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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_210853-xt4y0zjk
wandb: Run `wandb offline` to turn off syncing.
wandb: Syncing run rerun_bestrun_wgas1_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/xt4y0zjk
/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 21:08:58 - 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 [04:16<2:36:51,  1.57it/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 [07:09<2:43:21,  1.50it/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: iiiiiiैं



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


  Prediction: iiं



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


  Prediction: iंअ



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


  Prediction: iैंैंैंै



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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{'loss': 4.8123, 'grad_norm': 44.33531188964844, 'learning_rate': 0.000234, 'epoch': 0.16}
{'loss': 3.9295, 'grad_norm': 13.5255126953125, 'learning_rate': 0.0002352, 'epoch': 0.16}
{'loss': 3.7041, 'grad_norm': 7.930710792541504, 'learning_rate': 0.0002364, 'epoch': 0.16}
{'loss': 4.1326, 'grad_norm': 15.819513320922852, 'learning_rate': 0.0002376, 'epoch': 0.16}
{'loss': 3.6596, 'grad_norm': 2.3044705390930176, 'learning_rate': 0.0002388, 'epoch': 0.16}
{'loss': 3.6752, 'grad_norm': 4.406660556793213, 'learning_rate': 0.00023999999999999998, 'epoch': 0.16}
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{'loss': 2.5818, 'grad_norm': 1.7448837757110596, 'learning_rate': 0.0003096, 'epoch': 0.21}
{'loss': 2.6296, 'grad_norm': 4.226468563079834, 'learning_rate': 0.00031079999999999997, 'epoch': 0.21}
{'loss': 2.7109, 'grad_norm': 4.208134174346924, 'learning_rate': 0.000312, 'epoch': 0.21}
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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 [10:14<2:42:32,  1.50it/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 imps में एक परसतुति docmnt बनाना और बुनियादी fरmtiंग के इस spocn ttorl 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: बात हइस ttorl मेंहमimps वindo के बागों के बारे में सीखेंगे और कैसे sld nsrt करेंऔर coपpी करेंfon तता font को foरmt 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: यहा हम अपने oपरेtin सstm के रूप में जnू lins और libr ofis वrजन pn pn  का उपयोग कर रैं



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


  Prediction: चलिए अपनी परसतति smpl imps opn करते हैंजिसे पिले ttर ं



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


  Prediction: मं बनाया ाचलिए देखते हैं कि सcrin पर कया 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 दवारा अनवादित हआt मmंबइ की और से मैं रवि कुमार अब आपसे विद्या लेता हूंहमसे जुने के लिए दन्यवाद
{'eval_loss': 2.35416841506958, 'eval_cer': 0.4552909365087619, 'eval_wer': 0.7076121347478131, 'eval_runtime': 111.1233, 'eval_samples_per_second': 28.221, 'eval_steps_per_second': 1.764, 'epoch': 0.24}
{'loss': 5.2518, 'grad_norm': 31.012039184570312, 'learning_rate': 0.00035399999999999993, 'epoch': 0.24}
{'loss': 2.1213, 'grad_norm': 2.0471251010894775, 'learning_rate': 0.00035519999999999996, 'epoch': 0.24}
{'loss': 2.5048, 'grad_norm': 3.0729475021362305, 'learning_rate': 0.00035639999999999994, 'epoch': 0.24}
{'loss': 2.6125, 'grad_norm': 3.5748157501220703, 'learning_rate': 0.00035759999999999996, 'epoch': 0.24}
{'loss': 1.9878, 'grad_norm': 2.88887095451355, 'learning_rate': 0.00035879999999999994, 'epoch': 0.24}
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  3%|▎         | 500/15000 [13:07<2:23:14,  1.69it/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 प्रस्तुती document बनाना और बुनियादी foरrmating के इस s्poken tutoral में आपका स्वागैगैै



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 imprेs vindo के भागों के बारे में सीखेंगे और कैसे slid insrt करें और copी करेंfond 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 oprating sis्tem के रूप में janू linecs और libr ofis vrjon थथथ0 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 sैmpl impres open करते हैं जिसे पिछले tutoरa ंं



