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wandb: wandb version 0.18.3 is available!  To upgrade, please run:
wandb:  $ pip install wandb --upgrade
wandb: Tracking run with wandb version 0.17.6
wandb: Run data is saved locally in /scratch/elec/t405-puhe/p/palp3/MUCS/wandb/run-20241015_221820-qq3iase9
wandb: Run `wandb offline` to turn off syncing.
wandb: Syncing run rerun_bestrun_wgas1fp16false_indicw2v_ad0_3_hd_02_featd_0_3_lr6e-4_warmup500_s300_shuff100
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wandb: 🚀 View run at https://wandb.ai/priyanshipal/huggingface/runs/qq3iase9
/scratch/work/palp3/myenv/lib/python3.11/site-packages/transformers/training_args.py:1545: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead
  warnings.warn(
10/15/2024 22:18:25 - WARNING - __main__ - device: cuda:0, n_gpu: 116-bits training: True
/scratch/work/palp3/myenv/lib/python3.11/site-packages/transformers/models/auto/configuration_auto.py:991: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.
  warnings.warn(
/scratch/work/palp3/myenv/lib/python3.11/site-packages/transformers/configuration_utils.py:302: UserWarning: Passing `gradient_checkpointing` to a config initialization is deprecated and will be removed in v5 Transformers. Using `model.gradient_checkpointing_enable()` instead, or if you are using the `Trainer` API, pass `gradient_checkpointing=True` in your `TrainingArguments`.
  warnings.warn(
/scratch/work/palp3/myenv/lib/python3.11/site-packages/transformers/models/auto/feature_extraction_auto.py:331: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.
  warnings.warn(
/scratch/elec/puhe/p/palp3/MUCS/finetune_script_indicw2v_partdata.py:509: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
  state_dict = torch.load(f"{model_args.model_name_or_path}/pytorch_model.bin")
Some weights of Wav2Vec2ForCTC were not initialized from the model checkpoint at /m/triton/scratch/elec/puhe/p/palp3/MUCS/indicwav2vec-hindi and are newly initialized: ['lm_head.bias', 'lm_head.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
/scratch/work/palp3/myenv/lib/python3.11/site-packages/accelerate/accelerator.py:494: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead.
  self.scaler = torch.cuda.amp.GradScaler(**kwargs)
max_steps is given, it will override any value given in num_train_epochs
Wav2Vec2CTCTokenizer(name_or_path='', vocab_size=149, model_max_length=1000000000000000019884624838656, is_fast=False, padding_side='right', truncation_side='right', special_tokens={'bos_token': '<s>', 'eos_token': '</s>', 'unk_token': '[UNK]', 'pad_token': '[PAD]'}, clean_up_tokenization_spaces=False),  added_tokens_decoder={
	147: AddedToken("[UNK]", rstrip=True, lstrip=True, single_word=False, normalized=False, special=False),
	148: AddedToken("[PAD]", rstrip=True, lstrip=True, single_word=False, normalized=False, special=False),
	149: AddedToken("<s>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
	150: AddedToken("</s>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
}
CHECK MODEL PARAMS Wav2Vec2ForCTC(
  (wav2vec2): Wav2Vec2Model(
    (feature_extractor): Wav2Vec2FeatureEncoder(
      (conv_layers): ModuleList(
        (0): Wav2Vec2LayerNormConvLayer(
          (conv): Conv1d(1, 512, kernel_size=(10,), stride=(5,))
          (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
          (activation): GELUActivation()
        )
        (1-4): 4 x Wav2Vec2LayerNormConvLayer(
          (conv): Conv1d(512, 512, kernel_size=(3,), stride=(2,))
          (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
          (activation): GELUActivation()
        )
        (5-6): 2 x Wav2Vec2LayerNormConvLayer(
          (conv): Conv1d(512, 512, kernel_size=(2,), stride=(2,))
          (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
          (activation): GELUActivation()
        )
      )
    )
    (feature_projection): Wav2Vec2FeatureProjection(
      (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
      (projection): Linear(in_features=512, out_features=1024, bias=True)
      (dropout): Dropout(p=0.3, inplace=False)
    )
    (encoder): Wav2Vec2EncoderStableLayerNorm(
      (pos_conv_embed): Wav2Vec2PositionalConvEmbedding(
        (conv): ParametrizedConv1d(
          1024, 1024, kernel_size=(128,), stride=(1,), padding=(64,), groups=16
          (parametrizations): ModuleDict(
            (weight): ParametrizationList(
              (0): _WeightNorm()
            )
          )
        )
        (padding): Wav2Vec2SamePadLayer()
        (activation): GELUActivation()
      )
      (layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
      (dropout): Dropout(p=0.2, inplace=False)
      (layers): ModuleList(
        (0-23): 24 x Wav2Vec2EncoderLayerStableLayerNorm(
          (attention): Wav2Vec2SdpaAttention(
            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
          )
          (dropout): Dropout(p=0.2, inplace=False)
          (layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
          (feed_forward): Wav2Vec2FeedForward(
            (intermediate_dropout): Dropout(p=0.0, inplace=False)
            (intermediate_dense): Linear(in_features=1024, out_features=4096, bias=True)
            (intermediate_act_fn): GELUActivation()
            (output_dense): Linear(in_features=4096, out_features=1024, bias=True)
            (output_dropout): Dropout(p=0.2, inplace=False)
          )
          (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        )
      )
    )
  )
  (dropout): Dropout(p=0.0, inplace=False)
  (lm_head): Linear(in_features=1024, out_features=151, bias=True)
)

  0%|          | 0/15000 [00:00<?, ?it/s]/scratch/work/palp3/myenv/lib/python3.11/site-packages/transformers/models/wav2vec2/processing_wav2vec2.py:157: UserWarning: `as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your labels by using the argument `text` of the regular `__call__` method (either in the same call as your audio inputs, or in a separate call.
  warnings.warn(
/scratch/work/palp3/myenv/lib/python3.11/site-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.
  with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):  # type: ignore[attr-defined]

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  1%|▏         | 200/15000 [03:11<1:36:07,  2.57it/s]Printing predictions for a few samples:
Sample 1:
  Reference: लिबर ऑफिस impress में एक प्रस्तुति document बनाना और बुनियादी formatting के इस spoken tutorial में आपका स्वागत है
######


  Prediction: 



Sample 2:
  Reference: इस tutorial में हम impress window के भागों के बारे में सीखेंगे और कैसे स्लाइड इन्सर्ट करें और कॉपी करें फॉन्ट तथा फॉन्ट को फॉर्मेट करना सीखेंगे
######


  Prediction: 



Sample 3:
  Reference: यहाँ हम अपने ऑपरेटिंग सिस्टम के रूप में gnu/linux और लिबरऑफिस वर्जन 334 का उपयोग कर रहे हैं
######


  Prediction: 



Sample 4:
  Reference: चलिए अपनी प्रस्तुति प्रेजैटेशन sample impress open करते हैं जिसे पिछले tutorial में बनाया था
######


  Prediction: 



