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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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  5%|▌         | 800/15000 [17:47<1:29:44,  2.64it/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 impres में एक प्रस्तुति documeंt बनाना और बुनियादी fomating के इस spoken tutorial 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 tutoral में हम impres window के भागों के बारे में सीखेंगे और कैसे slid insrt करें और copी करें fod तथ fot को foरmat 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 oprading sistm के रूप में gnu linxx और libr ofis wrson थ का उपयोग कर रहैं



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


  Prediction: चलए अपनी प्रस्तुति sampl imprs op करते हैं जसे पिछले tutoैै



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


  Prediction: चए देे हैं कि scrीन 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 लतa दवारा अनुवादित है आiआईटी मुमबई की और से मैं रवी कुमार अब आपसे विदा लेता हूँ हमसे जुड़ने के लिए धन्यवा
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{'loss': 1.711, 'grad_norm': 2.1838691234588623, 'learning_rate': 0.0005918896551724138, 'epoch': 0.28}
{'loss': 1.5707, 'grad_norm': 2.115309715270996, 'learning_rate': 0.0005918482758620689, 'epoch': 0.28}
{'loss': 1.8096, 'grad_norm': 2.248124122619629, 'learning_rate': 0.0005918068965517241, 'epoch': 0.28}
{'loss': 1.1201, 'grad_norm': 3.41158127784729, 'learning_rate': 0.0005917655172413792, 'epoch': 0.28}
{'loss': 2.1531, 'grad_norm': 2.472078323364258, 'learning_rate': 0.0005917241379310344, 'epoch': 0.28}
{'loss': 1.2054, 'grad_norm': 2.0882015228271484, 'learning_rate': 0.0005916827586206897, 'epoch': 0.28}
{'loss': 1.296, 'grad_norm': 1.790144443511963, 'learning_rate': 0.0005916413793103447, 'epoch': 0.28}
{'loss': 1.6942, 'grad_norm': 4.462668418884277, 'learning_rate': 0.0005916, 'epoch': 0.28}
{'loss': 1.4937, 'grad_norm': 1.6038415431976318, 'learning_rate': 0.0005915586206896551, 'epoch': 0.28}
{'loss': 1.1961, 'grad_norm': 1.8274813890457153, 'learning_rate': 0.0005915172413793103, 'epoch': 0.28}
{'loss': 1.1887, 'grad_norm': 2.016632556915283, 'learning_rate': 0.0005914758620689654, 'epoch': 0.28}
{'loss': 2.0348, 'grad_norm': 3.401322841644287, 'learning_rate': 0.0005914344827586207, 'epoch': 0.28}
{'loss': 1.2195, 'grad_norm': 2.3689587116241455, 'learning_rate': 0.0005913931034482758, 'epoch': 0.29}
{'loss': 1.202, 'grad_norm': 2.109053611755371, 'learning_rate': 0.000591351724137931, 'epoch': 0.29}
{'loss': 1.2404, 'grad_norm': 1.9748375415802002, 'learning_rate': 0.0005913103448275861, 'epoch': 0.29}
{'loss': 1.4719, 'grad_norm': 1.3651127815246582, 'learning_rate': 0.0005912689655172413, 'epoch': 0.29}
{'loss': 1.2857, 'grad_norm': 1.8783410787582397, 'learning_rate': 0.0005912275862068966, 'epoch': 0.29}
{'loss': 1.3789, 'grad_norm': 1.6520543098449707, 'learning_rate': 0.0005911862068965517, 'epoch': 0.29}
{'loss': 2.8401, 'grad_norm': 6.08381462097168, 'learning_rate': 0.0005911448275862069, 'epoch': 0.29}
{'loss': 1.3944, 'grad_norm': 2.0706722736358643, 'learning_rate': 0.000591103448275862, 'epoch': 0.29}
{'loss': 1.6212, 'grad_norm': 2.412391424179077, 'learning_rate': 0.0005910620689655172, 'epoch': 0.29}
{'loss': 1.597, 'grad_norm': 1.4182956218719482, 'learning_rate': 0.0005910206896551724, 'epoch': 0.29}
{'loss': 1.5448, 'grad_norm': 1.738808512687683, 'learning_rate': 0.0005909793103448275, 'epoch': 0.29}
{'loss': 1.019, 'grad_norm': 1.730510950088501, 'learning_rate': 0.0005909379310344827, 'epoch': 0.29}
{'loss': 1.3682, 'grad_norm': 1.3860887289047241, 'learning_rate': 0.0005908965517241379, 'epoch': 0.29}
{'loss': 1.8092, 'grad_norm': 4.032029628753662, 'learning_rate': 0.000590855172413793, 'epoch': 0.29}
{'loss': 1.5999, 'grad_norm': 1.499061107635498, 'learning_rate': 0.0005908137931034482, 'epoch': 0.29}
{'loss': 1.3909, 'grad_norm': 1.729883074760437, 'learning_rate': 0.0005907724137931034, 'epoch': 0.29}
{'loss': 1.1639, 'grad_norm': 4.955617904663086, 'learning_rate': 0.0005907310344827586, 'epoch': 0.29}
{'loss': 1.7685, 'grad_norm': 2.7260191440582275, 'learning_rate': 0.0005906896551724138, 'epoch': 0.29}
{'loss': 1.142, 'grad_norm': 1.7496634721755981, 'learning_rate': 0.0005906482758620689, 'epoch': 0.29}
{'loss': 1.921, 'grad_norm': 2.2988076210021973, 'learning_rate': 0.0005906068965517241, 'epoch': 0.29}
{'loss': 1.319, 'grad_norm': 2.0166690349578857, 'learning_rate': 0.0005905655172413793, 'epoch': 0.29}
{'loss': 1.8343, 'grad_norm': 1.9566967487335205, 'learning_rate': 0.0005905241379310344, 'epoch': 0.29}
{'loss': 1.872, 'grad_norm': 1.8502064943313599, 'learning_rate': 0.0005904827586206896, 'epoch': 0.29}
{'loss': 2.2023, 'grad_norm': 2.7952170372009277, 'learning_rate': 0.0005904413793103448, 'epoch': 0.29}
{'loss': 1.1585, 'grad_norm': 3.4202959537506104, 'learning_rate': 0.0005903999999999999, 'epoch': 0.29}
{'loss': 1.5116, 'grad_norm': 1.6191118955612183, 'learning_rate': 0.0005903586206896551, 'epoch': 0.3}
{'loss': 1.543, 'grad_norm': 1.9514490365982056, 'learning_rate': 0.0005903172413793102, 'epoch': 0.3}
{'loss': 1.36, 'grad_norm': 2.595632314682007, 'learning_rate': 0.0005902758620689655, 'epoch': 0.3}
{'loss': 1.898, 'grad_norm': 4.149548530578613, 'learning_rate': 0.0005902344827586207, 'epoch': 0.3}
{'loss': 1.6217, 'grad_norm': 1.923248291015625, 'learning_rate': 0.0005901931034482758, 'epoch': 0.3}
{'loss': 1.297, 'grad_norm': 2.5620779991149902, 'learning_rate': 0.000590151724137931, 'epoch': 0.3}
{'loss': 2.0822, 'grad_norm': 2.8223459720611572, 'learning_rate': 0.0005901103448275861, 'epoch': 0.3}
{'loss': 1.3102, 'grad_norm': 2.312930107116699, 'learning_rate': 0.0005900689655172414, 'epoch': 0.3}
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{'loss': 2.0704, 'grad_norm': 2.142591714859009, 'learning_rate': 0.0005899862068965517, 'epoch': 0.3}
{'loss': 1.9793, 'grad_norm': 4.919168949127197, 'learning_rate': 0.0005899448275862068, 'epoch': 0.3}
{'loss': 1.7969, 'grad_norm': 1.9778062105178833, 'learning_rate': 0.0005899034482758621, 'epoch': 0.3}
{'loss': 0.0, 'grad_norm': nan, 'learning_rate': 0.0005899034482758621, 'epoch': 0.3}
{'loss': 1.2527, 'grad_norm': 1.7709628343582153, 'learning_rate': 0.0005898620689655171, 'epoch': 0.3}
{'loss': 2.7612, 'grad_norm': 8.728433609008789, 'learning_rate': 0.0005898206896551724, 'epoch': 0.3}
{'loss': 2.0286, 'grad_norm': 2.755829334259033, 'learning_rate': 0.0005897793103448275, 'epoch': 0.3}
{'loss': 1.8518, 'grad_norm': 5.461521148681641, 'learning_rate': 0.0005897379310344827, 'epoch': 0.3}
{'loss': 1.5177, 'grad_norm': 2.9472265243530273, 'learning_rate': 0.0005896965517241379, 'epoch': 0.3}
{'loss': 1.4976, 'grad_norm': 1.8816511631011963, 'learning_rate': 0.000589655172413793, 'epoch': 0.3}
{'loss': 2.2076, 'grad_norm': 2.512596607208252, 'learning_rate': 0.0005896137931034483, 'epoch': 0.3}
{'loss': 1.6094, 'grad_norm': 3.4800477027893066, 'learning_rate': 0.0005895724137931034, 'epoch': 0.3}
{'loss': 1.749, 'grad_norm': 3.0851073265075684, 'learning_rate': 0.0005895310344827586, 'epoch': 0.3}
{'loss': 1.1389, 'grad_norm': 2.376063585281372, 'learning_rate': 0.0005894896551724137, 'epoch': 0.3}
{'loss': 1.4995, 'grad_norm': 1.3033183813095093, 'learning_rate': 0.0005894482758620689, 'epoch': 0.3}
{'loss': 1.0246, 'grad_norm': 1.559168815612793, 'learning_rate': 0.000589406896551724, 'epoch': 0.3}
{'loss': 1.5008, 'grad_norm': 2.0087711811065674, 'learning_rate': 0.0005893655172413793, 'epoch': 0.31}
{'loss': 0.9547, 'grad_norm': 1.1878690719604492, 'learning_rate': 0.0005893241379310344, 'epoch': 0.31}
{'loss': 1.0679, 'grad_norm': 2.065190315246582, 'learning_rate': 0.0005892827586206896, 'epoch': 0.31}
{'loss': 0.9561, 'grad_norm': 1.3885300159454346, 'learning_rate': 0.0005892413793103449, 'epoch': 0.31}
{'loss': 1.7266, 'grad_norm': 2.1077563762664795, 'learning_rate': 0.0005891999999999999, 'epoch': 0.31}
{'loss': 1.7713, 'grad_norm': 2.6957547664642334, 'learning_rate': 0.0005891586206896552, 'epoch': 0.31}
{'loss': 1.8261, 'grad_norm': 2.7139129638671875, 'learning_rate': 0.0005891172413793103, 'epoch': 0.31}
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{'loss': 2.1747, 'grad_norm': 2.0236310958862305, 'learning_rate': 0.0005883310344827585, 'epoch': 0.32}
{'loss': 1.4894, 'grad_norm': 1.9639248847961426, 'learning_rate': 0.0005882896551724138, 'epoch': 0.32}
{'loss': 0.889, 'grad_norm': 2.0216288566589355, 'learning_rate': 0.0005882482758620688, 'epoch': 0.32}
{'loss': 1.4948, 'grad_norm': 2.178797960281372, 'learning_rate': 0.0005882068965517241, 'epoch': 0.32}
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{'loss': 1.6044, 'grad_norm': 2.0475571155548096, 'learning_rate': 0.0005880413793103448, 'epoch': 0.32}
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{'loss': 1.7543, 'grad_norm': 3.0093488693237305, 'learning_rate': 0.0005879586206896551, 'epoch': 0.32}
{'loss': 1.3761, 'grad_norm': 2.5983715057373047, 'learning_rate': 0.0005879172413793103, 'epoch': 0.32}
{'loss': 2.3897, 'grad_norm': 5.947105407714844, 'learning_rate': 0.0005878758620689654, 'epoch': 0.32}
{'loss': 1.6616, 'grad_norm': 2.2814366817474365, 'learning_rate': 0.0005878344827586207, 'epoch': 0.32}


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  6%|▌         | 900/15000 [20:11<1:21:47,  2.87it/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 impरes में एक प्रस्तुति document बनाना और बुनियादी formatting के इस spoken tutorial में आपका स्वागताह ात



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 window के भागों के बारे में सीखेंगे और कैसे slide insrt करें और copy करें fot तथा font 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 linकx और liber ofis verson 60 का उपयोग कर रह हं



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


  Prediction: चलिए अपनी प्रस्तुति sampl imprs open करते हैं जिसे पिछले tutoर हैहैहैह



