Whisper Small Fine-tuned with Uyghur Common Voice
This model is a fine-tuned version of openai/whisper-small on the Uyghur Common Voice dataset.
This model achieves the following results on the evaluation set:
- Loss: 1.5920
- Wer Ortho: 42.9701
- Wer: 28.2995
- Cer: 10.8968
Training and evaluation data
The training was done using the combined train and dev set of common_voice_15_0 (11215 recordings, ~20hrs of audio).
The testing was done using the test set of THUYG20 as the standard benchmark for Uyghur speech models.
Training procedure
Finetuning code avaiblable in https://github.com/ixxan/ug-speech
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 300
- training_steps: 4000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer | Cer |
|---|---|---|---|---|---|---|
| 0.574400 | 0.7133 | 500 | 1.413890 | 59.765522 | 48.561550 | 17.639905 |
| 0.299600 | 1.4256 | 1000 | 1.283326 | 52.819004 | 41.377838 | 14.717958 |
| 0.130600 | 2.1398 | 1500 | 1.379338 | 52.265742 | 38.953389 | 16.260934 |
| 0.122500 | 2.8531 | 2000 | 1.313730 | 50.245894 | 36.494793 | 14.762585 |
| 0.060500 | 3.5663 | 2500 | 1.434626 | 47.589356 | 32.998976 | 12.185938 |
| 0.019500 | 4.2796 | 3000 | 1.526625 | 45.345570 | 30.975756 | 11.307346 |
| 0.015300 | 4.9929 | 3500 | 1.531676 | 44.120488 | 29.285470 | 11.690021 |
| 0.003300 | 5.7061 | 4000 | 1.592020 | 42.970054 | 28.299471 | 10.896778 |
Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3