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


  Prediction: मं बनाया थाचलिए देखते हैं कि sc्rine 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 आitी मुmबई की और से मैं रवी कुमार अब आपसे विदा लेता हूँ हमसे जुड़ने के लिए धन्यवाद
{'eval_loss': 2.4520702362060547, 'eval_cer': 0.4125318496178046, 'eval_wer': 0.6250299114620723, 'eval_runtime': 110.209, 'eval_samples_per_second': 28.455, 'eval_steps_per_second': 1.778, 'epoch': 0.32}
{'loss': 3.3713, 'grad_norm': 9.622496604919434, 'learning_rate': 0.0004728, 'epoch': 0.32}
{'loss': 3.2062, 'grad_norm': 7.592476844787598, 'learning_rate': 0.000474, 'epoch': 0.32}
{'loss': 3.1904, 'grad_norm': 11.068998336791992, 'learning_rate': 0.0004752, 'epoch': 0.32}
{'loss': 1.8687, 'grad_norm': 4.264267921447754, 'learning_rate': 0.0004764, 'epoch': 0.32}
{'loss': 1.9795, 'grad_norm': 5.0423665046691895, 'learning_rate': 0.0004776, 'epoch': 0.32}
{'loss': 2.113, 'grad_norm': 6.721738815307617, 'learning_rate': 0.0004788, 'epoch': 0.32}
{'loss': 2.1734, 'grad_norm': 3.764658212661743, 'learning_rate': 0.00047999999999999996, 'epoch': 0.33}
{'loss': 2.4073, 'grad_norm': 2.553842544555664, 'learning_rate': 0.0004812, 'epoch': 0.33}
{'loss': 2.0969, 'grad_norm': 3.2715542316436768, 'learning_rate': 0.00048239999999999996, 'epoch': 0.33}
{'loss': 1.6303, 'grad_norm': 1.814467430114746, 'learning_rate': 0.0004836, 'epoch': 0.33}
{'loss': 1.3806, 'grad_norm': 0.9853479266166687, 'learning_rate': 0.00048479999999999997, 'epoch': 0.33}
{'loss': 1.7154, 'grad_norm': 4.047312259674072, 'learning_rate': 0.000486, 'epoch': 0.33}
{'loss': 1.3877, 'grad_norm': 2.40771222114563, 'learning_rate': 0.00048719999999999997, 'epoch': 0.33}
{'loss': 1.6674, 'grad_norm': 2.062441349029541, 'learning_rate': 0.0004883999999999999, 'epoch': 0.33}
{'loss': 1.6538, 'grad_norm': 1.9290614128112793, 'learning_rate': 0.0004896, 'epoch': 0.33}
{'loss': 1.7653, 'grad_norm': 2.0009119510650635, 'learning_rate': 0.0004907999999999999, 'epoch': 0.33}
{'loss': 1.2382, 'grad_norm': 1.4536744356155396, 'learning_rate': 0.0004919999999999999, 'epoch': 0.33}
{'loss': 1.7138, 'grad_norm': 1.432706356048584, 'learning_rate': 0.0004932, 'epoch': 0.33}
{'loss': 1.3929, 'grad_norm': 2.0988097190856934, 'learning_rate': 0.0004944, 'epoch': 0.34}
{'loss': 1.8244, 'grad_norm': 1.9379551410675049, 'learning_rate': 0.0004955999999999999, 'epoch': 0.34}
{'loss': 1.7236, 'grad_norm': 1.8364909887313843, 'learning_rate': 0.0004967999999999999, 'epoch': 0.34}
{'loss': 1.0383, 'grad_norm': 1.1317616701126099, 'learning_rate': 0.000498, 'epoch': 0.34}
{'loss': 1.7776, 'grad_norm': 1.7506917715072632, 'learning_rate': 0.0004991999999999999, 'epoch': 0.34}
{'loss': 1.5318, 'grad_norm': 1.0113252401351929, 'learning_rate': 0.0005003999999999999, 'epoch': 0.34}
{'loss': 1.1355, 'grad_norm': 1.5814828872680664, 'learning_rate': 0.0005015999999999999, 'epoch': 0.34}
{'loss': 1.0664, 'grad_norm': 1.6495469808578491, 'learning_rate': 0.0005028, 'epoch': 0.34}
{'loss': 1.2866, 'grad_norm': 1.6560667753219604, 'learning_rate': 0.0005039999999999999, 'epoch': 0.34}
{'loss': 1.0897, 'grad_norm': 1.6408244371414185, 'learning_rate': 0.0005051999999999999, 'epoch': 0.34}
{'loss': 1.5413, 'grad_norm': 1.8058329820632935, 'learning_rate': 0.0005064, 'epoch': 0.34}
{'loss': 1.7002, 'grad_norm': 3.712158203125, 'learning_rate': 0.0005076, 'epoch': 0.34}
{'loss': 1.3931, 'grad_norm': 1.998500108718872, 'learning_rate': 0.0005087999999999999, 'epoch': 0.34}
{'loss': 1.9027, 'grad_norm': 2.115840196609497, 'learning_rate': 0.0005099999999999999, 'epoch': 0.35}
{'loss': 1.9201, 'grad_norm': 2.0319206714630127, 'learning_rate': 0.0005112, 'epoch': 0.35}
{'loss': 1.7284, 'grad_norm': 2.1958181858062744, 'learning_rate': 0.0005124, 'epoch': 0.35}
{'loss': 1.6264, 'grad_norm': 1.7242988348007202, 'learning_rate': 0.0005135999999999999, 'epoch': 0.35}
{'loss': 1.0129, 'grad_norm': 1.247483730316162, 'learning_rate': 0.0005147999999999999, 'epoch': 0.35}
{'loss': 1.1768, 'grad_norm': 1.5689575672149658, 'learning_rate': 0.000516, 'epoch': 0.35}
{'loss': 1.8038, 'grad_norm': 2.353442907333374, 'learning_rate': 0.0005172, 'epoch': 0.35}
{'loss': 1.2842, 'grad_norm': 1.9209372997283936, 'learning_rate': 0.0005183999999999999, 'epoch': 0.35}
{'loss': 0.8987, 'grad_norm': 1.3712620735168457, 'learning_rate': 0.0005195999999999999, 'epoch': 0.35}
{'loss': 1.6802, 'grad_norm': 2.0958011150360107, 'learning_rate': 0.0005208, 'epoch': 0.35}
{'loss': 1.4684, 'grad_norm': 2.796006441116333, 'learning_rate': 0.000522, 'epoch': 0.35}
{'loss': 1.7857, 'grad_norm': 2.1561906337738037, 'learning_rate': 0.0005231999999999999, 'epoch': 0.35}
{'loss': 1.5094, 'grad_norm': 1.8679171800613403, 'learning_rate': 0.0005244, 'epoch': 0.36}
{'loss': 1.826, 'grad_norm': 1.6648530960083008, 'learning_rate': 0.0005256, 'epoch': 0.36}
{'loss': 1.2792, 'grad_norm': 2.125572681427002, 'learning_rate': 0.0005267999999999999, 'epoch': 0.36}
{'loss': 1.7125, 'grad_norm': 1.597325086593628, 'learning_rate': 0.0005279999999999999, 'epoch': 0.36}
{'loss': 1.7701, 'grad_norm': 2.8514344692230225, 'learning_rate': 0.0005292, 'epoch': 0.36}
{'loss': 2.0764, 'grad_norm': 1.9149036407470703, 'learning_rate': 0.0005304, 'epoch': 0.36}
{'loss': 2.5126, 'grad_norm': 2.442195415496826, 'learning_rate': 0.0005315999999999999, 'epoch': 0.36}
{'loss': 6.7322, 'grad_norm': 37.38871383666992, 'learning_rate': 0.0005327999999999999, 'epoch': 0.36}
{'loss': 3.0498, 'grad_norm': 7.534951686859131, 'learning_rate': 0.000534, 'epoch': 0.36}
{'loss': 2.5051, 'grad_norm': 5.540412425994873, 'learning_rate': 0.0005352, 'epoch': 0.36}
{'loss': 1.6387, 'grad_norm': 4.399904251098633, 'learning_rate': 0.0005363999999999999, 'epoch': 0.36}
{'loss': 1.7854, 'grad_norm': 5.46937370300293, 'learning_rate': 0.0005376, 'epoch': 0.36}
{'loss': 2.1615, 'grad_norm': 2.6823713779449463, 'learning_rate': 0.0005388, 'epoch': 0.36}
{'loss': 1.6518, 'grad_norm': 5.212604522705078, 'learning_rate': 0.00054, 'epoch': 0.37}
{'loss': 1.9313, 'grad_norm': 2.130959987640381, 'learning_rate': 0.0005411999999999999, 'epoch': 0.37}
{'loss': 1.4991, 'grad_norm': 2.1140379905700684, 'learning_rate': 0.0005424, 'epoch': 0.37}
{'loss': 1.8197, 'grad_norm': 1.6964654922485352, 'learning_rate': 0.0005436, 'epoch': 0.37}
{'loss': 1.1808, 'grad_norm': 1.361877202987671, 'learning_rate': 0.0005448, 'epoch': 0.37}
{'loss': 1.1463, 'grad_norm': 1.0626742839813232, 'learning_rate': 0.0005459999999999999, 'epoch': 0.37}
{'loss': 1.2746, 'grad_norm': 1.3022620677947998, 'learning_rate': 0.0005472, 'epoch': 0.37}
{'loss': 1.5344, 'grad_norm': 3.946754217147827, 'learning_rate': 0.0005484, 'epoch': 0.37}
{'loss': 1.487, 'grad_norm': 1.8056219816207886, 'learning_rate': 0.0005496, 'epoch': 0.37}
{'loss': 0.9653, 'grad_norm': 0.9918379783630371, 'learning_rate': 0.0005507999999999999, 'epoch': 0.37}
{'loss': 1.2347, 'grad_norm': 1.278193712234497, 'learning_rate': 0.000552, 'epoch': 0.37}
{'loss': 1.2505, 'grad_norm': 1.3993587493896484, 'learning_rate': 0.0005532, 'epoch': 0.37}
{'loss': 0.9676, 'grad_norm': 1.343614935874939, 'learning_rate': 0.0005544, 'epoch': 0.38}
{'loss': 2.4302, 'grad_norm': 7.252395153045654, 'learning_rate': 0.0005556, 'epoch': 0.38}
{'loss': 1.3925, 'grad_norm': 1.3693439960479736, 'learning_rate': 0.0005568, 'epoch': 0.38}
{'loss': 1.1046, 'grad_norm': 1.3953487873077393, 'learning_rate': 0.000558, 'epoch': 0.38}
{'loss': 1.5929, 'grad_norm': 1.2658900022506714, 'learning_rate': 0.0005591999999999999, 'epoch': 0.38}
{'loss': 1.0919, 'grad_norm': 1.3098078966140747, 'learning_rate': 0.0005604, 'epoch': 0.38}
{'loss': 1.0624, 'grad_norm': 0.9656120538711548, 'learning_rate': 0.0005616, 'epoch': 0.38}
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{'loss': 1.592, 'grad_norm': 1.4318585395812988, 'learning_rate': 0.00057, 'epoch': 0.39}
{'loss': 1.3203, 'grad_norm': 2.1180155277252197, 'learning_rate': 0.0005711999999999999, 'epoch': 0.39}
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{'loss': 1.6122, 'grad_norm': 1.794335961341858, 'learning_rate': 0.0005795999999999999, 'epoch': 0.39}
{'loss': 1.4827, 'grad_norm': 2.277660369873047, 'learning_rate': 0.0005807999999999999, 'epoch': 0.39}
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{'loss': 1.903, 'grad_norm': 3.973555564880371, 'learning_rate': 0.0005868, 'epoch': 0.4}
{'loss': 2.112, 'grad_norm': 2.072765588760376, 'learning_rate': 0.000588, 'epoch': 0.4}
{'loss': 2.3134, 'grad_norm': 2.914924144744873, 'learning_rate': 0.0005891999999999999, 'epoch': 0.4}
{'loss': 2.6424, 'grad_norm': 2.967578649520874, 'learning_rate': 0.0005903999999999999, 'epoch': 0.4}
{'loss': 1.8059, 'grad_norm': 2.532670021057129, 'learning_rate': 0.0005916, 'epoch': 0.4}


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  4%|▍         | 600/15000 [16:01<2:24:41,  1.66it/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 oफiस impes 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 tutoral में हम impres vindo के भागों के बारे में सीखेंगे और कैसे slide insrt करें और cॉpy करें fornt तथा font को फoरmat 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 oपrating sisteme के रूप में gnu linixस और libr oफiस verson 1का उपयोग कर रे हैं



