| wandb: Currently logged in as: priyanshi-pal (priyanshipal). Use `wandb login --relogin` to force relogin |
| wandb: wandb version 0.17.7 is available! To upgrade, please run: |
| wandb: $ pip install wandb --upgrade |
| wandb: Tracking run with wandb version 0.17.6 |
| wandb: Run data is saved locally in /scratch/elec/t405-puhe/p/palp3/MUCS/wandb/run-20240822_174142-upry9j53 |
| wandb: Run `wandb offline` to turn off syncing. |
| wandb: Syncing run eval_pd20000_w500_s300_shuff100_hinglish |
| wandb: ⭐️ View project at https://wandb.ai/priyanshipal/huggingface |
| wandb: 🚀 View run at https://wandb.ai/priyanshipal/huggingface/runs/upry9j53 |
| /scratch/work/palp3/myenv/lib/python3.11/site-packages/transformers/training_args.py:1525: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead |
| warnings.warn( |
|
Generating train split: 0 examples [00:00, ? examples/s]
Generating train split: 572 examples [00:00, 27863.95 examples/s] |
| /scratch/work/palp3/myenv/lib/python3.11/site-packages/transformers/models/auto/configuration_auto.py:957: 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:329: 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=True), 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 |
| 08/22/2024 17:41:50 - 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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| Printing predictions for a few samples: |
| Sample 1: |
| Reference: हम उनका उपयोग ऐसे ही कर सकते हैं या आवश्यकता अनुसार कुछ बदलाव करके उपयोग कर सकते हैं |
| ###### |
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| Prediction: हम उनका उपयोग ऐसे ही कर सकते हैं |
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| Sample 2: |
| Reference: अतः शीर्षक इस तरह से जोड़ सकते हैं |
| ###### |
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| Prediction: अतः शीर्ष है |
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| Sample 3: |
| Reference: प्रेसेंटेशन के अंत में आपने स्लाइड की एक कॉपी बना ली है |
| ###### |
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| Prediction: presentation के अंत में आपने स ैंैं |
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| Sample 4: |
| Reference: चलिए अब फोंट्स और फोंट्स को फॉर्मेट करने के कुछ तरीके देखते हैं |
| ###### |
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| Prediction: चलिए अब fonts और fonts को format करने के कुछ तरीके देेहं |
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| Sample 5: |
| Reference: यह एक डायलॉग बॉक्स खोलेगा जिसमें हम अपनी आवश्यकतानुसार फॉन्ट स्टाइल और साइज़ सेट कर सकते हैं |
| ###### |
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| Prediction: यह एक dialog box खोलेगा जिसमें हम अपनी आवश्यकत हैहै |
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| 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 |
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| 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 = 0:00:31.20 |
| eval_samples = 572 |
| eval_samples_per_second = 18.329 |
| eval_steps_per_second = 1.154 |
| eval_wer = 0.5264 |
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Upload 3 LFS files: 100%|██████████| 3/3 [00:38<00:00, 12.89s/it] |
| wandb: - 0.005 MB of 0.005 MB uploaded
wandb: \ 0.037 MB of 0.037 MB uploaded
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 ▁ |
| wandb: eval_samples_per_second ▁ |
| 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 31.2077 |
| wandb: eval/samples_per_second 18.329 |
| wandb: eval/steps_per_second 1.154 |
| 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 31.2077 |
| wandb: eval_samples 572 |
| wandb: eval_samples_per_second 18.329 |
| wandb: eval_steps_per_second 1.154 |
| wandb: eval_wer 0.5264 |
| wandb: train/global_step 0 |
| wandb: |
| wandb: 🚀 View run eval_pd20000_w500_s300_shuff100_hinglish at: https://wandb.ai/priyanshipal/huggingface/runs/upry9j53 |
| wandb: ⭐️ View project at: https://wandb.ai/priyanshipal/huggingface |
| wandb: Synced 6 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s) |
| wandb: Find logs at: ./wandb/run-20240822_174142-upry9j53/logs |
| wandb: WARNING The new W&B backend becomes opt-out in version 0.18.0; try it out with `wandb.require("core")`! See https://wandb.me/wandb-core for more information. |
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