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--- |
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license: apache-2.0 |
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base_model: arun100/whisper-base-hi-2 |
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tags: |
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- whisper-event |
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- generated_from_trainer |
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datasets: |
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- google/fleurs |
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metrics: |
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- wer |
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model-index: |
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- name: Whisper Base Hindi |
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results: |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: google/fleurs hi_in |
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type: google/fleurs |
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config: hi_in |
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split: test |
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args: hi_in |
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metrics: |
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- name: Wer |
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type: wer |
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value: 27.72060783790989 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# Whisper Base Hindi |
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This model is a fine-tuned version of [arun100/whisper-base-hi-2](https://huggingface.co/arun100/whisper-base-hi-2) on the google/fleurs hi_in dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4468 |
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- Wer: 27.7206 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-07 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 64 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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- training_steps: 5000 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Wer | |
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|:-------------:|:-----:|:----:|:---------------:|:-------:| |
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| 0.4805 | 33.0 | 250 | 0.4868 | 30.4186 | |
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| 0.3559 | 66.0 | 500 | 0.4417 | 29.0909 | |
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| 0.2655 | 99.0 | 750 | 0.4307 | 28.2165 | |
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| 0.1987 | 133.0 | 1000 | 0.4350 | 27.8326 | |
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| 0.1472 | 166.0 | 1250 | 0.4468 | 27.7206 | |
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| 0.1061 | 199.0 | 1500 | 0.4640 | 28.0992 | |
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| 0.0767 | 233.0 | 1750 | 0.4835 | 28.5737 | |
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| 0.0541 | 266.0 | 2000 | 0.5032 | 28.6857 | |
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| 0.0396 | 299.0 | 2250 | 0.5202 | 28.7763 | |
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| 0.03 | 333.0 | 2500 | 0.5353 | 29.2029 | |
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| 0.0237 | 366.0 | 2750 | 0.5479 | 28.9096 | |
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| 0.0195 | 399.0 | 3000 | 0.5587 | 28.9096 | |
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| 0.0163 | 433.0 | 3250 | 0.5683 | 28.9469 | |
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| 0.014 | 466.0 | 3500 | 0.5767 | 29.1336 | |
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| 0.0121 | 499.0 | 3750 | 0.5838 | 29.3415 | |
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| 0.0108 | 533.0 | 4000 | 0.5900 | 29.2775 | |
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| 0.01 | 566.0 | 4250 | 0.5951 | 29.6081 | |
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| 0.0093 | 599.0 | 4500 | 0.5988 | 29.4855 | |
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| 0.0088 | 633.0 | 4750 | 0.6012 | 29.5281 | |
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| 0.0087 | 666.0 | 5000 | 0.6020 | 29.4268 | |
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### Framework versions |
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- Transformers 4.37.0.dev0 |
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- Pytorch 2.1.2+cu121 |
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- Datasets 2.16.2.dev0 |
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- Tokenizers 0.15.0 |
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