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


  Prediction: चलिए अपनी प्रस्तुति sempl imprs open करते हैं जिसे पिछले tut



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


  Prediction: बनाया थाचलिए देखते हैं कि सcrene 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 lतa द्वारा अनुवादित है आईtी मुमंबई की और से मैं रवि कुमार अब आपसे विदा लेता हूँ हहमसे जुड़ने के लिए धन्यवाद
{'eval_loss': 2.063480854034424, 'eval_cer': 0.3823878113462638, 'eval_wer': 0.5590385791390816, 'eval_runtime': 110.176, 'eval_samples_per_second': 28.464, 'eval_steps_per_second': 1.779, 'epoch': 0.4}
{'loss': 4.9529, 'grad_norm': 22.416309356689453, 'learning_rate': 0.0005928, 'epoch': 0.4}
{'loss': 2.8488, 'grad_norm': 8.623217582702637, 'learning_rate': 0.0005939999999999999, 'epoch': 0.4}
{'loss': 1.6508, 'grad_norm': 1.6001296043395996, 'learning_rate': 0.0005951999999999999, 'epoch': 0.4}
{'loss': 2.5635, 'grad_norm': 5.380290985107422, 'learning_rate': 0.0005964, 'epoch': 0.4}
{'loss': 1.7229, 'grad_norm': 2.321347713470459, 'learning_rate': 0.0005976, 'epoch': 0.4}
{'loss': 1.5573, 'grad_norm': 2.4642832279205322, 'learning_rate': 0.0005987999999999999, 'epoch': 0.4}
{'loss': 2.244, 'grad_norm': 2.532890796661377, 'learning_rate': 0.0006, 'epoch': 0.41}
{'loss': 1.5616, 'grad_norm': 4.478203296661377, 'learning_rate': 0.0005999586206896551, 'epoch': 0.41}
{'loss': 1.84, 'grad_norm': 1.8151098489761353, 'learning_rate': 0.0005999172413793103, 'epoch': 0.41}
{'loss': 1.4796, 'grad_norm': 4.065229415893555, 'learning_rate': 0.0005998758620689655, 'epoch': 0.41}
{'loss': 1.1766, 'grad_norm': 1.6631847620010376, 'learning_rate': 0.0005998344827586206, 'epoch': 0.41}
{'loss': 1.5513, 'grad_norm': 1.2163468599319458, 'learning_rate': 0.0005997931034482758, 'epoch': 0.41}
{'loss': 1.3731, 'grad_norm': 2.7793924808502197, 'learning_rate': 0.000599751724137931, 'epoch': 0.41}
{'loss': 1.1285, 'grad_norm': 1.9624617099761963, 'learning_rate': 0.0005997103448275861, 'epoch': 0.41}
{'loss': 1.1158, 'grad_norm': 1.8284677267074585, 'learning_rate': 0.0005996689655172413, 'epoch': 0.41}
{'loss': 1.2951, 'grad_norm': 1.602000117301941, 'learning_rate': 0.0005996275862068965, 'epoch': 0.41}
{'loss': 1.2041, 'grad_norm': 1.2240982055664062, 'learning_rate': 0.0005995862068965517, 'epoch': 0.41}
{'loss': 1.1588, 'grad_norm': 1.2553801536560059, 'learning_rate': 0.0005995448275862068, 'epoch': 0.41}
{'loss': 1.169, 'grad_norm': 1.2255510091781616, 'learning_rate': 0.000599503448275862, 'epoch': 0.42}
{'loss': 1.5039, 'grad_norm': 2.2664201259613037, 'learning_rate': 0.0005994620689655171, 'epoch': 0.42}
{'loss': 0.993, 'grad_norm': 1.5108261108398438, 'learning_rate': 0.0005994206896551724, 'epoch': 0.42}
{'loss': 0.8906, 'grad_norm': 0.8835381269454956, 'learning_rate': 0.0005993793103448276, 'epoch': 0.42}
{'loss': 1.1201, 'grad_norm': 1.2143915891647339, 'learning_rate': 0.0005993379310344827, 'epoch': 0.42}
{'loss': 1.0322, 'grad_norm': 1.4565423727035522, 'learning_rate': 0.0005992965517241379, 'epoch': 0.42}
{'loss': 1.3578, 'grad_norm': 1.813416600227356, 'learning_rate': 0.000599255172413793, 'epoch': 0.42}
{'loss': 1.545, 'grad_norm': 2.4473183155059814, 'learning_rate': 0.0005992137931034483, 'epoch': 0.42}
{'loss': 1.3672, 'grad_norm': 1.5755915641784668, 'learning_rate': 0.0005991724137931033, 'epoch': 0.42}
{'loss': 1.2865, 'grad_norm': 1.4933589696884155, 'learning_rate': 0.0005991310344827586, 'epoch': 0.42}
{'loss': 1.5637, 'grad_norm': 1.2568109035491943, 'learning_rate': 0.0005990896551724137, 'epoch': 0.42}
{'loss': 1.7427, 'grad_norm': 1.382275104522705, 'learning_rate': 0.0005990482758620689, 'epoch': 0.42}
{'loss': 2.3204, 'grad_norm': 2.0553746223449707, 'learning_rate': 0.000599006896551724, 'epoch': 0.42}
{'loss': 1.155, 'grad_norm': 1.279197096824646, 'learning_rate': 0.0005989655172413793, 'epoch': 0.43}
{'loss': 1.3167, 'grad_norm': 1.3615517616271973, 'learning_rate': 0.0005989241379310345, 'epoch': 0.43}
{'loss': 1.3268, 'grad_norm': 1.5457457304000854, 'learning_rate': 0.0005988827586206896, 'epoch': 0.43}
{'loss': 1.2408, 'grad_norm': 1.2720369100570679, 'learning_rate': 0.0005988413793103448, 'epoch': 0.43}
{'loss': 1.4032, 'grad_norm': 1.672280192375183, 'learning_rate': 0.0005987999999999999, 'epoch': 0.43}
{'loss': 1.6429, 'grad_norm': 1.9361270666122437, 'learning_rate': 0.0005987586206896552, 'epoch': 0.43}
{'loss': 2.052, 'grad_norm': 1.7501822710037231, 'learning_rate': 0.0005987172413793102, 'epoch': 0.43}
{'loss': 1.874, 'grad_norm': 2.6883723735809326, 'learning_rate': 0.0005986758620689655, 'epoch': 0.43}
{'loss': 1.1242, 'grad_norm': 3.4362456798553467, 'learning_rate': 0.0005986344827586206, 'epoch': 0.43}
{'loss': 1.9288, 'grad_norm': 2.3784518241882324, 'learning_rate': 0.0005985931034482758, 'epoch': 0.43}
{'loss': 2.1756, 'grad_norm': 2.4396467208862305, 'learning_rate': 0.000598551724137931, 'epoch': 0.43}
{'loss': 0.936, 'grad_norm': 1.6945518255233765, 'learning_rate': 0.0005985103448275861, 'epoch': 0.43}
{'loss': 1.1534, 'grad_norm': 1.6726940870285034, 'learning_rate': 0.0005984689655172414, 'epoch': 0.44}
{'loss': 1.5566, 'grad_norm': 1.8183149099349976, 'learning_rate': 0.0005984275862068965, 'epoch': 0.44}
{'loss': 1.4601, 'grad_norm': 2.308500051498413, 'learning_rate': 0.0005983862068965517, 'epoch': 0.44}
{'loss': 2.0904, 'grad_norm': 3.5451548099517822, 'learning_rate': 0.0005983448275862068, 'epoch': 0.44}
{'loss': 1.9004, 'grad_norm': 1.918361783027649, 'learning_rate': 0.000598303448275862, 'epoch': 0.44}
{'loss': 1.5618, 'grad_norm': 2.2224361896514893, 'learning_rate': 0.0005982620689655172, 'epoch': 0.44}
{'loss': 2.2505, 'grad_norm': 4.1373395919799805, 'learning_rate': 0.0005982206896551724, 'epoch': 0.44}
{'loss': 7.9284, 'grad_norm': 42.398223876953125, 'learning_rate': 0.0005981793103448275, 'epoch': 0.44}
{'loss': 1.401, 'grad_norm': 1.7623811960220337, 'learning_rate': 0.0005981379310344827, 'epoch': 0.44}
{'loss': 1.6064, 'grad_norm': 1.713548183441162, 'learning_rate': 0.000598096551724138, 'epoch': 0.44}
{'loss': 1.5934, 'grad_norm': 1.7544000148773193, 'learning_rate': 0.000598055172413793, 'epoch': 0.44}
{'loss': 1.6158, 'grad_norm': 1.5748591423034668, 'learning_rate': 0.0005980137931034483, 'epoch': 0.44}
{'loss': 1.5541, 'grad_norm': 1.686427116394043, 'learning_rate': 0.0005979724137931034, 'epoch': 0.44}
{'loss': 1.3154, 'grad_norm': 2.805879831314087, 'learning_rate': 0.0005979310344827586, 'epoch': 0.45}
{'loss': 1.5867, 'grad_norm': 1.4843370914459229, 'learning_rate': 0.0005978896551724137, 'epoch': 0.45}
{'loss': 1.5355, 'grad_norm': 1.673445463180542, 'learning_rate': 0.0005978482758620689, 'epoch': 0.45}
{'loss': 1.1532, 'grad_norm': 1.166231393814087, 'learning_rate': 0.0005978068965517241, 'epoch': 0.45}
{'loss': 1.5332, 'grad_norm': 3.0401833057403564, 'learning_rate': 0.0005977655172413793, 'epoch': 0.45}
{'loss': 2.1526, 'grad_norm': 7.1963114738464355, 'learning_rate': 0.0005977241379310344, 'epoch': 0.45}
{'loss': 1.3451, 'grad_norm': 1.0466963052749634, 'learning_rate': 0.0005976827586206896, 'epoch': 0.45}
{'loss': 0.8485, 'grad_norm': 1.0985534191131592, 'learning_rate': 0.0005976413793103447, 'epoch': 0.45}
{'loss': 0.7492, 'grad_norm': 1.0326215028762817, 'learning_rate': 0.0005976, 'epoch': 0.45}
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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 [19:02<2:19:50,  1.70it/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 oफis impres में एक प्रस्तुती docuमेंnt बनाना और बुनियादी formating के इस spoken tutorial में आपका सहै



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 वindो के भागों के बारे में सीखेंगे और कैसे slide insrt करें और copी करेंford तथा farlt 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 sistem के रूप में ganu linux और libr ofiस vrsion 3 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 imprs open करते हैंजिसे पिछले t