Sample 5:
  Reference: चलिए देखते हैं कि screen पर क्या क्या है
######


  Prediction: 



last Reference string यह स्क्रिप्ट लता द्वारा अनुवादित है आईआईटी मुंबई की ओर से मैं रवि कुमार अब आपसे विदा लेता हूँहमसे जुड़ने के लिए धन्यवाद


last prediction string 
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  2%|▏         | 300/15000 [05:34<1:29:41,  2.73it/s]Printing predictions for a few samples:
Sample 1:
  Reference: लिबर ऑफिस impress में एक प्रस्तुति document बनाना और बुनियादी formatting के इस spoken tutorial में आपका स्वागत है
######


  Prediction: ंं



Sample 2:
  Reference: इस tutorial में हम impress window के भागों के बारे में सीखेंगे और कैसे स्लाइड इन्सर्ट करें और कॉपी करें फॉन्ट तथा फॉन्ट को फॉर्मेट करना सीखेंगे
######


  Prediction: iiंiiं



Sample 3:
  Reference: यहाँ हम अपने ऑपरेटिंग सिस्टम के रूप में gnu/linux और लिबरऑफिस वर्जन 334 का उपयोग कर रहे हैं
######


  Prediction: ंंiं



Sample 4:
  Reference: चलिए अपनी प्रस्तुति प्रेजैटेशन sample impress open करते हैं जिसे पिछले tutorial में बनाया था
######


  Prediction: iं



Sample 5:
  Reference: चलिए देखते हैं कि screen पर क्या क्या है
######


  Prediction: iं



last Reference string यह स्क्रिप्ट लता द्वारा अनुवादित है आईआईटी मुंबई की ओर से मैं रवि कुमार अब आपसे विदा लेता हूँहमसे जुड़ने के लिए धन्यवाद


last prediction string ंं
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                                                 /scratch/work/palp3/myenv/lib/python3.11/site-packages/transformers/models/wav2vec2/processing_wav2vec2.py:157: UserWarning: `as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your labels by using the argument `text` of the regular `__call__` method (either in the same call as your audio inputs, or in a separate call.
  warnings.warn(
/scratch/work/palp3/myenv/lib/python3.11/site-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.
  with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):  # type: ignore[attr-defined]

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  3%|▎         | 400/15000 [08:06<1:24:12,  2.89it/s]Printing predictions for a few samples:
Sample 1:
  Reference: लिबर ऑफिस impress में एक प्रस्तुति document बनाना और बुनियादी formatting के इस spoken tutorial में आपका स्वागत है
######


  Prediction: s में क परसतत  बाना र बनाी म के स pक  में पका सा



Sample 2:
  Reference: इस tutorial में हम impress window के भागों के बारे में सीखेंगे और कैसे स्लाइड इन्सर्ट करें और कॉपी करें फॉन्ट तथा फॉन्ट को फॉर्मेट करना सीखेंगे
######


  Prediction: त हस  मेंम  o के बाों के बारे में सीेंेर कसे   करेंर कपी करें ता  को म करना सीेंेै



Sample 3:
  Reference: यहाँ हम अपने ऑपरेटिंग सिस्टम के रूप में gnu/linux और लिबरऑफिस वर्जन 334 का उपयोग कर रहे हैं
######


  Prediction: यहा हम पने  सम के रप में  र   बरन    का पयो कर रहैैै



Sample 4:
  Reference: चलिए अपनी प्रस्तुति प्रेजैटेशन sample impress open करते हैं जिसे पिछले tutorial में बनाया था
######


  Prediction: अपनी परसततp  पन करते हसे पिे o ेैंै



Sample 5:
  Reference: चलिए देखते हैं कि screen पर क्या क्या है
######


  Prediction: ें बनाया ाि ेते है कस न पर का का है



last Reference string यह स्क्रिप्ट लता द्वारा अनुवादित है आईआईटी मुंबई की ओर से मैं रवि कुमार अब आपसे विदा लेता हूँहमसे जुड़ने के लिए धन्यवाद


