tensor var_6207_0, tensor var_6207_1, tensor var_6207_2 = split(axis = var_6207_axis_0, split_sizes = tile_58, x = var_6206)[name = tensor("op_6207")]; + tensor var_6211 = const()[name = tensor("op_6211"), val = tensor([1, 1804, 4, 128])]; + tensor var_6212 = reshape(shape = var_6211, x = var_6207_0)[name = tensor("op_6212")]; + tensor var_6214 = const()[name = tensor("op_6214"), val = tensor([1, 1804, 4, 128])]; + tensor var_6215 = reshape(shape = var_6214, x = var_6207_1)[name = tensor("op_6215")]; + tensor var_6217 = const()[name = tensor("op_6217"), val = tensor([1, 1804, 4, 128])]; + tensor var_6218 = reshape(shape = var_6217, x = var_6207_2)[name = tensor("op_6218")]; + tensor value_117_perm_0 = const()[name = tensor("value_117_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor inputs_117 = mul(x = var_6207_2, y = mask_7)[name = tensor("inputs_117")]; + tensor input_1755_perm_0 = const()[name = tensor("input_1755_perm_0"), val = tensor([0, 2, 1])]; + tensor const_661 = const()[name = tensor("const_661"), val = tensor(0x0p+0)]; + tensor input_1757_pad_0 = const()[name = tensor("input_1757_pad_0"), val = tensor([0, 0, 0, 0, 5, 5])]; + tensor input_1757_mode_0 = const()[name = tensor("input_1757_mode_0"), val = tensor("constant")]; + tensor input_1755 = transpose(perm = input_1755_perm_0, x = inputs_117)[name = tensor("transpose_420")]; + tensor input_1757 = pad(constant_val = const_661, mode = input_1757_mode_0, pad = input_1757_pad_0, x = input_1755)[name = tensor("input_1757")]; + tensor x_585_pad_type_0 = const()[name = tensor("x_585_pad_type_0"), val = tensor("valid")]; + tensor x_585_groups_0 = const()[name = tensor("x_585_groups_0"), val = tensor(512)]; + tensor x_585_strides_0 = const()[name = tensor("x_585_strides_0"), val = tensor([1])]; + tensor x_585_pad_0 = const()[name = tensor("x_585_pad_0"), val = tensor([0, 0])]; + tensor x_585_dilations_0 = const()[name = tensor("x_585_dilations_0"), val = tensor([1])]; + tensor x_585 = conv(dilations = x_585_dilations_0, groups = x_585_groups_0, pad = x_585_pad_0, pad_type = x_585_pad_type_0, strides = x_585_strides_0, weight = encoder_tp_encoders_8_self_attn_fsmn_block_weight, x = input_1757)[name = tensor("x_585")]; + tensor x_587_perm_0 = const()[name = tensor("x_587_perm_0"), val = tensor([0, 2, 1])]; + tensor x_587 = transpose(perm = x_587_perm_0, x = x_585)[name = tensor("transpose_419")]; + tensor input_1759 = add(x = x_587, y = inputs_117)[name = tensor("input_1759")]; + tensor fsmn_memory_117 = mul(x = input_1759, y = mask_7)[name = tensor("fsmn_memory_117")]; + tensor var_6237 = const()[name = tensor("op_6237"), val = tensor(0x1.6a09e6p-4)]; + tensor q_h_235 = mul(x = var_6212, y = var_6237)[name = tensor("q_h_235")]; + tensor scores_233_transpose_x_0 = const()[name = tensor("scores_233_transpose_x_0"), val = tensor(false)]; + tensor scores_233_transpose_y_0 = const()[name = tensor("scores_233_transpose_y_0"), val = tensor(false)]; + tensor transpose_326_perm_0 = const()[name = tensor("transpose_326_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_327_perm_0 = const()[name = tensor("transpose_327_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_327 = transpose(perm = transpose_327_perm_0, x = var_6215)[name = tensor("transpose_417")]; + tensor transpose_326 = transpose(perm = transpose_326_perm_0, x = q_h_235)[name = tensor("transpose_418")]; + tensor scores_233 = matmul(transpose_x = scores_233_transpose_x_0, transpose_y = scores_233_transpose_y_0, x = transpose_326, y = transpose_327)[name = tensor("scores_233")]; + tensor scores_235 = select(a = var_48, b = scores_233, cond = mask_9)[name = tensor("scores_235")]; + tensor var_6245 = softmax(axis = var_61, x = scores_235)[name = tensor("op_6245")]; + tensor input_1761 = select(a = var_53, b = var_6245, cond = mask_9)[name = tensor("input_1761")]; + tensor x_591_transpose_x_0 = const()[name = tensor("x_591_transpose_x_0"), val = tensor(false)]; + tensor x_591_transpose_y_0 = const()[name = tensor("x_591_transpose_y_0"), val = tensor(false)]; + tensor value_117 = transpose(perm = value_117_perm_0, x = var_6218)[name = tensor("transpose_421")]; + tensor x_591 = matmul(transpose_x = x_591_transpose_x_0, transpose_y = x_591_transpose_y_0, x = input_1761, y = value_117)[name = tensor("x_591")]; + tensor var_6249_perm_0 = const()[name = tensor("op_6249_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_6251 = const()[name = tensor("op_6251"), val = tensor([1, -1, 512])]; + tensor var_6249 = transpose(perm = var_6249_perm_0, x = x_591)[name = tensor("transpose_416")]; + tensor input_1763 = reshape(shape = var_6251, x = var_6249)[name = tensor("input_1763")]; + tensor att_outs_117 = linear(bias = encoder_tp_encoders_8_self_attn_linear_out_bias, weight = encoder_tp_encoders_8_self_attn_linear_out_weight, x = input_1763)[name = tensor("linear_233")]; + tensor input_1765 = add(x = att_outs_117, y = fsmn_memory_117)[name = tensor("input_1765")]; + tensor input_1767 = add(x = input_1751, y = input_1765)[name = tensor("input_1767")]; + tensor const_663 = const()[name = tensor("const_663"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(939999616)))]; + tensor const_664 = const()[name = tensor("const_664"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(940001728)))]; + tensor output_237_axes_0 = const()[name = tensor("output_237_axes_0"), val = tensor([-1])]; + tensor output_237 = layer_norm(axes = output_237_axes_0, beta = const_664, epsilon = var_46, gamma = const_663, x = input_1767)[name = tensor("output_237")]; + tensor input_1773 = linear(bias = encoder_tp_encoders_8_feed_forward_w_1_bias, weight = encoder_tp_encoders_8_feed_forward_w_1_weight, x = output_237)[name = tensor("linear_234")]; + tensor input_1775 = relu(x = input_1773)[name = tensor("input_1775")]; + tensor input_1779 = linear(bias = encoder_tp_encoders_8_feed_forward_w_2_bias, weight = encoder_tp_encoders_8_feed_forward_w_2_weight, x = input_1775)[name = tensor("linear_235")]; + tensor input_1781 = add(x = input_1767, y = input_1779)[name = tensor("input_1781")]; + tensor const_665 = const()[name = tensor("const_665"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(940003840)))]; + tensor const_666 = const()[name = tensor("const_666"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(940005952)))]; + tensor output_239_axes_0 = const()[name = tensor("output_239_axes_0"), val = tensor([-1])]; + tensor output_239 = layer_norm(axes = output_239_axes_0, beta = const_666, epsilon = var_46, gamma = const_665, x = input_1781)[name = tensor("output_239")]; + tensor var_6308 = linear(bias = encoder_tp_encoders_9_self_attn_linear_q_k_v_bias, weight = encoder_tp_encoders_9_self_attn_linear_q_k_v_weight, x = output_239)[name = tensor("linear_236")]; + tensor tile_59 = const()[name = tensor("tile_59"), val = tensor([512, 512, 512])]; + tensor var_6309_axis_0 = const()[name = tensor("op_6309_axis_0"), val = tensor(-1)]; + tensor var_6309_0, tensor var_6309_1, tensor var_6309_2 = split(axis = var_6309_axis_0, split_sizes = tile_59, x = var_6308)[name = tensor("op_6309")]; + tensor var_6313 = const()[name = tensor("op_6313"), val = tensor([1, 1804, 4, 128])]; + tensor var_6314 = reshape(shape = var_6313, x = var_6309_0)[name = tensor("op_6314")]; + tensor var_6316 = const()[name = tensor("op_6316"), val = tensor([1, 1804, 4, 128])]; + tensor var_6317 = reshape(shape = var_6316, x = var_6309_1)[name = tensor("op_6317")]; + tensor var_6319 = const()[name = tensor("op_6319"), val = tensor([1, 1804, 4, 128])]; + tensor var_6320 = reshape(shape = var_6319, x = var_6309_2)[name = tensor("op_6320")]; + tensor value_119_perm_0 = const()[name = tensor("value_119_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor inputs_119 = mul(x = var_6309_2, y = mask_7)[name = tensor("inputs_119")]; + tensor input_1785_perm_0 = const()[name = tensor("input_1785_perm_0"), val = tensor([0, 2, 1])]; + tensor const_672 = const()[name = tensor("const_672"), val = tensor(0x0p+0)]; + tensor input_1787_pad_0 = const()[name = tensor("input_1787_pad_0"), val = tensor([0, 0, 0, 0, 5, 5])]; + tensor input_1787_mode_0 = const()[name = tensor("input_1787_mode_0"), val = tensor("constant")]; + tensor input_1785 = transpose(perm = input_1785_perm_0, x = inputs_119)[name = tensor("transpose_414")]; + tensor input_1787 = pad(constant_val = const_672, mode = input_1787_mode_0, pad = input_1787_pad_0, x = input_1785)[name = tensor("input_1787")]; + tensor x_595_pad_type_0 = const()[name = tensor("x_595_pad_type_0"), val = tensor("valid")]; + tensor x_595_groups_0 = const()[name = tensor("x_595_groups_0"), val = tensor(512)]; + tensor x_595_strides_0 = const()[name = tensor("x_595_strides_0"), val = tensor([1])]; + tensor x_595_pad_0 = const()[name = tensor("x_595_pad_0"), val = tensor([0, 0])]; + tensor x_595_dilations_0 = const()[name = tensor("x_595_dilations_0"), val = tensor([1])]; + tensor x_595 = conv(dilations = x_595_dilations_0, groups = x_595_groups_0, pad = x_595_pad_0, pad_type = x_595_pad_type_0, strides = x_595_strides_0, weight = encoder_tp_encoders_9_self_attn_fsmn_block_weight, x = input_1787)[name = tensor("x_595")]; + tensor x_597_perm_0 = const()[name = tensor("x_597_perm_0"), val = tensor([0, 2, 1])]; + tensor x_597 = transpose(perm = x_597_perm_0, x = x_595)[name = tensor("transpose_413")]; + tensor input_1789 = add(x = x_597, y = inputs_119)[name = tensor("input_1789")]; + tensor fsmn_memory_119 = mul(x = input_1789, y = mask_7)[name = tensor("fsmn_memory_119")]; + tensor var_6339 = const()[name = tensor("op_6339"), val = tensor(0x1.6a09e6p-4)]; + tensor q_h_239 = mul(x = var_6314, y = var_6339)[name = tensor("q_h_239")]; + tensor scores_237_transpose_x_0 = const()[name = tensor("scores_237_transpose_x_0"), val = tensor(false)]; + tensor scores_237_transpose_y_0 = const()[name = tensor("scores_237_transpose_y_0"), val = tensor(false)]; + tensor transpose_328_perm_0 = const()[name = tensor("transpose_328_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_329_perm_0 = const()[name = tensor("transpose_329_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_329 = transpose(perm = transpose_329_perm_0, x = var_6317)[name = tensor("transpose_411")]; + tensor transpose_328 = transpose(perm = transpose_328_perm_0, x = q_h_239)[name = tensor("transpose_412")]; + tensor scores_237 = matmul(transpose_x = scores_237_transpose_x_0, transpose_y = scores_237_transpose_y_0, x = transpose_328, y = transpose_329)[name = tensor("scores_237")]; + tensor scores_239 = select(a = var_48, b = scores_237, cond = mask_9)[name = tensor("scores_239")]; + tensor var_6347 = softmax(axis = var_61, x = scores_239)[name = tensor("op_6347")]; + tensor input_1791 = select(a = var_53, b = var_6347, cond = mask_9)[name = tensor("input_1791")]; + tensor x_601_transpose_x_0 = const()[name = tensor("x_601_transpose_x_0"), val = tensor(false)]; + tensor x_601_transpose_y_0 = const()[name = tensor("x_601_transpose_y_0"), val = tensor(false)]; + tensor value_119 = transpose(perm = value_119_perm_0, x = var_6320)[name = tensor("transpose_415")]; + tensor x_601 = matmul(transpose_x = x_601_transpose_x_0, transpose_y = x_601_transpose_y_0, x = input_1791, y = value_119)[name = tensor("x_601")]; + tensor var_6351_perm_0 = const()[name = tensor("op_6351_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_6353 = const()[name = tensor("op_6353"), val = tensor([1, -1, 512])]; + tensor var_6351 = transpose(perm = var_6351_perm_0, x = x_601)[name = tensor("transpose_410")]; + tensor input_1793 = reshape(shape = var_6353, x = var_6351)[name = tensor("input_1793")]; + tensor att_outs_119 = linear(bias = encoder_tp_encoders_9_self_attn_linear_out_bias, weight = encoder_tp_encoders_9_self_attn_linear_out_weight, x = input_1793)[name = tensor("linear_237")]; + tensor input_1795 = add(x = att_outs_119, y = fsmn_memory_119)[name = tensor("input_1795")]; + tensor input_1797 = add(x = input_1781, y = input_1795)[name = tensor("input_1797")]; + tensor const_674 = const()[name = tensor("const_674"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(940008064)))]; + tensor const_675 = const()[name = tensor("const_675"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(940010176)))]; + tensor output_241_axes_0 = const()[name = tensor("output_241_axes_0"), val = tensor([-1])]; + tensor output_241 = layer_norm(axes = output_241_axes_0, beta = const_675, epsilon = var_46, gamma = const_674, x = input_1797)[name = tensor("output_241")]; + tensor input_1803 = linear(bias = encoder_tp_encoders_9_feed_forward_w_1_bias, weight = encoder_tp_encoders_9_feed_forward_w_1_weight, x = output_241)[name = tensor("linear_238")]; + tensor input_1805 = relu(x = input_1803)[name = tensor("input_1805")]; + tensor input_1809 = linear(bias = encoder_tp_encoders_9_feed_forward_w_2_bias, weight = encoder_tp_encoders_9_feed_forward_w_2_weight, x = input_1805)[name = tensor("linear_239")]; + tensor input_1811 = add(x = input_1797, y = input_1809)[name = tensor("input_1811")]; + tensor const_676 = const()[name = tensor("const_676"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(940012288)))]; + tensor const_677 = const()[name = tensor("const_677"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(940014400)))]; + tensor output_243_axes_0 = const()[name = tensor("output_243_axes_0"), val = tensor([-1])]; + tensor output_243 = layer_norm(axes = output_243_axes_0, beta = const_677, epsilon = var_46, gamma = const_676, x = input_1811)[name = tensor("output_243")]; + tensor var_6410 = linear(bias = encoder_tp_encoders_10_self_attn_linear_q_k_v_bias, weight = encoder_tp_encoders_10_self_attn_linear_q_k_v_weight, x = output_243)[name = tensor("linear_240")]; + tensor tile_60 = const()[name = tensor("tile_60"), val = tensor([512, 512, 512])]; + tensor var_6411_axis_0 = const()[name = tensor("op_6411_axis_0"), val = tensor(-1)]; + tensor var_6411_0, tensor var_6411_1, tensor var_6411_2 = split(axis = var_6411_axis_0, split_sizes = tile_60, x = var_6410)[name = tensor("op_6411")]; + tensor var_6415 = const()[name = tensor("op_6415"), val = tensor([1, 1804, 4, 128])]; + tensor var_6416 = reshape(shape = var_6415, x = var_6411_0)[name = tensor("op_6416")]; + tensor var_6418 = const()[name = tensor("op_6418"), val = tensor([1, 1804, 4, 128])]; + tensor var_6419 = reshape(shape = var_6418, x = var_6411_1)[name = tensor("op_6419")]; + tensor var_6421 = const()[name = tensor("op_6421"), val = tensor([1, 1804, 4, 128])]; + tensor var_6422 = reshape(shape = var_6421, x = var_6411_2)[name = tensor("op_6422")]; + tensor value_121_perm_0 = const()[name = tensor("value_121_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor inputs_121 = mul(x = var_6411_2, y = mask_7)[name = tensor("inputs_121")]; + tensor input_1815_perm_0 = const()[name = tensor("input_1815_perm_0"), val = tensor([0, 2, 1])]; + tensor const_683 = const()[name = tensor("const_683"), val = tensor(0x0p+0)]; + tensor input_1817_pad_0 = const()[name = tensor("input_1817_pad_0"), val = tensor([0, 0, 0, 0, 5, 5])]; + tensor input_1817_mode_0 = const()[name = tensor("input_1817_mode_0"), val = tensor("constant")]; + tensor input_1815 = transpose(perm = input_1815_perm_0, x = inputs_121)[name = tensor("transpose_408")]; + tensor input_1817 = pad(constant_val = const_683, mode = input_1817_mode_0, pad = input_1817_pad_0, x = input_1815)[name = tensor("input_1817")]; + tensor x_605_pad_type_0 = const()[name = tensor("x_605_pad_type_0"), val = tensor("valid")]; + tensor x_605_groups_0 = const()[name = tensor("x_605_groups_0"), val = tensor(512)]; + tensor x_605_strides_0 = const()[name = tensor("x_605_strides_0"), val = tensor([1])]; + tensor x_605_pad_0 = const()[name = tensor("x_605_pad_0"), val = tensor([0, 0])]; + tensor x_605_dilations_0 = const()[name = tensor("x_605_dilations_0"), val = tensor([1])]; + tensor x_605 = conv(dilations = x_605_dilations_0, groups = x_605_groups_0, pad = x_605_pad_0, pad_type = x_605_pad_type_0, strides = x_605_strides_0, weight = encoder_tp_encoders_10_self_attn_fsmn_block_weight, x = input_1817)[name = tensor("x_605")]; + tensor x_607_perm_0 = const()[name = tensor("x_607_perm_0"), val = tensor([0, 2, 1])]; + tensor x_607 = transpose(perm = x_607_perm_0, x = x_605)[name = tensor("transpose_407")]; + tensor input_1819 = add(x = x_607, y = inputs_121)[name = tensor("input_1819")]; + tensor fsmn_memory_121 = mul(x = input_1819, y = mask_7)[name = tensor("fsmn_memory_121")]; + tensor var_6441 = const()[name = tensor("op_6441"), val = tensor(0x1.6a09e6p-4)]; + tensor q_h_243 = mul(x = var_6416, y = var_6441)[name = tensor("q_h_243")]; + tensor scores_241_transpose_x_0 = const()[name = tensor("scores_241_transpose_x_0"), val = tensor(false)]; + tensor scores_241_transpose_y_0 = const()[name = tensor("scores_241_transpose_y_0"), val = tensor(false)]; + tensor transpose_330_perm_0 = const()[name = tensor("transpose_330_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_331_perm_0 = const()[name = tensor("transpose_331_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_331 = transpose(perm = transpose_331_perm_0, x = var_6419)[name = tensor("transpose_405")]; + tensor transpose_330 = transpose(perm = transpose_330_perm_0, x = q_h_243)[name = tensor("transpose_406")]; + tensor scores_241 = matmul(transpose_x = scores_241_transpose_x_0, transpose_y = scores_241_transpose_y_0, x = transpose_330, y = transpose_331)[name = tensor("scores_241")]; + tensor scores_243 = select(a = var_48, b = scores_241, cond = mask_9)[name = tensor("scores_243")]; + tensor var_6449 = softmax(axis = var_61, x = scores_243)[name = tensor("op_6449")]; + tensor input_1821 = select(a = var_53, b = var_6449, cond = mask_9)[name = tensor("input_1821")]; + tensor x_611_transpose_x_0 = const()[name = tensor("x_611_transpose_x_0"), val = tensor(false)]; + tensor x_611_transpose_y_0 = const()[name = tensor("x_611_transpose_y_0"), val = tensor(false)]; + tensor value_121 = transpose(perm = value_121_perm_0, x = var_6422)[name = tensor("transpose_409")]; + tensor x_611 = matmul(transpose_x = x_611_transpose_x_0, transpose_y = x_611_transpose_y_0, x = input_1821, y = value_121)[name = tensor("x_611")]; + tensor var_6453_perm_0 = const()[name = tensor("op_6453_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_6455 = const()[name = tensor("op_6455"), val = tensor([1, -1, 512])]; + tensor var_6453 = transpose(perm = var_6453_perm_0, x = x_611)[name = tensor("transpose_404")]; + tensor input_1823 = reshape(shape = var_6455, x = var_6453)[name = tensor("input_1823")]; + tensor att_outs_121 = linear(bias = encoder_tp_encoders_10_self_attn_linear_out_bias, weight = encoder_tp_encoders_10_self_attn_linear_out_weight, x = input_1823)[name = tensor("linear_241")]; + tensor input_1825 = add(x = att_outs_121, y = fsmn_memory_121)[name = tensor("input_1825")]; + tensor input_1827 = add(x = input_1811, y = input_1825)[name = tensor("input_1827")]; + tensor const_685 = const()[name = tensor("const_685"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(940016512)))]; + tensor const_686 = const()[name = tensor("const_686"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(940018624)))]; + tensor output_245_axes_0 = const()[name = tensor("output_245_axes_0"), val = tensor([-1])]; + tensor output_245 = layer_norm(axes = output_245_axes_0, beta = const_686, epsilon = var_46, gamma = const_685, x = input_1827)[name = tensor("output_245")]; + tensor input_1833 = linear(bias = encoder_tp_encoders_10_feed_forward_w_1_bias, weight = encoder_tp_encoders_10_feed_forward_w_1_weight, x = output_245)[name = tensor("linear_242")]; + tensor input_1835 = relu(x = input_1833)[name = tensor("input_1835")]; + tensor input_1839 = linear(bias = encoder_tp_encoders_10_feed_forward_w_2_bias, weight = encoder_tp_encoders_10_feed_forward_w_2_weight, x = input_1835)[name = tensor("linear_243")]; + tensor input_1841 = add(x = input_1827, y = input_1839)[name = tensor("input_1841")]; + tensor const_687 = const()[name = tensor("const_687"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(940020736)))]; + tensor const_688 = const()[name = tensor("const_688"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(940022848)))]; + tensor output_247_axes_0 = const()[name = tensor("output_247_axes_0"), val = tensor([-1])]; + tensor output_247 = layer_norm(axes = output_247_axes_0, beta = const_688, epsilon = var_46, gamma = const_687, x = input_1841)[name = tensor("output_247")]; + tensor var_6512 = linear(bias = encoder_tp_encoders_11_self_attn_linear_q_k_v_bias, weight = encoder_tp_encoders_11_self_attn_linear_q_k_v_weight, x = output_247)[name = tensor("linear_244")]; + tensor tile_61 = const()[name = tensor("tile_61"), val = tensor([512, 512, 512])]; + tensor