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


  Prediction: बनाया थाचलिए देखत हैं कि scren 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 आiआt mुmबई की और से मैं रवि कुमार अब आपसे विदा लेता हूँ हमसे जोड़ने के लिए धन्यवाद
{'eval_loss': 2.113497018814087, 'eval_cer': 0.3747855025739691, 'eval_wer': 0.56265454255404, 'eval_runtime': 109.0138, 'eval_samples_per_second': 28.767, 'eval_steps_per_second': 3.596, 'epoch': 0.32}
{'loss': 3.5186, 'grad_norm': 8.09139347076416, 'learning_rate': 0.0005877931034482757, 'epoch': 0.32}
{'loss': 3.411, 'grad_norm': 8.221309661865234, 'learning_rate': 0.000587751724137931, 'epoch': 0.32}
{'loss': 2.4718, 'grad_norm': 7.913961410522461, 'learning_rate': 0.0005877103448275862, 'epoch': 0.32}
{'loss': 1.7814, 'grad_norm': 2.8902628421783447, 'learning_rate': 0.0005876689655172413, 'epoch': 0.32}
{'loss': 1.6798, 'grad_norm': 2.477668523788452, 'learning_rate': 0.0005876275862068965, 'epoch': 0.32}
{'loss': 1.4893, 'grad_norm': 3.8905258178710938, 'learning_rate': 0.0005875862068965516, 'epoch': 0.32}
{'loss': 2.0605, 'grad_norm': 3.315901517868042, 'learning_rate': 0.0005875448275862069, 'epoch': 0.32}
{'loss': 1.4026, 'grad_norm': 3.187060594558716, 'learning_rate': 0.000587503448275862, 'epoch': 0.32}
{'loss': 2.7172, 'grad_norm': 3.7421469688415527, 'learning_rate': 0.0005874620689655172, 'epoch': 0.32}
{'loss': 1.2428, 'grad_norm': 4.060311794281006, 'learning_rate': 0.0005874206896551723, 'epoch': 0.32}
{'loss': 1.3195, 'grad_norm': 3.2724180221557617, 'learning_rate': 0.0005873793103448275, 'epoch': 0.32}
{'loss': 1.0438, 'grad_norm': 1.7093439102172852, 'learning_rate': 0.0005873379310344827, 'epoch': 0.32}
{'loss': 2.0996, 'grad_norm': 4.952425479888916, 'learning_rate': 0.0005872965517241379, 'epoch': 0.33}
{'loss': 1.3836, 'grad_norm': 1.273587703704834, 'learning_rate': 0.0005872551724137931, 'epoch': 0.33}
{'loss': 1.3235, 'grad_norm': 1.3366081714630127, 'learning_rate': 0.0005872137931034482, 'epoch': 0.33}
{'loss': 1.9058, 'grad_norm': 2.0751209259033203, 'learning_rate': 0.0005871724137931035, 'epoch': 0.33}
{'loss': 0.9176, 'grad_norm': 1.6061197519302368, 'learning_rate': 0.0005871310344827585, 'epoch': 0.33}
{'loss': 1.7001, 'grad_norm': 2.116816520690918, 'learning_rate': 0.0005870896551724138, 'epoch': 0.33}
{'loss': 1.5563, 'grad_norm': 1.498201847076416, 'learning_rate': 0.0005870482758620689, 'epoch': 0.33}
{'loss': 0.9646, 'grad_norm': 1.0481873750686646, 'learning_rate': 0.0005870068965517241, 'epoch': 0.33}
{'loss': 1.153, 'grad_norm': 1.2067315578460693, 'learning_rate': 0.0005869655172413792, 'epoch': 0.33}
{'loss': 1.9439, 'grad_norm': 1.5271618366241455, 'learning_rate': 0.0005869241379310344, 'epoch': 0.33}
{'loss': 1.5771, 'grad_norm': 3.3871960639953613, 'learning_rate': 0.0005868827586206896, 'epoch': 0.33}
{'loss': 1.3659, 'grad_norm': 1.814846158027649, 'learning_rate': 0.0005868413793103448, 'epoch': 0.33}
{'loss': 1.3777, 'grad_norm': 1.426160454750061, 'learning_rate': 0.0005868, 'epoch': 0.33}
{'loss': 0.7107, 'grad_norm': 1.3465638160705566, 'learning_rate': 0.0005867586206896551, 'epoch': 0.33}
{'loss': 0.7831, 'grad_norm': 1.5093027353286743, 'learning_rate': 0.0005867172413793103, 'epoch': 0.33}
{'loss': 1.2532, 'grad_norm': 2.905425548553467, 'learning_rate': 0.0005866758620689654, 'epoch': 0.33}
{'loss': 0.7266, 'grad_norm': 1.4891449213027954, 'learning_rate': 0.0005866344827586207, 'epoch': 0.33}
{'loss': 1.6132, 'grad_norm': 1.7670483589172363, 'learning_rate': 0.0005865931034482758, 'epoch': 0.33}
{'loss': 0.9624, 'grad_norm': 2.1997294425964355, 'learning_rate': 0.000586551724137931, 'epoch': 0.33}
{'loss': 0.9769, 'grad_norm': 2.0361111164093018, 'learning_rate': 0.0005865103448275862, 'epoch': 0.33}
{'loss': 1.7302, 'grad_norm': 1.7128384113311768, 'learning_rate': 0.0005864689655172413, 'epoch': 0.33}
{'loss': 1.7898, 'grad_norm': 2.531914710998535, 'learning_rate': 0.0005864275862068965, 'epoch': 0.33}
{'loss': 1.5737, 'grad_norm': 2.9484355449676514, 'learning_rate': 0.0005863862068965517, 'epoch': 0.33}
{'loss': 1.3695, 'grad_norm': 3.0550053119659424, 'learning_rate': 0.0005863448275862068, 'epoch': 0.33}
{'loss': 1.5186, 'grad_norm': 1.8972245454788208, 'learning_rate': 0.000586303448275862, 'epoch': 0.33}
{'loss': 1.3833, 'grad_norm': 1.4994868040084839, 'learning_rate': 0.0005862620689655172, 'epoch': 0.34}
{'loss': 0.7513, 'grad_norm': 1.5003745555877686, 'learning_rate': 0.0005862206896551724, 'epoch': 0.34}
{'loss': 1.2213, 'grad_norm': 5.997674942016602, 'learning_rate': 0.0005861793103448275, 'epoch': 0.34}
{'loss': 1.3892, 'grad_norm': 1.958757758140564, 'learning_rate': 0.0005861379310344827, 'epoch': 0.34}
{'loss': 1.5148, 'grad_norm': 2.5659127235412598, 'learning_rate': 0.0005860965517241379, 'epoch': 0.34}
{'loss': 1.0358, 'grad_norm': 2.517404079437256, 'learning_rate': 0.000586055172413793, 'epoch': 0.34}
{'loss': 1.864, 'grad_norm': 1.8189629316329956, 'learning_rate': 0.0005860137931034482, 'epoch': 0.34}
{'loss': 2.144, 'grad_norm': 2.8897640705108643, 'learning_rate': 0.0005859724137931034, 'epoch': 0.34}
{'loss': 1.2, 'grad_norm': 2.0305874347686768, 'learning_rate': 0.0005859310344827586, 'epoch': 0.34}
{'loss': 1.2052, 'grad_norm': 2.3711161613464355, 'learning_rate': 0.0005858896551724137, 'epoch': 0.34}
{'loss': 1.2497, 'grad_norm': 2.4664366245269775, 'learning_rate': 0.0005858482758620689, 'epoch': 0.34}
{'loss': 1.8508, 'grad_norm': 4.674132347106934, 'learning_rate': 0.000585806896551724, 'epoch': 0.34}
{'loss': 2.8199, 'grad_norm': 2.7254140377044678, 'learning_rate': 0.0005857655172413793, 'epoch': 0.34}
{'loss': 2.2697, 'grad_norm': 7.265655040740967, 'learning_rate': 0.0005857241379310344, 'epoch': 0.34}
{'loss': 1.8842, 'grad_norm': 4.829804420471191, 'learning_rate': 0.0005856827586206896, 'epoch': 0.34}
{'loss': 4.4666, 'grad_norm': 16.029399871826172, 'learning_rate': 0.0005856413793103448, 'epoch': 0.34}
{'loss': 1.7117, 'grad_norm': 1.6040375232696533, 'learning_rate': 0.0005855999999999999, 'epoch': 0.34}
{'loss': 1.5487, 'grad_norm': 1.537297010421753, 'learning_rate': 0.0005855586206896552, 'epoch': 0.34}
{'loss': 1.3935, 'grad_norm': 3.0603699684143066, 'learning_rate': 0.0005855172413793102, 'epoch': 0.34}
{'loss': 1.5674, 'grad_norm': 2.5580198764801025, 'learning_rate': 0.0005854758620689655, 'epoch': 0.34}
{'loss': 2.9112, 'grad_norm': 8.373856544494629, 'learning_rate': 0.0005854344827586206, 'epoch': 0.34}
{'loss': 1.6689, 'grad_norm': 2.8395578861236572, 'learning_rate': 0.0005853931034482758, 'epoch': 0.34}
{'loss': 1.5068, 'grad_norm': 3.6766064167022705, 'learning_rate': 0.000585351724137931, 'epoch': 0.34}
{'loss': 1.1796, 'grad_norm': 3.888636350631714, 'learning_rate': 0.0005853103448275862, 'epoch': 0.34}
{'loss': 1.4677, 'grad_norm': 2.8241145610809326, 'learning_rate': 0.0005852689655172414, 'epoch': 0.34}
{'loss': 1.15, 'grad_norm': 2.3614697456359863, 'learning_rate': 0.0005852275862068965, 'epoch': 0.35}
{'loss': 1.4227, 'grad_norm': 1.722853183746338, 'learning_rate': 0.0005851862068965517, 'epoch': 0.35}
{'loss': 1.1782, 'grad_norm': 2.1625068187713623, 'learning_rate': 0.0005851448275862068, 'epoch': 0.35}
{'loss': 0.6465, 'grad_norm': 1.3588907718658447, 'learning_rate': 0.0005851034482758621, 'epoch': 0.35}
{'loss': 1.2798, 'grad_norm': 4.83656120300293, 'learning_rate': 0.0005850620689655171, 'epoch': 0.35}
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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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  7%|▋         | 1000/15000 [22:42<1:18:27,  2.97it/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: lिber ofिs impres में एक प्रस्तुति documeंt बनाना और बुनियादी formating के इस spoken tutoral में आपका सहैै



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 mpres windo के भागों के बारे में सीखेंगे और कैसे slid insert करें और copी करें fot तथा font 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 operainग sstem के रूप में janू linuxx और lिber ofिs वr्soन 333 का उपयोग कर रहै



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


  Prediction: चलिए अपनी प्रस्तुति sampl imprs open करते हैं जिसे पिछले tं



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


  Prediction: चलिए देखते हैं कि सcren 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 है आई आई टी मुंबई की ओर से मैं रवि कुमार अब आपसे विदा लेता हूँ हमसे जुड़ने के लिए धन्यवादहै
{'eval_loss': 1.763370394706726, 'eval_cer': 0.3583432998804014, 'eval_wer': 0.5376618542447689, 'eval_runtime': 108.8976, 'eval_samples_per_second': 28.798, 'eval_steps_per_second': 3.6, 'epoch': 0.36}
{'loss': 1.2975, 'grad_norm': 3.0794479846954346, 'learning_rate': 0.0005836551724137931, 'epoch': 0.36}
{'loss': 2.0686, 'grad_norm': 2.8902060985565186, 'learning_rate': 0.0005836137931034482, 'epoch': 0.36}
{'loss': 1.7263, 'grad_norm': 3.7465689182281494, 'learning_rate': 0.0005835724137931034, 'epoch': 0.36}
{'loss': 1.5116, 'grad_norm': 1.435352087020874, 'learning_rate': 0.0005835310344827585, 'epoch': 0.36}
{'loss': 1.472, 'grad_norm': 2.1863062381744385, 'learning_rate': 0.0005834896551724138, 'epoch': 0.36}
{'loss': 0.7822, 'grad_norm': 1.7186282873153687, 'learning_rate': 0.0005834482758620688, 'epoch': 0.36}
{'loss': 1.75, 'grad_norm': 2.8512065410614014, 'learning_rate': 0.0005834068965517241, 'epoch': 0.36}
{'loss': 1.4932, 'grad_norm': 1.7590235471725464, 'learning_rate': 0.0005833655172413793, 'epoch': 0.36}
{'loss': 1.9758, 'grad_norm': 2.3558247089385986, 'learning_rate': 0.0005833241379310344, 'epoch': 0.36}
{'loss': 1.089, 'grad_norm': 1.8008754253387451, 'learning_rate': 0.0005832827586206896, 'epoch': 0.36}
{'loss': 0.872, 'grad_norm': 1.1822811365127563, 'learning_rate': 0.0005832413793103448, 'epoch': 0.36}
{'loss': 1.1377, 'grad_norm': 1.9720146656036377, 'learning_rate': 0.0005832, 'epoch': 0.36}
{'loss': 0.9727, 'grad_norm': 1.5535717010498047, 'learning_rate': 0.0005831586206896551, 'epoch': 0.37}
{'loss': 1.8171, 'grad_norm': 5.558025360107422, 'learning_rate': 0.0005831172413793103, 'epoch': 0.37}
{'loss': 0.9358, 'grad_norm': 1.7187446355819702, 'learning_rate': 0.0005830758620689654, 'epoch': 0.37}
{'loss': 0.9424, 'grad_norm': 1.131671667098999, 'learning_rate': 0.0005830344827586207, 'epoch': 0.37}
{'loss': 1.0295, 'grad_norm': 1.3320086002349854, 'learning_rate': 0.0005829931034482757, 'epoch': 0.37}
{'loss': 0.7522, 'grad_norm': 1.0949654579162598, 'learning_rate': 0.000582951724137931, 'epoch': 0.37}
{'loss': 0.8567, 'grad_norm': 1.1100084781646729, 'learning_rate': 0.0005829103448275862, 'epoch': 0.37}
{'loss': 2.0619, 'grad_norm': 5.027128219604492, 'learning_rate': 0.0005828689655172413, 'epoch': 0.37}
{'loss': 3.2182, 'grad_norm': 8.351059913635254, 'learning_rate': 0.0005828275862068966, 'epoch': 0.37}
{'loss': 1.1478, 'grad_norm': 1.8608063459396362, 'learning_rate': 0.0005827862068965516, 'epoch': 0.37}
{'loss': 1.0399, 'grad_norm': 2.072645664215088, 'learning_rate': 0.0005827448275862069, 'epoch': 0.37}
{'loss': 1.2577, 'grad_norm': 1.3193167448043823, 'learning_rate': 0.000582703448275862, 'epoch': 0.37}
{'loss': 1.0668, 'grad_norm': 2.0414414405822754, 'learning_rate': 0.0005826620689655172, 'epoch': 0.37}
{'loss': 1.8467, 'grad_norm': 2.318455219268799, 'learning_rate': 0.0005826206896551723, 'epoch': 0.37}
{'loss': 1.16, 'grad_norm': 2.600895404815674, 'learning_rate': 0.0005825793103448276, 'epoch': 0.37}
{'loss': 0.9443, 'grad_norm': 1.279796838760376, 'learning_rate': 0.0005825379310344827, 'epoch': 0.37}
{'loss': 0.9068, 'grad_norm': 1.4747859239578247, 'learning_rate': 0.0005824965517241379, 'epoch': 0.37}
{'loss': 1.1944, 'grad_norm': 1.4343931674957275, 'learning_rate': 0.000582455172413793, 'epoch': 0.37}
{'loss': 1.7253, 'grad_norm': 2.1070916652679443, 'learning_rate': 0.0005824137931034482, 'epoch': 0.37}
{'loss': 0.872, 'grad_norm': 2.1483888626098633, 'learning_rate': 0.0005823724137931035, 'epoch': 0.37}
{'loss': 1.0919, 'grad_norm': 1.8406040668487549, 'learning_rate': 0.0005823310344827585, 'epoch': 0.37}
{'loss': 1.5654, 'grad_norm': 1.998806118965149, 'learning_rate': 0.0005822896551724138, 'epoch': 0.37}
{'loss': 1.8579, 'grad_norm': 3.3941240310668945, 'learning_rate': 0.0005822482758620689, 'epoch': 0.37}
{'loss': 1.31, 'grad_norm': 2.7270736694335938, 'learning_rate': 0.0005822068965517241, 'epoch': 0.37}
{'loss': 1.3577, 'grad_norm': 2.1992948055267334, 'learning_rate': 0.0005821655172413792, 'epoch': 0.37}
{'loss': 1.5714, 'grad_norm': 2.281238555908203, 'learning_rate': 0.0005821241379310344, 'epoch': 0.38}
{'loss': 1.0239, 'grad_norm': 1.7528656721115112, 'learning_rate': 0.0005820827586206896, 'epoch': 0.38}
{'loss': 1.6527, 'grad_norm': 2.9133195877075195, 'learning_rate': 0.0005820413793103448, 'epoch': 0.38}
{'loss': 2.0844, 'grad_norm': 3.38217830657959, 'learning_rate': 0.0005819999999999999, 'epoch': 0.38}
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  7%|▋         | 1100/15000 [25:05<1:26:37,  2.67it/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: lिbr oफिस imprec में एक प्रस्तुति document बनाना और बुनियादी formating के इस spoken tutoial 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 tutoral में हम imprec vindo के भागों के बारे में सीखेंगे और कैसे slid insrt करें और copy fot तथा font 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 operating yसtm के रूप में gnu linuxस और libr oफस vrsion 3p3 4 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 campl imprs open करते हैं



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


  Prediction: चलिए देखते हैं कि सcren पर क्या कया 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 लता द्वारा अनुवादित iit mुंबy की ओर से मैं रवि कुमार अब आपसे विदा लेता हूँ हमसे जुड़ने के लिए धन्यवाद
{'eval_loss': 1.8754785060882568, 'eval_cer': 0.35016899797202433, 'eval_wer': 0.5350828215149823, 'eval_runtime': 108.474, 'eval_samples_per_second': 28.91, 'eval_steps_per_second': 3.614, 'epoch': 0.4}
{'loss': 1.4634, 'grad_norm': 1.8088257312774658, 'learning_rate': 0.0005795172413793103, 'epoch': 0.4}
{'loss': 3.4391, 'grad_norm': 11.102457046508789, 'learning_rate': 0.0005794758620689655, 'epoch': 0.4}
{'loss': 2.1321, 'grad_norm': 4.285269737243652, 'learning_rate': 0.0005794344827586206, 'epoch': 0.4}
{'loss': 1.0479, 'grad_norm': 1.6026911735534668, 'learning_rate': 0.0005793931034482758, 'epoch': 0.4}
{'loss': 1.6403, 'grad_norm': 1.993919849395752, 'learning_rate': 0.000579351724137931, 'epoch': 0.4}
{'loss': 1.3146, 'grad_norm': 2.342257499694824, 'learning_rate': 0.0005793103448275862, 'epoch': 0.4}
{'loss': 1.9909, 'grad_norm': 5.831331729888916, 'learning_rate': 0.0005792689655172413, 'epoch': 0.4}
{'loss': 1.4868, 'grad_norm': 1.8961278200149536, 'learning_rate': 0.0005792275862068965, 'epoch': 0.4}
{'loss': 1.5657, 'grad_norm': 1.8074991703033447, 'learning_rate': 0.0005791862068965516, 'epoch': 0.4}
{'loss': 1.2085, 'grad_norm': 3.748798370361328, 'learning_rate': 0.0005791448275862069, 'epoch': 0.4}
{'loss': 2.1137, 'grad_norm': 3.8494410514831543, 'learning_rate': 0.000579103448275862, 'epoch': 0.4}
{'loss': 1.4059, 'grad_norm': 3.627728223800659, 'learning_rate': 0.0005790620689655172, 'epoch': 0.4}
{'loss': 1.0373, 'grad_norm': 3.439666748046875, 'learning_rate': 0.0005790206896551723, 'epoch': 0.41}
{'loss': 1.5971, 'grad_norm': 2.9347527027130127, 'learning_rate': 0.0005789793103448276, 'epoch': 0.41}
{'loss': 1.3079, 'grad_norm': 2.5919582843780518, 'learning_rate': 0.0005789379310344827, 'epoch': 0.41}
{'loss': 1.079, 'grad_norm': 2.0599286556243896, 'learning_rate': 0.0005788965517241379, 'epoch': 0.41}
{'loss': 1.3207, 'grad_norm': 1.4827386140823364, 'learning_rate': 0.0005788551724137931, 'epoch': 0.41}
{'loss': 1.0952, 'grad_norm': 1.7784048318862915, 'learning_rate': 0.0005788137931034482, 'epoch': 0.41}
{'loss': 0.7333, 'grad_norm': 1.6224281787872314, 'learning_rate': 0.0005787724137931034, 'epoch': 0.41}
{'loss': 1.3049, 'grad_norm': 1.4817919731140137, 'learning_rate': 0.0005787310344827585, 'epoch': 0.41}
{'loss': 1.0933, 'grad_norm': 2.0309460163116455, 'learning_rate': 0.0005786896551724138, 'epoch': 0.41}
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  8%|▊         | 1200/15000 [27:27<1:20:01,  2.87it/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: lिber ऑफस impres में एक प्रस्तुति document बनाना और बुनियादी formating के इस spoken tutorial में आपका स्वाग