last prediction string ा ारा नाित ह म की र से मं री कमार अब पसे िा ेता हहमसे ने के ि नाै
{'eval_loss': 2.6875200271606445, 'eval_cer': 0.6375955488534137, 'eval_wer': 0.8605461168275239, 'eval_runtime': 110.04, 'eval_samples_per_second': 28.499, 'eval_steps_per_second': 3.562, 'epoch': 0.12}
{'loss': 2.3693, 'grad_norm': 2.21392560005188, 'learning_rate': 0.00035519999999999996, 'epoch': 0.12}
{'loss': 3.8565, 'grad_norm': 19.431764602661133, 'learning_rate': 0.00035639999999999994, 'epoch': 0.12}
{'loss': 2.8138, 'grad_norm': 8.469930648803711, 'learning_rate': 0.00035759999999999996, 'epoch': 0.12}
{'loss': 3.056, 'grad_norm': 2.8307647705078125, 'learning_rate': 0.00035879999999999994, 'epoch': 0.12}
{'loss': 4.2551, 'grad_norm': 21.769601821899414, 'learning_rate': 0.00035999999999999997, 'epoch': 0.12}
{'loss': 2.5649, 'grad_norm': 6.5430731773376465, 'learning_rate': 0.00036119999999999994, 'epoch': 0.12}
{'loss': 2.6847, 'grad_norm': 9.003012657165527, 'learning_rate': 0.00036239999999999997, 'epoch': 0.12}
{'loss': 2.6808, 'grad_norm': 6.23049259185791, 'learning_rate': 0.00036359999999999995, 'epoch': 0.12}
{'loss': 2.3324, 'grad_norm': 5.494946002960205, 'learning_rate': 0.0003648, 'epoch': 0.12}
{'loss': 3.8814, 'grad_norm': 15.659469604492188, 'learning_rate': 0.00036599999999999995, 'epoch': 0.12}
{'loss': 2.0751, 'grad_norm': 2.092240333557129, 'learning_rate': 0.0003672, 'epoch': 0.12}
{'loss': 2.7125, 'grad_norm': 5.324711322784424, 'learning_rate': 0.00036839999999999996, 'epoch': 0.12}
{'loss': 2.3976, 'grad_norm': 2.1855404376983643, 'learning_rate': 0.0003696, 'epoch': 0.13}
{'loss': 2.1444, 'grad_norm': 2.469062089920044, 'learning_rate': 0.00037079999999999996, 'epoch': 0.13}
{'loss': 2.4314, 'grad_norm': 1.6100339889526367, 'learning_rate': 0.000372, 'epoch': 0.13}
{'loss': 2.4405, 'grad_norm': 6.247311115264893, 'learning_rate': 0.00037319999999999996, 'epoch': 0.13}
{'loss': 1.8289, 'grad_norm': 1.556992530822754, 'learning_rate': 0.0003744, 'epoch': 0.13}
{'loss': 2.0842, 'grad_norm': 2.5153632164001465, 'learning_rate': 0.00037559999999999997, 'epoch': 0.13}
{'loss': 2.2031, 'grad_norm': 1.9754118919372559, 'learning_rate': 0.00037679999999999994, 'epoch': 0.13}
{'loss': 2.2736, 'grad_norm': 1.7561548948287964, 'learning_rate': 0.00037799999999999997, 'epoch': 0.13}
{'loss': 2.1288, 'grad_norm': 2.142244338989258, 'learning_rate': 0.00037919999999999995, 'epoch': 0.13}
{'loss': 2.1143, 'grad_norm': 2.3002707958221436, 'learning_rate': 0.0003804, 'epoch': 0.13}
{'loss': 2.1201, 'grad_norm': 1.937534213066101, 'learning_rate': 0.00038159999999999995, 'epoch': 0.13}
{'loss': 1.7457, 'grad_norm': 2.1132678985595703, 'learning_rate': 0.0003828, 'epoch': 0.13}
{'loss': 2.1161, 'grad_norm': 1.7769768238067627, 'learning_rate': 0.00038399999999999996, 'epoch': 0.13}
{'loss': 1.9675, 'grad_norm': 1.8603218793869019, 'learning_rate': 0.0003852, 'epoch': 0.13}
{'loss': 1.6495, 'grad_norm': 2.5247581005096436, 'learning_rate': 0.00038639999999999996, 'epoch': 0.13}
{'loss': 2.2256, 'grad_norm': 4.731106281280518, 'learning_rate': 0.0003876, 'epoch': 0.13}
{'loss': 1.9076, 'grad_norm': 2.348209857940674, 'learning_rate': 0.00038879999999999996, 'epoch': 0.13}
{'loss': 1.5723, 'grad_norm': 1.841052770614624, 'learning_rate': 0.00039, 'epoch': 0.13}
{'loss': 2.1343, 'grad_norm': 2.6915109157562256, 'learning_rate': 0.00039119999999999997, 'epoch': 0.13}
{'loss': 1.7951, 'grad_norm': 2.055569887161255, 'learning_rate': 0.0003924, 'epoch': 0.13}
{'loss': 1.8054, 'grad_norm': 2.3700554370880127, 'learning_rate': 0.00039359999999999997, 'epoch': 0.13}
{'loss': 1.9693, 'grad_norm': 1.84112548828125, 'learning_rate': 0.0003948, 'epoch': 0.13}
{'loss': 2.5455, 'grad_norm': 3.789865255355835, 'learning_rate': 0.000396, 'epoch': 0.13}
{'loss': 2.7769, 'grad_norm': 7.156352996826172, 'learning_rate': 0.0003972, 'epoch': 0.13}
{'loss': 2.5651, 'grad_norm': 3.7505874633789062, 'learning_rate': 0.0003984, 'epoch': 0.13}
{'loss': 2.5979, 'grad_norm': 3.4813008308410645, 'learning_rate': 0.0003996, 'epoch': 0.14}
{'loss': 2.5471, 'grad_norm': 3.8392105102539062, 'learning_rate': 0.0004008, 'epoch': 0.14}
{'loss': 2.2596, 'grad_norm': 2.6831040382385254, 'learning_rate': 0.000402, 'epoch': 0.14}
{'loss': 1.6111, 'grad_norm': 2.312537431716919, 'learning_rate': 0.0004032, 'epoch': 0.14}
{'loss': 1.8245, 'grad_norm': 3.20222806930542, 'learning_rate': 0.0004044, 'epoch': 0.14}
{'loss': 2.5544, 'grad_norm': 4.593980312347412, 'learning_rate': 0.0004056, 'epoch': 0.14}
{'loss': 2.9319, 'grad_norm': 7.26917839050293, 'learning_rate': 0.00040679999999999997, 'epoch': 0.14}
{'loss': 2.2855, 'grad_norm': 7.082774639129639, 'learning_rate': 0.000408, 'epoch': 0.14}
{'loss': 1.7308, 'grad_norm': 2.9662764072418213, 'learning_rate': 0.00040919999999999997, 'epoch': 0.14}
{'loss': 2.8599, 'grad_norm': 3.201094388961792, 'learning_rate': 0.0004104, 'epoch': 0.14}
{'loss': 3.0389, 'grad_norm': 6.703475475311279, 'learning_rate': 0.0004116, 'epoch': 0.14}
{'loss': 2.0929, 'grad_norm': 4.2044291496276855, 'learning_rate': 0.00041279999999999995, 'epoch': 0.14}
{'loss': 3.1795, 'grad_norm': 4.09753942489624, 'learning_rate': 0.0004139999999999999, 'epoch': 0.14}
{'loss': 2.6903, 'grad_norm': 9.047749519348145, 'learning_rate': 0.00041519999999999995, 'epoch': 0.14}
{'loss': 1.809, 'grad_norm': 4.272243499755859, 'learning_rate': 0.00041639999999999993, 'epoch': 0.14}
{'loss': 2.061, 'grad_norm': 4.085626602172852, 'learning_rate': 0.00041759999999999996, 'epoch': 0.14}
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{'loss': 1.9722, 'grad_norm': 2.053496837615967, 'learning_rate': 0.00041999999999999996, 'epoch': 0.14}
{'loss': 1.5403, 'grad_norm': 3.7039904594421387, 'learning_rate': 0.00042119999999999994, 'epoch': 0.14}
{'loss': 1.7012, 'grad_norm': 3.2297189235687256, 'learning_rate': 0.0004223999999999999, 'epoch': 0.14}
{'loss': 1.8181, 'grad_norm': 2.513806104660034, 'learning_rate': 0.00042359999999999994, 'epoch': 0.14}
{'loss': 1.7642, 'grad_norm': 2.4321508407592773, 'learning_rate': 0.0004247999999999999, 'epoch': 0.14}
{'loss': 1.5303, 'grad_norm': 2.105113983154297, 'learning_rate': 0.00042599999999999995, 'epoch': 0.14}
{'loss': 1.6585, 'grad_norm': 2.039120674133301, 'learning_rate': 0.0004271999999999999, 'epoch': 0.14}
{'loss': 1.2803, 'grad_norm': 2.758521795272827, 'learning_rate': 0.00042839999999999995, 'epoch': 0.14}
{'loss': 1.8043, 'grad_norm': 3.2673959732055664, 'learning_rate': 0.0004295999999999999, 'epoch': 0.15}
{'loss': 1.7912, 'grad_norm': 2.4577527046203613, 'learning_rate': 0.00043079999999999995, 'epoch': 0.15}
{'loss': 1.4285, 'grad_norm': 1.7472435235977173, 'learning_rate': 0.00043199999999999993, 'epoch': 0.15}
{'loss': 1.4038, 'grad_norm': 2.05169939994812, 'learning_rate': 0.00043319999999999996, 'epoch': 0.15}
{'loss': 1.9732, 'grad_norm': 2.229142665863037, 'learning_rate': 0.00043439999999999993, 'epoch': 0.15}
{'loss': 1.2128, 'grad_norm': 1.7169595956802368, 'learning_rate': 0.00043559999999999996, 'epoch': 0.15}
{'loss': 1.6774, 'grad_norm': 1.5378077030181885, 'learning_rate': 0.00043679999999999994, 'epoch': 0.15}
{'loss': 1.4687, 'grad_norm': 1.9067835807800293, 'learning_rate': 0.00043799999999999997, 'epoch': 0.15}
{'loss': 1.2386, 'grad_norm': 1.996898889541626, 'learning_rate': 0.00043919999999999994, 'epoch': 0.15}
{'loss': 2.1704, 'grad_norm': 4.134831428527832, 'learning_rate': 0.00044039999999999997, 'epoch': 0.15}
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{'loss': 1.3236, 'grad_norm': 2.2175774574279785, 'learning_rate': 0.00045359999999999997, 'epoch': 0.15}
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{'loss': 4.1513, 'grad_norm': 6.65028190612793, 'learning_rate': 0.000474, 'epoch': 0.16}


