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/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(
/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/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/work/palp3/myenv/lib/python3.11/site-packages/accelerate/accelerator.py:488: 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)
)
check the eval set length 572
10/14/2024 23:39:37 - INFO - __main__ - *** Evaluate ***
/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(

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/scratch/work/palp3/myenv/lib/python3.11/site-packages/huggingface_hub/hf_api.py:3889: UserWarning: It seems that you are about to commit a data file (json/default-b60d5edd0f197c71/0.0.0/7483f22a71512872c377524b97484f6d20c275799bb9e7cd8fb3198178d8220a/json-train.arrow) to a model repository. You are sure this is intended? If you are trying to upload a dataset, please set `repo_type='dataset'` or `--repo-type=dataset` in a CLI.
  warnings.warn(
Printing predictions for a few samples:
Sample 1:
  Reference (English): हम उनका उपयोग ऐसे ही कर सकते हैं या आवश्यकता अनुसार कुछ बदलाव करके उपयोग कर सकते हैं
 True Reference: हम उनका उपयोग ऐसे ही कर सकते हैं या आवश्यकता अनुसार कुछ बदलाव करके उपयोग कर सकते हैं
######
  Prediction (English): हम उनका उपयोग ऐसे ही कर सकते हैं
 True Prediction: हम उनका उपयोग ऐसे ही कर सकते हैं



Sample 2:
  Reference (English): अतः शीर्षक इस तरह से जोड़ सकते हैं
 True Reference: अतः शीर्षक इस तरह से जोड़ सकते हैं
######
  Prediction (English): अतः शीर्ष है
 True Prediction: अतः शीर्ष है



Sample 3:
  Reference (English): प्रेसेंटेशन के अंत में आपने स्लाइड की एक कॉपी बना ली है
 True Reference: प्रेसेंटेशन के अंत में आपने स्लाइड की एक कॉपी बना ली है
######
  Prediction (English): presentation के अंत में आपने स ैंैं
 True Prediction: presentation के अंत में आपने स ैंैं



Sample 4:
  Reference (English): चलिए अब फोंट्स और फोंट्स को फॉर्मेट करने के कुछ तरीके देखते हैं
 True Reference: चलिए अब फोंट्स और फोंट्स को फॉर्मेट करने के कुछ तरीके देखते हैं
######
  Prediction (English): चलिए अब fonts और fonts को format करने के कुछ तरीके देेहं
 True Prediction: चलिए अब fonts और fonts को format करने के कुछ तरीके देेहं



Sample 5:
  Reference (English): यह एक डायलॉग बॉक्स खोलेगा जिसमें हम अपनी आवश्यकतानुसार फॉन्ट स्टाइल और साइज़ सेट कर सकते हैं
 True Reference: यह एक डायलॉग बॉक्स खोलेगा जिसमें हम अपनी आवश्यकतानुसार फॉन्ट स्टाइल और साइज़ सेट कर सकते हैं
######
  Prediction (English): यह एक dialog box खोलेगा जिसमें हम अपनी आवश्यकत हैहै
 True Prediction: यह एक dialog box खोलेगा जिसमें हम अपनी आवश्यकत हैहै



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 मुmबई की ओर से मैं रवि कुमार अब आपसे विदा लेता हूँ हमसे जुड़ने के लिए धन्यवाद
***** eval metrics *****
  eval_cer                    =     0.4569
  eval_loss                   =     2.2188
  eval_model_preparation_time =     0.0045
  eval_runtime                = 0:00:39.02
  eval_samples                =        572
  eval_samples_per_second     =     14.659
  eval_steps_per_second       =      0.923
  eval_wer                    =     0.5264

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wandb: - 0.005 MB of 0.005 MB uploaded
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wandb: 
wandb: Run history:
wandb:                    eval/cer ▁
wandb:                   eval/loss ▁
wandb: eval/model_preparation_time ▁
wandb:                eval/runtime ▁
wandb:     eval/samples_per_second ▁
wandb:       eval/steps_per_second ▁
wandb:                    eval/wer ▁
wandb:                    eval_cer ▁
wandb:                   eval_loss ▁
wandb: eval_model_preparation_time ▁
wandb:                eval_runtime ▁
wandb:                eval_samples ▁
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wandb:       eval_steps_per_second ▁
wandb:                    eval_wer ▁
wandb:           train/global_step ▁▁
wandb: 
wandb: Run summary:
wandb:                    eval/cer 0.4569
wandb:                   eval/loss 2.21876
wandb: eval/model_preparation_time 0.0045
wandb:                eval/runtime 39.0212
wandb:     eval/samples_per_second 14.659
wandb:       eval/steps_per_second 0.923
wandb:                    eval/wer 0.5264
wandb:                    eval_cer 0.4569
wandb:                   eval_loss 2.21876
wandb: eval_model_preparation_time 0.0045
wandb:                eval_runtime 39.0212
wandb:                eval_samples 572
wandb:     eval_samples_per_second 14.659
wandb:       eval_steps_per_second 0.923
wandb:                    eval_wer 0.5264
wandb:           train/global_step 0
wandb: 
wandb: 🚀 View run transliterated_wer_glamorous_tree_37 at: https://wandb.ai/priyanshipal/huggingface/runs/6ill7u88
wandb: ⭐️ View project at: https://wandb.ai/priyanshipal/huggingface
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