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 impres window के भागों के बारे में सीखेंगे और कैसे slid insrt करें और copी करेंfot तथा font को foरmat 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 oपpeरेting system के रूप में jnu लिनकस और लिbr ofिस वrजon 2 का उपयोग कर रहं



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


  Prediction: चलिए अपनी प्रस्तुति campl implus open करते हैं ेपिह



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


  Prediction: चलिए देखते हैं कि सcrीn 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 लता द्वारा अनुवादित है आईआईटी मुम्बई की ओर से मैं रविकुमार अब आपसे विदा लेता हूँ हमसे जोड़ने के लिए धन्यवाद
{'eval_loss': 1.7561842203140259, 'eval_cer': 0.33700795590452914, 'eval_wer': 0.4988700114328255, 'eval_runtime': 107.0775, 'eval_samples_per_second': 29.287, 'eval_steps_per_second': 3.661, 'epoch': 0.44}
{'loss': 6.6873, 'grad_norm': 30.482227325439453, 'learning_rate': 0.0005753793103448275, 'epoch': 0.44}
{'loss': 2.8237, 'grad_norm': 8.308656692504883, 'learning_rate': 0.0005753379310344827, 'epoch': 0.44}
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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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  9%|▊         | 1300/15000 [29:56<1:31:41,  2.49it/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: liber offis impes में एक प्रस्तुती document बनाना और बुनियादी formating के इस spoken tutorial में आपका  है



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 window के भागों के बारे में सीखेंगे और कैसे slide insert करें और copy ford तथा font 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 operating system के रूप में gnu linux और liber ofis version 4 का उपयोग कर रहैं



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


  Prediction: चिए अपनी प्रस्तुति sampl imps open करते हैं ज  tहैै



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


  Prediction: लिए देखते हैं कि scren पर क्या क्या



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 ltा द्वारा अनुवादित है आई आई टी मुम्बई की ओर से मैं रवि कुमार अब आपसे विदा लेता हूँहमसे जुड़ने के लिए धन्यवा
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  9%|▉         | 1400/15000 [32:18<1:09:25,  3.26it/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: liber ऑffiस impres में एक प्रस्तुती document बनाना और बुनियादी formating के इस spoken t्utoriयl में आपका सैै



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 im pres window के भागों के बारे में सीखेंगे और कैसे slide insert करें और copyfornd तथा font 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 ऑperating system के रूप में gnu linux और liber ऑfiस version 35 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 sample imps open करते हैं जसल ै



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


  Prediction: चलिए देखते हैं कि सcren 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 लता द्वारा अनुवादित है आईआईटी बॉmbई की ओर से मैं रवि कुमार अब आपसे विदा लेता हूँहमसे जुड़ने के लिए धन्यवाद
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{'loss': 6.6514, 'grad_norm': 32.6164436340332, 'learning_rate': 0.0005671034482758621, 'epoch': 0.52}
{'loss': 2.6111, 'grad_norm': 8.046913146972656, 'learning_rate': 0.0005670620689655172, 'epoch': 0.52}
{'loss': 2.242, 'grad_norm': 5.487936973571777, 'learning_rate': 0.0005670206896551724, 'epoch': 0.52}
{'loss': 1.7792, 'grad_norm': 3.002572774887085, 'learning_rate': 0.0005669793103448275, 'epoch': 0.52}
{'loss': 2.2088, 'grad_norm': 6.153054714202881, 'learning_rate': 0.0005669379310344827, 'epoch': 0.52}
{'loss': 1.1687, 'grad_norm': 1.6907706260681152, 'learning_rate': 0.0005668965517241378, 'epoch': 0.52}
{'loss': 1.0567, 'grad_norm': 1.709630012512207, 'learning_rate': 0.0005668551724137931, 'epoch': 0.52}
{'loss': 1.6884, 'grad_norm': 1.8379902839660645, 'learning_rate': 0.0005668137931034483, 'epoch': 0.52}
{'loss': 1.2267, 'grad_norm': 2.92991042137146, 'learning_rate': 0.0005667724137931034, 'epoch': 0.52}
{'loss': 1.3389, 'grad_norm': 1.9532591104507446, 'learning_rate': 0.0005667310344827586, 'epoch': 0.52}
{'loss': 1.3149, 'grad_norm': 1.574538230895996, 'learning_rate': 0.0005666896551724137, 'epoch': 0.52}
{'loss': 0.9474, 'grad_norm': 1.6551812887191772, 'learning_rate': 0.000566648275862069, 'epoch': 0.52}
{'loss': 0.9099, 'grad_norm': 1.2598047256469727, 'learning_rate': 0.000566606896551724, 'epoch': 0.53}
{'loss': 1.2111, 'grad_norm': 1.9808896780014038, 'learning_rate': 0.0005665655172413793, 'epoch': 0.53}
{'loss': 1.1024, 'grad_norm': 1.764464020729065, 'learning_rate': 0.0005665241379310344, 'epoch': 0.53}
{'loss': 0.9545, 'grad_norm': 1.55327570438385, 'learning_rate': 0.0005664827586206896, 'epoch': 0.53}
{'loss': 0.5894, 'grad_norm': 1.1670303344726562, 'learning_rate': 0.0005664413793103447, 'epoch': 0.53}
{'loss': 1.6512, 'grad_norm': 1.7760355472564697, 'learning_rate': 0.0005663999999999999, 'epoch': 0.53}
{'loss': 0.8652, 'grad_norm': 1.2191481590270996, 'learning_rate': 0.0005663586206896552, 'epoch': 0.53}
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{'loss': 1.533, 'grad_norm': 1.7233994007110596, 'learning_rate': 0.0005662758620689655, 'epoch': 0.53}
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{'loss': 1.3484, 'grad_norm': 1.2548363208770752, 'learning_rate': 0.0005661931034482759, 'epoch': 0.53}
{'loss': 1.2557, 'grad_norm': 2.25128173828125, 'learning_rate': 0.000566151724137931, 'epoch': 0.53}
{'loss': 0.8276, 'grad_norm': 1.7279512882232666, 'learning_rate': 0.0005661103448275862, 'epoch': 0.53}
{'loss': 1.1374, 'grad_norm': 2.103109121322632, 'learning_rate': 0.0005660689655172413, 'epoch': 0.53}
{'loss': 1.3416, 'grad_norm': 1.7957643270492554, 'learning_rate': 0.0005660275862068965, 'epoch': 0.53}
{'loss': 1.4945, 'grad_norm': 1.7539247274398804, 'learning_rate': 0.0005659862068965517, 'epoch': 0.53}
{'loss': 1.2283, 'grad_norm': 1.3741440773010254, 'learning_rate': 0.0005659448275862068, 'epoch': 0.53}
{'loss': 1.3751, 'grad_norm': 1.3716015815734863, 'learning_rate': 0.000565903448275862, 'epoch': 0.53}
{'loss': 1.1535, 'grad_norm': 1.1672292947769165, 'learning_rate': 0.0005658620689655172, 'epoch': 0.53}
{'loss': 1.0352, 'grad_norm': 1.3138678073883057, 'learning_rate': 0.0005658206896551724, 'epoch': 0.53}
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{'loss': 1.1739, 'grad_norm': 1.8624886274337769, 'learning_rate': 0.0005657379310344827, 'epoch': 0.53}
{'loss': 1.0628, 'grad_norm': 1.9753748178482056, 'learning_rate': 0.0005656965517241379, 'epoch': 0.53}
{'loss': 1.6893, 'grad_norm': 1.9955335855484009, 'learning_rate': 0.0005656551724137931, 'epoch': 0.53}
{'loss': 1.8649, 'grad_norm': 1.8318431377410889, 'learning_rate': 0.0005656137931034482, 'epoch': 0.53}
{'loss': 1.0862, 'grad_norm': 1.8575928211212158, 'learning_rate': 0.0005655724137931034, 'epoch': 0.54}
{'loss': 1.4287, 'grad_norm': 1.419586181640625, 'learning_rate': 0.0005655310344827585, 'epoch': 0.54}
{'loss': 1.6556, 'grad_norm': 1.8935896158218384, 'learning_rate': 0.0005654896551724137, 'epoch': 0.54}
{'loss': 1.2479, 'grad_norm': 1.9776411056518555, 'learning_rate': 0.0005654482758620689, 'epoch': 0.54}
{'loss': 0.5173, 'grad_norm': 2.0596790313720703, 'learning_rate': 0.0005654068965517241, 'epoch': 0.54}
{'loss': 1.8213, 'grad_norm': 1.975089192390442, 'learning_rate': 0.0005653655172413792, 'epoch': 0.54}
{'loss': 1.4563, 'grad_norm': 2.4187560081481934, 'learning_rate': 0.0005653241379310345, 'epoch': 0.54}
{'loss': 1.3203, 'grad_norm': 1.5047487020492554, 'learning_rate': 0.0005652827586206896, 'epoch': 0.54}
{'loss': 1.5433, 'grad_norm': 1.5806580781936646, 'learning_rate': 0.0005652413793103448, 'epoch': 0.54}
{'loss': 1.0472, 'grad_norm': 2.1407384872436523, 'learning_rate': 0.0005652, 'epoch': 0.54}
{'loss': 2.0003, 'grad_norm': 2.794060707092285, 'learning_rate': 0.0005651586206896551, 'epoch': 0.54}
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{'loss': 2.6739, 'grad_norm': 6.611154556274414, 'learning_rate': 0.0005650344827586207, 'epoch': 0.54}
{'loss': 3.3583, 'grad_norm': 12.77904987335205, 'learning_rate': 0.0005649931034482758, 'epoch': 0.54}
{'loss': 1.2592, 'grad_norm': 1.7515536546707153, 'learning_rate': 0.000564951724137931, 'epoch': 0.54}
{'loss': 1.6228, 'grad_norm': 1.7660462856292725, 'learning_rate': 0.0005649103448275861, 'epoch': 0.54}
{'loss': 1.6975, 'grad_norm': 2.41253399848938, 'learning_rate': 0.0005648689655172413, 'epoch': 0.54}
{'loss': 1.1561, 'grad_norm': 2.4980580806732178, 'learning_rate': 0.0005648275862068965, 'epoch': 0.54}
{'loss': 1.3718, 'grad_norm': 2.2681756019592285, 'learning_rate': 0.0005647862068965517, 'epoch': 0.54}
{'loss': 1.5229, 'grad_norm': 2.604771375656128, 'learning_rate': 0.0005647448275862069, 'epoch': 0.54}
{'loss': 0.8339, 'grad_norm': 1.4891250133514404, 'learning_rate': 0.000564703448275862, 'epoch': 0.54}
{'loss': 0.9591, 'grad_norm': 1.6357184648513794, 'learning_rate': 0.0005646620689655172, 'epoch': 0.54}
{'loss': 1.0823, 'grad_norm': 1.6959325075149536, 'learning_rate': 0.0005646206896551723, 'epoch': 0.54}
{'loss': 0.7039, 'grad_norm': 1.3780635595321655, 'learning_rate': 0.0005645793103448276, 'epoch': 0.54}
{'loss': 1.1006, 'grad_norm': 1.4493426084518433, 'learning_rate': 0.0005645379310344826, 'epoch': 0.55}
{'loss': 0.8776, 'grad_norm': 1.8949341773986816, 'learning_rate': 0.0005644965517241379, 'epoch': 0.55}
{'loss': 1.2749, 'grad_norm': 1.3911073207855225, 'learning_rate': 0.000564455172413793, 'epoch': 0.55}
{'loss': 1.0776, 'grad_norm': 2.1128616333007812, 'learning_rate': 0.0005644137931034482, 'epoch': 0.55}
{'loss': 1.3408, 'grad_norm': 1.323582649230957, 'learning_rate': 0.0005643724137931035, 'epoch': 0.55}
{'loss': 2.1558, 'grad_norm': 1.9844741821289062, 'learning_rate': 0.0005643310344827586, 'epoch': 0.55}
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{'loss': 2.0747, 'grad_norm': 3.1359336376190186, 'learning_rate': 0.000563006896551724, 'epoch': 0.56}


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 10%|█         | 1500/15000 [34:39<1:32:08,  2.44it/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 ofस impres में एक प्रस्तुति document बनाना और बुनियादी formating के इस spoken tutorial में आपका स ा हैहैह



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 mpres windo के भागों के बारे में सीखेंगे और कैसे slid insrt करें और copy करें fot तथ fot 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 oprेting ystem के रूप में gnu linux और libr ofiस vrsion 334 का उपयोग कर रहैंैं



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


  Prediction: चलिए अपनी प्रस्तुति campl imps open करते हैं िे



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


  Prediction: चलिए देखते हैं कि scren 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 लतa द्वारा अनुवादित है आई आईटी बॉमbई की ओर से मैं रविकुमार अब आपसे विदा लेता हूँहमसे जुड़ने के लिए धन्यवाद
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{'loss': 2.2537, 'grad_norm': 1.8673219680786133, 'learning_rate': 0.0005629241379310345, 'epoch': 0.56}
{'loss': 1.5288, 'grad_norm': 2.4735469818115234, 'learning_rate': 0.0005628827586206896, 'epoch': 0.56}
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{'loss': 1.0048, 'grad_norm': 1.6854146718978882, 'learning_rate': 0.0005627999999999999, 'epoch': 0.56}
{'loss': 0.9297, 'grad_norm': 1.2204288244247437, 'learning_rate': 0.0005627586206896551, 'epoch': 0.56}
{'loss': 1.0402, 'grad_norm': 1.092132329940796, 'learning_rate': 0.0005627172413793103, 'epoch': 0.56}
{'loss': 1.1723, 'grad_norm': 1.3360257148742676, 'learning_rate': 0.0005626758620689655, 'epoch': 0.56}
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{'loss': 0.7656, 'grad_norm': 1.1494708061218262, 'learning_rate': 0.0005625103448275862, 'epoch': 0.56}
{'loss': 0.9577, 'grad_norm': 1.8790632486343384, 'learning_rate': 0.0005624689655172413, 'epoch': 0.57}
{'loss': 1.0579, 'grad_norm': 1.8038742542266846, 'learning_rate': 0.0005624275862068965, 'epoch': 0.57}
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{'loss': 0.7756, 'grad_norm': 1.0184121131896973, 'learning_rate': 0.0005623448275862068, 'epoch': 0.57}
{'loss': 1.0217, 'grad_norm': 1.7344228029251099, 'learning_rate': 0.0005623034482758621, 'epoch': 0.57}
{'loss': 0.8719, 'grad_norm': 1.0978368520736694, 'learning_rate': 0.0005622620689655172, 'epoch': 0.57}
{'loss': 3.4198, 'grad_norm': 13.407425880432129, 'learning_rate': 0.0005622206896551724, 'epoch': 0.57}
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{'loss': 1.6986, 'grad_norm': 1.9758626222610474, 'learning_rate': 0.0005620551724137931, 'epoch': 0.57}
{'loss': 1.0854, 'grad_norm': 2.504441499710083, 'learning_rate': 0.0005620137931034482, 'epoch': 0.57}
{'loss': 1.2532, 'grad_norm': 1.7605282068252563, 'learning_rate': 0.0005619724137931034, 'epoch': 0.57}
{'loss': 1.1551, 'grad_norm': 1.197166085243225, 'learning_rate': 0.0005619310344827586, 'epoch': 0.57}
{'loss': 0.679, 'grad_norm': 1.3288516998291016, 'learning_rate': 0.0005618896551724137, 'epoch': 0.57}
{'loss': 0.5182, 'grad_norm': 1.5184783935546875, 'learning_rate': 0.000561848275862069, 'epoch': 0.57}
{'loss': 0.7231, 'grad_norm': 1.2794119119644165, 'learning_rate': 0.000561806896551724, 'epoch': 0.57}
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{'loss': 0.9214, 'grad_norm': 1.470470666885376, 'learning_rate': 0.0005616, 'epoch': 0.57}
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{'loss': 0.7906, 'grad_norm': 1.346225380897522, 'learning_rate': 0.000561351724137931, 'epoch': 0.58}
{'loss': 1.161, 'grad_norm': 1.4292958974838257, 'learning_rate': 0.0005613103448275862, 'epoch': 0.58}
{'loss': 0.8972, 'grad_norm': 1.7373254299163818, 'learning_rate': 0.0005612689655172413, 'epoch': 0.58}
{'loss': 1.5412, 'grad_norm': 1.511770486831665, 'learning_rate': 0.0005612275862068965, 'epoch': 0.58}
{'loss': 0.752, 'grad_norm': 1.5465128421783447, 'learning_rate': 0.0005611862068965517, 'epoch': 0.58}
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{'loss': 0.9819, 'grad_norm': 1.1829485893249512, 'learning_rate': 0.0005606896551724138, 'epoch': 0.58}
{'loss': 2.1182, 'grad_norm': 3.5633976459503174, 'learning_rate': 0.0005606482758620689, 'epoch': 0.58}
{'loss': 1.4131, 'grad_norm': 1.9722304344177246, 'learning_rate': 0.0005606068965517241, 'epoch': 0.58}
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{'loss': 1.0797, 'grad_norm': 1.5685163736343384, 'learning_rate': 0.0005604, 'epoch': 0.59}
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{'loss': 0.812, 'grad_norm': 1.1394420862197876, 'learning_rate': 0.0005603172413793103, 'epoch': 0.59}
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{'loss': 2.527, 'grad_norm': 4.580041408538818, 'learning_rate': 0.0005588689655172414, 'epoch': 0.6}