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  3%|▎         | 500/15000 [10:28<1:40:07,  2.41it/s]Printing predictions for a few samples:
Sample 1:
  Reference: लिबर ऑफिस impress में एक प्रस्तुति document बनाना और बुनियादी formatting के इस spoken tutorial में आपका स्वागत है
######


  Prediction: libr ofिs empas में एक प्रस्तुति documnt बनाना और बुनियादी formating के इस spocen tutorl में आपका स्वााा



Sample 2:
  Reference: इस tutorial में हम impress window के भागों के बारे में सीखेंगे और कैसे स्लाइड इन्सर्ट करें और कॉपी करें फॉन्ट तथा फॉन्ट को फॉर्मेट करना सीखेंगे
######


  Prediction: इस tutoril में हम impres vindo के भागों के बारे में सीखेंगे और कैसे slid insrt करें और copी करेंfond तथा fond को format करना सीखेंगे



Sample 3:
  Reference: यहाँ हम अपने ऑपरेटिंग सिस्टम के रूप में gnu/linux और लिबरऑफिस वर्जन 334 का उपयोग कर रहे हैं
######


  Prediction: यहां हम अपने oprating sिs्tm के रूप में janulincs और libr ofis vrdon  का उपयोग कर रहैं



Sample 4:
  Reference: चलिए अपनी प्रस्तुति प्रेजैटेशन sample impress open करते हैं जिसे पिछले tutorial में बनाया था
######


  Prediction: चलिए अपनी प्रस्तुति sempl imprs opn करते हैं जिसे पिछले tutेंैं



Sample 5:
  Reference: चलिए देखते हैं कि screen पर क्या क्या है
######


  Prediction: बनाया ाचलिए देखते हैं कि scrin पर क्या क्या है



last Reference string यह स्क्रिप्ट लता द्वारा अनुवादित है आईआईटी मुंबई की ओर से मैं रवि कुमार अब आपसे विदा लेता हूँहमसे जुड़ने के लिए धन्यवाद