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


  Prediction: मं बनाया थाचलिए 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 lता द्वारा अनुवादित है आईआईटी मुम्बई की ओर से मैं रवी कुमार अब आपसे विदा लेता हूँ हहमसे जुड़ने के लिए धन्यवाद
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{'loss': 3.6425, 'grad_norm': 9.610684394836426, 'learning_rate': 0.0005961103448275861, 'epoch': 0.48}
{'loss': 1.56, 'grad_norm': 1.9226266145706177, 'learning_rate': 0.0005960689655172413, 'epoch': 0.48}
{'loss': 1.126, 'grad_norm': 1.4089831113815308, 'learning_rate': 0.0005960275862068966, 'epoch': 0.48}
{'loss': 2.331, 'grad_norm': 5.7033305168151855, 'learning_rate': 0.0005959862068965516, 'epoch': 0.48}
{'loss': 1.349, 'grad_norm': 1.6135114431381226, 'learning_rate': 0.0005959448275862069, 'epoch': 0.48}
{'loss': 1.7172, 'grad_norm': 2.2275140285491943, 'learning_rate': 0.000595903448275862, 'epoch': 0.48}
{'loss': 2.3508, 'grad_norm': 3.0225791931152344, 'learning_rate': 0.0005958620689655172, 'epoch': 0.49}
{'loss': 1.614, 'grad_norm': 3.8021223545074463, 'learning_rate': 0.0005958206896551724, 'epoch': 0.49}
{'loss': 1.2519, 'grad_norm': 3.858461380004883, 'learning_rate': 0.0005957793103448275, 'epoch': 0.49}
{'loss': 1.5994, 'grad_norm': 1.6742503643035889, 'learning_rate': 0.0005957379310344827, 'epoch': 0.49}
{'loss': 1.3956, 'grad_norm': 1.6299188137054443, 'learning_rate': 0.0005956965517241379, 'epoch': 0.49}
{'loss': 1.7532, 'grad_norm': 1.6947338581085205, 'learning_rate': 0.000595655172413793, 'epoch': 0.49}
{'loss': 1.2102, 'grad_norm': 1.0825295448303223, 'learning_rate': 0.0005956137931034482, 'epoch': 0.49}
{'loss': 1.2751, 'grad_norm': 0.9115064144134521, 'learning_rate': 0.0005955724137931033, 'epoch': 0.49}
{'loss': 1.5901, 'grad_norm': 1.4332678318023682, 'learning_rate': 0.0005955310344827586, 'epoch': 0.49}
{'loss': 0.909, 'grad_norm': 0.8407877683639526, 'learning_rate': 0.0005954896551724138, 'epoch': 0.49}
{'loss': 1.0663, 'grad_norm': 1.1478828191757202, 'learning_rate': 0.0005954482758620689, 'epoch': 0.49}
{'loss': 1.3291, 'grad_norm': 1.2347513437271118, 'learning_rate': 0.0005954068965517241, 'epoch': 0.49}
{'loss': 1.8371, 'grad_norm': 1.8986964225769043, 'learning_rate': 0.0005953655172413793, 'epoch': 0.5}
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{'loss': 0.9254, 'grad_norm': 1.4439136981964111, 'learning_rate': 0.0005952827586206896, 'epoch': 0.5}
{'loss': 1.8428, 'grad_norm': 1.9494640827178955, 'learning_rate': 0.0005952413793103448, 'epoch': 0.5}
{'loss': 1.5328, 'grad_norm': 2.0461411476135254, 'learning_rate': 0.0005951999999999999, 'epoch': 0.5}
{'loss': 1.4576, 'grad_norm': 1.4995301961898804, 'learning_rate': 0.0005951586206896551, 'epoch': 0.5}
{'loss': 1.4282, 'grad_norm': 1.5061633586883545, 'learning_rate': 0.0005951172413793102, 'epoch': 0.5}
{'loss': 1.1977, 'grad_norm': 1.2395235300064087, 'learning_rate': 0.0005950758620689655, 'epoch': 0.5}
{'loss': 1.227, 'grad_norm': 2.120986223220825, 'learning_rate': 0.0005950344827586207, 'epoch': 0.5}
{'loss': 1.0529, 'grad_norm': 3.0553135871887207, 'learning_rate': 0.0005949931034482758, 'epoch': 0.5}
{'loss': 0.9782, 'grad_norm': 1.8950954675674438, 'learning_rate': 0.000594951724137931, 'epoch': 0.5}
{'loss': 1.2712, 'grad_norm': 1.3218441009521484, 'learning_rate': 0.0005949103448275861, 'epoch': 0.5}
{'loss': 1.1195, 'grad_norm': 1.5978167057037354, 'learning_rate': 0.0005948689655172414, 'epoch': 0.5}
{'loss': 1.2676, 'grad_norm': 1.502376675605774, 'learning_rate': 0.0005948275862068965, 'epoch': 0.51}
{'loss': 1.0898, 'grad_norm': 1.5277307033538818, 'learning_rate': 0.0005947862068965517, 'epoch': 0.51}
{'loss': 1.2522, 'grad_norm': 1.7791085243225098, 'learning_rate': 0.0005947448275862068, 'epoch': 0.51}
{'loss': 1.1278, 'grad_norm': 1.7304997444152832, 'learning_rate': 0.0005947034482758621, 'epoch': 0.51}
{'loss': 1.5974, 'grad_norm': 1.3274544477462769, 'learning_rate': 0.0005946620689655171, 'epoch': 0.51}
{'loss': 1.2693, 'grad_norm': 1.3884472846984863, 'learning_rate': 0.0005946206896551724, 'epoch': 0.51}
{'loss': 1.0023, 'grad_norm': 1.7881792783737183, 'learning_rate': 0.0005945793103448276, 'epoch': 0.51}
{'loss': 0.9127, 'grad_norm': 1.7465835809707642, 'learning_rate': 0.0005945379310344827, 'epoch': 0.51}
{'loss': 1.1482, 'grad_norm': 1.2514567375183105, 'learning_rate': 0.0005944965517241379, 'epoch': 0.51}
{'loss': 1.5495, 'grad_norm': 1.753526210784912, 'learning_rate': 0.000594455172413793, 'epoch': 0.51}
{'loss': 1.2932, 'grad_norm': 1.598852515220642, 'learning_rate': 0.0005944137931034483, 'epoch': 0.51}
{'loss': 2.087, 'grad_norm': 5.297712326049805, 'learning_rate': 0.0005943724137931033, 'epoch': 0.51}
{'loss': 1.4096, 'grad_norm': 1.4666143655776978, 'learning_rate': 0.0005943310344827586, 'epoch': 0.52}
{'loss': 1.349, 'grad_norm': 1.6063132286071777, 'learning_rate': 0.0005942896551724137, 'epoch': 0.52}
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{'loss': 2.604, 'grad_norm': 2.5103201866149902, 'learning_rate': 0.000594206896551724, 'epoch': 0.52}
{'loss': 1.5213, 'grad_norm': 1.2484166622161865, 'learning_rate': 0.0005941655172413793, 'epoch': 0.52}
{'loss': 2.1494, 'grad_norm': 1.7289602756500244, 'learning_rate': 0.0005941241379310345, 'epoch': 0.52}
{'loss': 1.9376, 'grad_norm': 1.6923683881759644, 'learning_rate': 0.0005940827586206896, 'epoch': 0.52}
{'loss': 6.0494, 'grad_norm': 30.088640213012695, 'learning_rate': 0.0005940413793103448, 'epoch': 0.52}
{'loss': 4.3615, 'grad_norm': 17.713655471801758, 'learning_rate': 0.0005939999999999999, 'epoch': 0.52}
{'loss': 1.7418, 'grad_norm': 4.541439533233643, 'learning_rate': 0.0005939586206896552, 'epoch': 0.52}
{'loss': 2.271, 'grad_norm': 5.822942733764648, 'learning_rate': 0.0005939172413793103, 'epoch': 0.52}
{'loss': 1.4424, 'grad_norm': 1.533124327659607, 'learning_rate': 0.0005938758620689655, 'epoch': 0.52}
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{'loss': 1.2789, 'grad_norm': 2.5658531188964844, 'learning_rate': 0.0005937931034482758, 'epoch': 0.53}
{'loss': 1.7735, 'grad_norm': 3.331835985183716, 'learning_rate': 0.000593751724137931, 'epoch': 0.53}
{'loss': 1.4763, 'grad_norm': 3.131972312927246, 'learning_rate': 0.0005937103448275861, 'epoch': 0.53}
{'loss': 1.3077, 'grad_norm': 1.8016350269317627, 'learning_rate': 0.0005936689655172413, 'epoch': 0.53}
{'loss': 1.1834, 'grad_norm': 1.8749202489852905, 'learning_rate': 0.0005936275862068965, 'epoch': 0.53}
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{'loss': 2.1189, 'grad_norm': 3.334155321121216, 'learning_rate': 0.0005920137931034482, 'epoch': 0.56}


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  5%|▌         | 800/15000 [21:56<2:35:20,  1.52it/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 ofis impres में एक प्रस्तुती documnt बनाना और बुनियादी formating के इस spoken tutoril 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 vindow के भागों के बारे में सीखेंगे और कैसे slid insert करें और copy करेंford तथा fon 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 सstem के रूप में gएnu linux और liber ofis version 3का 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 cैmpl impres open करते हैं े ै



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 lta द्वारा अनुवादित है आईआईटी बoमबई की ओर से मैं रवि कुमार अब आपसे विधा लेता हूँ हहमसे जुड़ने के लिए धन्यवाद
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{'loss': 0.9184, 'grad_norm': 1.7675949335098267, 'learning_rate': 0.0005879172413793103, 'epoch': 0.64}
{'loss': 1.8942, 'grad_norm': 3.1207735538482666, 'learning_rate': 0.0005878758620689654, 'epoch': 0.64}


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  6%|▌         | 900/15000 [24:50<2:47:15,  1.41it/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: liber ofis impres में एक परस्तुति document बनाना और बुनियादी formating के इस spoken tutoral में आपक



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 winडो के भागों के बारे में सीखेंगे और कैसे slide insert करें और copy करेंfod तथा fon 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 oeraing system के रूप में gnu linx और liber ofis version 33 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 sampl impres open करते हैंि