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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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 11%|█         | 1600/15000 [37:11<1:23:22,  2.68it/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: liber offiस impres में एक प्रस्तुति document बनाना और बुनियादी fॉrmating के इस spoken tutorial में आपका स्वा गा े



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 impre+ window के भागों के बारे में सीखेंगे और कैसे slide इnsert करें और cॉpy करें fort तथा font को fॉrmaट 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 operating system के रूप में gnu liनuक् और liber ofिस version 334 का उपयोग कर रह हं



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


  Prediction: चलिए अपनी प्रस्तुति cample impl+ open करते हैं जिसे पिछले tं



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


  Prediction: चलिए देखते हैं कि scren 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 लता द्वारा अनुवादित है आई आई टी बॉम्बy की ओर से मैं रवि कुमार अब आपसे विदा लेता हूँ हमसे जुड़ने के लिए धन्यवाद
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{'loss': 2.5029, 'grad_norm': 2.6998393535614014, 'learning_rate': 0.000554855172413793, 'epoch': 0.64}
{'loss': 1.242, 'grad_norm': 1.9452300071716309, 'learning_rate': 0.0005548137931034482, 'epoch': 0.64}
{'loss': 1.0282, 'grad_norm': 1.390939712524414, 'learning_rate': 0.0005547724137931034, 'epoch': 0.64}
{'loss': 1.9367, 'grad_norm': 2.072896957397461, 'learning_rate': 0.0005547310344827586, 'epoch': 0.64}


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 11%|█▏        | 1700/15000 [39:33<1:29:42,  2.47it/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: lिber ऑfिस impres में एक प्रस्तुति document बनना और बुनियाद fomating के इस spoken tutorial ै



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 mpres wिdो के भागों के बारे में सीखेंगे और कैसे slide nsert करें और copy कें fort तथा font 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 operating system के रूप में gnu lिनx और lिber ffिस version 334 का उपयोग कर हैै



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


  Prediction: चिएअपनी प्रस्तुि sampl imprs open करते हैं



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


  Prediction: चलिए देखते हैं कि scren 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 ा द्वरा अनवदि हई ई मबई की ओर से मैं रविकुमार ब पसे विदा लेता हूँ मसे डनि
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{'loss': 1.7832, 'grad_norm': 2.7563538551330566, 'learning_rate': 0.0005546896551724137, 'epoch': 0.64}
{'loss': 1.6838, 'grad_norm': 2.574582815170288, 'learning_rate': 0.0005546482758620689, 'epoch': 0.64}
{'loss': 2.1999, 'grad_norm': 8.967336654663086, 'learning_rate': 0.0005546068965517242, 'epoch': 0.64}
{'loss': 1.797, 'grad_norm': 1.4387661218643188, 'learning_rate': 0.0005545655172413792, 'epoch': 0.64}
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{'loss': 1.9462, 'grad_norm': 3.349374532699585, 'learning_rate': 0.0005544827586206896, 'epoch': 0.64}
{'loss': 0.8313, 'grad_norm': 1.8992623090744019, 'learning_rate': 0.0005544413793103448, 'epoch': 0.64}
{'loss': 1.3977, 'grad_norm': 2.2072153091430664, 'learning_rate': 0.0005544, 'epoch': 0.64}
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{'loss': 1.7023, 'grad_norm': 1.2865604162216187, 'learning_rate': 0.0005510482758620689, 'epoch': 0.68}
{'loss': 0.9168, 'grad_norm': 1.0310213565826416, 'learning_rate': 0.0005510068965517241, 'epoch': 0.68}
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{'loss': 1.6648, 'grad_norm': 2.8278703689575195, 'learning_rate': 0.0005508413793103448, 'epoch': 0.68}
{'loss': 0.7754, 'grad_norm': 1.8417835235595703, 'learning_rate': 0.0005507999999999999, 'epoch': 0.68}
{'loss': 0.4978, 'grad_norm': 1.9994232654571533, 'learning_rate': 0.0005507586206896551, 'epoch': 0.68}
{'loss': 1.5362, 'grad_norm': 1.7182801961898804, 'learning_rate': 0.0005507172413793102, 'epoch': 0.68}
{'loss': 2.0442, 'grad_norm': 2.4052021503448486, 'learning_rate': 0.0005506758620689655, 'epoch': 0.68}
{'loss': 1.3618, 'grad_norm': 1.8194621801376343, 'learning_rate': 0.0005506344827586207, 'epoch': 0.68}
{'loss': 1.6041, 'grad_norm': 3.0581767559051514, 'learning_rate': 0.0005505931034482758, 'epoch': 0.68}


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 12%|█▏        | 1800/15000 [41:55<1:27:38,  2.51it/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: liber ऑfic impress में एक प्रस्तुति document बनाना और बुनियादी fॉrmating के इस spoken tutorial में आपका हैह



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 window के भागों के बारे में सीखेंगे और कैसे slide insert करें और copy करें frnt तथा fant को fॉrmat 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 ऑperating system के रूप में gnu linux और liber ऑfiसe version 334 का उपयोग कर रहैंं



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


  Prediction: चलिए अपनी प्रस्तुति sample impres open करते हैं



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


  Prediction: चलिए देखते हैं कि sकrीन 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 लता द्वारा अनुवादित हआई आई टी बॉमबई की ओर से मैं रविकुमार अब आपसे विदा लेता हूँहमसे जुड़ने कधन्यवा
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{'loss': 1.0545, 'grad_norm': 1.3965269327163696, 'learning_rate': 0.000550551724137931, 'epoch': 0.68}
{'loss': 1.9476, 'grad_norm': 3.4058761596679688, 'learning_rate': 0.0005505103448275861, 'epoch': 0.68}
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{'loss': 1.0615, 'grad_norm': 2.95066499710083, 'learning_rate': 0.000550303448275862, 'epoch': 0.68}
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{'loss': 1.5452, 'grad_norm': 3.3623666763305664, 'learning_rate': 0.0005481103448275862, 'epoch': 0.7}
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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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 13%|█▎        | 1900/15000 [44:25<1:14:59,  2.91it/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: liberofiस impress में एक प्रस्तुति document बनाना और बुनियादी formating के इस spoken tutorial में आपका स् हैहैह



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 window के भागों के बारे में सीखेंगे और कैसे slide insert करें और copy करें ford तथा font 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 opeरating system के रूप में jnu linuxस और liber ऑfiस version 334 का उपयोग कर रह हैं



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


  Prediction: चलिए अपनी प्रस्तुति sample impres open करते हैं जिसे पिछे  हं



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


  Prediction: चलिए देखते हैं कि scren 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 लता द्वारा अनुवादित है आई आईटी bॉम्bi की ओर से मैं रवि कुमार अब आपसे विदा लेता हूँ हमसे जुड़ने के लिए धन्यवा
{'eval_loss': 1.5831369161605835, 'eval_cer': 0.31393583277000675, 'eval_wer': 0.45114461194863203, 'eval_runtime': 108.8835, 'eval_samples_per_second': 28.801, 'eval_steps_per_second': 3.6, 'epoch': 0.72}
{'loss': 1.3447, 'grad_norm': 1.2728232145309448, 'learning_rate': 0.0005464137931034483, 'epoch': 0.72}
{'loss': 3.0234, 'grad_norm': 8.846041679382324, 'learning_rate': 0.0005463724137931033, 'epoch': 0.72}
{'loss': 1.039, 'grad_norm': 1.7851070165634155, 'learning_rate': 0.0005463310344827586, 'epoch': 0.72}
{'loss': 1.0608, 'grad_norm': 2.243628740310669, 'learning_rate': 0.0005462896551724138, 'epoch': 0.72}
{'loss': 1.4958, 'grad_norm': 1.117526888847351, 'learning_rate': 0.0005462482758620689, 'epoch': 0.72}
{'loss': 1.4345, 'grad_norm': 2.473588466644287, 'learning_rate': 0.0005462068965517241, 'epoch': 0.72}
{'loss': 0.9589, 'grad_norm': 1.4729325771331787, 'learning_rate': 0.0005461655172413792, 'epoch': 0.72}
{'loss': 1.1739, 'grad_norm': 1.4850056171417236, 'learning_rate': 0.0005461241379310345, 'epoch': 0.72}
{'loss': 0.9624, 'grad_norm': 1.7359071969985962, 'learning_rate': 0.0005460827586206895, 'epoch': 0.72}
{'loss': 1.3644, 'grad_norm': 1.2711845636367798, 'learning_rate': 0.0005460413793103448, 'epoch': 0.72}
{'loss': 0.6966, 'grad_norm': 1.5666863918304443, 'learning_rate': 0.0005459999999999999, 'epoch': 0.72}
{'loss': 2.1795, 'grad_norm': 3.5630381107330322, 'learning_rate': 0.0005459586206896551, 'epoch': 0.72}
{'loss': 0.7955, 'grad_norm': 1.2938557863235474, 'learning_rate': 0.0005459172413793103, 'epoch': 0.73}
{'loss': 1.3712, 'grad_norm': 2.5508573055267334, 'learning_rate': 0.0005458758620689655, 'epoch': 0.73}
{'loss': 0.9597, 'grad_norm': 1.5231380462646484, 'learning_rate': 0.0005458344827586207, 'epoch': 0.73}
{'loss': 1.4485, 'grad_norm': 2.689833164215088, 'learning_rate': 0.0005457931034482758, 'epoch': 0.73}
{'loss': 1.6706, 'grad_norm': 2.0492734909057617, 'learning_rate': 0.000545751724137931, 'epoch': 0.73}
{'loss': 0.9253, 'grad_norm': 1.5168957710266113, 'learning_rate': 0.0005457103448275861, 'epoch': 0.73}
{'loss': 1.0287, 'grad_norm': 1.1175618171691895, 'learning_rate': 0.0005456689655172414, 'epoch': 0.73}
{'loss': 0.561, 'grad_norm': 1.0077041387557983, 'learning_rate': 0.0005456275862068965, 'epoch': 0.73}
{'loss': 1.8407, 'grad_norm': 2.247011661529541, 'learning_rate': 0.0005455862068965517, 'epoch': 0.73}
{'loss': 0.6489, 'grad_norm': 1.1056792736053467, 'learning_rate': 0.0005455448275862068, 'epoch': 0.73}
{'loss': 0.6947, 'grad_norm': 0.9935020208358765, 'learning_rate': 0.000545503448275862, 'epoch': 0.73}
{'loss': 1.6584, 'grad_norm': 2.017566680908203, 'learning_rate': 0.0005454620689655172, 'epoch': 0.73}
{'loss': 0.5805, 'grad_norm': 1.344721794128418, 'learning_rate': 0.0005454206896551723, 'epoch': 0.73}
{'loss': 0.4041, 'grad_norm': 1.2267000675201416, 'learning_rate': 0.0005453793103448276, 'epoch': 0.73}
{'loss': 1.382, 'grad_norm': 1.9068725109100342, 'learning_rate': 0.0005453379310344827, 'epoch': 0.73}
{'loss': 1.466, 'grad_norm': 1.3093942403793335, 'learning_rate': 0.0005452965517241379, 'epoch': 0.73}
{'loss': 1.3982, 'grad_norm': 1.7432563304901123, 'learning_rate': 0.000545255172413793, 'epoch': 0.73}
{'loss': 1.0104, 'grad_norm': 1.4845340251922607, 'learning_rate': 0.0005452137931034483, 'epoch': 0.73}
{'loss': 1.3711, 'grad_norm': 1.655010461807251, 'learning_rate': 0.0005451724137931034, 'epoch': 0.73}
{'loss': 1.3364, 'grad_norm': 2.113186836242676, 'learning_rate': 0.0005451310344827586, 'epoch': 0.73}
{'loss': 1.2057, 'grad_norm': 1.904127597808838, 'learning_rate': 0.0005450896551724137, 'epoch': 0.73}
{'loss': 2.6429, 'grad_norm': 2.3251023292541504, 'learning_rate': 0.0005450482758620689, 'epoch': 0.73}
{'loss': 1.5723, 'grad_norm': 2.4815738201141357, 'learning_rate': 0.0005450068965517241, 'epoch': 0.73}
{'loss': 1.141, 'grad_norm': 1.4882811307907104, 'learning_rate': 0.0005449655172413793, 'epoch': 0.73}
{'loss': 0.9987, 'grad_norm': 1.7666832208633423, 'learning_rate': 0.0005449241379310344, 'epoch': 0.73}
{'loss': 0.7664, 'grad_norm': 1.4958058595657349, 'learning_rate': 0.0005448827586206896, 'epoch': 0.74}
{'loss': 0.6732, 'grad_norm': 1.3187624216079712, 'learning_rate': 0.0005448413793103448, 'epoch': 0.74}
{'loss': 1.5833, 'grad_norm': 1.819538950920105, 'learning_rate': 0.0005448, 'epoch': 0.74}
{'loss': 1.735, 'grad_norm': 2.2420146465301514, 'learning_rate': 0.0005447586206896551, 'epoch': 0.74}
{'loss': 1.712, 'grad_norm': 1.6333922147750854, 'learning_rate': 0.0005447172413793103, 'epoch': 0.74}
{'loss': 1.9254, 'grad_norm': 1.561193585395813, 'learning_rate': 0.0005446758620689655, 'epoch': 0.74}
{'loss': 1.1838, 'grad_norm': 1.6394083499908447, 'learning_rate': 0.0005446344827586206, 'epoch': 0.74}
{'loss': 1.0078, 'grad_norm': 2.0634100437164307, 'learning_rate': 0.0005445931034482758, 'epoch': 0.74}
{'loss': 1.3367, 'grad_norm': 2.046290159225464, 'learning_rate': 0.0005445517241379309, 'epoch': 0.74}
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 13%|█▎        | 2000/15000 [46:50<1:14:42,  2.90it/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: libere office impress में एक प्रस्तुति document बनाना और बुनियादी formating के इस spoken tutorial में आपका स्व हैहैहह



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 window के भागों के बारे में सीखेंगे और कैसे slide insert करें और copy करें fornt तथा fornt 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 operेting system के रूप में jnu linux और libere office version 334 का उपयोग कर रह हैं



