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--- |
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library_name: transformers |
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language: |
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- ug |
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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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metrics: |
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- cer |
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- wer |
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model-index: |
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- name: Whisper Small Fine-tuned with Uyghur Common Voice |
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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: Common Voice 15 |
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type: mozilla-foundation/common_voice_15_0 |
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metrics: |
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- name: Wer |
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type: wer |
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value: 28.29947071879802 |
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- name: Cer |
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type: cer |
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value: 10.896777936451267 |
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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 Fine-tuned with Uyghur Common Voice |
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This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Uyghur Common Voice dataset. |
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This model achieves the following results on the evaluation set: |
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- Loss: 1.5920 |
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- Wer Ortho: 42.9701 |
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- Wer: 28.2995 |
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- Cer: 10.8968 |
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## Training and evaluation data |
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The training was done using the combined train and dev set of common_voice_15_0 (11215 recordings, \~20hrs of audio). |
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The testing was done using the test set of THUYG20 as the standard benchmark for Uyghur speech models. |
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## Training procedure |
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Finetuning code avaiblable in https://github.com/ixxan/ug-speech |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0001 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 16 |
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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: 300 |
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- training_steps: 4000 |
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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 | Cer | |
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|:-------------:|:-------:|:----:|:---------------:|:---------:|:---------:|:---------:| |
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| 0.574400 | 0.7133 | 500 | 1.413890 | 59.765522 | 48.561550 | 17.639905 | |
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| 0.299600 | 1.4256 | 1000 | 1.283326 | 52.819004 | 41.377838 | 14.717958 | |
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| 0.130600 | 2.1398 | 1500 | 1.379338 | 52.265742 | 38.953389 | 16.260934 | |
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| 0.122500 | 2.8531 | 2000 | 1.313730 | 50.245894 | 36.494793 | 14.762585 | |
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| 0.060500 | 3.5663 | 2500 | 1.434626 | 47.589356 | 32.998976 | 12.185938 | |
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| 0.019500 | 4.2796 | 3000 | 1.526625 | 45.345570 | 30.975756 | 11.307346 | |
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| 0.015300 | 4.9929 | 3500 | 1.531676 | 44.120488 | 29.285470 | 11.690021 | |
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| 0.003300 | 5.7061 | 4000 | 1.592020 | 42.970054 | 28.299471 | 10.896778 | |
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### Framework versions |
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- Transformers 4.46.2 |
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- Pytorch 2.5.1+cu121 |
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- Datasets 3.1.0 |
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- Tokenizers 0.20.3 |
