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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: openai/whisper-large-v3 |
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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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- OUTCOMESAI/medical_n_common_speech_corpus_50_50 |
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metrics: |
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- wer |
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model-index: |
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- name: Whisper Large V3 Common n Medical 50 50 |
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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: OUTCOMESAI/medical_n_common_speech_corpus_50_50 en |
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type: OUTCOMESAI/medical_n_common_speech_corpus_50_50 |
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metrics: |
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- name: Wer |
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type: wer |
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value: 5.218643517767322 |
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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 Large V3 Common n Medical 50 50 |
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This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the OUTCOMESAI/medical_n_common_speech_corpus_50_50 en dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3196 |
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- Wer: 5.2186 |
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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: 64 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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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: 250 |
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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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| 5.126 | 0.0969 | 250 | 0.3694 | 5.6601 | |
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| 4.367 | 0.1938 | 500 | 0.3586 | 5.8156 | |
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| 4.1514 | 0.2907 | 750 | 0.3511 | 5.8839 | |
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| 3.962 | 0.3876 | 1000 | 0.3450 | 5.7805 | |
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| 3.9038 | 0.4845 | 1250 | 0.3403 | 6.1746 | |
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| 3.8313 | 0.5814 | 1500 | 0.3359 | 5.9738 | |
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| 3.7778 | 0.6783 | 1750 | 0.3333 | 5.9218 | |
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| 3.7421 | 0.7752 | 2000 | 0.3306 | 6.1327 | |
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| 3.7367 | 0.8721 | 2250 | 0.3281 | 5.6561 | |
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| 3.6878 | 0.9690 | 2500 | 0.3257 | 5.5154 | |
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| 3.6769 | 1.0659 | 2750 | 0.3242 | 5.4803 | |
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| 3.6508 | 1.1628 | 3000 | 0.3235 | 5.4634 | |
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| 3.6292 | 1.2597 | 3250 | 0.3220 | 5.3512 | |
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| 3.6179 | 1.3566 | 3500 | 0.3210 | 5.2254 | |
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| 3.6032 | 1.4535 | 3750 | 0.3206 | 5.2207 | |
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| 3.5922 | 1.5504 | 4000 | 0.3201 | 5.3038 | |
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| 3.5743 | 1.6473 | 4250 | 0.3198 | 5.2633 | |
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| 3.5882 | 1.7442 | 4500 | 0.3198 | 5.2254 | |
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| 3.6021 | 1.8411 | 4750 | 0.3196 | 5.2186 | |
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| 3.5865 | 1.9380 | 5000 | 0.3193 | 5.2213 | |
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### Framework versions |
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- Transformers 4.48.0.dev0 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 3.2.1.dev0 |
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- Tokenizers 0.21.0 |
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