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


  Prediction: चलिए अपनी प्रस्तुति sample imprus open करते हैंजिे



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


  Prediction: चलिए देखते हैं कि scren 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 लतa द्वारा अनुवादित है आई आई टी बॉमबy की ओर से मैं रवि कुमार अब आपसे विदा लेता हूँ हमसे जुड़ने के लिए धन्यवाद
{'eval_loss': 1.5932414531707764, 'eval_cer': 0.3176797878425459, 'eval_wer': 0.4630294328786791, 'eval_runtime': 108.3684, 'eval_samples_per_second': 28.938, 'eval_steps_per_second': 3.617, 'epoch': 0.76}
{'loss': 5.6766, 'grad_norm': 25.15976333618164, 'learning_rate': 0.0005422758620689655, 'epoch': 0.76}
{'loss': 1.8849, 'grad_norm': 5.871236801147461, 'learning_rate': 0.0005422344827586207, 'epoch': 0.76}
{'loss': 2.7792, 'grad_norm': 8.483572959899902, 'learning_rate': 0.0005421931034482758, 'epoch': 0.76}
{'loss': 1.6369, 'grad_norm': 4.964258193969727, 'learning_rate': 0.000542151724137931, 'epoch': 0.76}
{'loss': 1.1447, 'grad_norm': 1.5091110467910767, 'learning_rate': 0.0005421103448275861, 'epoch': 0.76}
{'loss': 1.2837, 'grad_norm': 1.3172553777694702, 'learning_rate': 0.0005420689655172414, 'epoch': 0.76}
{'loss': 1.0829, 'grad_norm': 1.8255559206008911, 'learning_rate': 0.0005420275862068964, 'epoch': 0.76}
{'loss': 1.5364, 'grad_norm': 2.5679914951324463, 'learning_rate': 0.0005419862068965517, 'epoch': 0.76}
{'loss': 1.0285, 'grad_norm': 1.9658007621765137, 'learning_rate': 0.0005419448275862069, 'epoch': 0.76}
{'loss': 0.7724, 'grad_norm': 1.1866735219955444, 'learning_rate': 0.000541903448275862, 'epoch': 0.76}
{'loss': 1.2083, 'grad_norm': 1.4293495416641235, 'learning_rate': 0.0005418620689655172, 'epoch': 0.76}
{'loss': 1.7489, 'grad_norm': 2.379923105239868, 'learning_rate': 0.0005418206896551723, 'epoch': 0.76}
{'loss': 1.0922, 'grad_norm': 1.0156636238098145, 'learning_rate': 0.0005417793103448276, 'epoch': 0.77}
{'loss': 0.8064, 'grad_norm': 1.0535211563110352, 'learning_rate': 0.0005417379310344827, 'epoch': 0.77}
{'loss': 0.4925, 'grad_norm': 1.1472920179367065, 'learning_rate': 0.0005416965517241379, 'epoch': 0.77}
{'loss': 0.5918, 'grad_norm': 1.0086278915405273, 'learning_rate': 0.000541655172413793, 'epoch': 0.77}
{'loss': 0.8186, 'grad_norm': 1.2053087949752808, 'learning_rate': 0.0005416137931034483, 'epoch': 0.77}
{'loss': 1.1315, 'grad_norm': 1.5646709203720093, 'learning_rate': 0.0005415724137931033, 'epoch': 0.77}
{'loss': 1.3888, 'grad_norm': 1.259419560432434, 'learning_rate': 0.0005415310344827586, 'epoch': 0.77}
{'loss': 0.6438, 'grad_norm': 1.0682919025421143, 'learning_rate': 0.0005414896551724138, 'epoch': 0.77}
{'loss': 1.5904, 'grad_norm': 1.504800796508789, 'learning_rate': 0.0005414482758620689, 'epoch': 0.77}
{'loss': 0.7419, 'grad_norm': 1.2231844663619995, 'learning_rate': 0.0005414068965517242, 'epoch': 0.77}
{'loss': 0.7431, 'grad_norm': 1.5558377504348755, 'learning_rate': 0.0005413655172413792, 'epoch': 0.77}
{'loss': 0.6368, 'grad_norm': 1.8001765012741089, 'learning_rate': 0.0005413241379310345, 'epoch': 0.77}
{'loss': 0.8397, 'grad_norm': 1.3737030029296875, 'learning_rate': 0.0005412827586206896, 'epoch': 0.77}
{'loss': 0.8992, 'grad_norm': 1.0345020294189453, 'learning_rate': 0.0005412413793103448, 'epoch': 0.77}
{'loss': 0.7535, 'grad_norm': 1.0259613990783691, 'learning_rate': 0.0005411999999999999, 'epoch': 0.77}
{'loss': 1.5524, 'grad_norm': 1.5945600271224976, 'learning_rate': 0.0005411586206896551, 'epoch': 0.77}
{'loss': 0.7423, 'grad_norm': 1.3085721731185913, 'learning_rate': 0.0005411172413793103, 'epoch': 0.77}
{'loss': 1.1561, 'grad_norm': 1.083601474761963, 'learning_rate': 0.0005410758620689655, 'epoch': 0.77}
{'loss': 0.8963, 'grad_norm': 2.1839725971221924, 'learning_rate': 0.0005410344827586206, 'epoch': 0.77}
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 14%|█▍        | 2100/15000 [49:13<1:15:17,  2.86it/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: liberoffice impres में एक प्रस्तुति document बनाना और बुनियादी formating के इस spoken tutorial में आपका स हैह



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 window के भागों के बारे में सीखेंगे और कैसे slide insert करें और copy करें fot तथा font 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 operेting system के रूप में gnu linux और liber office version 334 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 sampl imprus open करते हैं



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


  Prediction: चलिए देखते हैं कि scren 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 लता द्वारा अनुवादित ह आई आई टी muमby की ओर से मैं रवि कुमार अब आपसे विदा लेता हूहमसे जुड़ने के लिए धन्यवाद
{'eval_loss': 1.6316254138946533, 'eval_cer': 0.31675939888721333, 'eval_wer': 0.456993964531653, 'eval_runtime': 109.0242, 'eval_samples_per_second': 28.764, 'eval_steps_per_second': 3.596, 'epoch': 0.8}
{'loss': 1.2273, 'grad_norm': 1.4380937814712524, 'learning_rate': 0.0005381379310344827, 'epoch': 0.8}
{'loss': 0.944, 'grad_norm': 1.3887938261032104, 'learning_rate': 0.0005380965517241379, 'epoch': 0.8}
{'loss': 1.3426, 'grad_norm': 1.3330237865447998, 'learning_rate': 0.000538055172413793, 'epoch': 0.8}
{'loss': 2.203, 'grad_norm': 4.159045696258545, 'learning_rate': 0.0005380137931034482, 'epoch': 0.8}
{'loss': 2.1865, 'grad_norm': 2.9507055282592773, 'learning_rate': 0.0005379724137931034, 'epoch': 0.8}
{'loss': 1.5716, 'grad_norm': 3.422333240509033, 'learning_rate': 0.0005379310344827586, 'epoch': 0.8}
{'loss': 1.1107, 'grad_norm': 2.205335855484009, 'learning_rate': 0.0005378896551724137, 'epoch': 0.8}
{'loss': 0.8881, 'grad_norm': 1.8896450996398926, 'learning_rate': 0.0005378482758620689, 'epoch': 0.8}
{'loss': 1.4023, 'grad_norm': 2.1919093132019043, 'learning_rate': 0.0005378068965517241, 'epoch': 0.8}
{'loss': 0.6932, 'grad_norm': 1.206391453742981, 'learning_rate': 0.0005377655172413792, 'epoch': 0.8}
{'loss': 0.9681, 'grad_norm': 1.461532711982727, 'learning_rate': 0.0005377241379310345, 'epoch': 0.8}
{'loss': 0.6057, 'grad_norm': 1.3309721946716309, 'learning_rate': 0.0005376827586206896, 'epoch': 0.8}
{'loss': 0.635, 'grad_norm': 1.122302770614624, 'learning_rate': 0.0005376413793103448, 'epoch': 0.81}
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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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 15%|█▍        | 2200/15000 [51:42<1:25:40,  2.49it/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: liber office impress में एक प्रस्तुति document बनाना और बुनियादी formating के इस spoken tutorial में आपका स्वा हैहहह



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 impress window के भागों के बारे में सीखेंगे और कैसे slide insert करें और copy करें font तथा front 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 oपeरेting sys्tem के रूप में gnu lिनक और liber office version 3334 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 sampl impres open करते हैं जिसे पिछले t्यuोर हैहैहैह



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


  Prediction: चलिए देखते हैं कि scren 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 लता द्वारा अनुवादित है आई आई टी मुमबई की ओर से मैं रवि कुमार अब आपसे विदा लेता हूँ हमसे जुड़ने के लिए धन्यवाद
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 15%|█▌        | 2300/15000 [54:07<1:28:53,  2.38it/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: liberofice impress में एक प्रस्तुति document बनाना और बुनियादी formating के इस spoken tutorial में आपका स्वै



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 window के भागों के बारे में सीखेंगे और कैसे slide insert करें और copy करें fot तथा font 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 operating system के रूप में jnu लिनकस और liberofice वर्sion 334 का उपयोग कर रह हैं



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


  Prediction: चलिए अपनी प्रस्तुति sampal imprs open करते हैं ज



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


  Prediction: चलिए देखते हैं कि scren पर क्या क्य 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 लता द्वारा अनुवादित ह आई आई टी मुम्बई की ओर से मैं रवि कुमार अब आपसे विदा लेता हूँ हमसे जुड़ने के लिए धन्यवाद
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{'loss': 0.7437, 'grad_norm': 0.9954508543014526, 'learning_rate': 0.0005298620689655172, 'epoch': 0.88}
{'loss': 2.4852, 'grad_norm': 3.797497272491455, 'learning_rate': 0.0005298206896551723, 'epoch': 0.88}
{'loss': 1.572, 'grad_norm': 1.7269701957702637, 'learning_rate': 0.0005297793103448275, 'epoch': 0.88}
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{'loss': 1.7183, 'grad_norm': 2.6435434818267822, 'learning_rate': 0.0005296965517241378, 'epoch': 0.88}
{'loss': 1.0584, 'grad_norm': 1.5048909187316895, 'learning_rate': 0.0005296551724137931, 'epoch': 0.88}
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{'loss': 1.1558, 'grad_norm': 1.4964033365249634, 'learning_rate': 0.0005283724137931033, 'epoch': 0.89}
{'loss': 0.8453, 'grad_norm': 1.6162021160125732, 'learning_rate': 0.0005283310344827586, 'epoch': 0.9}
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{'loss': 1.4439, 'grad_norm': 2.3958523273468018, 'learning_rate': 0.0005281655172413792, 'epoch': 0.9}
{'loss': 1.8911, 'grad_norm': 1.4908806085586548, 'learning_rate': 0.0005281241379310345, 'epoch': 0.9}
{'loss': 1.6392, 'grad_norm': 2.776564836502075, 'learning_rate': 0.0005280827586206896, 'epoch': 0.9}
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{'loss': 1.602, 'grad_norm': 1.6787211894989014, 'learning_rate': 0.000527751724137931, 'epoch': 0.9}
{'loss': 1.1135, 'grad_norm': 1.5228450298309326, 'learning_rate': 0.0005277103448275861, 'epoch': 0.9}
{'loss': 2.6526, 'grad_norm': 7.300190448760986, 'learning_rate': 0.0005276689655172414, 'epoch': 0.9}
{'loss': 1.8347, 'grad_norm': 2.5623672008514404, 'learning_rate': 0.0005276275862068965, 'epoch': 0.9}
{'loss': 0.9791, 'grad_norm': 1.21243417263031, 'learning_rate': 0.0005275862068965517, 'epoch': 0.9}
{'loss': 0.8406, 'grad_norm': 1.146185040473938, 'learning_rate': 0.0005275448275862068, 'epoch': 0.9}
{'loss': 1.2297, 'grad_norm': 1.9172124862670898, 'learning_rate': 0.000527503448275862, 'epoch': 0.9}
{'loss': 1.5491, 'grad_norm': 2.3153085708618164, 'learning_rate': 0.0005274620689655172, 'epoch': 0.9}
{'loss': 1.3129, 'grad_norm': 1.6830394268035889, 'learning_rate': 0.0005274206896551724, 'epoch': 0.9}
{'loss': 3.6221, 'grad_norm': 10.968546867370605, 'learning_rate': 0.0005273793103448275, 'epoch': 0.9}
{'loss': 1.8555, 'grad_norm': 1.6481847763061523, 'learning_rate': 0.0005273379310344827, 'epoch': 0.9}
{'loss': 0.7174, 'grad_norm': 1.0084160566329956, 'learning_rate': 0.000527296551724138, 'epoch': 0.91}
{'loss': 2.1386, 'grad_norm': 4.796565055847168, 'learning_rate': 0.000527255172413793, 'epoch': 0.91}
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{'loss': 0.0, 'grad_norm': nan, 'learning_rate': 0.0005258068965517241, 'epoch': 0.92}


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 16%|█▌        | 2400/15000 [56:31<1:21:05,  2.59it/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: liber oफiस impess में एक प्रस्तुति document बनाना और बुनियादी formating के इस spoken tutorial में आपका स्वाहैहैह



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 impres window के भागों के बारे में सीखेंगे और कैसे slide insert करें और copy करें fon तथा font 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 operating system के रूप में gnu लिनकस और liber ofic version 334 का उपयोग कर रह हैंं



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


  Prediction: चलिए अपनी प्रस्तुति sample impres open करते हैंिि



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


  Prediction: चलिए देखते हैं कि screen 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 लता द्वारा अनुवदित है आई आई टी mुmbई की ओर से मैं रवि कुमार अब आपसे विदा लेता हूँ हमसे जुड़ने के लिए धन्यवाद
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{'loss': 0.5887, 'grad_norm': 1.3858288526535034, 'learning_rate': 0.0005255586206896551, 'epoch': 0.92}
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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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 17%|█▋        | 2500/15000 [59:38<53:33,  3.89it/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: liberoffice impress में एक प्रस्तुति document बनाना और बुनियादी formating के इस spoken tutorial में आपका स्वा गहै



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 impres window के भागों के बारे में सीखेंगे और कैसे slide insert करें और copy करें font तथा font 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 operating system के रूप में jnu linux और liber office versin 334 का उपयोग कर रहं



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


  Prediction: चलिए अपनी प्रस्तुति sample impres open करते हैंजिसे िल tंं



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


  Prediction: चलिए देखते हैं कि scren 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 नता द्वरनुवादित ह आई ई ट मुmबई की ओर से मैं रवि कुमार अब आपसे विदा लेता हूँहमस जुडने केिए धन्यवाद
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{'loss': 6.8486, 'grad_norm': 40.751182556152344, 'learning_rate': 0.0005216275862068965, 'epoch': 0.96}
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 17%|█▋        | 2600/15000 [1:02:23<1:53:11,  1.83it/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: libereoffice impress में एक प्रस्तुति document बनाना और बुनियादी formatting के इस spoken tutorial में आपका स्वा हैहैह



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 impress window के भागों के बारे में सीखेंगे और कैसे slide insert करें और copy करें fornt तथा font 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 operating system के रूप में gnu liनक्स और liber office version 334 का उपयोग कर रह हंैं



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


  Prediction: चलिए अपनी प्रस्तुति sample impres open करते हैं जिसे पिल tहैंं



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


  Prediction: चलिए देखते हैं कि scren 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 लता द्वारा अनुवादित है आई आई टी मुम्बई की ओर से मैं रवि कुमार अब आपसे विदा लेता हूँ हमसे जुड़ने के लिए धन्यवाद
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 18%|█▊        | 2700/15000 [1:05:07<2:04:10,  1.65it/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: liber office impress में एक प्रस्तुती document बनाना और बुनियादी formating के इस spoken tutorial में आपका स्वा गहैहैह



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 window के भागों के बारे में सीखेंगे और कैसे slide insert करें और copy करें font तथा front 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 operating system के रूप में gnu lिनx् और liber office ver्sion 334 का उपयोग कर रह हैं



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


  Prediction: चलिए अपनी प्रस्तुति cample impres open करते हैं जिसे पिछले tयूtoर हैंंं



