Automatic Speech Recognition
Transformers
NeMo
Safetensors
PyTorch
parakeet_tdt
feature-extraction
speech
audio
Transducer
Transformer
TDT
FastConformer
Conformer
NeMo
hf-asr-leaderboard
Transformers
Eval Results (legacy)
Eval Results
Instructions to use nvidia/parakeet-tdt-0.6b-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nvidia/parakeet-tdt-0.6b-v3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="nvidia/parakeet-tdt-0.6b-v3", device_map="auto")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("nvidia/parakeet-tdt-0.6b-v3", dtype="auto", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
File size: 1,153 Bytes
593ce35 | 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 | {
"architectures": [
"ParakeetForTDT"
],
"blank_token_id": 8192,
"decoder_hidden_size": 640,
"dtype": "float32",
"durations": [
0,
1,
2,
3,
4
],
"encoder_config": {
"activation_dropout": 0.1,
"attention_bias": false,
"attention_dropout": 0.1,
"conv_kernel_size": 9,
"convolution_bias": false,
"dropout": 0.1,
"dropout_positions": 0.0,
"hidden_act": "silu",
"hidden_size": 1024,
"initializer_range": 0.02,
"intermediate_size": 4096,
"layerdrop": 0.1,
"max_position_embeddings": 5000,
"model_type": "parakeet_encoder",
"num_attention_heads": 8,
"num_hidden_layers": 24,
"num_key_value_heads": 8,
"num_mel_bins": 128,
"scale_input": false,
"subsampling_conv_channels": 256,
"subsampling_conv_kernel_size": 3,
"subsampling_conv_stride": 2,
"subsampling_factor": 8
},
"hidden_act": "relu",
"initializer_range": 0.02,
"is_encoder_decoder": true,
"max_symbols_per_step": 10,
"model_type": "parakeet_tdt",
"num_decoder_layers": 2,
"pad_token_id": 2,
"transformers_version": "5.6.0.dev0",
"vocab_size": 8193
}
|