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 lta द्वारा अनुवादित है आईईटी बuमबई की ओर से मैं रवि कुमार अब आपसे विदा लेता हूँ हमसे जुड़ने के लिए धन्यवाद
{'eval_loss': 1.7647607326507568, 'eval_cer': 0.336409963080443, 'eval_wer': 0.48653319507590864, 'eval_runtime': 110.6132, 'eval_samples_per_second': 28.351, 'eval_steps_per_second': 1.772, 'epoch': 0.64}
{'loss': 1.8223, 'grad_norm': 2.1060497760772705, 'learning_rate': 0.0005878344827586207, 'epoch': 0.64}
{'loss': 1.3789, 'grad_norm': 1.854649305343628, 'learning_rate': 0.0005877931034482757, 'epoch': 0.64}
{'loss': 2.6302, 'grad_norm': 6.581136703491211, 'learning_rate': 0.000587751724137931, 'epoch': 0.64}
{'loss': 1.594, 'grad_norm': 1.7134705781936646, 'learning_rate': 0.0005877103448275862, 'epoch': 0.64}
{'loss': 1.2786, 'grad_norm': 1.901299238204956, 'learning_rate': 0.0005876689655172413, 'epoch': 0.64}
{'loss': 1.9983, 'grad_norm': 5.519202709197998, 'learning_rate': 0.0005876275862068965, 'epoch': 0.64}
{'loss': 0.9869, 'grad_norm': 2.387113094329834, 'learning_rate': 0.0005875862068965516, 'epoch': 0.65}
{'loss': 1.3227, 'grad_norm': 2.4941344261169434, 'learning_rate': 0.0005875448275862069, 'epoch': 0.65}
{'loss': 1.7397, 'grad_norm': 1.434406042098999, 'learning_rate': 0.000587503448275862, 'epoch': 0.65}
{'loss': 0.9758, 'grad_norm': 1.3343299627304077, 'learning_rate': 0.0005874620689655172, 'epoch': 0.65}
{'loss': 1.4532, 'grad_norm': 1.1546649932861328, 'learning_rate': 0.0005874206896551723, 'epoch': 0.65}
{'loss': 1.1676, 'grad_norm': 1.1700540781021118, 'learning_rate': 0.0005873793103448275, 'epoch': 0.65}
{'loss': 0.9189, 'grad_norm': 1.3167747259140015, 'learning_rate': 0.0005873379310344827, 'epoch': 0.65}
{'loss': 1.2475, 'grad_norm': 1.949277400970459, 'learning_rate': 0.0005872965517241379, 'epoch': 0.65}
{'loss': 1.1008, 'grad_norm': 1.9752020835876465, 'learning_rate': 0.0005872551724137931, 'epoch': 0.65}
{'loss': 0.9252, 'grad_norm': 1.358701229095459, 'learning_rate': 0.0005872137931034482, 'epoch': 0.65}
{'loss': 0.8594, 'grad_norm': 0.9534769654273987, 'learning_rate': 0.0005871724137931035, 'epoch': 0.65}
{'loss': 1.1055, 'grad_norm': 1.2225638628005981, 'learning_rate': 0.0005871310344827585, 'epoch': 0.65}
{'loss': 1.4807, 'grad_norm': 1.0106825828552246, 'learning_rate': 0.0005870896551724138, 'epoch': 0.66}
{'loss': 1.4027, 'grad_norm': 2.0789005756378174, 'learning_rate': 0.0005870482758620689, 'epoch': 0.66}
{'loss': 1.1259, 'grad_norm': 1.1729044914245605, 'learning_rate': 0.0005870068965517241, 'epoch': 0.66}
{'loss': 1.1986, 'grad_norm': 1.9779002666473389, 'learning_rate': 0.0005869655172413792, 'epoch': 0.66}
{'loss': 1.1476, 'grad_norm': 2.4892494678497314, 'learning_rate': 0.0005869241379310344, 'epoch': 0.66}
{'loss': 1.1918, 'grad_norm': 1.4584962129592896, 'learning_rate': 0.0005868827586206896, 'epoch': 0.66}
{'loss': 1.3738, 'grad_norm': 2.293195962905884, 'learning_rate': 0.0005868413793103448, 'epoch': 0.66}
{'loss': 0.9198, 'grad_norm': 1.079431176185608, 'learning_rate': 0.0005868, 'epoch': 0.66}
{'loss': 1.2445, 'grad_norm': 0.9649025797843933, 'learning_rate': 0.0005867586206896551, 'epoch': 0.66}
{'loss': 0.8454, 'grad_norm': 1.1180777549743652, 'learning_rate': 0.0005867172413793103, 'epoch': 0.66}
{'loss': 1.6061, 'grad_norm': 1.6239745616912842, 'learning_rate': 0.0005866758620689654, 'epoch': 0.66}
{'loss': 1.0418, 'grad_norm': 1.8363871574401855, 'learning_rate': 0.0005866344827586207, 'epoch': 0.66}
{'loss': 0.7934, 'grad_norm': 1.446305274963379, 'learning_rate': 0.0005865931034482758, 'epoch': 0.66}
{'loss': 1.4256, 'grad_norm': 1.9750399589538574, 'learning_rate': 0.000586551724137931, 'epoch': 0.67}
{'loss': 0.9466, 'grad_norm': 1.287217140197754, 'learning_rate': 0.0005865103448275862, 'epoch': 0.67}
{'loss': 1.1185, 'grad_norm': 1.3368726968765259, 'learning_rate': 0.0005864689655172413, 'epoch': 0.67}
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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 [27:54<2:35:26,  1.50it/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 प्रस्तुति document बनाना और बुनियादी formating के इस spoken tutoral में आप का 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 imprec window के भागों के बारे में सीखेंगे और कैसे slide inseर्t करें और copy करेford तथा fort को foर्mat 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 oपerेtng systेm के रूप में jnu लinux और libr ffic version 33 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 हैंहैंैैैह



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


  Prediction: चलिए देखते हैं कि सc्ren 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 आईआई टी बॉम्बई की ओर से मैं रवि कुमार अब आपसे विदा लेता हूँ हमसे जोड़ने के लिए धन्यवाद
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{'loss': 2.732, 'grad_norm': 6.955453872680664, 'learning_rate': 0.0005836551724137931, 'epoch': 0.72}
{'loss': 1.4677, 'grad_norm': 2.1897571086883545, 'learning_rate': 0.0005836137931034482, 'epoch': 0.72}
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{'loss': 1.2908, 'grad_norm': 1.4592558145523071, 'learning_rate': 0.0005835310344827585, 'epoch': 0.72}
{'loss': 1.539, 'grad_norm': 1.8143867254257202, 'learning_rate': 0.0005834896551724138, 'epoch': 0.72}
{'loss': 1.2376, 'grad_norm': 2.6475701332092285, 'learning_rate': 0.0005834482758620688, 'epoch': 0.73}
{'loss': 1.4317, 'grad_norm': 2.3188207149505615, 'learning_rate': 0.0005834068965517241, 'epoch': 0.73}
{'loss': 1.2264, 'grad_norm': 2.621372699737549, 'learning_rate': 0.0005833655172413793, 'epoch': 0.73}
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{'loss': 1.1258, 'grad_norm': 1.7464282512664795, 'learning_rate': 0.0005832827586206896, 'epoch': 0.73}
{'loss': 1.1753, 'grad_norm': 1.642560362815857, 'learning_rate': 0.0005832413793103448, 'epoch': 0.73}
{'loss': 1.3391, 'grad_norm': 1.7781301736831665, 'learning_rate': 0.0005832, 'epoch': 0.73}
{'loss': 1.2312, 'grad_norm': 1.5776126384735107, 'learning_rate': 0.0005831586206896551, 'epoch': 0.73}
{'loss': 0.9738, 'grad_norm': 0.8773081302642822, 'learning_rate': 0.0005831172413793103, 'epoch': 0.73}
{'loss': 1.0107, 'grad_norm': 0.8047522902488708, 'learning_rate': 0.0005830758620689654, 'epoch': 0.73}
{'loss': 1.3486, 'grad_norm': 2.3557088375091553, 'learning_rate': 0.0005830344827586207, 'epoch': 0.73}
{'loss': 0.8337, 'grad_norm': 0.9371004700660706, 'learning_rate': 0.0005829931034482757, 'epoch': 0.73}
{'loss': 1.3114, 'grad_norm': 1.1932120323181152, 'learning_rate': 0.000582951724137931, 'epoch': 0.74}
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{'loss': 1.264, 'grad_norm': 1.1349596977233887, 'learning_rate': 0.0005825793103448276, 'epoch': 0.74}
{'loss': 1.2742, 'grad_norm': 1.7588207721710205, 'learning_rate': 0.0005825379310344827, 'epoch': 0.74}
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{'loss': 1.7538, 'grad_norm': 2.5294296741485596, 'learning_rate': 0.0005823310344827585, 'epoch': 0.75}
{'loss': 0.7759, 'grad_norm': 1.1457370519638062, 'learning_rate': 0.0005822896551724138, 'epoch': 0.75}
{'loss': 1.0471, 'grad_norm': 1.4419732093811035, 'learning_rate': 0.0005822482758620689, 'epoch': 0.75}
{'loss': 1.4256, 'grad_norm': 1.5055546760559082, 'learning_rate': 0.0005822068965517241, 'epoch': 0.75}
{'loss': 0.9108, 'grad_norm': 1.8637267351150513, 'learning_rate': 0.0005821655172413792, 'epoch': 0.75}
{'loss': 1.5888, 'grad_norm': 1.6786298751831055, 'learning_rate': 0.0005821241379310344, 'epoch': 0.75}
{'loss': 1.0885, 'grad_norm': 1.4557009935379028, 'learning_rate': 0.0005820827586206896, 'epoch': 0.75}
{'loss': 1.6414, 'grad_norm': 1.7649375200271606, 'learning_rate': 0.0005820413793103448, 'epoch': 0.75}
{'loss': 1.7963, 'grad_norm': 1.7127431631088257, 'learning_rate': 0.0005819999999999999, 'epoch': 0.75}
{'loss': 1.2704, 'grad_norm': 1.4084855318069458, 'learning_rate': 0.0005819586206896551, 'epoch': 0.75}
{'loss': 1.3967, 'grad_norm': 2.0224783420562744, 'learning_rate': 0.0005819172413793102, 'epoch': 0.76}
{'loss': 0.9223, 'grad_norm': 1.1966300010681152, 'learning_rate': 0.0005818758620689655, 'epoch': 0.76}
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{'loss': 3.2803, 'grad_norm': 14.045222282409668, 'learning_rate': 0.0005816275862068965, 'epoch': 0.76}
{'loss': 1.6475, 'grad_norm': 3.2479794025421143, 'learning_rate': 0.0005815862068965517, 'epoch': 0.76}
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{'loss': 1.8683, 'grad_norm': 1.7608100175857544, 'learning_rate': 0.000581503448275862, 'epoch': 0.76}
{'loss': 1.585, 'grad_norm': 2.236809730529785, 'learning_rate': 0.0005814620689655171, 'epoch': 0.76}
{'loss': 1.1072, 'grad_norm': 2.723594903945923, 'learning_rate': 0.0005814206896551724, 'epoch': 0.76}
{'loss': 2.0457, 'grad_norm': 4.082906723022461, 'learning_rate': 0.0005813793103448276, 'epoch': 0.77}
{'loss': 1.4599, 'grad_norm': 3.8369736671447754, 'learning_rate': 0.0005813379310344827, 'epoch': 0.77}
{'loss': 0.9487, 'grad_norm': 3.0807173252105713, 'learning_rate': 0.0005812965517241379, 'epoch': 0.77}
{'loss': 1.0933, 'grad_norm': 2.944730043411255, 'learning_rate': 0.000581255172413793, 'epoch': 0.77}
{'loss': 1.6462, 'grad_norm': 1.8234058618545532, 'learning_rate': 0.0005812137931034482, 'epoch': 0.77}
{'loss': 0.8169, 'grad_norm': 1.236390471458435, 'learning_rate': 0.0005811724137931034, 'epoch': 0.77}
{'loss': 0.9681, 'grad_norm': 1.0344562530517578, 'learning_rate': 0.0005811310344827586, 'epoch': 0.77}
{'loss': 0.9938, 'grad_norm': 1.1346018314361572, 'learning_rate': 0.0005810896551724137, 'epoch': 0.77}
{'loss': 0.6212, 'grad_norm': 1.027980089187622, 'learning_rate': 0.000581048275862069, 'epoch': 0.77}
{'loss': 1.4777, 'grad_norm': 1.565483808517456, 'learning_rate': 0.000581006896551724, 'epoch': 0.77}
{'loss': 1.154, 'grad_norm': 2.1391916275024414, 'learning_rate': 0.0005809655172413793, 'epoch': 0.77}
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{'loss': 3.394, 'grad_norm': 4.654911041259766, 'learning_rate': 0.0005795999999999999, 'epoch': 0.8}