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


  Prediction: चलिए देखते हैं कि scren 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 नैता द्वारा अनुवादित है आई आई टी मुम्बई की ओर से मैं रवि कुमार अब आपसे विदा लेता हूँ हमसे जुड़ने के लिए धन्यवाद
{'eval_loss': 1.6404063701629639, 'eval_cer': 0.3040819510165878, 'eval_wer': 0.4262582755045067, 'eval_runtime': 108.8672, 'eval_samples_per_second': 28.806, 'eval_steps_per_second': 3.601, 'epoch': 1.04}
{'loss': 1.0594, 'grad_norm': 3.238098621368408, 'learning_rate': 0.000513351724137931, 'epoch': 1.04}
{'loss': 0.9981, 'grad_norm': 1.3624919652938843, 'learning_rate': 0.0005133103448275862, 'epoch': 1.04}
{'loss': 0.5566, 'grad_norm': 0.9932172894477844, 'learning_rate': 0.0005132689655172413, 'epoch': 1.04}
{'loss': 1.8596, 'grad_norm': 3.07936954498291, 'learning_rate': 0.0005132275862068965, 'epoch': 1.04}
{'loss': 1.7657, 'grad_norm': 3.5009236335754395, 'learning_rate': 0.0005131862068965517, 'epoch': 1.04}
{'loss': 1.2194, 'grad_norm': 1.1729313135147095, 'learning_rate': 0.0005131448275862068, 'epoch': 1.04}
{'loss': 1.3714, 'grad_norm': 1.419417142868042, 'learning_rate': 0.0005131034482758621, 'epoch': 1.04}
{'loss': 0.9918, 'grad_norm': 2.027456283569336, 'learning_rate': 0.0005130620689655172, 'epoch': 1.04}
{'loss': 1.2219, 'grad_norm': 2.3161702156066895, 'learning_rate': 0.0005130206896551724, 'epoch': 1.04}
{'loss': 1.1394, 'grad_norm': 1.4224910736083984, 'learning_rate': 0.0005129793103448275, 'epoch': 1.04}
{'loss': 0.6693, 'grad_norm': 1.3339043855667114, 'learning_rate': 0.0005129379310344827, 'epoch': 1.04}
{'loss': 0.8655, 'grad_norm': 0.861128032207489, 'learning_rate': 0.0005128965517241379, 'epoch': 1.04}
{'loss': 1.0987, 'grad_norm': 1.687015414237976, 'learning_rate': 0.0005128551724137931, 'epoch': 1.05}
{'loss': 1.8808, 'grad_norm': 3.318044662475586, 'learning_rate': 0.0005128137931034482, 'epoch': 1.05}
{'loss': 0.5544, 'grad_norm': 0.850831925868988, 'learning_rate': 0.0005127724137931034, 'epoch': 1.05}
{'loss': 0.751, 'grad_norm': 0.9701075553894043, 'learning_rate': 0.0005127310344827585, 'epoch': 1.05}
{'loss': 1.0028, 'grad_norm': 1.2017261981964111, 'learning_rate': 0.0005126896551724138, 'epoch': 1.05}
{'loss': 0.8733, 'grad_norm': 1.5517467260360718, 'learning_rate': 0.0005126482758620689, 'epoch': 1.05}
{'loss': 0.6629, 'grad_norm': 1.0907726287841797, 'learning_rate': 0.0005126068965517241, 'epoch': 1.05}
{'loss': 0.4966, 'grad_norm': 1.2253700494766235, 'learning_rate': 0.0005125655172413793, 'epoch': 1.05}
{'loss': 1.3435, 'grad_norm': 2.433143138885498, 'learning_rate': 0.0005125241379310344, 'epoch': 1.05}
{'loss': 0.4869, 'grad_norm': 1.2576591968536377, 'learning_rate': 0.0005124827586206896, 'epoch': 1.05}
{'loss': 1.1156, 'grad_norm': 1.2053848505020142, 'learning_rate': 0.0005124413793103448, 'epoch': 1.05}
{'loss': 1.3203, 'grad_norm': 2.246276617050171, 'learning_rate': 0.0005124, 'epoch': 1.05}
{'loss': 1.6111, 'grad_norm': 1.4134451150894165, 'learning_rate': 0.0005123586206896551, 'epoch': 1.05}
{'loss': 1.1047, 'grad_norm': 1.9930723905563354, 'learning_rate': 0.0005123172413793103, 'epoch': 1.05}
{'loss': 0.7809, 'grad_norm': 1.3072404861450195, 'learning_rate': 0.0005122758620689654, 'epoch': 1.05}
{'loss': 0.8441, 'grad_norm': 1.165319800376892, 'learning_rate': 0.0005122344827586207, 'epoch': 1.05}
{'loss': 1.9051, 'grad_norm': 3.1468565464019775, 'learning_rate': 0.0005121931034482758, 'epoch': 1.05}
{'loss': 1.4985, 'grad_norm': 1.5420725345611572, 'learning_rate': 0.000512151724137931, 'epoch': 1.05}
{'loss': 0.3344, 'grad_norm': 1.2049915790557861, 'learning_rate': 0.0005121103448275862, 'epoch': 1.05}
{'loss': 1.0682, 'grad_norm': 1.5615836381912231, 'learning_rate': 0.0005120689655172413, 'epoch': 1.05}
{'loss': 1.315, 'grad_norm': 2.1360385417938232, 'learning_rate': 0.0005120275862068965, 'epoch': 1.05}
{'loss': 0.5715, 'grad_norm': 1.7102206945419312, 'learning_rate': 0.0005119862068965516, 'epoch': 1.05}
{'loss': 1.1387, 'grad_norm': 2.2430877685546875, 'learning_rate': 0.0005119448275862069, 'epoch': 1.05}
{'loss': 2.2395, 'grad_norm': 1.9811503887176514, 'learning_rate': 0.000511903448275862, 'epoch': 1.05}
{'loss': 1.1876, 'grad_norm': 1.889750361442566, 'learning_rate': 0.0005118620689655172, 'epoch': 1.05}
{'loss': 0.8625, 'grad_norm': 1.4714722633361816, 'learning_rate': 0.0005118206896551723, 'epoch': 1.06}
{'loss': 1.1003, 'grad_norm': 1.5893300771713257, 'learning_rate': 0.0005117793103448275, 'epoch': 1.06}
{'loss': 1.1184, 'grad_norm': 1.795811653137207, 'learning_rate': 0.0005117379310344828, 'epoch': 1.06}
{'loss': 0.9734, 'grad_norm': 2.571471929550171, 'learning_rate': 0.0005116965517241379, 'epoch': 1.06}
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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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 19%|█▊        | 2800/15000 [1:08:03<2:20:27,  1.45it/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: liber office impress में एक प्रस्तुती document बनाना और बुनियादी formating के इस spoken tutoril में आपका स्वा हैहैैै



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 impress window के भागों के बारे में सीखेंगे और कैसे slide insert करें और copy करें fot तथा front 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 operating system के रूप में jnu linux और liber office version 334 का उपयोग कर रहे हैं



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


  Prediction: चलिए अपनी प्रस्तुति sample impress open करते हैंजिसेिल ं



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


  Prediction: चलिए देखते हैं कि scren 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 लाता द्वारा अनुवादित ह आi आit मुम्bi की ओर से मैं रवि कुमार अब आपसे विदा लेता हूँ हमसे जुड़ने के लिए धन्यवाद
{'eval_loss': 1.7064540386199951, 'eval_cer': 0.309749883001404, 'eval_wer': 0.43662758235622556, 'eval_runtime': 110.9334, 'eval_samples_per_second': 28.269, 'eval_steps_per_second': 3.534, 'epoch': 1.08}
{'loss': 1.0934, 'grad_norm': 1.1682382822036743, 'learning_rate': 0.0005092137931034483, 'epoch': 1.08}
{'loss': 0.9207, 'grad_norm': 1.3840662240982056, 'learning_rate': 0.0005091724137931034, 'epoch': 1.08}
{'loss': 1.7124, 'grad_norm': 3.955160617828369, 'learning_rate': 0.0005091310344827586, 'epoch': 1.08}
{'loss': 4.8184, 'grad_norm': 15.46823787689209, 'learning_rate': 0.0005090896551724137, 'epoch': 1.08}
{'loss': 2.4675, 'grad_norm': 3.1153149604797363, 'learning_rate': 0.0005090482758620689, 'epoch': 1.08}
{'loss': 2.8801, 'grad_norm': 7.214519500732422, 'learning_rate': 0.0005090068965517241, 'epoch': 1.08}
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100%|█████████▉| 391/392 [01:43<00:00,  4.35it/s]
                                                        

                                                 

 19%|█▊        | 2800/15000 [1:09:52<2:20:27,  1.45it/s]

100%|██████████| 392/392 [01:48<00:00,  4.35it/s]

                                                 
 19%|█▊        | 2801/15000 [1:09:53<114:10:13, 33.69s/it]
                                                          

 19%|█▊        | 2801/15000 [1:09:53<114:10:13, 33.69s/it]
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 19%|█▊        | 2802/15000 [1:09:54<80:48:11, 23.85s/it]
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 19%|█▊        | 2808/15000 [1:09:59<11:49:41,  3.49s/it]
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 19%|█▊        | 2809/15000 [1:10:00<8:50:37,  2.61s/it]
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 19%|█▊        | 2810/15000 [1:10:00<6:51:33,  2.03s/it]
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 19%|█▊        | 2811/15000 [1:10:01<5:23:55,  1.59s/it]
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 19%|█▉        | 2815/15000 [1:10:03<2:47:01,  1.22it/s]
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 19%|█▉        | 2816/15000 [1:10:04<2:29:32,  1.36it/s]
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 19%|█▉        | 2817/15000 [1:10:04<2:21:38,  1.43it/s]
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 19%|█▉        | 2821/15000 [1:10:07<2:04:29,  1.63it/s]
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 19%|█▉        | 2822/15000 [1:10:07<1:59:50,  1.69it/s]
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 19%|█▉        | 2828/15000 [1:10:10<1:40:43,  2.01it/s]
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 19%|█▉        | 2829/15000 [1:10:10<1:33:32,  2.17it/s]
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 19%|█▉        | 2830/15000 [1:10:11<1:33:55,  2.16it/s]
 19%|█▉        | 2831/15000 [1:10:11<1:27:42,  2.31it/s]
                                                        

 19%|█▉        | 2831/15000 [1:10:11<1:27:42,  2.31it/s]
 19%|█▉        | 2832/15000 [1:10:12<1:30:50,  2.23it/s]
                                                        

 19%|█▉        | 2832/15000 [1:10:12<1:30:50,  2.23it/s]
 19%|█▉        | 2833/15000 [1:10:12<1:26:07,  2.35it/s]
                                                        

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 19%|█▉        | 2837/15000 [1:10:14<1:29:02,  2.28it/s]
 19%|█▉        | 2838/15000 [1:10:14<1:26:15,  2.35it/s]
                                                        

 19%|█▉        | 2838/15000 [1:10:14<1:26:15,  2.35it/s]
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 19%|█▉        | 2839/15000 [1:10:15<1:26:32,  2.34it/s]
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 19%|█▉        | 2840/15000 [1:10:15<1:26:44,  2.34it/s]
 19%|█▉        | 2841/15000 [1:10:16<1:23:35,  2.42it/s]
                                                        

 19%|█▉        | 2841/15000 [1:10:16<1:23:35,  2.42it/s]
 19%|█▉        | 2842/15000 [1:10:16<1:22:00,  2.47it/s]
                                                        

 19%|█▉        | 2842/15000 [1:10:16<1:22:00,  2.47it/s]
 19%|█▉        | 2843/15000 [1:10:16<1:21:25,  2.49it/s]
                                                        

 19%|█▉        | 2843/15000 [1:10:16<1:21:25,  2.49it/s]
 19%|█▉        | 2844/15000 [1:10:17<1:20:56,  2.50it/s]
                                                        

 19%|█▉        | 2844/15000 [1:10:17<1:20:56,  2.50it/s]
 19%|█▉        | 2845/15000 [1:10:17<1:23:43,  2.42it/s]
                                                        

 19%|█▉        | 2845/15000 [1:10:17<1:23:43,  2.42it/s]
 19%|█▉        | 2846/15000 [1:10:17<1:16:37,  2.64it/s]
                                                        

 19%|█▉        | 2846/15000 [1:10:17<1:16:37,  2.64it/s]
 19%|█▉        | 2847/15000 [1:10:18<1:08:10,  2.97it/s]
                                                        

 19%|█▉        | 2847/15000 [1:10:18<1:08:10,  2.97it/s]
 19%|█▉        | 2848/15000 [1:10:18<1:03:36,  3.18it/s]
                                                        

 19%|█▉        | 2848/15000 [1:10:18<1:03:36,  3.18it/s]
 19%|█▉        | 2849/15000 [1:10:18<58:13,  3.48it/s]  
                                                      

 19%|█▉        | 2849/15000 [1:10:18<58:13,  3.48it/s]
 19%|█▉        | 2850/15000 [1:10:19<1:49:53,  1.84it/s]
                                                        

 19%|█▉        | 2850/15000 [1:10:19<1:49:53,  1.84it/s]
 19%|█▉        | 2851/15000 [1:10:21<2:28:39,  1.36it/s]
                                                        

 19%|█▉        | 2851/15000 [1:10:21<2:28:39,  1.36it/s]
 19%|█▉        | 2852/15000 [1:10:21<2:32:51,  1.32it/s]
                                                        

 19%|█▉        | 2852/15000 [1:10:21<2:32:51,  1.32it/s]
 19%|█▉        | 2853/15000 [1:10:22<2:40:12,  1.26it/s]
                                                        

 19%|█▉        | 2853/15000 [1:10:22<2:40:12,  1.26it/s]
 19%|█▉        | 2854/15000 [1:10:23<2:42:57,  1.24it/s]
                                                        

 19%|█▉        | 2854/15000 [1:10:23<2:42:57,  1.24it/s]
 19%|█▉        | 2855/15000 [1:10:24<2:46:32,  1.22it/s]
                                                        

 19%|█▉        | 2855/15000 [1:10:24<2:46:32,  1.22it/s]
 19%|█▉        | 2856/15000 [1:10:25<2:39:24,  1.27it/s]
                                                        

 19%|█▉        | 2856/15000 [1:10:25<2:39:24,  1.27it/s]
 19%|█▉        | 2857/15000 [1:10:25<2:29:32,  1.35it/s]
                                                        

 19%|█▉        | 2857/15000 [1:10:25<2:29:32,  1.35it/s]
 19%|█▉        | 2858/15000 [1:10:26<2:17:17,  1.47it/s]
                                                        

 19%|█▉        | 2858/15000 [1:10:26<2:17:17,  1.47it/s]
 19%|█▉        | 2859/15000 [1:10:26<2:17:38,  1.47it/s]
                                                        

 19%|█▉        | 2859/15000 [1:10:26<2:17:38,  1.47it/s]
 19%|█▉        | 2860/15000 [1:10:27<2:09:22,  1.56it/s]
                                                        

 19%|█▉        | 2860/15000 [1:10:27<2:09:22,  1.56it/s]
 19%|█▉        | 2861/15000 [1:10:28<2:08:06,  1.58it/s]
                                                        

 19%|█▉        | 2861/15000 [1:10:28<2:08:06,  1.58it/s]
 19%|█▉        | 2862/15000 [1:10:28<2:03:00,  1.64it/s]
                                                        

 19%|█▉        | 2862/15000 [1:10:28<2:03:00,  1.64it/s]
 19%|█▉        | 2863/15000 [1:10:29<2:06:18,  1.60it/s]
                                                        

 19%|█▉        | 2863/15000 [1:10:29<2:06:18,  1.60it/s]
 19%|█▉        | 2864/15000 [1:10:29<1:58:52,  1.70it/s]
                                                        

 19%|█▉        | 2864/15000 [1:10:29<1:58:52,  1.70it/s]
 19%|█▉        | 2865/15000 [1:10:30<1:57:06,  1.73it/s]
                                                        

 19%|█▉        | 2865/15000 [1:10:30<1:57:06,  1.73it/s]
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 19%|█▉        | 2866/15000 [1:10:31<2:02:43,  1.65it/s]
 19%|█▉        | 2867/15000 [1:10:31<2:02:27,  1.65it/s]
                                                        

 19%|█▉        | 2867/15000 [1:10:31<2:02:27,  1.65it/s]
 19%|█▉        | 2868/15000 [1:10:32<1:57:35,  1.72it/s]
                                                        

 19%|█▉        | 2868/15000 [1:10:32<1:57:35,  1.72it/s]
 19%|█▉        | 2869/15000 [1:10:32<1:45:18,  1.92it/s]
                                                        

 19%|█▉        | 2869/15000 [1:10:32<1:45:18,  1.92it/s]
 19%|█▉        | 2870/15000 [1:10:33<1:42:05,  1.98it/s]
                                                        

 19%|█▉        | 2870/15000 [1:10:33<1:42:05,  1.98it/s]
 19%|█▉        | 2871/15000 [1:10:33<1:41:01,  2.00it/s]
                                                        

 19%|█▉        | 2871/15000 [1:10:33<1:41:01,  2.00it/s]
 19%|█▉        | 2872/15000 [1:10:33<1:39:47,  2.03it/s]
                                                        