last prediction string lता द्वारा अनुवादित ह आit mुंबई की और से मैं रवि कुमार अब आपसे विदा लेता हूँहमसे जुdने के लिए धन्यवाद
{'eval_loss': 2.2568418979644775, 'eval_cer': 0.4154334147990224, 'eval_wer': 0.6277950599558639, 'eval_runtime': 108.4152, 'eval_samples_per_second': 28.926, 'eval_steps_per_second': 3.616, 'epoch': 0.16}
{'loss': 3.1055, 'grad_norm': 10.63907527923584, 'learning_rate': 0.0004752, 'epoch': 0.16}
{'loss': 2.5712, 'grad_norm': 7.856226921081543, 'learning_rate': 0.0004764, 'epoch': 0.16}
{'loss': 2.0602, 'grad_norm': 2.7789902687072754, 'learning_rate': 0.0004776, 'epoch': 0.16}
{'loss': 1.8347, 'grad_norm': 3.9424784183502197, 'learning_rate': 0.0004788, 'epoch': 0.16}
{'loss': 1.8435, 'grad_norm': 2.155517339706421, 'learning_rate': 0.00047999999999999996, 'epoch': 0.16}
{'loss': 1.4428, 'grad_norm': 1.7009425163269043, 'learning_rate': 0.0004812, 'epoch': 0.16}
{'loss': 2.4481, 'grad_norm': 2.8969461917877197, 'learning_rate': 0.00048239999999999996, 'epoch': 0.16}
{'loss': 1.7013, 'grad_norm': 3.3479177951812744, 'learning_rate': 0.0004836, 'epoch': 0.16}
{'loss': 1.7989, 'grad_norm': 1.8520783185958862, 'learning_rate': 0.00048479999999999997, 'epoch': 0.16}
{'loss': 2.0989, 'grad_norm': 4.505759239196777, 'learning_rate': 0.000486, 'epoch': 0.16}
{'loss': 1.861, 'grad_norm': 2.9783542156219482, 'learning_rate': 0.00048719999999999997, 'epoch': 0.16}
{'loss': 1.6748, 'grad_norm': 2.845592975616455, 'learning_rate': 0.0004883999999999999, 'epoch': 0.16}
{'loss': 2.097, 'grad_norm': 3.7559475898742676, 'learning_rate': 0.0004896, 'epoch': 0.17}
{'loss': 1.3349, 'grad_norm': 4.264702320098877, 'learning_rate': 0.0004907999999999999, 'epoch': 0.17}
{'loss': 1.8101, 'grad_norm': 2.6709723472595215, 'learning_rate': 0.0004919999999999999, 'epoch': 0.17}
{'loss': 1.5654, 'grad_norm': 2.3186886310577393, 'learning_rate': 0.0004932, 'epoch': 0.17}
{'loss': 1.6074, 'grad_norm': 4.400014877319336, 'learning_rate': 0.0004944, 'epoch': 0.17}
{'loss': 1.4601, 'grad_norm': 1.6715278625488281, 'learning_rate': 0.0004955999999999999, 'epoch': 0.17}
{'loss': 1.2547, 'grad_norm': 1.9263966083526611, 'learning_rate': 0.0004967999999999999, 'epoch': 0.17}
{'loss': 1.4949, 'grad_norm': 1.8865171670913696, 'learning_rate': 0.000498, 'epoch': 0.17}
{'loss': 1.8605, 'grad_norm': 2.318582534790039, 'learning_rate': 0.0004991999999999999, 'epoch': 0.17}
{'loss': 1.2673, 'grad_norm': 1.4431250095367432, 'learning_rate': 0.0005003999999999999, 'epoch': 0.17}
{'loss': 1.0903, 'grad_norm': 1.6961430311203003, 'learning_rate': 0.0005015999999999999, 'epoch': 0.17}
{'loss': 1.2492, 'grad_norm': 1.5667531490325928, 'learning_rate': 0.0005028, 'epoch': 0.17}
{'loss': 1.2268, 'grad_norm': 1.7235971689224243, 'learning_rate': 0.0005039999999999999, 'epoch': 0.17}
{'loss': 2.0925, 'grad_norm': 2.2716994285583496, 'learning_rate': 0.0005051999999999999, 'epoch': 0.17}
{'loss': 1.7899, 'grad_norm': 3.057313919067383, 'learning_rate': 0.0005064, 'epoch': 0.17}
{'loss': 2.0368, 'grad_norm': 1.9482861757278442, 'learning_rate': 0.0005076, 'epoch': 0.17}
{'loss': 1.5866, 'grad_norm': 2.2377617359161377, 'learning_rate': 0.0005087999999999999, 'epoch': 0.17}
{'loss': 1.2362, 'grad_norm': 1.5615544319152832, 'learning_rate': 0.0005099999999999999, 'epoch': 0.17}
{'loss': 1.7395, 'grad_norm': 1.9075547456741333, 'learning_rate': 0.0005112, 'epoch': 0.17}
{'loss': 1.5563, 'grad_norm': 2.3841354846954346, 'learning_rate': 0.0005124, 'epoch': 0.17}
{'loss': 1.393, 'grad_norm': 2.5319297313690186, 'learning_rate': 0.0005135999999999999, 'epoch': 0.17}
{'loss': 1.8297, 'grad_norm': 2.3335015773773193, 'learning_rate': 0.0005147999999999999, 'epoch': 0.17}
{'loss': 1.6833, 'grad_norm': 2.419879198074341, 'learning_rate': 0.000516, 'epoch': 0.17}
{'loss': 1.8353, 'grad_norm': 2.148865222930908, 'learning_rate': 0.0005172, 'epoch': 0.17}
{'loss': 1.5478, 'grad_norm': 2.289213180541992, 'learning_rate': 0.0005183999999999999, 'epoch': 0.17}
{'loss': 1.4922, 'grad_norm': 3.3783493041992188, 'learning_rate': 0.0005195999999999999, 'epoch': 0.18}
{'loss': 1.9106, 'grad_norm': 2.4567673206329346, 'learning_rate': 0.0005208, 'epoch': 0.18}
{'loss': 1.8431, 'grad_norm': 2.240478277206421, 'learning_rate': 0.000522, 'epoch': 0.18}
{'loss': 2.3678, 'grad_norm': 2.029679775238037, 'learning_rate': 0.0005231999999999999, 'epoch': 0.18}
{'loss': 1.5806, 'grad_norm': 2.039754867553711, 'learning_rate': 0.0005244, 'epoch': 0.18}
{'loss': 2.7376, 'grad_norm': 3.5851993560791016, 'learning_rate': 0.0005256, 'epoch': 0.18}
{'loss': 1.4581, 'grad_norm': 2.961970806121826, 'learning_rate': 0.0005267999999999999, 'epoch': 0.18}
{'loss': 2.1674, 'grad_norm': 4.003596782684326, 'learning_rate': 0.0005279999999999999, 'epoch': 0.18}
{'loss': 1.9285, 'grad_norm': 4.428085803985596, 'learning_rate': 0.0005292, 'epoch': 0.18}
{'loss': 1.5585, 'grad_norm': 2.912794828414917, 'learning_rate': 0.0005304, 'epoch': 0.18}
{'loss': 1.8986, 'grad_norm': 3.141347646713257, 'learning_rate': 0.0005315999999999999, 'epoch': 0.18}
{'loss': 2.2395, 'grad_norm': 4.043821334838867, 'learning_rate': 0.0005327999999999999, 'epoch': 0.18}
{'loss': 2.8411, 'grad_norm': 5.761645793914795, 'learning_rate': 0.000534, 'epoch': 0.18}
{'loss': 4.5851, 'grad_norm': 19.885866165161133, 'learning_rate': 0.0005352, 'epoch': 0.18}
{'loss': 1.4378, 'grad_norm': 2.242046594619751, 'learning_rate': 0.0005363999999999999, 'epoch': 0.18}
{'loss': 1.8037, 'grad_norm': 2.6804563999176025, 'learning_rate': 0.0005376, 'epoch': 0.18}
{'loss': 4.4704, 'grad_norm': 16.926651000976562, 'learning_rate': 0.0005388, 'epoch': 0.18}
{'loss': 1.4779, 'grad_norm': 3.923008918762207, 'learning_rate': 0.00054, 'epoch': 0.18}
{'loss': 2.2539, 'grad_norm': 6.066555500030518, 'learning_rate': 0.0005411999999999999, 'epoch': 0.18}
{'loss': 2.071, 'grad_norm': 5.812263011932373, 'learning_rate': 0.0005424, 'epoch': 0.18}
{'loss': 1.7869, 'grad_norm': 3.733736753463745, 'learning_rate': 0.0005436, 'epoch': 0.18}
{'loss': 1.6823, 'grad_norm': 2.588632822036743, 'learning_rate': 0.0005448, 'epoch': 0.18}
{'loss': 1.4438, 'grad_norm': 1.6445374488830566, 'learning_rate': 0.0005459999999999999, 'epoch': 0.18}
{'loss': 1.2979, 'grad_norm': 1.633201241493225, 'learning_rate': 0.0005472, 'epoch': 0.18}
{'loss': 1.2576, 'grad_norm': 2.589153528213501, 'learning_rate': 0.0005484, 'epoch': 0.18}
{'loss': 1.2143, 'grad_norm': 2.4447247982025146, 'learning_rate': 0.0005496, 'epoch': 0.19}
{'loss': 2.8169, 'grad_norm': 6.226335048675537, 'learning_rate': 0.0005507999999999999, 'epoch': 0.19}
{'loss': 2.0583, 'grad_norm': 3.2706716060638428, 'learning_rate': 0.000552, 'epoch': 0.19}
{'loss': 1.13, 'grad_norm': 1.868854284286499, 'learning_rate': 0.0005532, 'epoch': 0.19}
{'loss': 1.0417, 'grad_norm': 1.4710438251495361, 'learning_rate': 0.0005544, 'epoch': 0.19}
{'loss': 1.1512, 'grad_norm': 2.0166754722595215, 'learning_rate': 0.0005556, 'epoch': 0.19}
{'loss': 2.6584, 'grad_norm': 8.030802726745605, 'learning_rate': 0.0005568, 'epoch': 0.19}
{'loss': 1.7608, 'grad_norm': 3.493773937225342, 'learning_rate': 0.000558, 'epoch': 0.19}
{'loss': 1.5206, 'grad_norm': 4.326634407043457, 'learning_rate': 0.0005591999999999999, 'epoch': 0.19}
{'loss': 1.535, 'grad_norm': 3.652657985687256, 'learning_rate': 0.0005604, 'epoch': 0.19}
{'loss': 2.5261, 'grad_norm': 2.5583713054656982, 'learning_rate': 0.0005616, 'epoch': 0.19}
{'loss': 1.1986, 'grad_norm': 2.078011989593506, 'learning_rate': 0.0005627999999999999, 'epoch': 0.19}
{'loss': 0.9899, 'grad_norm': 1.7389509677886963, 'learning_rate': 0.0005639999999999999, 'epoch': 0.19}
{'loss': 1.6448, 'grad_norm': 2.0091888904571533, 'learning_rate': 0.0005652, 'epoch': 0.19}
{'loss': 2.4667, 'grad_norm': 3.8182358741760254, 'learning_rate': 0.0005663999999999999, 'epoch': 0.19}
{'loss': 1.5887, 'grad_norm': 2.6458334922790527, 'learning_rate': 0.0005675999999999999, 'epoch': 0.19}
{'loss': 1.1928, 'grad_norm': 2.8256735801696777, 'learning_rate': 0.0005688, 'epoch': 0.19}
{'loss': 2.4022, 'grad_norm': 3.986029863357544, 'learning_rate': 0.00057, 'epoch': 0.19}
{'loss': 1.4459, 'grad_norm': 1.7533482313156128, 'learning_rate': 0.0005711999999999999, 'epoch': 0.19}
{'loss': 1.0761, 'grad_norm': 1.9787483215332031, 'learning_rate': 0.0005723999999999999, 'epoch': 0.19}
{'loss': 1.7317, 'grad_norm': 2.2873222827911377, 'learning_rate': 0.0005736, 'epoch': 0.19}
{'loss': 1.1974, 'grad_norm': 1.890799880027771, 'learning_rate': 0.0005747999999999999, 'epoch': 0.19}
{'loss': 1.624, 'grad_norm': 2.4860143661499023, 'learning_rate': 0.0005759999999999999, 'epoch': 0.19}
{'loss': 1.6114, 'grad_norm': 2.4561805725097656, 'learning_rate': 0.0005771999999999999, 'epoch': 0.19}
{'loss': 1.6571, 'grad_norm': 2.1408402919769287, 'learning_rate': 0.0005784, 'epoch': 0.19}
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{'loss': 1.7938, 'grad_norm': 3.026120901107788, 'learning_rate': 0.0005819999999999999, 'epoch': 0.2}
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{'loss': 2.1969, 'grad_norm': 3.176147699356079, 'learning_rate': 0.0005843999999999999, 'epoch': 0.2}
{'loss': 2.6791, 'grad_norm': 3.4093942642211914, 'learning_rate': 0.0005855999999999999, 'epoch': 0.2}
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{'loss': 2.1306, 'grad_norm': 4.215576171875, 'learning_rate': 0.0005891999999999999, 'epoch': 0.2}
{'loss': 1.4102, 'grad_norm': 2.812678337097168, 'learning_rate': 0.0005903999999999999, 'epoch': 0.2}
{'loss': 1.4933, 'grad_norm': 3.7179956436157227, 'learning_rate': 0.0005916, 'epoch': 0.2}
{'loss': 1.6343, 'grad_norm': 2.661510467529297, 'learning_rate': 0.0005928, 'epoch': 0.2}
{'loss': 2.3713, 'grad_norm': 3.627727508544922, 'learning_rate': 0.0005939999999999999, 'epoch': 0.2}