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  7%|▋         | 1100/15000 [30:46<2:36:03,  1.48it/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 प्रस्तुति document बनाना और बुनियादी formating के इस spoken tutoril में आपका स ैैैै



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 करेford 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 सsteम के रूप में jnu linux और liber offic vrsion 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 sampl impres open करते हैं जिेपै



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 अनुवादितआई आई टी मुम्बई की ओर से मैं रवि कुमार अब आपसे विदा लेता हूँहमसे जुड़ने के लिए धन्यवाद
{'eval_loss': 1.7430150508880615, 'eval_cer': 0.3240341115906609, 'eval_wer': 0.46778867884395525, 'eval_runtime': 111.4877, 'eval_samples_per_second': 28.129, 'eval_steps_per_second': 1.758, 'epoch': 0.8}
{'loss': 1.5326, 'grad_norm': 2.1737260818481445, 'learning_rate': 0.0005795586206896551, 'epoch': 0.8}
{'loss': 1.4253, 'grad_norm': 2.1404078006744385, 'learning_rate': 0.0005795172413793103, 'epoch': 0.8}
{'loss': 1.4953, 'grad_norm': 2.299166679382324, 'learning_rate': 0.0005794758620689655, 'epoch': 0.8}
{'loss': 1.9327, 'grad_norm': 3.59151029586792, 'learning_rate': 0.0005794344827586206, 'epoch': 0.8}
{'loss': 1.6581, 'grad_norm': 1.6993526220321655, 'learning_rate': 0.0005793931034482758, 'epoch': 0.8}
{'loss': 1.7516, 'grad_norm': 1.9592061042785645, 'learning_rate': 0.000579351724137931, 'epoch': 0.8}
{'loss': 1.114, 'grad_norm': 2.217750072479248, 'learning_rate': 0.0005793103448275862, 'epoch': 0.81}
{'loss': 0.809, 'grad_norm': 2.4080827236175537, 'learning_rate': 0.0005792689655172413, 'epoch': 0.81}
{'loss': 1.0945, 'grad_norm': 2.1364572048187256, 'learning_rate': 0.0005792275862068965, 'epoch': 0.81}
{'loss': 1.122, 'grad_norm': 1.2930113077163696, 'learning_rate': 0.0005791862068965516, 'epoch': 0.81}
{'loss': 1.526, 'grad_norm': 1.9616435766220093, 'learning_rate': 0.0005791448275862069, 'epoch': 0.81}
{'loss': 0.96, 'grad_norm': 0.9473212361335754, 'learning_rate': 0.000579103448275862, 'epoch': 0.81}
{'loss': 0.6484, 'grad_norm': 0.947380781173706, 'learning_rate': 0.0005790620689655172, 'epoch': 0.81}
{'loss': 1.587, 'grad_norm': 2.6783204078674316, 'learning_rate': 0.0005790206896551723, 'epoch': 0.81}
{'loss': 1.1792, 'grad_norm': 1.4205185174942017, 'learning_rate': 0.0005789793103448276, 'epoch': 0.81}
{'loss': 0.8479, 'grad_norm': 1.0596907138824463, 'learning_rate': 0.0005789379310344827, 'epoch': 0.81}
{'loss': 1.1584, 'grad_norm': 1.730775237083435, 'learning_rate': 0.0005788965517241379, 'epoch': 0.81}
{'loss': 0.7529, 'grad_norm': 1.0375272035598755, 'learning_rate': 0.0005788551724137931, 'epoch': 0.81}
{'loss': 0.7186, 'grad_norm': 0.8387724161148071, 'learning_rate': 0.0005788137931034482, 'epoch': 0.82}
{'loss': 0.6535, 'grad_norm': 1.0978877544403076, 'learning_rate': 0.0005787724137931034, 'epoch': 0.82}
{'loss': 1.34, 'grad_norm': 1.844658613204956, 'learning_rate': 0.0005787310344827585, 'epoch': 0.82}
{'loss': 1.6369, 'grad_norm': 1.394779920578003, 'learning_rate': 0.0005786896551724138, 'epoch': 0.82}
{'loss': 0.8804, 'grad_norm': 1.0424656867980957, 'learning_rate': 0.0005786482758620689, 'epoch': 0.82}
{'loss': 1.9034, 'grad_norm': 1.65019690990448, 'learning_rate': 0.0005786068965517241, 'epoch': 0.82}
{'loss': 1.4254, 'grad_norm': 1.0185081958770752, 'learning_rate': 0.0005785655172413792, 'epoch': 0.82}
{'loss': 1.2366, 'grad_norm': 1.4375725984573364, 'learning_rate': 0.0005785241379310344, 'epoch': 0.82}
{'loss': 1.2534, 'grad_norm': 1.06988525390625, 'learning_rate': 0.0005784827586206896, 'epoch': 0.82}
{'loss': 0.9093, 'grad_norm': 1.2914669513702393, 'learning_rate': 0.0005784413793103448, 'epoch': 0.82}
{'loss': 1.4892, 'grad_norm': 1.3867543935775757, 'learning_rate': 0.0005784, 'epoch': 0.82}
{'loss': 1.2454, 'grad_norm': 1.1370289325714111, 'learning_rate': 0.0005783586206896551, 'epoch': 0.82}
{'loss': 0.6344, 'grad_norm': 0.7477738261222839, 'learning_rate': 0.0005783172413793104, 'epoch': 0.82}
{'loss': 0.8749, 'grad_norm': 1.3737530708312988, 'learning_rate': 0.0005782758620689654, 'epoch': 0.83}
{'loss': 0.7149, 'grad_norm': 1.3285287618637085, 'learning_rate': 0.0005782344827586207, 'epoch': 0.83}
{'loss': 1.1796, 'grad_norm': 1.662576675415039, 'learning_rate': 0.0005781931034482758, 'epoch': 0.83}
{'loss': 0.7719, 'grad_norm': 1.088865876197815, 'learning_rate': 0.000578151724137931, 'epoch': 0.83}
{'loss': 1.1611, 'grad_norm': 1.402590036392212, 'learning_rate': 0.0005781103448275861, 'epoch': 0.83}
{'loss': 1.1465, 'grad_norm': 1.3320173025131226, 'learning_rate': 0.0005780689655172413, 'epoch': 0.83}
{'loss': 1.7409, 'grad_norm': 1.7776877880096436, 'learning_rate': 0.0005780275862068966, 'epoch': 0.83}
{'loss': 1.1262, 'grad_norm': 1.6084744930267334, 'learning_rate': 0.0005779862068965517, 'epoch': 0.83}
{'loss': 1.4873, 'grad_norm': 1.6403446197509766, 'learning_rate': 0.0005779448275862069, 'epoch': 0.83}
{'loss': 0.9893, 'grad_norm': 1.2440085411071777, 'learning_rate': 0.000577903448275862, 'epoch': 0.83}
{'loss': 0.8044, 'grad_norm': 1.1196553707122803, 'learning_rate': 0.0005778620689655172, 'epoch': 0.83}
{'loss': 1.7307, 'grad_norm': 2.0302839279174805, 'learning_rate': 0.0005778206896551723, 'epoch': 0.83}
{'loss': 1.6071, 'grad_norm': 1.2174124717712402, 'learning_rate': 0.0005777793103448276, 'epoch': 0.84}
{'loss': 1.2126, 'grad_norm': 1.7687053680419922, 'learning_rate': 0.0005777379310344827, 'epoch': 0.84}
{'loss': 0.8385, 'grad_norm': 1.3215792179107666, 'learning_rate': 0.0005776965517241379, 'epoch': 0.84}
{'loss': 2.0095, 'grad_norm': 1.8157902956008911, 'learning_rate': 0.000577655172413793, 'epoch': 0.84}
{'loss': 1.4815, 'grad_norm': 1.615418553352356, 'learning_rate': 0.0005776137931034482, 'epoch': 0.84}
{'loss': 2.1643, 'grad_norm': 2.395775079727173, 'learning_rate': 0.0005775724137931035, 'epoch': 0.84}
{'loss': 1.651, 'grad_norm': 2.1626031398773193, 'learning_rate': 0.0005775310344827585, 'epoch': 0.84}
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{'loss': 1.2764, 'grad_norm': 1.2223045825958252, 'learning_rate': 0.0005774482758620689, 'epoch': 0.84}
{'loss': 1.9701, 'grad_norm': 3.992225408554077, 'learning_rate': 0.0005774068965517241, 'epoch': 0.84}
{'loss': 1.5524, 'grad_norm': 1.5884290933609009, 'learning_rate': 0.0005773655172413793, 'epoch': 0.84}
{'loss': 0.9604, 'grad_norm': 1.104845404624939, 'learning_rate': 0.0005773241379310344, 'epoch': 0.84}
{'loss': 2.5652, 'grad_norm': 3.4478087425231934, 'learning_rate': 0.0005772827586206896, 'epoch': 0.84}
{'loss': 0.9261, 'grad_norm': 1.9719113111495972, 'learning_rate': 0.0005772413793103448, 'epoch': 0.85}
{'loss': 1.692, 'grad_norm': 2.5427818298339844, 'learning_rate': 0.0005771999999999999, 'epoch': 0.85}
{'loss': 1.1254, 'grad_norm': 2.1420998573303223, 'learning_rate': 0.0005771586206896551, 'epoch': 0.85}
{'loss': 1.1397, 'grad_norm': 1.4160470962524414, 'learning_rate': 0.0005771172413793103, 'epoch': 0.85}
{'loss': 1.0544, 'grad_norm': 1.3062032461166382, 'learning_rate': 0.0005770758620689655, 'epoch': 0.85}
{'loss': 1.1583, 'grad_norm': 1.2905148267745972, 'learning_rate': 0.0005770344827586207, 'epoch': 0.85}
{'loss': 0.948, 'grad_norm': 1.0324835777282715, 'learning_rate': 0.0005769931034482758, 'epoch': 0.85}
{'loss': 1.1342, 'grad_norm': 2.084989547729492, 'learning_rate': 0.000576951724137931, 'epoch': 0.85}
{'loss': 0.751, 'grad_norm': 1.1444343328475952, 'learning_rate': 0.0005769103448275862, 'epoch': 0.85}
{'loss': 1.0942, 'grad_norm': 0.9838349223136902, 'learning_rate': 0.0005768689655172413, 'epoch': 0.85}
{'loss': 0.9179, 'grad_norm': 0.912041187286377, 'learning_rate': 0.0005768275862068965, 'epoch': 0.85}
{'loss': 1.4996, 'grad_norm': 1.9841991662979126, 'learning_rate': 0.0005767862068965517, 'epoch': 0.85}
{'loss': 0.7585, 'grad_norm': 1.1156262159347534, 'learning_rate': 0.0005767448275862068, 'epoch': 0.86}
{'loss': 0.7241, 'grad_norm': 0.9746763110160828, 'learning_rate': 0.000576703448275862, 'epoch': 0.86}
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  8%|▊         | 1200/15000 [33:42<2:27:03,  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: liber ofiस impes में एक प्रस्तुति document बनाना और बुनियादी formating के इस spoken tuटorial में आपका सवहै