 19%|█▉        | 2872/15000 [1:10:34<1:39:47,  2.03it/s]
 19%|█▉        | 2873/15000 [1:10:34<1:40:40,  2.01it/s]
                                                        

 19%|█▉        | 2873/15000 [1:10:34<1:40:40,  2.01it/s]
 19%|█▉        | 2874/15000 [1:10:34<1:36:12,  2.10it/s]
                                                        

 19%|█▉        | 2874/15000 [1:10:34<1:36:12,  2.10it/s]
 19%|█▉        | 2875/15000 [1:10:35<1:33:56,  2.15it/s]
                                                        

 19%|█▉        | 2875/15000 [1:10:35<1:33:56,  2.15it/s]
 19%|█▉        | 2876/15000 [1:10:35<1:35:10,  2.12it/s]
                                                        

 19%|█▉        | 2876/15000 [1:10:35<1:35:10,  2.12it/s]
 19%|█▉        | 2877/15000 [1:10:36<1:32:30,  2.18it/s]
                                                        

 19%|█▉        | 2877/15000 [1:10:36<1:32:30,  2.18it/s]
 19%|█▉        | 2878/15000 [1:10:36<1:29:36,  2.25it/s]
                                                        

 19%|█▉        | 2878/15000 [1:10:36<1:29:36,  2.25it/s]
 19%|█▉        | 2879/15000 [1:10:37<1:25:32,  2.36it/s]
                                                        

 19%|█▉        | 2879/15000 [1:10:37<1:25:32,  2.36it/s]
 19%|█▉        | 2880/15000 [1:10:37<1:29:47,  2.25it/s]
                                                        

 19%|█▉        | 2880/15000 [1:10:37<1:29:47,  2.25it/s]
 19%|█▉        | 2881/15000 [1:10:37<1:29:07,  2.27it/s]
                                                        

 19%|█▉        | 2881/15000 [1:10:37<1:29:07,  2.27it/s]
 19%|█▉        | 2882/15000 [1:10:38<1:28:01,  2.29it/s]
                                                        

 19%|█▉        | 2882/15000 [1:10:38<1:28:01,  2.29it/s]
 19%|█▉        | 2883/15000 [1:10:38<1:24:17,  2.40it/s]
                                                        

 19%|█▉        | 2883/15000 [1:10:38<1:24:17,  2.40it/s]
 19%|█▉        | 2884/15000 [1:10:39<1:20:56,  2.49it/s]
                                                        

 19%|█▉        | 2884/15000 [1:10:39<1:20:56,  2.49it/s]
 19%|█▉        | 2885/15000 [1:10:39<1:22:26,  2.45it/s]
                                                        

 19%|█▉        | 2885/15000 [1:10:39<1:22:26,  2.45it/s]
 19%|█▉        | 2886/15000 [1:10:40<1:24:55,  2.38it/s]
                                                        

 19%|█▉        | 2886/15000 [1:10:40<1:24:55,  2.38it/s]
 19%|█▉        | 2887/15000 [1:10:40<1:30:12,  2.24it/s]
                                                        

 19%|█▉        | 2887/15000 [1:10:40<1:30:12,  2.24it/s]
 19%|█▉        | 2888/15000 [1:10:40<1:24:12,  2.40it/s]
                                                        

 19%|█▉        | 2888/15000 [1:10:40<1:24:12,  2.40it/s]
 19%|█▉        | 2889/15000 [1:10:41<1:18:34,  2.57it/s]
                                                        

 19%|█▉        | 2889/15000 [1:10:41<1:18:34,  2.57it/s]
 19%|█▉        | 2890/15000 [1:10:41<1:15:23,  2.68it/s]
                                                        

 19%|█▉        | 2890/15000 [1:10:41<1:15:23,  2.68it/s]
 19%|█▉        | 2891/15000 [1:10:41<1:11:25,  2.83it/s]
                                                        

 19%|█▉        | 2891/15000 [1:10:41<1:11:25,  2.83it/s]
 19%|█▉        | 2892/15000 [1:10:42<1:10:40,  2.86it/s]
                                                        

 19%|█▉        | 2892/15000 [1:10:42<1:10:40,  2.86it/s]
 19%|█▉        | 2893/15000 [1:10:42<1:07:33,  2.99it/s]
                                                        

 19%|█▉        | 2893/15000 [1:10:42<1:07:33,  2.99it/s]
 19%|█▉        | 2894/15000 [1:10:42<1:13:42,  2.74it/s]
                                                        

 19%|█▉        | 2894/15000 [1:10:42<1:13:42,  2.74it/s]
 19%|█▉        | 2895/15000 [1:10:43<1:11:42,  2.81it/s]
                                                        

 19%|█▉        | 2895/15000 [1:10:43<1:11:42,  2.81it/s]
 19%|█▉        | 2896/15000 [1:10:43<1:12:09,  2.80it/s]
                                                        

 19%|█▉        | 2896/15000 [1:10:43<1:12:09,  2.80it/s]
 19%|█▉        | 2897/15000 [1:10:43<1:09:07,  2.92it/s]
                                                        

 19%|█▉        | 2897/15000 [1:10:43<1:09:07,  2.92it/s]
 19%|█▉        | 2898/15000 [1:10:44<1:05:56,  3.06it/s]
                                                        

 19%|█▉        | 2898/15000 [1:10:44<1:05:56,  3.06it/s]
 19%|█▉        | 2899/15000 [1:10:44<56:03,  3.60it/s]  
                                                      

 19%|█▉        | 2899/15000 [1:10:44<56:03,  3.60it/s]
 19%|█▉        | 2900/15000 [1:10:45<1:50:32,  1.82it/s]
                                                        

 19%|█▉        | 2900/15000 [1:10:45<1:50:32,  1.82it/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 offिस impres में एक प्रस्तुती document बनाना और बुनियादी formating के इस spoken tutoral में आपका स्वा हैह



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 window के भागों के बारे में सीखेंगे और कैसे slide insert करें और copy करेंfot तथा font को frmat 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 operating sysटem के रूप में jnu lिnक और libr offिस vरsion 334 का उपयोग कर रहैंैं



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


  Prediction: चलिए अपनी प्रस्तुति sample impres open करते हैं जिसे पिछले t हैंैंैह



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


  Prediction: चलिए देखते हैं कि screen 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 मैता द्वारा अनुवादित है आई आई टी मुमबई की ओर से मैं रवि कुमार अब आपसे विदा लेता हूँ हमसे जुड़ने के लिए धन्यवाद
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{'loss': 2.3079, 'grad_norm': 6.331419467926025, 'learning_rate': 0.0005050344827586206, 'epoch': 1.12}
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{'loss': 0.9537, 'grad_norm': 1.820931077003479, 'learning_rate': 0.0005028, 'epoch': 1.14}
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{'loss': 1.4484, 'grad_norm': 3.640172004699707, 'learning_rate': 0.0005027172413793104, 'epoch': 1.14}
{'loss': 1.1758, 'grad_norm': 1.5199528932571411, 'learning_rate': 0.0005026758620689654, 'epoch': 1.14}
{'loss': 0.5975, 'grad_norm': 1.356781005859375, 'learning_rate': 0.0005026344827586207, 'epoch': 1.14}
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 20%|██        | 3000/15000 [1:13:26<1:34:31,  2.12it/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: lिbreऑfिस impress में एक प्रस्तुती डocument बनाना और बुनियादी formating के इस spoken tutorial में आपका स्वा गै



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 wiिndow के भागों के बारे में सीखेंगे और कैसे slide insert करें और copy करें fot तथा font 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 operेting system के रूप में jnu lिnux और lिbrofिस वर्soन 334 का उपयोग कर रहे हैं



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


  Prediction: चलिए अपनी प्रस्तुति sample impres open करते हैंजिेपिलल



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


  Prediction: चलिए देखते हैं कि scren 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 लता द्वारा अनुवादित है आई आई टी मुम्बई की ओर से मैं रवि कुमार अब आपसे विदा लेता हूँ हमसे जुड़ने के लिए धन्यवाद
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{'loss': 1.1344, 'grad_norm': 1.5991801023483276, 'learning_rate': 0.0005008965517241379, 'epoch': 1.16}
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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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 21%|██        | 3100/15000 [1:16:16<1:46:42,  1.86it/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: libere ऑffice impress में एक प्रस्तुति document बनाना और बुनियादी formating के इस spoken tutorial में आपका स्वागहैहैहैहैहैह



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 window के भागों के बारे में सीखेंगे और कैसे slide insert करें और copy करें fot तथा font 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 ऑperेtiंg sysटem के रूप में jnu lिनx और liber ऑfिc vrsoन 334 का उपयोग कर रहे हैंंं



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


  Prediction: चलिए अपनी प्रस्तुति sample imprs open करते हैं जिसे पिछले tuto है



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


  Prediction: चलिए देखते हैं कि scren 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 नता द्वाहरा अनुवादित है आई आई टी मुम्बई की ओर से मैं रवि कुमार अब आपसे विदा लेता हूँ हमसे जुड़ने के लिए धन्यवाद
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{'loss': 1.5522, 'grad_norm': 1.64492666721344, 'learning_rate': 0.0004930758620689654, 'epoch': 1.24}
{'loss': 0.6415, 'grad_norm': 1.8319951295852661, 'learning_rate': 0.0004930344827586207, 'epoch': 1.24}
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{'loss': 0.6831, 'grad_norm': 2.398181915283203, 'learning_rate': 0.000492951724137931, 'epoch': 1.24}
{'loss': 1.2966, 'grad_norm': 3.03297758102417, 'learning_rate': 0.0004929103448275861, 'epoch': 1.24}
{'loss': 0.9455, 'grad_norm': 1.3297066688537598, 'learning_rate': 0.0004928689655172413, 'epoch': 1.24}
{'loss': 1.6417, 'grad_norm': 1.7982977628707886, 'learning_rate': 0.0004928275862068965, 'epoch': 1.24}
{'loss': 1.7264, 'grad_norm': 2.4908556938171387, 'learning_rate': 0.0004927862068965517, 'epoch': 1.24}
{'loss': 1.8014, 'grad_norm': 2.8599886894226074, 'learning_rate': 0.0004927448275862069, 'epoch': 1.24}
{'loss': 2.0566, 'grad_norm': 1.8401293754577637, 'learning_rate': 0.000492703448275862, 'epoch': 1.24}


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 21%|██▏       | 3200/15000 [1:18:58<2:13:30,  1.47it/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: liber offic impress में एक प्रस्तुती document बनाना और बुनियादी formatting के इस spoken tutorial में आपका स् ा हैह



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 window के भागों के बारे में सीखेंगे और कैसे slide insert करें और copy करें frnt तथा font 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 operating sिsटem के रूप में gnu lिnux और libr offic version 334 का उपयोग कर रह हैं



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


  Prediction: चलिए अपनी प्रस्तुति cample impruss oपen करते हैं जिस पिल   हैहहह



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


  Prediction: चलिए देखते हैं कि screen 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 लता द्वारा अनुवादित है आई आई टी मुंबई की ओर से मैं रवि कुमार अब आपसे विदा लेता हूँ हमसे जुडने के लिए धन्यवाद
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 22%|██▏       | 3300/15000 [1:21:39<1:42:42,  1.90it/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: liber ऑffic impress में एक प्रस्तुति document बनाना और बुनियादी formatting के इस spoken tutorial में आपका स्वागा



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 impres window के भागों के बारे में सीखेंगे और कैसे slide insert करें और copy कं fon तथा font 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 operating system के रूप में jnu linux और liber oऑffic version 334 का उपयोग कर रहेहं



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


  Prediction: चलिए अपनी प्रस्तुति sample impress open करते हैं जिसे पिछले tूtor हैंै



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


  Prediction: चलिए देखते हैं कि screen 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 लतa द्वारा अनुवादित है आई आई टी मुम्बई की ओर से मैं रवि कुमार अब आपसे विदा लेता हूँ हमसे जुड़ने के लिए धन्यवाद
{'eval_loss': 1.5341277122497559, 'eval_cer': 0.30282876605480735, 'eval_wer': 0.4284384887399963, 'eval_runtime': 108.5807, 'eval_samples_per_second': 28.882, 'eval_steps_per_second': 3.61, 'epoch': 1.28}
{'loss': 2.6412, 'grad_norm': 15.196290969848633, 'learning_rate': 0.0004885241379310345, 'epoch': 1.28}
{'loss': 1.5936, 'grad_norm': 3.6793572902679443, 'learning_rate': 0.0004884827586206896, 'epoch': 1.28}
{'loss': 1.8463, 'grad_norm': 7.942821979522705, 'learning_rate': 0.0004884413793103448, 'epoch': 1.28}
{'loss': 1.664, 'grad_norm': 4.548803806304932, 'learning_rate': 0.0004883999999999999, 'epoch': 1.28}
{'loss': 0.9992, 'grad_norm': 1.5449870824813843, 'learning_rate': 0.0004883586206896551, 'epoch': 1.28}
{'loss': 0.6398, 'grad_norm': 0.9733086824417114, 'learning_rate': 0.0004883172413793104, 'epoch': 1.28}
{'loss': 1.1758, 'grad_norm': 1.8481475114822388, 'learning_rate': 0.00048827586206896546, 'epoch': 1.28}
{'loss': 1.7035, 'grad_norm': 1.8280220031738281, 'learning_rate': 0.00048823448275862066, 'epoch': 1.28}
{'loss': 0.6877, 'grad_norm': 1.8358126878738403, 'learning_rate': 0.00048819310344827587, 'epoch': 1.28}
{'loss': 0.6241, 'grad_norm': 1.0939416885375977, 'learning_rate': 0.00048815172413793097, 'epoch': 1.28}
{'loss': 0.4785, 'grad_norm': 1.2757935523986816, 'learning_rate': 0.0004881103448275862, 'epoch': 1.28}
{'loss': 1.1116, 'grad_norm': 1.3357384204864502, 'learning_rate': 0.00048806896551724133, 'epoch': 1.28}
{'loss': 2.7663, 'grad_norm': 9.775795936584473, 'learning_rate': 0.0004880275862068965, 'epoch': 1.29}
{'loss': 1.0256, 'grad_norm': 1.2006868124008179, 'learning_rate': 0.0004879862068965517, 'epoch': 1.29}
{'loss': 0.7534, 'grad_norm': 1.7015979290008545, 'learning_rate': 0.00048794482758620684, 'epoch': 1.29}
{'loss': 0.7416, 'grad_norm': 1.6042076349258423, 'learning_rate': 0.00048790344827586205, 'epoch': 1.29}
{'loss': 0.5004, 'grad_norm': 0.9974443316459656, 'learning_rate': 0.0004878620689655172, 'epoch': 1.29}
{'loss': 1.0582, 'grad_norm': 1.227708339691162, 'learning_rate': 0.00048782068965517235, 'epoch': 1.29}
{'loss': 0.9653, 'grad_norm': 2.3534884452819824, 'learning_rate': 0.00048777931034482756, 'epoch': 1.29}
{'loss': 1.2744, 'grad_norm': 2.422630548477173, 'learning_rate': 0.0004877379310344827, 'epoch': 1.29}
{'loss': 0.8732, 'grad_norm': 1.7679169178009033, 'learning_rate': 0.00048769655172413787, 'epoch': 1.29}
{'loss': 1.0519, 'grad_norm': 1.277707815170288, 'learning_rate': 0.0004876551724137931, 'epoch': 1.29}
{'loss': 0.8014, 'grad_norm': 1.2692278623580933, 'learning_rate': 0.0004876137931034482, 'epoch': 1.29}
{'loss': 0.749, 'grad_norm': 1.3436559438705444, 'learning_rate': 0.00048757241379310343, 'epoch': 1.29}
{'loss': 0.6477, 'grad_norm': 0.8652753829956055, 'learning_rate': 0.0004875310344827586, 'epoch': 1.29}
{'loss': 0.6765, 'grad_norm': 1.0843740701675415, 'learning_rate': 0.00048748965517241374, 'epoch': 1.29}
{'loss': 0.7594, 'grad_norm': 1.1199299097061157, 'learning_rate': 0.00048744827586206895, 'epoch': 1.29}
{'loss': 2.3969, 'grad_norm': 2.7518153190612793, 'learning_rate': 0.00048740689655172404, 'epoch': 1.29}
{'loss': 0.5617, 'grad_norm': 1.305484414100647, 'learning_rate': 0.00048736551724137925, 'epoch': 1.29}
{'loss': 0.63, 'grad_norm': 1.1281580924987793, 'learning_rate': 0.00048732413793103446, 'epoch': 1.29}
{'loss': 0.903, 'grad_norm': 1.2427219152450562, 'learning_rate': 0.0004872827586206896, 'epoch': 1.29}
{'loss': 0.5829, 'grad_norm': 1.5127191543579102, 'learning_rate': 0.0004872413793103448, 'epoch': 1.29}
{'loss': 0.6938, 'grad_norm': 1.0132231712341309, 'learning_rate': 0.00048719999999999997, 'epoch': 1.29}
{'loss': 0.8946, 'grad_norm': 1.8186728954315186, 'learning_rate': 0.0004871586206896551, 'epoch': 1.29}
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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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 23%|██▎       | 3400/15000 [1:24:26<1:33:23,  2.07it/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: libere ऑफिसe impress में एक प्रस्तुति document बनाना और बुनियादी formatting के इस spoken tutorial में आपका स्वा गै