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  4%|▍         | 600/15000 [12:51<1:26:52,  2.76it/s]Printing predictions for a few samples:
Sample 1:
  Reference: लिबर ऑफिस impress में एक प्रस्तुति document बनाना और बुनियादी formatting के इस spoken tutorial में आपका स्वागत है
######


  Prediction: libr ofis impras mेn एक प्रस्तुति documnt बनाना और बुनियादी formating के इस spoken tutorial में आपका्



Sample 2:
  Reference: इस tutorial में हम impress window के भागों के बारे में सीखेंगे और कैसे स्लाइड इन्सर्ट करें और कॉपी करें फॉन्ट तथा फॉन्ट को फॉर्मेट करना सीखेंगे
######


  Prediction: इस tutoral में हम impras windo के भागों के बारे में सीखेंगे और कैसे slid इinsrt करें और copी करेंford तथा fond को format करना सीखेंगे



Sample 3:
  Reference: यहाँ हम अपने ऑपरेटिंग सिस्टम के रूप में gnu/linux और लिबरऑफिस वर्जन 334 का उपयोग कर रहे हैं
######


  Prediction: यहाँ हम अपने oprating sistm के रूप में ganu lincs और libr ofis vrgon थ  का उपयोग करहं



Sample 4:
  Reference: चलिए अपनी प्रस्तुति प्रेजैटेशन sample impress open करते हैं जिसे पिछले tutorial में बनाया था
######


  Prediction: चलिए अपनी प्रस्तुति sampl impras opn करते हैंजसे पिछले tutं



Sample 5:
  Reference: चलिए देखते हैं कि screen पर क्या क्या है
######


  Prediction: बाया थाचलिए देखते हैं कि सcrin पर क्या क्या है



last Reference string यह स्क्रिप्ट लता द्वारा अनुवादित है आईआईटी मुंबई की ओर से मैं रवि कुमार अब आपसे विदा लेता हूँहमसे जुड़ने के लिए धन्यवाद