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 tuटorial में हम impres वंdow के भागों के बारे में सीखेंगे और कैसे slid इnserट करें और copy करेfo तथा font को formaट करना सीखेंे



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 लिनकस और liber ofiस वrsioन 335 का उपयोग कर रहे हैंं



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


  Prediction: चलिए अपनी प्रस्तुति sampl imps open करते हैं



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


  Prediction: चलिए देखते हैं कि scrीn 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 हैआई आई टी मुम्बई की ओर से मैं रवि कुमार अब आपसे विदा लेता हूँ हमसे जुड़ने के लिए धन्यवाद
{'eval_loss': 1.492153286933899, 'eval_cer': 0.33180801830378037, 'eval_wer': 0.4839275743798357, 'eval_runtime': 110.5817, 'eval_samples_per_second': 28.359, 'eval_steps_per_second': 1.772, 'epoch': 0.88}
{'loss': 1.1461, 'grad_norm': 1.201806664466858, 'learning_rate': 0.0005754206896551724, 'epoch': 0.88}
{'loss': 2.4356, 'grad_norm': 3.224534273147583, 'learning_rate': 0.0005753793103448275, 'epoch': 0.88}
{'loss': 1.3961, 'grad_norm': 1.570224642753601, 'learning_rate': 0.0005753379310344827, 'epoch': 0.88}
{'loss': 2.1454, 'grad_norm': 6.379323482513428, 'learning_rate': 0.0005752965517241379, 'epoch': 0.88}
{'loss': 1.8163, 'grad_norm': 2.2744860649108887, 'learning_rate': 0.0005752551724137931, 'epoch': 0.88}
{'loss': 1.057, 'grad_norm': 1.1360559463500977, 'learning_rate': 0.0005752137931034482, 'epoch': 0.88}
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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 [36:47<2:25:22,  1.57it/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: liber office impes में एक प्रस्तुति document बनाना और बुनियादी formating के इस spoken tutorial में आपका स्वा गैै



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 करेंfont 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 opereting system के रूप में jn linuकस और liber 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 impres open करते हैं जिसे पिछल t 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 लातार द्वारा अनुवादित है आई आई ट मुम्बई की ओर से मैं रवि कुमार अब आपसे विदा लेता हूँहमसे जुड़ने के लिए धन्यवाद
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{'loss': 5.4868, 'grad_norm': 26.70827293395996, 'learning_rate': 0.0005713241379310344, 'epoch': 0.96}
{'loss': 2.7424, 'grad_norm': 7.779392719268799, 'learning_rate': 0.0005712827586206896, 'epoch': 0.96}
{'loss': 1.2021, 'grad_norm': 1.7913457155227661, 'learning_rate': 0.0005712413793103447, 'epoch': 0.96}
{'loss': 1.7324, 'grad_norm': 1.760551929473877, 'learning_rate': 0.0005711999999999999, 'epoch': 0.96}
{'loss': 1.7695, 'grad_norm': 2.5545730590820312, 'learning_rate': 0.0005711586206896552, 'epoch': 0.96}
{'loss': 1.2859, 'grad_norm': 3.367995262145996, 'learning_rate': 0.0005711172413793103, 'epoch': 0.96}
{'loss': 1.1425, 'grad_norm': 2.67651104927063, 'learning_rate': 0.0005710758620689655, 'epoch': 0.97}
{'loss': 1.2813, 'grad_norm': 1.7702245712280273, 'learning_rate': 0.0005710344827586206, 'epoch': 0.97}
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{'loss': 0.9666, 'grad_norm': 0.9068742394447327, 'learning_rate': 0.0005709103448275862, 'epoch': 0.97}
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{'loss': 1.0087, 'grad_norm': 1.2329061031341553, 'learning_rate': 0.0005708275862068965, 'epoch': 0.97}
{'loss': 1.1756, 'grad_norm': 1.1002187728881836, 'learning_rate': 0.0005707862068965518, 'epoch': 0.97}
{'loss': 1.4149, 'grad_norm': 2.14863920211792, 'learning_rate': 0.0005707448275862068, 'epoch': 0.97}
{'loss': 0.756, 'grad_norm': 0.8424788117408752, 'learning_rate': 0.0005707034482758621, 'epoch': 0.97}
{'loss': 0.9421, 'grad_norm': 0.9140539169311523, 'learning_rate': 0.0005706620689655172, 'epoch': 0.97}
{'loss': 1.4061, 'grad_norm': 1.0328311920166016, 'learning_rate': 0.0005706206896551724, 'epoch': 0.97}
{'loss': 0.9327, 'grad_norm': 1.1561588048934937, 'learning_rate': 0.0005705793103448275, 'epoch': 0.98}
{'loss': 1.0851, 'grad_norm': 0.998042106628418, 'learning_rate': 0.0005705379310344827, 'epoch': 0.98}
{'loss': 1.156, 'grad_norm': 0.9896425604820251, 'learning_rate': 0.0005704965517241379, 'epoch': 0.98}
{'loss': 0.8168, 'grad_norm': 1.1115268468856812, 'learning_rate': 0.0005704551724137931, 'epoch': 0.98}
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{'loss': 1.1559, 'grad_norm': 1.2686210870742798, 'learning_rate': 0.0005703724137931034, 'epoch': 0.98}
{'loss': 0.8931, 'grad_norm': 1.3486443758010864, 'learning_rate': 0.0005703310344827585, 'epoch': 0.98}
{'loss': 1.0435, 'grad_norm': 1.7373155355453491, 'learning_rate': 0.0005702896551724137, 'epoch': 0.98}
{'loss': 1.13, 'grad_norm': 1.5829644203186035, 'learning_rate': 0.000570248275862069, 'epoch': 0.98}
{'loss': 0.9729, 'grad_norm': 1.0416885614395142, 'learning_rate': 0.0005702068965517241, 'epoch': 0.98}
{'loss': 1.4197, 'grad_norm': 3.1023709774017334, 'learning_rate': 0.0005701655172413793, 'epoch': 0.98}
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{'loss': 1.6418, 'grad_norm': 1.392039179801941, 'learning_rate': 0.0005698758620689654, 'epoch': 0.99}
{'loss': 1.0414, 'grad_norm': 1.469576120376587, 'learning_rate': 0.0005698344827586207, 'epoch': 0.99}
{'loss': 1.0324, 'grad_norm': 1.0751391649246216, 'learning_rate': 0.0005697931034482758, 'epoch': 0.99}
{'loss': 0.9545, 'grad_norm': 1.2098734378814697, 'learning_rate': 0.000569751724137931, 'epoch': 0.99}
{'loss': 0.8175, 'grad_norm': 1.732841968536377, 'learning_rate': 0.0005697103448275862, 'epoch': 0.99}
{'loss': 1.8661, 'grad_norm': 2.4671740531921387, 'learning_rate': 0.0005696689655172413, 'epoch': 0.99}
{'loss': 1.0112, 'grad_norm': 1.2506730556488037, 'learning_rate': 0.0005696275862068965, 'epoch': 0.99}
{'loss': 1.3751, 'grad_norm': 1.1243518590927124, 'learning_rate': 0.0005695862068965517, 'epoch': 0.99}
{'loss': 1.2669, 'grad_norm': 1.3350924253463745, 'learning_rate': 0.0005695448275862069, 'epoch': 1.0}
{'loss': 0.9776, 'grad_norm': 0.9907298684120178, 'learning_rate': 0.000569503448275862, 'epoch': 1.0}
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{'loss': 1.0406, 'grad_norm': 1.4308171272277832, 'learning_rate': 0.0005694206896551723, 'epoch': 1.0}
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{'loss': 1.2051, 'grad_norm': 1.5520505905151367, 'learning_rate': 0.0005689655172413792, 'epoch': 1.01}
{'loss': 1.4329, 'grad_norm': 1.1957941055297852, 'learning_rate': 0.0005689241379310345, 'epoch': 1.01}
{'loss': 1.2222, 'grad_norm': 0.951712429523468, 'learning_rate': 0.0005688827586206896, 'epoch': 1.01}
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{'loss': 1.7321, 'grad_norm': 1.7893060445785522, 'learning_rate': 0.0005672275862068965, 'epoch': 1.04}


