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--- |
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base_model: openai/whisper-small |
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library_name: transformers |
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license: apache-2.0 |
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metrics: |
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- wer |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: whisper-small-sc |
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results: [] |
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datasets: |
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- janaab/supreme-court-speech |
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language: |
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- en |
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pipeline_tag: automatic-speech-recognition |
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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-small-sc |
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This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on [janaab/supreme-court-speech](https://huggingface.co/datasets/janaab/supreme-court-speech) dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2992 |
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- Wer Ortho: 10.7269 |
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- Wer: 10.2317 |
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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: 32 |
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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: 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: 250 |
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- training_steps: 2500 |
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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 Ortho | Wer | |
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|:-------------:|:------:|:----:|:---------------:|:---------:|:-------:| |
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| 0.2799 | 0.7704 | 250 | 0.2614 | 12.1130 | 11.6261 | |
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| 0.1454 | 1.5408 | 500 | 0.2367 | 11.0523 | 10.6016 | |
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| 0.072 | 2.3112 | 750 | 0.2428 | 10.6736 | 10.2154 | |
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| 0.0488 | 3.0817 | 1000 | 0.2638 | 10.8335 | 10.3623 | |
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| 0.0294 | 3.8521 | 1250 | 0.2689 | 11.2821 | 10.7793 | |
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| 0.012 | 4.6225 | 1500 | 0.2992 | 10.7269 | 10.2317 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.4.0 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