last prediction string lता द्वारा अनुवादित ैआइआईtी मुmbइ की और से मैं रवि कुमार अब आपसे विदा लेता हूँहमसे जुड़ने के लिए धन्यवा
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{'loss': 2.9049, 'grad_norm': 8.620933532714844, 'learning_rate': 0.0005951999999999999, 'epoch': 0.2}
{'loss': 1.8373, 'grad_norm': 5.055453300476074, 'learning_rate': 0.0005964, 'epoch': 0.2}
{'loss': 2.5777, 'grad_norm': 3.4175736904144287, 'learning_rate': 0.0005976, 'epoch': 0.2}
{'loss': 1.6549, 'grad_norm': 2.403773069381714, 'learning_rate': 0.0005987999999999999, 'epoch': 0.2}
{'loss': 2.2092, 'grad_norm': 2.8520140647888184, 'learning_rate': 0.0006, 'epoch': 0.2}
{'loss': 1.3653, 'grad_norm': 2.81533145904541, 'learning_rate': 0.0005999586206896551, 'epoch': 0.2}
{'loss': 2.1099, 'grad_norm': 3.9412333965301514, 'learning_rate': 0.0005999172413793103, 'epoch': 0.2}
{'loss': 1.711, 'grad_norm': 2.4239866733551025, 'learning_rate': 0.0005998758620689655, 'epoch': 0.2}
{'loss': 1.2783, 'grad_norm': 1.6030797958374023, 'learning_rate': 0.0005998344827586206, 'epoch': 0.2}
{'loss': 1.97, 'grad_norm': 2.041012763977051, 'learning_rate': 0.0005997931034482758, 'epoch': 0.2}
{'loss': 1.3324, 'grad_norm': 1.9867337942123413, 'learning_rate': 0.000599751724137931, 'epoch': 0.2}
{'loss': 1.5472, 'grad_norm': 3.10821270942688, 'learning_rate': 0.0005997103448275861, 'epoch': 0.2}
{'loss': 1.5301, 'grad_norm': 2.958217144012451, 'learning_rate': 0.0005996689655172413, 'epoch': 0.21}
{'loss': 1.1618, 'grad_norm': 1.4882193803787231, 'learning_rate': 0.0005996275862068965, 'epoch': 0.21}
{'loss': 1.5415, 'grad_norm': 1.7506566047668457, 'learning_rate': 0.0005995862068965517, 'epoch': 0.21}
{'loss': 1.5324, 'grad_norm': 1.9622406959533691, 'learning_rate': 0.0005995448275862068, 'epoch': 0.21}
{'loss': 1.6604, 'grad_norm': 2.141575336456299, 'learning_rate': 0.000599503448275862, 'epoch': 0.21}
{'loss': 2.4964, 'grad_norm': 2.7557806968688965, 'learning_rate': 0.0005994620689655171, 'epoch': 0.21}
{'loss': 1.9322, 'grad_norm': 2.355375289916992, 'learning_rate': 0.0005994206896551724, 'epoch': 0.21}
{'loss': 1.1032, 'grad_norm': 1.548173427581787, 'learning_rate': 0.0005993793103448276, 'epoch': 0.21}
{'loss': 1.302, 'grad_norm': 3.306942939758301, 'learning_rate': 0.0005993379310344827, 'epoch': 0.21}
{'loss': 1.046, 'grad_norm': 1.735605001449585, 'learning_rate': 0.0005992965517241379, 'epoch': 0.21}
{'loss': 1.1501, 'grad_norm': 1.620605230331421, 'learning_rate': 0.000599255172413793, 'epoch': 0.21}
{'loss': 1.3633, 'grad_norm': 1.3116556406021118, 'learning_rate': 0.0005992137931034483, 'epoch': 0.21}
{'loss': 1.5051, 'grad_norm': 1.4407860040664673, 'learning_rate': 0.0005991724137931033, 'epoch': 0.21}
{'loss': 1.618, 'grad_norm': 1.843397617340088, 'learning_rate': 0.0005991310344827586, 'epoch': 0.21}
{'loss': 2.9144, 'grad_norm': 2.372600793838501, 'learning_rate': 0.0005990896551724137, 'epoch': 0.21}
{'loss': 1.3389, 'grad_norm': 1.8624999523162842, 'learning_rate': 0.0005990482758620689, 'epoch': 0.21}
{'loss': 1.1232, 'grad_norm': 2.1637954711914062, 'learning_rate': 0.000599006896551724, 'epoch': 0.21}
{'loss': 1.4703, 'grad_norm': 1.9150300025939941, 'learning_rate': 0.0005989655172413793, 'epoch': 0.21}
{'loss': 1.2947, 'grad_norm': 1.683755874633789, 'learning_rate': 0.0005989241379310345, 'epoch': 0.21}
{'loss': 2.0408, 'grad_norm': 2.6447227001190186, 'learning_rate': 0.0005988827586206896, 'epoch': 0.21}
{'loss': 0.9247, 'grad_norm': 1.6903175115585327, 'learning_rate': 0.0005988413793103448, 'epoch': 0.21}
{'loss': 1.7494, 'grad_norm': 2.4379537105560303, 'learning_rate': 0.0005987999999999999, 'epoch': 0.21}
{'loss': 1.3643, 'grad_norm': 2.0866823196411133, 'learning_rate': 0.0005987586206896552, 'epoch': 0.21}
{'loss': 1.2849, 'grad_norm': 2.5197439193725586, 'learning_rate': 0.0005987172413793102, 'epoch': 0.21}
{'loss': 0.8433, 'grad_norm': 1.3611820936203003, 'learning_rate': 0.0005986758620689655, 'epoch': 0.21}
{'loss': 1.1127, 'grad_norm': 4.326557636260986, 'learning_rate': 0.0005986344827586206, 'epoch': 0.22}
{'loss': 4.0215, 'grad_norm': 9.487196922302246, 'learning_rate': 0.0005985931034482758, 'epoch': 0.22}
{'loss': 1.1526, 'grad_norm': 2.3789398670196533, 'learning_rate': 0.000598551724137931, 'epoch': 0.22}
{'loss': 2.0065, 'grad_norm': 2.346961259841919, 'learning_rate': 0.0005985103448275861, 'epoch': 0.22}
{'loss': 1.9587, 'grad_norm': 2.559351682662964, 'learning_rate': 0.0005984689655172414, 'epoch': 0.22}
{'loss': 2.1139, 'grad_norm': 2.3643903732299805, 'learning_rate': 0.0005984275862068965, 'epoch': 0.22}
{'loss': 1.798, 'grad_norm': 2.4121475219726562, 'learning_rate': 0.0005983862068965517, 'epoch': 0.22}
{'loss': 1.761, 'grad_norm': 2.4712910652160645, 'learning_rate': 0.0005983448275862068, 'epoch': 0.22}
{'loss': 1.8396, 'grad_norm': 2.314969778060913, 'learning_rate': 0.000598303448275862, 'epoch': 0.22}
{'loss': 1.9997, 'grad_norm': 2.4038658142089844, 'learning_rate': 0.0005982620689655172, 'epoch': 0.22}
{'loss': 2.2795, 'grad_norm': 1.9356986284255981, 'learning_rate': 0.0005982206896551724, 'epoch': 0.22}
{'loss': 1.8879, 'grad_norm': 3.2914559841156006, 'learning_rate': 0.0005981793103448275, 'epoch': 0.22}
{'loss': 2.9297, 'grad_norm': 3.0275511741638184, 'learning_rate': 0.0005981379310344827, 'epoch': 0.22}
{'loss': 2.2926, 'grad_norm': 8.098669052124023, 'learning_rate': 0.000598096551724138, 'epoch': 0.22}
{'loss': 1.2659, 'grad_norm': 2.1478710174560547, 'learning_rate': 0.000598055172413793, 'epoch': 0.22}
{'loss': 1.491, 'grad_norm': 1.9794830083847046, 'learning_rate': 0.0005980137931034483, 'epoch': 0.22}
{'loss': 3.0264, 'grad_norm': 5.560781955718994, 'learning_rate': 0.0005979724137931034, 'epoch': 0.22}
{'loss': 2.0048, 'grad_norm': 6.047894477844238, 'learning_rate': 0.0005979310344827586, 'epoch': 0.22}
{'loss': 1.3296, 'grad_norm': 2.9243884086608887, 'learning_rate': 0.0005978896551724137, 'epoch': 0.22}
{'loss': 1.461, 'grad_norm': 3.350033760070801, 'learning_rate': 0.0005978482758620689, 'epoch': 0.22}
{'loss': 1.1274, 'grad_norm': 2.7459893226623535, 'learning_rate': 0.0005978068965517241, 'epoch': 0.22}
{'loss': 1.3654, 'grad_norm': 2.4179863929748535, 'learning_rate': 0.0005977655172413793, 'epoch': 0.22}
{'loss': 1.8164, 'grad_norm': 1.5426034927368164, 'learning_rate': 0.0005977241379310344, 'epoch': 0.22}
{'loss': 2.3478, 'grad_norm': 3.923177719116211, 'learning_rate': 0.0005976827586206896, 'epoch': 0.22}
{'loss': 1.7029, 'grad_norm': 2.4501774311065674, 'learning_rate': 0.0005976413793103447, 'epoch': 0.22}
{'loss': 1.1962, 'grad_norm': 1.742348551750183, 'learning_rate': 0.0005976, 'epoch': 0.23}
{'loss': 1.4131, 'grad_norm': 2.3669848442077637, 'learning_rate': 0.0005975586206896552, 'epoch': 0.23}
{'loss': 1.8151, 'grad_norm': 2.664574384689331, 'learning_rate': 0.0005975172413793103, 'epoch': 0.23}
{'loss': 1.1741, 'grad_norm': 2.0037319660186768, 'learning_rate': 0.0005974758620689655, 'epoch': 0.23}
{'loss': 1.1272, 'grad_norm': 2.4384982585906982, 'learning_rate': 0.0005974344827586207, 'epoch': 0.23}
{'loss': 1.592, 'grad_norm': 1.5745062828063965, 'learning_rate': 0.0005973931034482758, 'epoch': 0.23}
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{'loss': 1.9152, 'grad_norm': 2.314288854598999, 'learning_rate': 0.0005960689655172413, 'epoch': 0.24}


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                                                 /scratch/work/palp3/myenv/lib/python3.11/site-packages/transformers/models/wav2vec2/processing_wav2vec2.py:157: UserWarning: `as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your labels by using the argument `text` of the regular `__call__` method (either in the same call as your audio inputs, or in a separate call.
  warnings.warn(
/scratch/work/palp3/myenv/lib/python3.11/site-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.
  with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):  # type: ignore[attr-defined]

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  5%|▍         | 700/15000 [15:24<1:32:45,  2.57it/s]Printing predictions for a few samples:
Sample 1:
  Reference: लिबर ऑफिस impress में एक प्रस्तुति document बनाना और बुनियादी formatting के इस spoken tutorial में आपका स्वागत है
######


