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--- |
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library_name: transformers |
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language: |
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- eng |
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license: apache-2.0 |
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base_model: openai/whisper-small |
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tags: |
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- generated_from_trainer |
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datasets: |
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- kavyamanohar/supreme-court-speech |
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metrics: |
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- wer |
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model-index: |
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- name: kavyamanohar/whisper-supreme-court-asr |
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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: Supreme Court Hearing Corpus - Subset |
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type: kavyamanohar/supreme-court-speech |
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args: 'split: test' |
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metrics: |
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- name: Wer |
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type: wer |
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value: 69.86706056129985 |
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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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# kavyamanohar/whisper-supreme-court-asr |
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This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Supreme Court Hearing Corpus - Subset dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.8703 |
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- Wer: 69.8671 |
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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: 1e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 32 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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- training_steps: 1000 |
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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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| 1.8763 | 5.5714 | 100 | 1.8904 | 162.7770 | |
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| 0.7801 | 11.1143 | 200 | 1.5403 | 55.0222 | |
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| 0.0716 | 16.6857 | 300 | 1.5505 | 56.7947 | |
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| 0.0118 | 22.2286 | 400 | 1.6573 | 56.8685 | |
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| 0.0076 | 27.8 | 500 | 1.7218 | 83.3087 | |
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| 0.0014 | 33.3429 | 600 | 1.7909 | 79.2467 | |
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| 0.0005 | 38.9143 | 700 | 1.8290 | 67.8730 | |
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| 0.0003 | 44.4571 | 800 | 1.8555 | 69.4239 | |
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| 0.0002 | 50.0 | 900 | 1.8669 | 69.7932 | |
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| 0.0002 | 55.5714 | 1000 | 1.8703 | 69.8671 | |
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### Framework versions |
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- Transformers 4.48.2 |
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- Pytorch 2.6.0+cu124 |
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- Datasets 3.2.0 |
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- Tokenizers 0.21.0 |
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