var_6513_axis_0 = const()[name = tensor("op_6513_axis_0"), val = tensor(-1)]; + tensor var_6513_0, tensor var_6513_1, tensor var_6513_2 = split(axis = var_6513_axis_0, split_sizes = tile_61, x = var_6512)[name = tensor("op_6513")]; + tensor var_6517 = const()[name = tensor("op_6517"), val = tensor([1, 1804, 4, 128])]; + tensor var_6518 = reshape(shape = var_6517, x = var_6513_0)[name = tensor("op_6518")]; + tensor var_6520 = const()[name = tensor("op_6520"), val = tensor([1, 1804, 4, 128])]; + tensor var_6521 = reshape(shape = var_6520, x = var_6513_1)[name = tensor("op_6521")]; + tensor var_6523 = const()[name = tensor("op_6523"), val = tensor([1, 1804, 4, 128])]; + tensor var_6524 = reshape(shape = var_6523, x = var_6513_2)[name = tensor("op_6524")]; + tensor value_123_perm_0 = const()[name = tensor("value_123_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor inputs_123 = mul(x = var_6513_2, y = mask_7)[name = tensor("inputs_123")]; + tensor input_1845_perm_0 = const()[name = tensor("input_1845_perm_0"), val = tensor([0, 2, 1])]; + tensor const_694 = const()[name = tensor("const_694"), val = tensor(0x0p+0)]; + tensor input_1847_pad_0 = const()[name = tensor("input_1847_pad_0"), val = tensor([0, 0, 0, 0, 5, 5])]; + tensor input_1847_mode_0 = const()[name = tensor("input_1847_mode_0"), val = tensor("constant")]; + tensor input_1845 = transpose(perm = input_1845_perm_0, x = inputs_123)[name = tensor("transpose_402")]; + tensor input_1847 = pad(constant_val = const_694, mode = input_1847_mode_0, pad = input_1847_pad_0, x = input_1845)[name = tensor("input_1847")]; + tensor x_615_pad_type_0 = const()[name = tensor("x_615_pad_type_0"), val = tensor("valid")]; + tensor x_615_groups_0 = const()[name = tensor("x_615_groups_0"), val = tensor(512)]; + tensor x_615_strides_0 = const()[name = tensor("x_615_strides_0"), val = tensor([1])]; + tensor x_615_pad_0 = const()[name = tensor("x_615_pad_0"), val = tensor([0, 0])]; + tensor x_615_dilations_0 = const()[name = tensor("x_615_dilations_0"), val = tensor([1])]; + tensor x_615 = conv(dilations = x_615_dilations_0, groups = x_615_groups_0, pad = x_615_pad_0, pad_type = x_615_pad_type_0, strides = x_615_strides_0, weight = encoder_tp_encoders_11_self_attn_fsmn_block_weight, x = input_1847)[name = tensor("x_615")]; + tensor x_617_perm_0 = const()[name = tensor("x_617_perm_0"), val = tensor([0, 2, 1])]; + tensor x_617 = transpose(perm = x_617_perm_0, x = x_615)[name = tensor("transpose_401")]; + tensor input_1849 = add(x = x_617, y = inputs_123)[name = tensor("input_1849")]; + tensor fsmn_memory_123 = mul(x = input_1849, y = mask_7)[name = tensor("fsmn_memory_123")]; + tensor var_6543 = const()[name = tensor("op_6543"), val = tensor(0x1.6a09e6p-4)]; + tensor q_h_247 = mul(x = var_6518, y = var_6543)[name = tensor("q_h_247")]; + tensor scores_245_transpose_x_0 = const()[name = tensor("scores_245_transpose_x_0"), val = tensor(false)]; + tensor scores_245_transpose_y_0 = const()[name = tensor("scores_245_transpose_y_0"), val = tensor(false)]; + tensor transpose_332_perm_0 = const()[name = tensor("transpose_332_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_333_perm_0 = const()[name = tensor("transpose_333_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_333 = transpose(perm = transpose_333_perm_0, x = var_6521)[name = tensor("transpose_399")]; + tensor transpose_332 = transpose(perm = transpose_332_perm_0, x = q_h_247)[name = tensor("transpose_400")]; + tensor scores_245 = matmul(transpose_x = scores_245_transpose_x_0, transpose_y = scores_245_transpose_y_0, x = transpose_332, y = transpose_333)[name = tensor("scores_245")]; + tensor scores_247 = select(a = var_48, b = scores_245, cond = mask_9)[name = tensor("scores_247")]; + tensor var_6551 = softmax(axis = var_61, x = scores_247)[name = tensor("op_6551")]; + tensor input_1851 = select(a = var_53, b = var_6551, cond = mask_9)[name = tensor("input_1851")]; + tensor x_621_transpose_x_0 = const()[name = tensor("x_621_transpose_x_0"), val = tensor(false)]; + tensor x_621_transpose_y_0 = const()[name = tensor("x_621_transpose_y_0"), val = tensor(false)]; + tensor value_123 = transpose(perm = value_123_perm_0, x = var_6524)[name = tensor("transpose_403")]; + tensor x_621 = matmul(transpose_x = x_621_transpose_x_0, transpose_y = x_621_transpose_y_0, x = input_1851, y = value_123)[name = tensor("x_621")]; + tensor var_6555_perm_0 = const()[name = tensor("op_6555_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_6557 = const()[name = tensor("op_6557"), val = tensor([1, -1, 512])]; + tensor var_6555 = transpose(perm = var_6555_perm_0, x = x_621)[name = tensor("transpose_398")]; + tensor input_1853 = reshape(shape = var_6557, x = var_6555)[name = tensor("input_1853")]; + tensor att_outs_123 = linear(bias = encoder_tp_encoders_11_self_attn_linear_out_bias, weight = encoder_tp_encoders_11_self_attn_linear_out_weight, x = input_1853)[name = tensor("linear_245")]; + tensor input_1855 = add(x = att_outs_123, y = fsmn_memory_123)[name = tensor("input_1855")]; + tensor input_1857 = add(x = input_1841, y = input_1855)[name = tensor("input_1857")]; + tensor const_696 = const()[name = tensor("const_696"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(940024960)))]; + tensor const_697 = const()[name = tensor("const_697"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(940027072)))]; + tensor output_249_axes_0 = const()[name = tensor("output_249_axes_0"), val = tensor([-1])]; + tensor output_249 = layer_norm(axes = output_249_axes_0, beta = const_697, epsilon = var_46, gamma = const_696, x = input_1857)[name = tensor("output_249")]; + tensor input_1863 = linear(bias = encoder_tp_encoders_11_feed_forward_w_1_bias, weight = encoder_tp_encoders_11_feed_forward_w_1_weight, x = output_249)[name = tensor("linear_246")]; + tensor input_1865 = relu(x = input_1863)[name = tensor("input_1865")]; + tensor input_1869 = linear(bias = encoder_tp_encoders_11_feed_forward_w_2_bias, weight = encoder_tp_encoders_11_feed_forward_w_2_weight, x = input_1865)[name = tensor("linear_247")]; + tensor input_1871 = add(x = input_1857, y = input_1869)[name = tensor("input_1871")]; + tensor const_698 = const()[name = tensor("const_698"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(940029184)))]; + tensor const_699 = const()[name = tensor("const_699"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(940031296)))]; + tensor output_251_axes_0 = const()[name = tensor("output_251_axes_0"), val = tensor([-1])]; + tensor output_251 = layer_norm(axes = output_251_axes_0, beta = const_699, epsilon = var_46, gamma = const_698, x = input_1871)[name = tensor("output_251")]; + tensor var_6614 = linear(bias = encoder_tp_encoders_12_self_attn_linear_q_k_v_bias, weight = encoder_tp_encoders_12_self_attn_linear_q_k_v_weight, x = output_251)[name = tensor("linear_248")]; + tensor tile_62 = const()[name = tensor("tile_62"), val = tensor([512, 512, 512])]; + tensor var_6615_axis_0 = const()[name = tensor("op_6615_axis_0"), val = tensor(-1)]; + tensor var_6615_0, tensor var_6615_1, tensor var_6615_2 = split(axis = var_6615_axis_0, split_sizes = tile_62, x = var_6614)[name = tensor("op_6615")]; + tensor var_6619 = const()[name = tensor("op_6619"), val = tensor([1, 1804, 4, 128])]; + tensor var_6620 = reshape(shape = var_6619, x = var_6615_0)[name = tensor("op_6620")]; + tensor var_6622 = const()[name = tensor("op_6622"), val = tensor([1, 1804, 4, 128])]; + tensor var_6623 = reshape(shape = var_6622, x = var_6615_1)[name = tensor("op_6623")]; + tensor var_6625 = const()[name = tensor("op_6625"), val = tensor([1, 1804, 4, 128])]; + tensor var_6626 = reshape(shape = var_6625, x = var_6615_2)[name = tensor("op_6626")]; + tensor value_125_perm_0 = const()[name = tensor("value_125_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor inputs_125 = mul(x = var_6615_2, y = mask_7)[name = tensor("inputs_125")]; + tensor input_1875_perm_0 = const()[name = tensor("input_1875_perm_0"), val = tensor([0, 2, 1])]; + tensor const_705 = const()[name = tensor("const_705"), val = tensor(0x0p+0)]; + tensor input_1877_pad_0 = const()[name = tensor("input_1877_pad_0"), val = tensor([0, 0, 0, 0, 5, 5])]; + tensor input_1877_mode_0 = const()[name = tensor("input_1877_mode_0"), val = tensor("constant")]; + tensor input_1875 = transpose(perm = input_1875_perm_0, x = inputs_125)[name = tensor("transpose_396")]; + tensor input_1877 = pad(constant_val = const_705, mode = input_1877_mode_0, pad = input_1877_pad_0, x = input_1875)[name = tensor("input_1877")]; + tensor x_625_pad_type_0 = const()[name = tensor("x_625_pad_type_0"), val = tensor("valid")]; + tensor x_625_groups_0 = const()[name = tensor("x_625_groups_0"), val = tensor(512)]; + tensor x_625_strides_0 = const()[name = tensor("x_625_strides_0"), val = tensor([1])]; + tensor x_625_pad_0 = const()[name = tensor("x_625_pad_0"), val = tensor([0, 0])]; + tensor x_625_dilations_0 = const()[name = tensor("x_625_dilations_0"), val = tensor([1])]; + tensor x_625 = conv(dilations = x_625_dilations_0, groups = x_625_groups_0, pad = x_625_pad_0, pad_type = x_625_pad_type_0, strides = x_625_strides_0, weight = encoder_tp_encoders_12_self_attn_fsmn_block_weight, x = input_1877)[name = tensor("x_625")]; + tensor x_627_perm_0 = const()[name = tensor("x_627_perm_0"), val = tensor([0, 2, 1])]; + tensor x_627 = transpose(perm = x_627_perm_0, x = x_625)[name = tensor("transpose_395")]; + tensor input_1879 = add(x = x_627, y = inputs_125)[name = tensor("input_1879")]; + tensor fsmn_memory_125 = mul(x = input_1879, y = mask_7)[name = tensor("fsmn_memory_125")]; + tensor var_6645 = const()[name = tensor("op_6645"), val = tensor(0x1.6a09e6p-4)]; + tensor q_h_251 = mul(x = var_6620, y = var_6645)[name = tensor("q_h_251")]; + tensor scores_249_transpose_x_0 = const()[name = tensor("scores_249_transpose_x_0"), val = tensor(false)]; + tensor scores_249_transpose_y_0 = const()[name = tensor("scores_249_transpose_y_0"), val = tensor(false)]; + tensor transpose_334_perm_0 = const()[name = tensor("transpose_334_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_335_perm_0 = const()[name = tensor("transpose_335_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_335 = transpose(perm = transpose_335_perm_0, x = var_6623)[name = tensor("transpose_393")]; + tensor transpose_334 = transpose(perm = transpose_334_perm_0, x = q_h_251)[name = tensor("transpose_394")]; + tensor scores_249 = matmul(transpose_x = scores_249_transpose_x_0, transpose_y = scores_249_transpose_y_0, x = transpose_334, y = transpose_335)[name = tensor("scores_249")]; + tensor scores_251 = select(a = var_48, b = scores_249, cond = mask_9)[name = tensor("scores_251")]; + tensor var_6653 = softmax(axis = var_61, x = scores_251)[name = tensor("op_6653")]; + tensor input_1881 = select(a = var_53, b = var_6653, cond = mask_9)[name = tensor("input_1881")]; + tensor x_631_transpose_x_0 = const()[name = tensor("x_631_transpose_x_0"), val = tensor(false)]; + tensor x_631_transpose_y_0 = const()[name = tensor("x_631_transpose_y_0"), val = tensor(false)]; + tensor value_125 = transpose(perm = value_125_perm_0, x = var_6626)[name = tensor("transpose_397")]; + tensor x_631 = matmul(transpose_x = x_631_transpose_x_0, transpose_y = x_631_transpose_y_0, x = input_1881, y = value_125)[name = tensor("x_631")]; + tensor var_6657_perm_0 = const()[name = tensor("op_6657_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_6659 = const()[name = tensor("op_6659"), val = tensor([1, -1, 512])]; + tensor var_6657 = transpose(perm = var_6657_perm_0, x = x_631)[name = tensor("transpose_392")]; + tensor input_1883 = reshape(shape = var_6659, x = var_6657)[name = tensor("input_1883")]; + tensor att_outs_125 = linear(bias = encoder_tp_encoders_12_self_attn_linear_out_bias, weight = encoder_tp_encoders_12_self_attn_linear_out_weight, x = input_1883)[name = tensor("linear_249")]; + tensor input_1885 = add(x = att_outs_125, y = fsmn_memory_125)[name = tensor("input_1885")]; + tensor input_1887 = add(x = input_1871, y = input_1885)[name = tensor("input_1887")]; + tensor const_707 = const()[name = tensor("const_707"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(940033408)))]; + tensor const_708 = const()[name = tensor("const_708"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(940035520)))]; + tensor output_253_axes_0 = const()[name = tensor("output_253_axes_0"), val = tensor([-1])]; + tensor output_253 = layer_norm(axes = output_253_axes_0, beta = const_708, epsilon = var_46, gamma = const_707, x = input_1887)[name = tensor("output_253")]; + tensor input_1893 = linear(bias = encoder_tp_encoders_12_feed_forward_w_1_bias, weight = encoder_tp_encoders_12_feed_forward_w_1_weight, x = output_253)[name = tensor("linear_250")]; + tensor input_1895 = relu(x = input_1893)[name = tensor("input_1895")]; + tensor input_1899 = linear(bias = encoder_tp_encoders_12_feed_forward_w_2_bias, weight = encoder_tp_encoders_12_feed_forward_w_2_weight, x = input_1895)[name = tensor("linear_251")]; + tensor input_1901 = add(x = input_1887, y = input_1899)[name = tensor("input_1901")]; + tensor const_709 = const()[name = tensor("const_709"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(940037632)))]; + tensor const_710 = const()[name = tensor("const_710"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(940039744)))]; + tensor output_255_axes_0 = const()[name = tensor("output_255_axes_0"), val = tensor([-1])]; + tensor output_255 = layer_norm(axes = output_255_axes_0, beta = const_710, epsilon = var_46, gamma = const_709, x = input_1901)[name = tensor("output_255")]; + tensor var_6716 = linear(bias = encoder_tp_encoders_13_self_attn_linear_q_k_v_bias, weight = encoder_tp_encoders_13_self_attn_linear_q_k_v_weight, x = output_255)[name = tensor("linear_252")]; + tensor tile_63 = const()[name = tensor("tile_63"), val = tensor([512, 512, 512])]; + tensor var_6717_axis_0 = const()[name = tensor("op_6717_axis_0"), val = tensor(-1)]; + tensor var_6717_0, tensor var_6717_1, tensor var_6717_2 = split(axis = var_6717_axis_0, split_sizes = tile_63, x = var_6716)[name = tensor("op_6717")]; + tensor var_6721 = const()[name = tensor("op_6721"), val = tensor([1, 1804, 4, 128])]; + tensor var_6722 = reshape(shape = var_6721, x = var_6717_0)[name = tensor("op_6722")]; + tensor var_6724 = const()[name = tensor("op_6724"), val = tensor([1, 1804, 4, 128])]; + tensor var_6725 = reshape(shape = var_6724, x = var_6717_1)[name = tensor("op_6725")]; + tensor var_6727 = const()[name = tensor("op_6727"), val = tensor([1, 1804, 4, 128])]; + tensor var_6728 = reshape(shape = var_6727, x = var_6717_2)[name = tensor("op_6728")]; + tensor value_127_perm_0 = const()[name = tensor("value_127_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor inputs_127 = mul(x = var_6717_2, y = mask_7)[name = tensor("inputs_127")]; + tensor input_1905_perm_0 = const()[name = tensor("input_1905_perm_0"), val = tensor([0, 2, 1])]; + tensor const_716 = const()[name = tensor("const_716"), val = tensor(0x0p+0)]; + tensor input_1907_pad_0 = const()[name = tensor("input_1907_pad_0"), val = tensor([0, 0, 0, 0, 5, 5])]; + tensor input_1907_mode_0 = const()[name = tensor("input_1907_mode_0"), val = tensor("constant")]; + tensor input_1905 = transpose(perm = input_1905_perm_0, x = inputs_127)[name = tensor("transpose_390")]; + tensor input_1907 = pad(constant_val = const_716, mode = input_1907_mode_0, pad = input_1907_pad_0, x = input_1905)[name = tensor("input_1907")]; + tensor x_635_pad_type_0 = const()[name = tensor("x_635_pad_type_0"), val = tensor("valid")]; + tensor x_635_groups_0 = const()[name = tensor("x_635_groups_0"), val = tensor(512)]; + tensor x_635_strides_0 = const()[name = tensor("x_635_strides_0"), val = tensor([1])]; + tensor x_635_pad_0 = const()[name = tensor("x_635_pad_0"), val = tensor([0, 0])]; + tensor x_635_dilations_0 = const()[name = tensor("x_635_dilations_0"), val = tensor([1])]; + tensor x_635 = conv(dilations = x_635_dilations_0, groups = x_635_groups_0, pad = x_635_pad_0, pad_type = x_635_pad_type_0, strides = x_635_strides_0, weight = encoder_tp_encoders_13_self_attn_fsmn_block_weight, x = input_1907)[name = tensor("x_635")]; + tensor x_637_perm_0 = const()[name = tensor("x_637_perm_0"), val = tensor([0, 2, 1])]; + tensor x_637 = transpose(perm = x_637_perm_0, x = x_635)[name = tensor("transpose_389")]; + tensor input_1909 = add(x = x_637, y = inputs_127)[name = tensor("input_1909")]; + tensor fsmn_memory_127 = mul(x = input_1909, y = mask_7)[name = tensor("fsmn_memory_127")]; + tensor var_6747 = const()[name = tensor("op_6747"), val = tensor(0x1.6a09e6p-4)]; + tensor q_h_255 = mul(x = var_6722, y = var_6747)[name = tensor("q_h_255")]; + tensor scores_253_transpose_x_0 = const()[name = tensor("scores_253_transpose_x_0"), val = tensor(false)]; + tensor scores_253_transpose_y_0 = const()[name = tensor("scores_253_transpose_y_0"), val = tensor(false)]; + tensor transpose_336_perm_0 = const()[name = tensor("transpose_336_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_337_perm_0 = const()[name = tensor("transpose_337_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_337 = transpose(perm = transpose_337_perm_0, x = var_6725)[name = tensor("transpose_387")]; + tensor transpose_336 = transpose(perm = transpose_336_perm_0, x = q_h_255)[name = tensor("transpose_388")]; + tensor scores_253 = matmul(transpose_x = scores_253_transpose_x_0, transpose_y = scores_253_transpose_y_0, x = transpose_336, y = transpose_337)[name = tensor("scores_253")]; + tensor scores_255 = select(a = var_48, b = scores_253, cond = mask_9)[name = tensor("scores_255")]; + tensor var_6755 = softmax(axis = var_61, x = scores_255)[name = tensor("op_6755")]; + tensor input_1911 = select(a = var_53, b = var_6755, cond = mask_9)[name = tensor("input_1911")]; + tensor x_641_transpose_x_0 = const()[name = tensor("x_641_transpose_x_0"), val = tensor(false)]; + tensor x_641_transpose_y_0 = const()[name = tensor("x_641_transpose_y_0"), val = tensor(false)]; + tensor value_127 = transpose(perm = value_127_perm_0, x = var_6728)[name = tensor("transpose_391")]; + tensor x_641 = matmul(transpose_x = x_641_transpose_x_0, transpose_y = x_641_transpose_y_0, x = input_1911, y = value_127)[name = tensor("x_641")]; + tensor var_6759_perm_0 = const()[name = tensor("op_6759_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_6761 = const()[name = tensor("op_6761"), val = tensor([1, -1, 512])]; + tensor var_6759 = transpose(perm = var_6759_perm_0, x = x_641)[name = tensor("transpose_386")]; + tensor input_1913 = reshape(shape = var_6761, x = var_6759)[name = tensor("input_1913")]; + tensor att_outs_127 = linear(bias = encoder_tp_encoders_13_self_attn_linear_out_bias, weight = encoder_tp_encoders_13_self_attn_linear_out_weight, x = input_1913)[name = tensor("linear_253")]; + tensor input_1915 = add(x = att_outs_127, y = fsmn_memory_127)[name = tensor("input_1915")]; + tensor input_1917 = add(x = input_1901, y = input_1915)[name = tensor("input_1917")]; + tensor const_718 = const()[name = tensor("const_718"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(940041856)))]; + tensor const_719 = const()[name = tensor("const_719"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(940043968)))]; + tensor output_257_axes_0 = const()[name = tensor("output_257_axes_0"), val = tensor([-1])]; + tensor output_257 = layer_norm(axes = output_257_axes_0, beta = const_719, epsilon = var_46, gamma = const_718, x = input_1917)[name = tensor("output_257")]; + tensor input_1923 = linear(bias = encoder_tp_encoders_13_feed_forward_w_1_bias, weight = encoder_tp_encoders_13_feed_forward_w_1_weight, x = output_257)[name = tensor("linear_254")]; + tensor input_1925 = relu(x = input_1923)[name = tensor("input_1925")]; + tensor input_1929 = linear(bias = encoder_tp_encoders_13_feed_forward_w_2_bias, weight = encoder_tp_encoders_13_feed_forward_w_2_weight, x = input_1925)[name = tensor("linear_255")]; + tensor input_1931 = add(x = input_1917, y = input_1929)[name = tensor("input_1931")]; + tensor const_720 = const()[name = tensor("const_720"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(940046080)))]; + tensor const_721 = const()[name = tensor("const_721"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(940048192)))]; + tensor output_259_axes_0 = const()[name = tensor("output_259_axes_0"), val = tensor([-1])]; + tensor output_259 = layer_norm(axes = output_259_axes_0, beta = const_721, epsilon = var_46, gamma = const_720, x = input_1931)[name = tensor("output_259")]; + tensor var_6818 = linear(bias = encoder_tp_encoders_14_self_attn_linear_q_k_v_bias, weight = encoder_tp_encoders_14_self_attn_linear_q_k_v_weight, x = output_259)[name = tensor("linear_256")]; + tensor