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  9%|▉         | 1400/15000 [39:40<2:24:30,  1.57it/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: liबer ऑfफice impls में एक प्रस्तुति documेंट बनाना और बुनियादी fॉrmating 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 tutorial में हम impes wiंdw के भागों के बारे में सीखेंगे और कैसे slide insert करें और copy करें fond तथा font को फॉrmaट 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 operेating sysटेm के रूप में janu लiनकस और libरr ofफic वeरsion 34 का उपयोग कर रहंं



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


  Prediction: चलिए अपनी प्रस्तुति sैmpl impls open करते हैं जिसे पल ं



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 आई आई टी मुम्बई की ओर से मैं रवि कुमार अब आपसे विदा लेता हूँ हमसे जुड़ने के लिए धन्यवाद
{'eval_loss': 1.5221022367477417, 'eval_cer': 0.32337891945296654, 'eval_wer': 0.4742495546515647, 'eval_runtime': 109.4481, 'eval_samples_per_second': 28.653, 'eval_steps_per_second': 1.791, 'epoch': 1.04}
{'loss': 2.3817, 'grad_norm': 7.3447065353393555, 'learning_rate': 0.0005671862068965517, 'epoch': 1.04}
{'loss': 2.9916, 'grad_norm': 8.334514617919922, 'learning_rate': 0.0005671448275862068, 'epoch': 1.04}
{'loss': 1.9584, 'grad_norm': 1.6009753942489624, 'learning_rate': 0.0005671034482758621, 'epoch': 1.04}
{'loss': 1.5078, 'grad_norm': 1.8130059242248535, 'learning_rate': 0.0005670620689655172, 'epoch': 1.04}
{'loss': 2.2165, 'grad_norm': 5.1066083908081055, 'learning_rate': 0.0005670206896551724, 'epoch': 1.04}
{'loss': 1.3203, 'grad_norm': 1.9374101161956787, 'learning_rate': 0.0005669793103448275, 'epoch': 1.04}
{'loss': 1.2888, 'grad_norm': 3.753363847732544, 'learning_rate': 0.0005669379310344827, 'epoch': 1.05}
{'loss': 1.2771, 'grad_norm': 3.9195988178253174, 'learning_rate': 0.0005668965517241378, 'epoch': 1.05}
{'loss': 0.9323, 'grad_norm': 2.3530914783477783, 'learning_rate': 0.0005668551724137931, 'epoch': 1.05}
{'loss': 1.3416, 'grad_norm': 2.4786367416381836, 'learning_rate': 0.0005668137931034483, 'epoch': 1.05}
{'loss': 1.1702, 'grad_norm': 1.600172996520996, 'learning_rate': 0.0005667724137931034, 'epoch': 1.05}
{'loss': 0.7729, 'grad_norm': 1.0938080549240112, 'learning_rate': 0.0005667310344827586, 'epoch': 1.05}
{'loss': 1.5844, 'grad_norm': 1.1928399801254272, 'learning_rate': 0.0005666896551724137, 'epoch': 1.05}
{'loss': 1.0041, 'grad_norm': 1.4972984790802002, 'learning_rate': 0.000566648275862069, 'epoch': 1.05}
{'loss': 1.3022, 'grad_norm': 1.9426178932189941, 'learning_rate': 0.000566606896551724, 'epoch': 1.05}
{'loss': 1.2804, 'grad_norm': 2.483213424682617, 'learning_rate': 0.0005665655172413793, 'epoch': 1.05}
{'loss': 1.0645, 'grad_norm': 1.3551641702651978, 'learning_rate': 0.0005665241379310344, 'epoch': 1.05}
{'loss': 0.9106, 'grad_norm': 2.438025951385498, 'learning_rate': 0.0005664827586206896, 'epoch': 1.05}
{'loss': 0.7074, 'grad_norm': 1.0822499990463257, 'learning_rate': 0.0005664413793103447, 'epoch': 1.06}
{'loss': 0.8298, 'grad_norm': 1.3512879610061646, 'learning_rate': 0.0005663999999999999, 'epoch': 1.06}
{'loss': 1.2905, 'grad_norm': 1.1012386083602905, 'learning_rate': 0.0005663586206896552, 'epoch': 1.06}
{'loss': 1.0917, 'grad_norm': 1.0698977708816528, 'learning_rate': 0.0005663172413793103, 'epoch': 1.06}
{'loss': 0.8662, 'grad_norm': 1.3840702772140503, 'learning_rate': 0.0005662758620689655, 'epoch': 1.06}
{'loss': 1.0794, 'grad_norm': 1.078434705734253, 'learning_rate': 0.0005662344827586206, 'epoch': 1.06}
{'loss': 0.7787, 'grad_norm': 0.952928364276886, 'learning_rate': 0.0005661931034482759, 'epoch': 1.06}
{'loss': 1.5544, 'grad_norm': 1.435452938079834, 'learning_rate': 0.000566151724137931, 'epoch': 1.06}
{'loss': 1.2294, 'grad_norm': 1.3062447309494019, 'learning_rate': 0.0005661103448275862, 'epoch': 1.06}
{'loss': 1.0086, 'grad_norm': 1.247671365737915, 'learning_rate': 0.0005660689655172413, 'epoch': 1.06}
{'loss': 1.2344, 'grad_norm': 1.1681708097457886, 'learning_rate': 0.0005660275862068965, 'epoch': 1.06}
{'loss': 1.0245, 'grad_norm': 1.2916287183761597, 'learning_rate': 0.0005659862068965517, 'epoch': 1.06}
{'loss': 1.167, 'grad_norm': 1.1037423610687256, 'learning_rate': 0.0005659448275862068, 'epoch': 1.06}
{'loss': 1.0979, 'grad_norm': 0.8714407086372375, 'learning_rate': 0.000565903448275862, 'epoch': 1.07}
{'loss': 1.0295, 'grad_norm': 1.0490474700927734, 'learning_rate': 0.0005658620689655172, 'epoch': 1.07}
{'loss': 1.2914, 'grad_norm': 0.9651049971580505, 'learning_rate': 0.0005658206896551724, 'epoch': 1.07}
{'loss': 1.4024, 'grad_norm': 0.9985311627388, 'learning_rate': 0.0005657793103448275, 'epoch': 1.07}
{'loss': 1.1468, 'grad_norm': 1.1960391998291016, 'learning_rate': 0.0005657379310344827, 'epoch': 1.07}
{'loss': 1.3406, 'grad_norm': 4.024636268615723, 'learning_rate': 0.0005656965517241379, 'epoch': 1.07}
{'loss': 0.9337, 'grad_norm': 1.279215931892395, 'learning_rate': 0.0005656551724137931, 'epoch': 1.07}
{'loss': 0.7471, 'grad_norm': 1.579846978187561, 'learning_rate': 0.0005656137931034482, 'epoch': 1.07}
{'loss': 1.2494, 'grad_norm': 1.339896321296692, 'learning_rate': 0.0005655724137931034, 'epoch': 1.07}
{'loss': 1.1451, 'grad_norm': 2.7946248054504395, 'learning_rate': 0.0005655310344827585, 'epoch': 1.07}
{'loss': 0.8641, 'grad_norm': 2.5526604652404785, 'learning_rate': 0.0005654896551724137, 'epoch': 1.07}
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 10%|█         | 1500/15000 [42:33<2:13:46,  1.68it/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: libr ofice imples में एक प्रस्तुति document बनाना और बुनियादी formating के इस spoken tutorial में आपका स ा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 tutorial में हम impres window के भागों के बारे में सीखेंगे और cैसे slide insert करें और copy करेंfont 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 offic vrsion 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 sampl implus open करते हैंजि



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 लातार द्वारा अनुवादित है आई आई टी बुम्बई 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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