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--- |
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library_name: transformers |
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language: |
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- en |
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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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- openslr/librispeech_asr |
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model-index: |
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- name: Fine Tune Whisper on LibriSpeech |
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results: [] |
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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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# Fine Tune Whisper on LibriSpeech |
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This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the LibriSpeech dataset. |
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It achieves the following results on the evaluation set: |
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- eval_loss: 0.2510 |
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- eval_wer: 8.8067 |
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- eval_runtime: 109.0599 |
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- eval_samples_per_second: 1.834 |
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- eval_steps_per_second: 0.229 |
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- epoch: 20.0 |
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- step: 1000 |
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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: 8 |
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- seed: 42 |
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- optimizer: Use OptimizerNames.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: 5000 |
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- mixed_precision_training: Native AMP |
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### Framework versions |
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- Transformers 4.52.0 |
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- Pytorch 2.9.0+cu126 |
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- Datasets 4.4.1 |
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- Tokenizers 0.21.4 |
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