tile_64 = const()[name = tensor("tile_64"), val = tensor([512, 512, 512])]; + tensor var_6819_axis_0 = const()[name = tensor("op_6819_axis_0"), val = tensor(-1)]; + tensor var_6819_0, tensor var_6819_1, tensor var_6819_2 = split(axis = var_6819_axis_0, split_sizes = tile_64, x = var_6818)[name = tensor("op_6819")]; + tensor var_6823 = const()[name = tensor("op_6823"), val = tensor([1, 1804, 4, 128])]; + tensor var_6824 = reshape(shape = var_6823, x = var_6819_0)[name = tensor("op_6824")]; + tensor var_6826 = const()[name = tensor("op_6826"), val = tensor([1, 1804, 4, 128])]; + tensor var_6827 = reshape(shape = var_6826, x = var_6819_1)[name = tensor("op_6827")]; + tensor var_6829 = const()[name = tensor("op_6829"), val = tensor([1, 1804, 4, 128])]; + tensor var_6830 = reshape(shape = var_6829, x = var_6819_2)[name = tensor("op_6830")]; + tensor value_129_perm_0 = const()[name = tensor("value_129_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor inputs_129 = mul(x = var_6819_2, y = mask_7)[name = tensor("inputs_129")]; + tensor input_1935_perm_0 = const()[name = tensor("input_1935_perm_0"), val = tensor([0, 2, 1])]; + tensor const_727 = const()[name = tensor("const_727"), val = tensor(0x0p+0)]; + tensor input_1937_pad_0 = const()[name = tensor("input_1937_pad_0"), val = tensor([0, 0, 0, 0, 5, 5])]; + tensor input_1937_mode_0 = const()[name = tensor("input_1937_mode_0"), val = tensor("constant")]; + tensor input_1935 = transpose(perm = input_1935_perm_0, x = inputs_129)[name = tensor("transpose_384")]; + tensor input_1937 = pad(constant_val = const_727, mode = input_1937_mode_0, pad = input_1937_pad_0, x = input_1935)[name = tensor("input_1937")]; + tensor x_645_pad_type_0 = const()[name = tensor("x_645_pad_type_0"), val = tensor("valid")]; + tensor x_645_groups_0 = const()[name = tensor("x_645_groups_0"), val = tensor(512)]; + tensor x_645_strides_0 = const()[name = tensor("x_645_strides_0"), val = tensor([1])]; + tensor x_645_pad_0 = const()[name = tensor("x_645_pad_0"), val = tensor([0, 0])]; + tensor x_645_dilations_0 = const()[name = tensor("x_645_dilations_0"), val = tensor([1])]; + tensor x_645 = conv(dilations = x_645_dilations_0, groups = x_645_groups_0, pad = x_645_pad_0, pad_type = x_645_pad_type_0, strides = x_645_strides_0, weight = encoder_tp_encoders_14_self_attn_fsmn_block_weight, x = input_1937)[name = tensor("x_645")]; + tensor x_647_perm_0 = const()[name = tensor("x_647_perm_0"), val = tensor([0, 2, 1])]; + tensor x_647 = transpose(perm = x_647_perm_0, x = x_645)[name = tensor("transpose_383")]; + tensor input_1939 = add(x = x_647, y = inputs_129)[name = tensor("input_1939")]; + tensor fsmn_memory_129 = mul(x = input_1939, y = mask_7)[name = tensor("fsmn_memory_129")]; + tensor var_6849 = const()[name = tensor("op_6849"), val = tensor(0x1.6a09e6p-4)]; + tensor q_h_259 = mul(x = var_6824, y = var_6849)[name = tensor("q_h_259")]; + tensor scores_257_transpose_x_0 = const()[name = tensor("scores_257_transpose_x_0"), val = tensor(false)]; + tensor scores_257_transpose_y_0 = const()[name = tensor("scores_257_transpose_y_0"), val = tensor(false)]; + tensor transpose_338_perm_0 = const()[name = tensor("transpose_338_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_339_perm_0 = const()[name = tensor("transpose_339_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_339 = transpose(perm = transpose_339_perm_0, x = var_6827)[name = tensor("transpose_381")]; + tensor transpose_338 = transpose(perm = transpose_338_perm_0, x = q_h_259)[name = tensor("transpose_382")]; + tensor scores_257 = matmul(transpose_x = scores_257_transpose_x_0, transpose_y = scores_257_transpose_y_0, x = transpose_338, y = transpose_339)[name = tensor("scores_257")]; + tensor scores_259 = select(a = var_48, b = scores_257, cond = mask_9)[name = tensor("scores_259")]; + tensor var_6857 = softmax(axis = var_61, x = scores_259)[name = tensor("op_6857")]; + tensor input_1941 = select(a = var_53, b = var_6857, cond = mask_9)[name = tensor("input_1941")]; + tensor x_651_transpose_x_0 = const()[name = tensor("x_651_transpose_x_0"), val = tensor(false)]; + tensor x_651_transpose_y_0 = const()[name = tensor("x_651_transpose_y_0"), val = tensor(false)]; + tensor value_129 = transpose(perm = value_129_perm_0, x = var_6830)[name = tensor("transpose_385")]; + tensor x_651 = matmul(transpose_x = x_651_transpose_x_0, transpose_y = x_651_transpose_y_0, x = input_1941, y = value_129)[name = tensor("x_651")]; + tensor var_6861_perm_0 = const()[name = tensor("op_6861_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_6863 = const()[name = tensor("op_6863"), val = tensor([1, -1, 512])]; + tensor var_6861 = transpose(perm = var_6861_perm_0, x = x_651)[name = tensor("transpose_380")]; + tensor input_1943 = reshape(shape = var_6863, x = var_6861)[name = tensor("input_1943")]; + tensor att_outs_129 = linear(bias = encoder_tp_encoders_14_self_attn_linear_out_bias, weight = encoder_tp_encoders_14_self_attn_linear_out_weight, x = input_1943)[name = tensor("linear_257")]; + tensor input_1945 = add(x = att_outs_129, y = fsmn_memory_129)[name = tensor("input_1945")]; + tensor input_1947 = add(x = input_1931, y = input_1945)[name = tensor("input_1947")]; + tensor const_729 = const()[name = tensor("const_729"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(940050304)))]; + tensor const_730 = const()[name = tensor("const_730"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(940052416)))]; + tensor output_261_axes_0 = const()[name = tensor("output_261_axes_0"), val = tensor([-1])]; + tensor output_261 = layer_norm(axes = output_261_axes_0, beta = const_730, epsilon = var_46, gamma = const_729, x = input_1947)[name = tensor("output_261")]; + tensor input_1953 = linear(bias = encoder_tp_encoders_14_feed_forward_w_1_bias, weight = encoder_tp_encoders_14_feed_forward_w_1_weight, x = output_261)[name = tensor("linear_258")]; + tensor input_1955 = relu(x = input_1953)[name = tensor("input_1955")]; + tensor input_1959 = linear(bias = encoder_tp_encoders_14_feed_forward_w_2_bias, weight = encoder_tp_encoders_14_feed_forward_w_2_weight, x = input_1955)[name = tensor("linear_259")]; + tensor input_1961 = add(x = input_1947, y = input_1959)[name = tensor("input_1961")]; + tensor const_731 = const()[name = tensor("const_731"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(940054528)))]; + tensor const_732 = const()[name = tensor("const_732"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(940056640)))]; + tensor output_263_axes_0 = const()[name = tensor("output_263_axes_0"), val = tensor([-1])]; + tensor output_263 = layer_norm(axes = output_263_axes_0, beta = const_732, epsilon = var_46, gamma = const_731, x = input_1961)[name = tensor("output_263")]; + tensor var_6920 = linear(bias = encoder_tp_encoders_15_self_attn_linear_q_k_v_bias, weight = encoder_tp_encoders_15_self_attn_linear_q_k_v_weight, x = output_263)[name = tensor("linear_260")]; + tensor tile_65 = const()[name = tensor("tile_65"), val = tensor([512, 512, 512])]; + tensor var_6921_axis_0 = const()[name = tensor("op_6921_axis_0"), val = tensor(-1)]; + tensor var_6921_0, tensor var_6921_1, tensor var_6921_2 = split(axis = var_6921_axis_0, split_sizes = tile_65, x = var_6920)[name = tensor("op_6921")]; + tensor var_6925 = const()[name = tensor("op_6925"), val = tensor([1, 1804, 4, 128])]; + tensor var_6926 = reshape(shape = var_6925, x = var_6921_0)[name = tensor("op_6926")]; + tensor var_6928 = const()[name = tensor("op_6928"), val = tensor([1, 1804, 4, 128])]; + tensor var_6929 = reshape(shape = var_6928, x = var_6921_1)[name = tensor("op_6929")]; + tensor var_6931 = const()[name = tensor("op_6931"), val = tensor([1, 1804, 4, 128])]; + tensor var_6932 = reshape(shape = var_6931, x = var_6921_2)[name = tensor("op_6932")]; + tensor value_131_perm_0 = const()[name = tensor("value_131_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor inputs_131 = mul(x = var_6921_2, y = mask_7)[name = tensor("inputs_131")]; + tensor input_1965_perm_0 = const()[name = tensor("input_1965_perm_0"), val = tensor([0, 2, 1])]; + tensor const_738 = const()[name = tensor("const_738"), val = tensor(0x0p+0)]; + tensor input_1967_pad_0 = const()[name = tensor("input_1967_pad_0"), val = tensor([0, 0, 0, 0, 5, 5])]; + tensor input_1967_mode_0 = const()[name = tensor("input_1967_mode_0"), val = tensor("constant")]; + tensor input_1965 = transpose(perm = input_1965_perm_0, x = inputs_131)[name = tensor("transpose_378")]; + tensor input_1967 = pad(constant_val = const_738, mode = input_1967_mode_0, pad = input_1967_pad_0, x = input_1965)[name = tensor("input_1967")]; + tensor x_655_pad_type_0 = const()[name = tensor("x_655_pad_type_0"), val = tensor("valid")]; + tensor x_655_groups_0 = const()[name = tensor("x_655_groups_0"), val = tensor(512)]; + tensor x_655_strides_0 = const()[name = tensor("x_655_strides_0"), val = tensor([1])]; + tensor x_655_pad_0 = const()[name = tensor("x_655_pad_0"), val = tensor([0, 0])]; + tensor x_655_dilations_0 = const()[name = tensor("x_655_dilations_0"), val = tensor([1])]; + tensor x_655 = conv(dilations = x_655_dilations_0, groups = x_655_groups_0, pad = x_655_pad_0, pad_type = x_655_pad_type_0, strides = x_655_strides_0, weight = encoder_tp_encoders_15_self_attn_fsmn_block_weight, x = input_1967)[name = tensor("x_655")]; + tensor x_657_perm_0 = const()[name = tensor("x_657_perm_0"), val = tensor([0, 2, 1])]; + tensor x_657 = transpose(perm = x_657_perm_0, x = x_655)[name = tensor("transpose_377")]; + tensor input_1969 = add(x = x_657, y = inputs_131)[name = tensor("input_1969")]; + tensor fsmn_memory_131 = mul(x = input_1969, y = mask_7)[name = tensor("fsmn_memory_131")]; + tensor var_6951 = const()[name = tensor("op_6951"), val = tensor(0x1.6a09e6p-4)]; + tensor q_h_263 = mul(x = var_6926, y = var_6951)[name = tensor("q_h_263")]; + tensor scores_261_transpose_x_0 = const()[name = tensor("scores_261_transpose_x_0"), val = tensor(false)]; + tensor scores_261_transpose_y_0 = const()[name = tensor("scores_261_transpose_y_0"), val = tensor(false)]; + tensor transpose_340_perm_0 = const()[name = tensor("transpose_340_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_341_perm_0 = const()[name = tensor("transpose_341_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_341 = transpose(perm = transpose_341_perm_0, x = var_6929)[name = tensor("transpose_375")]; + tensor