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 window के भागों के बारे में सीखेंगे और कैसे slide insert करें और copy करें fot तथा font 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 operेting system के रूप में jnu lिnuxस और libre ऑफिस vर्son 334 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 cample imprus open करते हैं जिसे पिछले tutoर हैंैंै



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


  Prediction: चलिए देखते हैं कि सcren 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 लता द्वारा अनुवादित ह आई आई टी मुंबई की ओर से मैं रवि कुमार अब आपसे विदा लेता हूँ हमसे जुड़ने के लिए धन्यवाद
{'eval_loss': 1.5173879861831665, 'eval_cer': 0.30048359419686965, 'eval_wer': 0.43083140570577755, 'eval_runtime': 107.7704, 'eval_samples_per_second': 29.099, 'eval_steps_per_second': 3.637, 'epoch': 1.32}
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 23%|██▎       | 3500/15000 [1:27:04<1:39:43,  1.92it/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: liberoफiसe impess में एक प्रस्तुति document बनाना और बुनियादी formating के इस spoken tutorial में आपका स्वा ाैाैह



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 windw के भागों के बारे में सीखेंगे और कैसे slide insert करें और copy करें font तथा font 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 operating system के रूप में jnu linx और liberoफic version 334 का उपयोग कर रह हैंं



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


  Prediction: चलिए अपनी प्रस्तुति sample imprus open करते हैं जिसे पिछले  हैै



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


  Prediction: चलिए देखते हैं कि scren 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 लतa द्वारा अनुवादित है आई आई टी मुmबई की ओर से मैं रवि कुमार अब आपसे विदा लेता हूँ हमसे जुड़ने के लिए धन्यवाद
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 24%|██▍       | 3600/15000 [1:29:44<1:53:43,  1.67it/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: lिbrऑfिस impress में एक प्रस्तुति dcument बनाना और बुनियादी formating के इस spoken tutorial में आपका स्वागा



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 impress window के भागों के बारे में सीखेंगे और कैसे slide insert करें और copy करें font तथा font 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 ऑperating system के रूप में jnu lिनx और lिbrऑffिस version 334 का उपयोग कर रहेहं



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


  Prediction: चलिए अपनी प्रस्तुति sample impres open करते हैं जिसे पिछले tuto हंैहैहै



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


  Prediction: चलिए देखते हैं कि scren 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 लता दवारा अनुवादित हैं आई आई टी मुम्बई की ओर से मैं रवि कुमार अब आपसे विदा लेता हूँ हमसे जुड़ने के लिए धन्यवाद
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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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 25%|██▍       | 3700/15000 [1:32:30<1:46:03,  1.78it/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: lिbr ऑffिसe impress में एक प्रस्तुति document बनाना और बुनियादी formatting के इस spoken tutorial में आपका स्वागा हैहैहैहैहैहै



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 impress windw के भागों के बारे में सीखेंगे और कैसे slide insert करें और copyेंfont तथा font 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 ऑperating system के रूप में jnu lिनकस और lिbर ऑffिस version 334 का उपयोग कर रहे हं



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


  Prediction: चलिए अपनी प्रस्तुति sample impres open करते हैं जिसे पिछले tuto हंैंैहैहैं



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


  Prediction: चलिए देखते हैं कि scren 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 लाता दोवारा अनुवादित हैं आई आई टी मुम्बई की ओर से मैं रवि कुमार अब आपसे विदा लेता हूँ हमसे जुड़ने के लिए धन्यवाद
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{'loss': 0.9016, 'grad_norm': 1.3668131828308105, 'learning_rate': 0.0004719724137931034, 'epoch': 1.44}
{'loss': 3.465, 'grad_norm': 14.639104843139648, 'learning_rate': 0.0004719310344827586, 'epoch': 1.44}
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 25%|██▌       | 3800/15000 [1:35:10<1:35:37,  1.95it/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: lिbr ऑfफिसe impress में एक प्रस्तुति document बनाना और बुनियादी formatting के इस spoken tutorial में आपका स्वा हैहैहैहै



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 window के भागों के बारे में सीखेंगे और कैसे slide insert करें और copy कं font तथा font 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 ऑपerेting system के रूप में jnu लिनकस और लिबर ऑfफिस version 334 का उपयोग कर रह हैं



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


  Prediction: चलिए अपनी प्रस्तुति sample imprus open करते हैं जिे पि  हैै



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


  Prediction: चलिए देखते हैं कि screen 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 लता द्वारा अनुवादित है आई आई टी मुंबई की ओर से मैं रवि कुमार अब आपसे विदा लेता हूँ हमसे जुड़ने के लिए धन्यवाद
{'eval_loss': 1.4047954082489014, 'eval_cer': 0.3166969996360044, 'eval_wer': 0.44186541171465793, 'eval_runtime': 109.7681, 'eval_samples_per_second': 28.569, 'eval_steps_per_second': 3.571, 'epoch': 1.48}
{'loss': 0.6739, 'grad_norm': 1.1036834716796875, 'learning_rate': 0.00046783448275862066, 'epoch': 1.48}
{'loss': 0.9349, 'grad_norm': 1.7980012893676758, 'learning_rate': 0.0004677931034482758, 'epoch': 1.48}
{'loss': 1.7719, 'grad_norm': 4.178752422332764, 'learning_rate': 0.00046775172413793096, 'epoch': 1.48}
{'loss': 1.506, 'grad_norm': 3.6636734008789062, 'learning_rate': 0.00046771034482758617, 'epoch': 1.48}
{'loss': 1.4075, 'grad_norm': 1.9649639129638672, 'learning_rate': 0.0004676689655172414, 'epoch': 1.48}
{'loss': 1.139, 'grad_norm': 1.9701650142669678, 'learning_rate': 0.0004676275862068965, 'epoch': 1.48}
{'loss': 0.7894, 'grad_norm': 1.7302477359771729, 'learning_rate': 0.0004675862068965517, 'epoch': 1.48}
{'loss': 0.9645, 'grad_norm': 1.1882426738739014, 'learning_rate': 0.0004675448275862069, 'epoch': 1.48}
{'loss': 0.9764, 'grad_norm': 1.3908957242965698, 'learning_rate': 0.000467503448275862, 'epoch': 1.48}
{'loss': 0.8757, 'grad_norm': 1.5152406692504883, 'learning_rate': 0.0004674620689655172, 'epoch': 1.48}
{'loss': 0.8542, 'grad_norm': 1.2025786638259888, 'learning_rate': 0.00046742068965517235, 'epoch': 1.48}
{'loss': 1.3332, 'grad_norm': 1.5462998151779175, 'learning_rate': 0.00046737931034482755, 'epoch': 1.48}
{'loss': 0.4243, 'grad_norm': 0.7167777419090271, 'learning_rate': 0.00046733793103448276, 'epoch': 1.49}
{'loss': 0.6614, 'grad_norm': 1.2574717998504639, 'learning_rate': 0.00046729655172413786, 'epoch': 1.49}
{'loss': 1.5644, 'grad_norm': 1.344095230102539, 'learning_rate': 0.00046725517241379307, 'epoch': 1.49}
{'loss': 1.6896, 'grad_norm': 1.5791733264923096, 'learning_rate': 0.00046721379310344827, 'epoch': 1.49}
{'loss': 0.5693, 'grad_norm': 0.9362877607345581, 'learning_rate': 0.00046717241379310337, 'epoch': 1.49}
{'loss': 0.515, 'grad_norm': 0.8952619433403015, 'learning_rate': 0.0004671310344827586, 'epoch': 1.49}
{'loss': 0.6131, 'grad_norm': 0.833836019039154, 'learning_rate': 0.00046708965517241373, 'epoch': 1.49}
{'loss': 0.9398, 'grad_norm': 1.0950225591659546, 'learning_rate': 0.00046704827586206894, 'epoch': 1.49}
{'loss': 0.902, 'grad_norm': 1.426284909248352, 'learning_rate': 0.00046700689655172414, 'epoch': 1.49}
{'loss': 1.0129, 'grad_norm': 1.6695430278778076, 'learning_rate': 0.00046696551724137924, 'epoch': 1.49}
{'loss': 0.9747, 'grad_norm': 1.597800374031067, 'learning_rate': 0.00046692413793103445, 'epoch': 1.49}
{'loss': 0.5019, 'grad_norm': 2.1425042152404785, 'learning_rate': 0.00046688275862068966, 'epoch': 1.49}
{'loss': 1.284, 'grad_norm': 1.6938822269439697, 'learning_rate': 0.00046684137931034476, 'epoch': 1.49}
{'loss': 1.1117, 'grad_norm': 0.8126702308654785, 'learning_rate': 0.00046679999999999996, 'epoch': 1.49}
{'loss': 1.2314, 'grad_norm': 1.284356713294983, 'learning_rate': 0.0004667586206896551, 'epoch': 1.49}
{'loss': 0.525, 'grad_norm': 1.0282694101333618, 'learning_rate': 0.0004667172413793103, 'epoch': 1.49}
{'loss': 0.8636, 'grad_norm': 1.1276110410690308, 'learning_rate': 0.0004666758620689655, 'epoch': 1.49}
{'loss': 0.8246, 'grad_norm': 1.1873314380645752, 'learning_rate': 0.00046663448275862063, 'epoch': 1.49}
{'loss': 0.9803, 'grad_norm': 1.1022231578826904, 'learning_rate': 0.00046659310344827583, 'epoch': 1.49}
{'loss': 1.7735, 'grad_norm': 1.69191575050354, 'learning_rate': 0.00046655172413793104, 'epoch': 1.49}
{'loss': 2.2169, 'grad_norm': 5.218369007110596, 'learning_rate': 0.00046651034482758614, 'epoch': 1.49}
{'loss': 0.8516, 'grad_norm': 1.1822980642318726, 'learning_rate': 0.00046646896551724135, 'epoch': 1.49}
{'loss': 0.6589, 'grad_norm': 1.1467937231063843, 'learning_rate': 0.0004664275862068965, 'epoch': 1.49}
{'loss': 0.9859, 'grad_norm': 1.4250408411026, 'learning_rate': 0.0004663862068965517, 'epoch': 1.49}
{'loss': 0.7819, 'grad_norm': 1.3348915576934814, 'learning_rate': 0.00046634482758620686, 'epoch': 1.49}
{'loss': 1.7563, 'grad_norm': 2.946835994720459, 'learning_rate': 0.000466303448275862, 'epoch': 1.5}
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 26%|██▌       | 3900/15000 [1:37:46<1:48:02,  1.71it/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: lिबr offic impress में एक प्रस्तुति document बनाना और बुनियादी formatting के इस spoken tutorial में आपका स् ता गहैहैहैहैहै



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 impress window के भागों के बारे में सीखेंगे और कैसे slide insert करें और copy करें font तथा font 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 operating system के रूप में jnu लिनक और लिबर offिc version 334 का उपयोग कर रह हैंैंैंैं



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


  Prediction: चलिए अपनी प्रस्तुति sample impress open करते हैं जिसे पिछले  है



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


  Prediction: चलिए देखते हैं कि scrीen 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 लाता द्वारा अनुवादित है आई आई टी मुम्बई की ओर से मैं रवि कुमार अब आपसे विदा लेता हूँ हमसे जुड़ने के लिए धन्यवाद
{'eval_loss': 1.5359928607940674, 'eval_cer': 0.29881441422702926, 'eval_wer': 0.4155433250910638, 'eval_runtime': 109.1155, 'eval_samples_per_second': 28.74, 'eval_steps_per_second': 3.593, 'epoch': 1.52}
{'loss': 2.0178, 'grad_norm': 5.418882369995117, 'learning_rate': 0.0004636965517241379, 'epoch': 1.52}
{'loss': 1.0128, 'grad_norm': 1.153792381286621, 'learning_rate': 0.0004636551724137931, 'epoch': 1.52}
{'loss': 4.1461, 'grad_norm': 10.214264869689941, 'learning_rate': 0.00046361379310344824, 'epoch': 1.52}
{'loss': 1.7184, 'grad_norm': 4.618348598480225, 'learning_rate': 0.0004635724137931034, 'epoch': 1.52}
{'loss': 1.0276, 'grad_norm': 1.1765382289886475, 'learning_rate': 0.0004635310344827586, 'epoch': 1.52}
{'loss': 0.6794, 'grad_norm': 1.4064607620239258, 'learning_rate': 0.0004634896551724137, 'epoch': 1.52}
{'loss': 0.6551, 'grad_norm': 1.3631408214569092, 'learning_rate': 0.0004634482758620689, 'epoch': 1.52}
{'loss': 1.0221, 'grad_norm': 1.4536281824111938, 'learning_rate': 0.0004634068965517241, 'epoch': 1.52}
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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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 27%|██▋       | 4000/15000 [1:40:32<1:57:39,  1.56it/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: libere office impress में एक प्रस्तुति document बनाना और बुनियादी formatting के इस spoken tutorial में आपका स्वा हैहैहै



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 window के भागों के बारे में सीखेंगे और कैसे slide insert करें और copy करें fornt तथा font 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 operating system के रूप में jnu lिनx और liber office version 334 का उपयोग कर रहेहैं



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


  Prediction: चलिए अपनी प्रस्तुति sample impru+ open करते हैं जिसे पिछल t हैंै



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


  Prediction: चलिए देखते हैं कि screen 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 लाता द्वहरा अनुवादित है आई आई टी मुं्बई की ओर से मैं रवि कुमार अब आपसे विदा लेता हूँ हमसे जुड़ने के लिए धन्यवाद
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{'loss': 2.8823, 'grad_norm': 8.794419288635254, 'learning_rate': 0.0004572413793103448, 'epoch': 1.58}
{'loss': 1.4392, 'grad_norm': 1.7220923900604248, 'learning_rate': 0.00045719999999999995, 'epoch': 1.58}
{'loss': 1.2839, 'grad_norm': 1.3149176836013794, 'learning_rate': 0.00045715862068965515, 'epoch': 1.58}
{'loss': 0.7874, 'grad_norm': 1.3435609340667725, 'learning_rate': 0.0004571172413793103, 'epoch': 1.58}
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 27%|██▋       | 4100/15000 [1:43:09<1:17:52,  2.33it/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 ofफice impress में एक प्रस्तुति document बनाना और बुनियादी formatting के इस spoken tutorial में आपका स्वा हैहै



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 window के भागों के बारे में सीखेंगे और कैसे slide insert करें और copy करें font तथा font 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 operating system के रूप में jnu lिनx और librofफic version 334 का उपयोग कर रहे ैं



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


  Prediction: चलिए अपनी प्रस्तुति sample impru+ open करते हैं जिसे पिछले t हैंैंैै



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


  Prediction: चलिए देखते हैं कि scren 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 लाता द्वारा अनुवादित है आई आई टी मुम्बई की ओर से मैं रवि कुमार अब आपसे विदा लेता हूँ हमसे जुड़ने के लिए धन्यवाद
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 28%|██▊       | 4200/15000 [1:45:41<1:21:36,  2.21it/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: libere ofफिce impress में एक प्रस्तुति document बनाना और बुनियादी formatting के इस spoken tutorial में आपका स्वागैह



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 impress window के भागों के बारे में सीखेंगे और कैसे slide insert करें और copy करें fornt तथा font 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 operेtiंg system के रूप में jnu lिनक्स और liber offिce version 334 का उपयोग कर रहे हं



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


  Prediction: चलिए अपनी प्रस्तुति sample impru+ open करते हैं जिसे पिछले tूtorर हंै



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


  Prediction: चलिए देखते हैं कि scren 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 लाता द्वारा अनुवादित है आई आई टी मुम्बई की ओर से मैं रवि कुमार अब आपसे विदा लेता हूँ हमसे जुड़ने के लिए धन्यवाद
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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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