  Prediction: libr oफi impres में एक प्रस्तुति document बनाना और बुनियादी फormating के इस spoken tutoral में आपका



Sample 2:
  Reference: इस tutorial में हम impress window के भागों के बारे में सीखेंगे और कैसे स्लाइड इन्सर्ट करें और कॉपी करें फॉन्ट तथा फॉन्ट को फॉर्मेट करना सीखेंगे
######


  Prediction: इस tutorial में हम impres windo के भागों के बारे में सीखेंगे और कैसे slid insr करें और cope करेंont तथा फont को फormat करना सीखेंगे



Sample 3:
  Reference: यहाँ हम अपने ऑपरेटिंग सिस्टम के रूप में gnu/linux और लिबरऑफिस वर्जन 334 का उपयोग कर रहे हैं
######


  Prediction: यहाँ हम अपने oprating सिstm के रूप में gnu linx और libr oफis wrtn  का उपयोग कर रह



Sample 4:
  Reference: चलिए अपनी प्रस्तुति प्रेजैटेशन sample impress open करते हैं जिसे पिछले tutorial में बनाया था
######


  Prediction: चलिए अपनी प्रस्तुति sempl impre opn करे  पिछले t



Sample 5:
  Reference: चलिए देखते हैं कि screen पर क्या क्या है
######


  Prediction: चिए देखते हैं कि इसcrin पर क्या क्या है



last Reference string यह स्क्रिप्ट लता द्वारा अनुवादित है आईआईटी मुंबई की ओर से मैं रवि कुमार अब आपसे विदा लेता हूँहमसे जुड़ने के लिए धन्यवाद


last prediction string ltदर अनुवादित हआiआईt मुमबई की और से मैं रवी कुमार अब आपसे विदा लेता हूँ हमसे जुड़ने के लिए धन्यवा
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{'loss': 3.241, 'grad_norm': 8.570022583007812, 'learning_rate': 0.0005960275862068966, 'epoch': 0.24}
{'loss': 1.6557, 'grad_norm': 2.837428569793701, 'learning_rate': 0.0005959862068965516, 'epoch': 0.24}
{'loss': 1.7194, 'grad_norm': 4.636868953704834, 'learning_rate': 0.0005959448275862069, 'epoch': 0.24}
{'loss': 1.9821, 'grad_norm': 3.187772274017334, 'learning_rate': 0.000595903448275862, 'epoch': 0.24}
{'loss': 2.2221, 'grad_norm': 2.383075714111328, 'learning_rate': 0.0005958620689655172, 'epoch': 0.24}
{'loss': 2.0181, 'grad_norm': 6.057743549346924, 'learning_rate': 0.0005958206896551724, 'epoch': 0.24}
{'loss': 1.7376, 'grad_norm': 3.810245990753174, 'learning_rate': 0.0005957793103448275, 'epoch': 0.24}
{'loss': 1.6624, 'grad_norm': 5.0666584968566895, 'learning_rate': 0.0005957379310344827, 'epoch': 0.24}
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{'loss': 1.8538, 'grad_norm': 2.441513776779175, 'learning_rate': 0.000595655172413793, 'epoch': 0.24}
{'loss': 1.5266, 'grad_norm': 1.392045259475708, 'learning_rate': 0.0005956137931034482, 'epoch': 0.24}
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{'loss': 1.5424, 'grad_norm': 2.5804779529571533, 'learning_rate': 0.0005954896551724138, 'epoch': 0.25}
{'loss': 2.184, 'grad_norm': 2.1346352100372314, 'learning_rate': 0.0005954482758620689, 'epoch': 0.25}
{'loss': 1.2072, 'grad_norm': 1.7911951541900635, 'learning_rate': 0.0005954068965517241, 'epoch': 0.25}
{'loss': 1.2428, 'grad_norm': 2.0166399478912354, 'learning_rate': 0.0005953655172413793, 'epoch': 0.25}
{'loss': 1.9162, 'grad_norm': 1.6486585140228271, 'learning_rate': 0.0005953241379310344, 'epoch': 0.25}
{'loss': 1.4292, 'grad_norm': 1.9564014673233032, 'learning_rate': 0.0005952827586206896, 'epoch': 0.25}
{'loss': 3.3983, 'grad_norm': 10.306204795837402, 'learning_rate': 0.0005952413793103448, 'epoch': 0.25}
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{'loss': 1.9115, 'grad_norm': 2.648489236831665, 'learning_rate': 0.0005949931034482758, 'epoch': 0.25}
{'loss': 1.5888, 'grad_norm': 1.848435640335083, 'learning_rate': 0.000594951724137931, 'epoch': 0.25}
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{'loss': 1.0395, 'grad_norm': 2.3986527919769287, 'learning_rate': 0.0005948275862068965, 'epoch': 0.25}
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{'loss': 2.134, 'grad_norm': 2.561382293701172, 'learning_rate': 0.0005945793103448276, 'epoch': 0.25}
{'loss': 1.1803, 'grad_norm': 2.7185795307159424, 'learning_rate': 0.0005945379310344827, 'epoch': 0.25}
{'loss': 1.2106, 'grad_norm': 2.0261006355285645, 'learning_rate': 0.0005944965517241379, 'epoch': 0.26}
{'loss': 2.7326, 'grad_norm': 3.7475523948669434, 'learning_rate': 0.000594455172413793, 'epoch': 0.26}
{'loss': 0.9727, 'grad_norm': 2.1572113037109375, 'learning_rate': 0.0005944137931034483, 'epoch': 0.26}
{'loss': 1.15, 'grad_norm': 1.8728227615356445, 'learning_rate': 0.0005943724137931033, 'epoch': 0.26}
{'loss': 2.4967, 'grad_norm': 2.524895668029785, 'learning_rate': 0.0005943310344827586, 'epoch': 0.26}
{'loss': 0.9945, 'grad_norm': 1.8149582147598267, 'learning_rate': 0.0005942896551724137, 'epoch': 0.26}
{'loss': 1.7571, 'grad_norm': 2.011138916015625, 'learning_rate': 0.0005942482758620689, 'epoch': 0.26}
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{'loss': 1.8036, 'grad_norm': 3.7251291275024414, 'learning_rate': 0.0005937103448275861, 'epoch': 0.26}
{'loss': 1.1959, 'grad_norm': 3.657550573348999, 'learning_rate': 0.0005936689655172413, 'epoch': 0.26}
{'loss': 1.7072, 'grad_norm': 2.107895612716675, 'learning_rate': 0.0005936275862068965, 'epoch': 0.26}
{'loss': 1.5954, 'grad_norm': 3.0020875930786133, 'learning_rate': 0.0005935862068965517, 'epoch': 0.26}
{'loss': 1.2947, 'grad_norm': 2.0830163955688477, 'learning_rate': 0.0005935448275862068, 'epoch': 0.26}
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{'loss': 0.9579, 'grad_norm': 2.042124032974243, 'learning_rate': 0.0005934620689655172, 'epoch': 0.27}
{'loss': 1.4942, 'grad_norm': 2.4121668338775635, 'learning_rate': 0.0005934206896551724, 'epoch': 0.27}
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{'loss': 1.7144, 'grad_norm': 4.403632640838623, 'learning_rate': 0.0005919310344827585, 'epoch': 0.28}


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