transpose_340 = transpose(perm = transpose_340_perm_0, x = q_h_263)[name = tensor("transpose_376")]; + tensor scores_261 = matmul(transpose_x = scores_261_transpose_x_0, transpose_y = scores_261_transpose_y_0, x = transpose_340, y = transpose_341)[name = tensor("scores_261")]; + tensor scores_263 = select(a = var_48, b = scores_261, cond = mask_9)[name = tensor("scores_263")]; + tensor var_6959 = softmax(axis = var_61, x = scores_263)[name = tensor("op_6959")]; + tensor input_1971 = select(a = var_53, b = var_6959, cond = mask_9)[name = tensor("input_1971")]; + tensor x_661_transpose_x_0 = const()[name = tensor("x_661_transpose_x_0"), val = tensor(false)]; + tensor x_661_transpose_y_0 = const()[name = tensor("x_661_transpose_y_0"), val = tensor(false)]; + tensor value_131 = transpose(perm = value_131_perm_0, x = var_6932)[name = tensor("transpose_379")]; + tensor x_661 = matmul(transpose_x = x_661_transpose_x_0, transpose_y = x_661_transpose_y_0, x = input_1971, y = value_131)[name = tensor("x_661")]; + tensor var_6963_perm_0 = const()[name = tensor("op_6963_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_6965 = const()[name = tensor("op_6965"), val = tensor([1, -1, 512])]; + tensor var_6963 = transpose(perm = var_6963_perm_0, x = x_661)[name = tensor("transpose_374")]; + tensor input_1973 = reshape(shape = var_6965, x = var_6963)[name = tensor("input_1973")]; + tensor att_outs_131 = linear(bias = encoder_tp_encoders_15_self_attn_linear_out_bias, weight = encoder_tp_encoders_15_self_attn_linear_out_weight, x = input_1973)[name = tensor("linear_261")]; + tensor input_1975 = add(x = att_outs_131, y = fsmn_memory_131)[name = tensor("input_1975")]; + tensor input_1977 = add(x = input_1961, y = input_1975)[name = tensor("input_1977")]; + tensor const_740 = const()[name = tensor("const_740"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(940058752)))]; + tensor const_741 = const()[name = tensor("const_741"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(940060864)))]; + tensor output_265_axes_0 = const()[name = tensor("output_265_axes_0"), val = tensor([-1])]; + tensor output_265 = layer_norm(axes = output_265_axes_0, beta = const_741, epsilon = var_46, gamma = const_740, x = input_1977)[name = tensor("output_265")]; + tensor input_1983 = linear(bias = encoder_tp_encoders_15_feed_forward_w_1_bias, weight = encoder_tp_encoders_15_feed_forward_w_1_weight, x = output_265)[name = tensor("linear_262")]; + tensor input_1985 = relu(x = input_1983)[name = tensor("input_1985")]; + tensor input_1989 = linear(bias = encoder_tp_encoders_15_feed_forward_w_2_bias, weight = encoder_tp_encoders_15_feed_forward_w_2_weight, x = input_1985)[name = tensor("linear_263")]; + tensor input_1991 = add(x = input_1977, y = input_1989)[name = tensor("input_1991")]; + tensor const_742 = const()[name = tensor("const_742"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(940062976)))]; + tensor const_743 = const()[name = tensor("const_743"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(940065088)))]; + tensor output_267_axes_0 = const()[name = tensor("output_267_axes_0"), val = tensor([-1])]; + tensor output_267 = layer_norm(axes = output_267_axes_0, beta = const_743, epsilon = var_46, gamma = const_742, x = input_1991)[name = tensor("output_267")]; + tensor var_7022 = linear(bias = encoder_tp_encoders_16_self_attn_linear_q_k_v_bias, weight = encoder_tp_encoders_16_self_attn_linear_q_k_v_weight, x = output_267)[name = tensor("linear_264")]; + tensor tile_66 = const()[name = tensor("tile_66"), val = tensor([512, 512, 512])]; + tensor var_7023_axis_0 = const()[name = tensor("op_7023_axis_0"), val = tensor(-1)]; + tensor var_7023_0, tensor var_7023_1, tensor var_7023_2 = split(axis = var_7023_axis_0, split_sizes = tile_66, x = var_7022)[name = tensor("op_7023")]; + tensor var_7027 = const()[name = tensor("op_7027"), val = tensor([1, 1804, 4, 128])]; + tensor var_7028 = reshape(shape = var_7027, x = var_7023_0)[name = tensor("op_7028")]; + tensor var_7030 = const()[name = tensor("op_7030"), val = tensor([1, 1804, 4, 128])]; + tensor var_7031 = reshape(shape = var_7030, x = var_7023_1)[name = tensor("op_7031")]; + tensor var_7033 = const()[name = tensor("op_7033"), val = tensor([1, 1804, 4, 128])]; + tensor var_7034 = reshape(shape = var_7033, x = var_7023_2)[name = tensor("op_7034")]; + tensor value_133_perm_0 = const()[name = tensor("value_133_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor inputs_133 = mul(x = var_7023_2, y = mask_7)[name = tensor("inputs_133")]; + tensor input_1995_perm_0 = const()[name = tensor("input_1995_perm_0"), val = tensor([0, 2, 1])]; + tensor const_749 = const()[name = tensor("const_749"), val = tensor(0x0p+0)]; + tensor input_1997_pad_0 = const()[name = tensor("input_1997_pad_0"), val = tensor([0, 0, 0, 0, 5, 5])]; + tensor input_1997_mode_0 = const()[name = tensor("input_1997_mode_0"), val = tensor("constant")]; + tensor input_1995 = transpose(perm = input_1995_perm_0, x = inputs_133)[name = tensor("transpose_372")]; + tensor input_1997 = pad(constant_val = const_749, mode = input_1997_mode_0, pad = input_1997_pad_0, x = input_1995)[name = tensor("input_1997")]; + tensor x_665_pad_type_0 = const()[name = tensor("x_665_pad_type_0"), val = tensor("valid")]; + tensor x_665_groups_0 = const()[name = tensor("x_665_groups_0"), val = tensor(512)]; + tensor x_665_strides_0 = const()[name = tensor("x_665_strides_0"), val = tensor([1])]; + tensor x_665_pad_0 = const()[name = tensor("x_665_pad_0"), val = tensor([0, 0])]; + tensor x_665_dilations_0 = const()[name = tensor("x_665_dilations_0"), val = tensor([1])]; + tensor x_665 = conv(dilations = x_665_dilations_0, groups = x_665_groups_0, pad = x_665_pad_0, pad_type = x_665_pad_type_0, strides = x_665_strides_0, weight = encoder_tp_encoders_16_self_attn_fsmn_block_weight, x = input_1997)[name = tensor("x_665")]; + tensor x_667_perm_0 = const()[name = tensor("x_667_perm_0"), val = tensor([0, 2, 1])]; + tensor x_667 = transpose(perm = x_667_perm_0, x = x_665)[name = tensor("transpose_371")]; + tensor input_1999 = add(x = x_667, y = inputs_133)[name = tensor("input_1999")]; + tensor fsmn_memory_133 = mul(x = input_1999, y = mask_7)[name = tensor("fsmn_memory_133")]; + tensor var_7053 = const()[name = tensor("op_7053"), val = tensor(0x1.6a09e6p-4)]; + tensor q_h_267 = mul(x = var_7028, y = var_7053)[name = tensor("q_h_267")]; + tensor scores_265_transpose_x_0 = const()[name = tensor("scores_265_transpose_x_0"), val = tensor(false)]; + tensor scores_265_transpose_y_0 = const()[name = tensor("scores_265_transpose_y_0"), val = tensor(false)]; + tensor transpose_342_perm_0 = const()[name = tensor("transpose_342_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_343_perm_0 = const()[name = tensor("transpose_343_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_343 = transpose(perm = transpose_343_perm_0, x = var_7031)[name = tensor("transpose_369")]; + tensor transpose_342 = transpose(perm = transpose_342_perm_0, x = q_h_267)[name = tensor("transpose_370")]; + tensor scores_265 = matmul(transpose_x = scores_265_transpose_x_0, transpose_y = scores_265_transpose_y_0, x = transpose_342, y = transpose_343)[name = tensor("scores_265")]; + tensor scores_267 = select(a = var_48, b = scores_265, cond = mask_9)[name = tensor("scores_267")]; + tensor var_7061 = softmax(axis = var_61, x = scores_267)[name = tensor("op_7061")]; + tensor input_2001 = select(a = var_53, b = var_7061, cond = mask_9)[name = tensor("input_2001")]; + tensor x_671_transpose_x_0 = const()[name = tensor("x_671_transpose_x_0"), val = tensor(false)]; + tensor x_671_transpose_y_0 = const()[name = tensor("x_671_transpose_y_0"), val = tensor(false)]; + tensor value_133 = transpose(perm = value_133_perm_0, x = var_7034)[name = tensor("transpose_373")]; + tensor x_671 = matmul(transpose_x = x_671_transpose_x_0, transpose_y = x_671_transpose_y_0, x = input_2001, y = value_133)[name = tensor("x_671")]; + tensor var_7065_perm_0 = const()[name = tensor("op_7065_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_7067 = const()[name = tensor("op_7067"), val = tensor([1, -1, 512])]; + tensor var_7065 = transpose(perm = var_7065_perm_0, x = x_671)[name = tensor("transpose_368")]; + tensor input_2003 = reshape(shape = var_7067, x = var_7065)[name = tensor("input_2003")]; + tensor att_outs_133 = linear(bias = encoder_tp_encoders_16_self_attn_linear_out_bias, weight = encoder_tp_encoders_16_self_attn_linear_out_weight, x = input_2003)[name = tensor("linear_265")]; + tensor input_2005 = add(x = att_outs_133, y = fsmn_memory_133)[name = tensor("input_2005")]; + tensor input_2007 = add(x = input_1991, y = input_2005)[name = tensor("input_2007")]; + tensor const_751 = const()[name = tensor("const_751"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(940067200)))]; + tensor const_752 = const()[name = tensor("const_752"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(940069312)))]; + tensor output_269_axes_0 = const()[name = tensor("output_269_axes_0"), val = tensor([-1])]; + tensor output_269 = layer_norm(axes = output_269_axes_0, beta = const_752, epsilon = var_46, gamma = const_751, x = input_2007)[name = tensor("output_269")]; + tensor input_2013 = linear(bias = encoder_tp_encoders_16_feed_forward_w_1_bias, weight = encoder_tp_encoders_16_feed_forward_w_1_weight, x = output_269)[name = tensor("linear_266")]; + tensor input_2015 = relu(x = input_2013)[name = tensor("input_2015")]; + tensor input_2019 = linear(bias = encoder_tp_encoders_16_feed_forward_w_2_bias, weight = encoder_tp_encoders_16_feed_forward_w_2_weight, x = input_2015)[name = tensor("linear_267")]; + tensor input_2021 = add(x = input_2007, y = input_2019)[name = tensor("input_2021")]; + tensor const_753 = const()[name = tensor("const_753"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(940071424)))]; + tensor const_754 = const()[name = tensor("const_754"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(940073536)))]; + tensor output_271_axes_0 = const()[name = tensor("output_271_axes_0"), val = tensor([-1])]; + tensor output_271 = layer_norm(axes = output_271_axes_0, beta = const_754, epsilon = var_46, gamma = const_753, x = input_2021)[name = tensor("output_271")]; + tensor var_7124 = linear(bias = encoder_tp_encoders_17_self_attn_linear_q_k_v_bias, weight = encoder_tp_encoders_17_self_attn_linear_q_k_v_weight, x = output_271)[name = tensor("linear_268")]; + tensor tile_67 = const()[name = tensor("tile_67"), val = tensor([512, 512, 512])]; + tensor var_7125_axis_0 = const()[name = tensor("op_7125_axis_0"), val = tensor(-1)]; + tensor var_7125_0, tensor var_7125_1, tensor var_7125_2 = split(axis = var_7125_axis_0, split_sizes = tile_67, x = var_7124)[name = tensor("op_7125")]; + tensor var_7129 = const()[name = tensor("op_7129"), val = tensor([1, 1804, 4, 128])]; + tensor var_7130 = reshape(shape = var_7129, x = var_7125_0)[name = tensor("op_7130")]; + tensor var_7132 = const()[name = tensor("op_7132"), val = tensor([1, 1804, 4, 128])]; + tensor var_7133 = reshape(shape = var_7132, x = var_7125_1)[name = tensor("op_7133")]; + tensor var_7135 = const()[name = tensor("op_7135"), val = tensor([1, 1804, 4, 128])]; + tensor var_7136 = reshape(shape = var_7135, x = var_7125_2)[name = tensor("op_7136")]; + tensor value_135_perm_0 = const()[name = tensor("value_135_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor inputs_135 = mul(x = var_7125_2, y = mask_7)[name = tensor("inputs_135")]; + tensor input_2025_perm_0 = const()[name = tensor("input_2025_perm_0"), val = tensor([0, 2, 1])]; + tensor const_760 = const()[name = tensor("const_760"), val = tensor(0x0p+0)]; + tensor input_2027_pad_0 = const()[name = tensor("input_2027_pad_0"), val = tensor([0, 0, 0, 0, 5, 5])]; + tensor input_2027_mode_0 = const()[name = tensor("input_2027_mode_0"), val = tensor("constant")]; + tensor input_2025 = transpose(perm = input_2025_perm_0, x = inputs_135)[name = tensor("transpose_366")]; + tensor input_2027 = pad(constant_val = const_760, mode = input_2027_mode_0, pad = input_2027_pad_0, x = input_2025)[name = tensor("input_2027")]; + tensor x_675_pad_type_0 = const()[name = tensor("x_675_pad_type_0"), val = tensor("valid")]; + tensor x_675_groups_0 = const()[name = tensor("x_675_groups_0"), val = tensor(512)]; + tensor x_675_strides_0 = const()[name = tensor("x_675_strides_0"), val = tensor([1])]; + tensor x_675_pad_0 = const()[name = tensor("x_675_pad_0"), val = tensor([0, 0])]; + tensor x_675_dilations_0 = const()[name = tensor("x_675_dilations_0"), val = tensor([1])]; + tensor x_675 = conv(dilations = x_675_dilations_0, groups = x_675_groups_0, pad = x_675_pad_0, pad_type = x_675_pad_type_0, strides = x_675_strides_0, weight = encoder_tp_encoders_17_self_attn_fsmn_block_weight, x = input_2027)[name = tensor("x_675")]; + tensor x_677_perm_0 = const()[name = tensor("x_677_perm_0"), val = tensor([0, 2, 1])]; + tensor x_677 = transpose(perm = x_677_perm_0, x = x_675)[name = tensor("transpose_365")]; + tensor input_2029 = add(x = x_677, y = inputs_135)[name = tensor("input_2029")]; + tensor fsmn_memory_135 = mul(x = input_2029, y = mask_7)[name = tensor("fsmn_memory_135")]; + tensor var_7155 = const()[name = tensor("op_7155"), val = tensor(0x1.6a09e6p-4)]; + tensor q_h_271 = mul(x = var_7130, y = var_7155)[name = tensor("q_h_271")]; + tensor scores_269_transpose_x_0 = const()[name = tensor("scores_269_transpose_x_0"), val = tensor(false)]; + tensor scores_269_transpose_y_0 = const()[name = tensor("scores_269_transpose_y_0"), val = tensor(false)]; + tensor transpose_344_perm_0 = const()[name = tensor("transpose_344_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_345_perm_0 = const()[name = tensor("transpose_345_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_345 = transpose(perm = transpose_345_perm_0, x = var_7133)[name = tensor("transpose_363")]; + tensor transpose_344 = transpose(perm = transpose_344_perm_0, x = q_h_271)[name = tensor("transpose_364")]; + tensor scores_269 = matmul(transpose_x = scores_269_transpose_x_0, transpose_y = scores_269_transpose_y_0, x = transpose_344, y = transpose_345)[name = tensor("scores_269")]; + tensor scores_271 = select(a = var_48, b = scores_269, cond = mask_9)[name = tensor("scores_271")]; + tensor var_7163 = softmax(axis = var_61, x = scores_271)[name = tensor("op_7163")]; + tensor input_2031 = select(a = var_53, b = var_7163, cond = mask_9)[name = tensor("input_2031")]; + tensor x_681_transpose_x_0 = const()[name = tensor("x_681_transpose_x_0"), val = tensor(false)]; + tensor x_681_transpose_y_0 = const()[name = tensor("x_681_transpose_y_0"), val = tensor(false)]; + tensor value_135 = transpose(perm = value_135_perm_0, x = var_7136)[name = tensor("transpose_367")]; + tensor x_681 = matmul(transpose_x = x_681_transpose_x_0, transpose_y = x_681_transpose_y_0, x = input_2031, y = value_135)[name = tensor("x_681")]; + tensor var_7167_perm_0 = const()[name = tensor("op_7167_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_7169 = const()[name = tensor("op_7169"), val = tensor([1, -1, 512])]; + tensor var_7167 = transpose(perm = var_7167_perm_0, x = x_681)[name = tensor("transpose_362")]; + tensor input_2033 = reshape(shape = var_7169, x = var_7167)[name = tensor("input_2033")]; + tensor att_outs_135 = linear(bias = encoder_tp_encoders_17_self_attn_linear_out_bias, weight = encoder_tp_encoders_17_self_attn_linear_out_weight, x = input_2033)[name = tensor("linear_269")]; + tensor input_2035 = add(x = att_outs_135, y = fsmn_memory_135)[name = tensor("input_2035")]; + tensor input_2037 = add(x = input_2021, y = input_2035)[name = tensor("input_2037")]; + tensor const_762 = const()[name = tensor("const_762"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(940075648)))]; + tensor const_763 = const()[name = tensor("const_763"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(940077760)))]; + tensor output_273_axes_0 = const()[name = tensor("output_273_axes_0"), val = tensor([-1])]; + tensor output_273 = layer_norm(axes = output_273_axes_0, beta = const_763, epsilon = var_46, gamma = const_762, x = input_2037)[name = tensor("output_273")]; + tensor input_2043 = linear(bias = encoder_tp_encoders_17_feed_forward_w_1_bias, weight = encoder_tp_encoders_17_feed_forward_w_1_weight, x = output_273)[name = tensor("linear_270")]; + tensor input_2045 = relu(x = input_2043)[name = tensor("input_2045")]; + tensor input_2049 = linear(bias = encoder_tp_encoders_17_feed_forward_w_2_bias, weight = encoder_tp_encoders_17_feed_forward_w_2_weight, x = input_2045)[name = tensor("linear_271")]; + tensor input_2051 = add(x = input_2037, y = input_2049)[name = tensor("input_2051")]; + tensor const_764 = const()[name = tensor("const_764"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(940079872)))]; + tensor const_765 = const()[name = tensor("const_765"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(940081984)))]; + tensor output_275_axes_0 = const()[name = tensor("output_275_axes_0"), val = tensor([-1])]; + tensor output_275 = layer_norm(axes = output_275_axes_0, beta = const_765, epsilon = var_46, gamma = const_764, x = input_2051)[name = tensor("output_275")]; + tensor var_7226 = linear(bias = encoder_tp_encoders_18_self_attn_linear_q_k_v_bias, weight = encoder_tp_encoders_18_self_attn_linear_q_k_v_weight, x = output_275)[name = tensor("linear_272")]; + tensor tile_68 = const()[name = tensor("tile_68"), val = tensor([512, 512, 512])]; + tensor var_7227_axis_0 = const()[name = tensor("op_7227_axis_0"), val = tensor(-1)]; + tensor var_7227_0, tensor var_7227_1, tensor var_7227_2 = split(axis = var_7227_axis_0, split_sizes = tile_68, x = var_7226)[name = tensor("op_7227")]; + tensor var_7231 = const()[name = tensor("op_7231"), val = tensor([1, 1804, 4, 128])]; + tensor var_7232 = reshape(shape = var_7231, x = var_7227_0)[name = tensor("op_7232")]; + tensor var_7234 = const()[name = tensor("op_7234"), val = tensor([1, 1804, 4, 128])]; + tensor var_7235 = reshape(shape = var_7234, x = var_7227_1)[name = tensor("op_7235")]; + tensor var_7237 = const()[name = tensor("op_7237"), val = tensor([1, 1804, 4, 128])]; + tensor var_7238 = reshape(shape = var_7237, x = var_7227_2)[name = tensor("op_7238")]; + tensor value_137_perm_0 = const()[name = tensor("value_137_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor inputs_137 = mul(x = var_7227_2, y = mask_7)[name = tensor("inputs_137")]; + tensor input_2055_perm_0 = const()[name = tensor("input_2055_perm_0"), val = tensor([0, 2, 1])]; + tensor const_771 = const()[name = tensor("const_771"), val = tensor(0x0p+0)]; + tensor input_2057_pad_0 = const()[name = tensor("input_2057_pad_0"), val = tensor([0, 0, 0, 0, 5, 5])]; + tensor input_2057_mode_0 = const()[name = tensor("input_2057_mode_0"), val = tensor("constant")]; + tensor input_2055 = transpose(perm = input_2055_perm_0, x = inputs_137)[name = tensor("transpose_360")]; + tensor input_2057 = pad(constant_val = const_771, mode = input_2057_mode_0, pad = input_2057_pad_0, x = input_2055)[name = tensor("input_2057")]; + tensor x_685_pad_type_0 = const()[name = tensor("x_685_pad_type_0"), val = tensor("valid")]; + tensor x_685_groups_0 = const()[name = tensor("x_685_groups_0"), val = tensor(512)]; + tensor x_685_strides_0 = const()[name = tensor("x_685_strides_0"), val = tensor([1])]; + tensor x_685_pad_0 = const()[name = tensor("x_685_pad_0"), val = tensor([0, 0])]; + tensor x_685_dilations_0 = const()[name = tensor("x_685_dilations_0"), val = tensor([1])]; + tensor x_685 = conv(dilations = x_685_dilations_0, groups = x_685_groups_0, pad = x_685_pad_0, pad_type = x_685_pad_type_0, strides = x_685_strides_0, weight = encoder_tp_encoders_18_self_attn_fsmn_block_weight, x = input_2057)[name = tensor("x_685")]; + tensor x_687_perm_0 = const()[name = tensor("x_687_perm_0"), val = tensor([0, 2, 1])]; + tensor x_687 = transpose(perm = x_687_perm_0, x = x_685)[name = tensor("transpose_359")]; + tensor input_2059 = add(x = x_687, y = inputs_137)[name = tensor("input_2059")]; + tensor fsmn_memory_137 = mul(x = input_2059, y = mask_7)[name = tensor("fsmn_memory_137")]; + tensor var_7257 = const()[name = tensor("op_7257"), val = tensor(0x1.6a09e6p-4)]; + tensor q_h_275 = mul(x = var_7232, y = var_7257)[name = tensor("q_h_275")]; + tensor scores_273_transpose_x_0 = const()[name = tensor("scores_273_transpose_x_0"), val = tensor(false)]; + tensor scores_273_transpose_y_0 = const()[name = tensor("scores_273_transpose_y_0"), val = tensor(false)]; + tensor transpose_346_perm_0 = const()[name = tensor("transpose_346_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_347_perm_0 = const()[name = tensor("transpose_347_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_347 = transpose(perm = transpose_347_perm_0, x = var_7235)[name = tensor("transpose_357")]; + tensor transpose_346 = transpose(perm = transpose_346_perm_0, x = q_h_275)[name = tensor("transpose_358")]; + tensor scores_273 = matmul(transpose_x = scores_273_transpose_x_0, transpose_y = scores_273_transpose_y_0, x = transpose_346, y = transpose_347)[name = tensor("scores_273")]; + tensor scores_275 = select(a = var_48, b = scores_273, cond = mask_9)[name = tensor("scores_275")]; + tensor var_7265 = softmax(axis = var_61, x = scores_275)[name = tensor("op_7265")]; + tensor input_2061 = select(a = var_53, b = var_7265, cond = mask_9)[name = tensor("input_2061")]; + tensor x_691_transpose_x_0 = const()[name = tensor("x_691_transpose_x_0"), val = tensor(false)]; + tensor x_691_transpose_y_0 = const()[name = tensor("x_691_transpose_y_0"), val = tensor(false)]; + tensor value_137 = transpose(perm = value_137_perm_0, x = var_7238)[name = tensor("transpose_361")]; + tensor x_691 = matmul(transpose_x = x_691_transpose_x_0, transpose_y = x_691_transpose_y_0, x = input_2061, y = value_137)[name = tensor("x_691")]; + tensor var_7269_perm_0 = const()[name = tensor("op_7269_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_7271 = const()[name = tensor("op_7271"), val = tensor([1, -1, 512])]; + tensor var_7269 = transpose(perm = var_7269_perm_0, x = x_691)[name = tensor("transpose_356")]; + tensor input_2063 = reshape(shape = var_7271, x = var_7269)[name = tensor("input_2063")]; + tensor att_outs_137 = linear(bias = encoder_tp_encoders_18_self_attn_linear_out_bias, weight = encoder_tp_encoders_18_self_attn_linear_out_weight, x = input_2063)[name = tensor("linear_273")]; + tensor input_2065 = add(x = att_outs_137, y = fsmn_memory_137)[name = tensor("input_2065")]; + tensor input_2067 = add(x = input_2051, y = input_2065)[name = tensor("input_2067")]; + tensor const_773 = const()[name = tensor("const_773"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(940084096)))]; + tensor const_774 = const()[name = tensor("const_774"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(940086208)))]; + tensor output_277_axes_0 = const()[name = tensor("output_277_axes_0"), val = tensor([-1])]; + tensor output_277 = layer_norm(axes = output_277_axes_0, beta = const_774, epsilon = var_46, gamma = const_773, x = input_2067)[name = tensor("output_277")]; + tensor input_2073 = linear(bias = encoder_tp_encoders_18_feed_forward_w_1_bias, weight = encoder_tp_encoders_18_feed_forward_w_1_weight, x = output_277)[name = tensor("linear_274")]; + tensor input_2075 = relu(x = input_2073)[name = tensor("input_2075")]; + tensor input_2079 = linear(bias = encoder_tp_encoders_18_feed_forward_w_2_bias, weight = encoder_tp_encoders_18_feed_forward_w_2_weight, x = input_2075)[name = tensor("linear_275")]; + tensor input_2081 = add(x = input_2067, y = input_2079)[name = tensor("input_2081")]; + tensor const_775 = const()[name = tensor("const_775"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(940088320)))]; + tensor const_776 = const()[name = tensor("const_776"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(940090432)))]; + tensor output_279_axes_0 = const()[name = tensor("output_279_axes_0"), val = tensor([-1])]; + tensor output_279 = layer_norm(axes = output_279_axes_0, beta = const_776, epsilon = var_46, gamma = const_775, x = input_2081)[name = tensor("output_279")]; + tensor var_7328 = linear(bias = encoder_tp_encoders_19_self_attn_linear_q_k_v_bias, weight = encoder_tp_encoders_19_self_attn_linear_q_k_v_weight, x = output_279)[name = tensor("linear_276")]; + tensor tile_69 = const()[name = tensor("tile_69"), val = tensor([512, 512, 512])]; + tensor var_7329_axis_0 = const()[name = tensor("op_7329_axis_0"), val = tensor(-1)]; + tensor var_7329_0, tensor var_7329_1, tensor var_7329_2 = split(axis = var_7329_axis_0, split_sizes = tile_69, x = var_7328)[name = tensor("op_7329")]; + tensor var_7333 = const()[name = tensor("op_7333"), val = tensor([1, 1804, 4, 128])]; + tensor var_7334 = reshape(shape = var_7333, x = var_7329_0)[name = tensor("op_7334")]; + tensor var_7336 = const()[name = tensor("op_7336"), val = tensor([1, 1804, 4, 128])]; + tensor var_7337 = reshape(shape = var_7336, x = var_7329_1)[name = tensor("op_7337")]; + tensor var_7339 = const()[name = tensor("op_7339"), val = tensor([1, 1804, 4, 128])]; + tensor var_7340 = reshape(shape = var_7339, x = var_7329_2)[name = tensor("op_7340")]; + tensor value_perm_0 = const()[name = tensor("value_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor inputs = mul(x = var_7329_2, y = mask_7)[name = tensor("inputs")]; + tensor input_2085_perm_0 = const()[name = tensor("input_2085_perm_0"), val = tensor([0, 2, 1])]; + tensor const_782 = const()[name = tensor("const_782"), val = tensor(0x0p+0)]; + tensor input_2087_pad_0 = const()[name = tensor("input_2087_pad_0"), val = tensor([0, 0, 0, 0, 5, 5])]; + tensor input_2087_mode_0 = const()[name = tensor("input_2087_mode_0"), val = tensor("constant")]; + tensor input_2085 = transpose(perm = input_2085_perm_0, x = inputs)[name = tensor("transpose_354")]; + tensor input_2087 = pad(constant_val = const_782, mode = input_2087_mode_0, pad = input_2087_pad_0, x = input_2085)[name = tensor("input_2087")]; + tensor x_695_pad_type_0 = const()[name = tensor("x_695_pad_type_0"), val = tensor("valid")]; + tensor x_695_groups_0 = const()[name = tensor("x_695_groups_0"), val = tensor(512)]; + tensor x_695_strides_0 = const()[name = tensor("x_695_strides_0"), val = tensor([1])]; + tensor x_695_pad_0 = const()[name = tensor("x_695_pad_0"), val = tensor([0, 0])]; + tensor x_695_dilations_0 = const()[name = tensor("x_695_dilations_0"), val = tensor([1])]; + tensor x_695 = conv(dilations = x_695_dilations_0, groups = x_695_groups_0, pad = x_695_pad_0, pad_type = x_695_pad_type_0, strides = x_695_strides_0, weight = encoder_tp_encoders_19_self_attn_fsmn_block_weight, x = input_2087)[name = tensor("x_695")]; + tensor x_697_perm_0 = const()[name = tensor("x_697_perm_0"), val = tensor([0, 2, 1])]; + tensor x_697 = transpose(perm = x_697_perm_0, x = x_695)[name = tensor("transpose_353")]; + tensor input_2089 = add(x = x_697, y = inputs)[name = tensor("input_2089")]; + tensor fsmn_memory = mul(x = input_2089, y = mask_7)[name = tensor("fsmn_memory")]; + tensor var_7359 = const()[name = tensor("op_7359"), val = tensor(0x1.6a09e6p-4)]; + tensor q_h = mul(x = var_7334, y = var_7359)[name = tensor("q_h")]; + tensor scores_277_transpose_x_0 = const()[name = tensor("scores_277_transpose_x_0"), val = tensor(false)]; + tensor scores_277_transpose_y_0 = const()[name = tensor("scores_277_transpose_y_0"), val = tensor(false)]; + tensor transpose_348_perm_0 = const()[name = tensor("transpose_348_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_349_perm_0 = const()[name = tensor("transpose_349_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_349 = transpose(perm = transpose_349_perm_0, x = var_7337)[name = tensor("transpose_351")]; + tensor transpose_348 = transpose(perm = transpose_348_perm_0, x = q_h)[name = tensor("transpose_352")]; + tensor scores_277 = matmul(transpose_x = scores_277_transpose_x_0, transpose_y = scores_277_transpose_y_0, x = transpose_348, y = transpose_349)[name = tensor("scores_277")]; + tensor scores = select(a = var_48, b = scores_277, cond = mask_9)[name = tensor("scores")]; + tensor var_7367 = softmax(axis = var_61, x = scores)[name = tensor("op_7367")]; + tensor input_2091 = select(a = var_53, b = var_7367, cond = mask_9)[name = tensor("input_2091")]; + tensor x_transpose_x_0 = const()[name = tensor("x_transpose_x_0"), val = tensor(false)]; + tensor x_transpose_y_0 = const()[name = tensor("x_transpose_y_0"), val = tensor(false)]; + tensor value = transpose(perm = value_perm_0, x = var_7340)[name = tensor("transpose_355")]; + tensor x = matmul(transpose_x = x_transpose_x_0, transpose_y = x_transpose_y_0, x = input_2091, y = value)[name = tensor("x")]; + tensor var_7371_perm_0 = const()[name = tensor("op_7371_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_7373 = const()[name = tensor("op_7373"), val = tensor([1, -1, 512])]; + tensor var_7371 = transpose(perm = var_7371_perm_0, x = x)[name = tensor("transpose_350")]; + tensor input_2093 = reshape(shape = var_7373, x = var_7371)[name = tensor("input_2093")]; + tensor att_outs = linear(bias = encoder_tp_encoders_19_self_attn_linear_out_bias, weight = encoder_tp_encoders_19_self_attn_linear_out_weight, x = input_2093)[name = tensor("linear_277")]; + tensor input_2095 = add(x = att_outs, y = fsmn_memory)[name = tensor("input_2095")]; + tensor input_2097 = add(x = input_2081, y = input_2095)[name = tensor("input_2097")]; + tensor const_784 = const()[name = tensor("const_784"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(940092544)))]; + tensor const_785 = const()[name = tensor("const_785"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(940094656)))]; + tensor output_281_axes_0 = const()[name = tensor("output_281_axes_0"), val = tensor([-1])]; + tensor output_281 = layer_norm(axes = output_281_axes_0, beta = const_785, epsilon = var_46, gamma = const_784, x = input_2097)[name = tensor("output_281")]; + tensor input_2103 = linear(bias = encoder_tp_encoders_19_feed_forward_w_1_bias, weight = encoder_tp_encoders_19_feed_forward_w_1_weight, x = output_281)[name = tensor("linear_278")]; + tensor input_2105 = relu(x = input_2103)[name = tensor("input_2105")]; + tensor input_2109 = linear(bias = encoder_tp_encoders_19_feed_forward_w_2_bias, weight = encoder_tp_encoders_19_feed_forward_w_2_weight, x = input_2105)[name = tensor("linear_279")]; + tensor input_2111 = add(x = input_2097, y = input_2109)[name = tensor("input_2111")]; + tensor const_786 = const()[name = tensor("const_786"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(940096768)))]; + tensor const_787 = const()[name = tensor("const_787"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(940098880)))]; + tensor output_axes_0 = const()[name = tensor("output_axes_0"), val = tensor([-1])]; + tensor output = layer_norm(axes = output_axes_0, beta = const_787, epsilon = var_46, gamma = const_786, x = input_2111)[name = tensor("output")]; + tensor ctc_logits = linear(bias = ctc_lo_bias, weight = ctc_lo_weight, x = output)[name = tensor("linear_280")]; + } -> (ctc_logits); +} \ No newline at end of file