WhisperTiny10hCommonVoice20hAugment
This model is a fine-tuned version of OpenAI/whisper-tiny on the audiofolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.9134
- Wer: 0.4361
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.9553 | 0.4492 | 500 | 1.0205 | 0.4894 |
| 0.7923 | 0.8985 | 1000 | 0.9367 | 0.4628 |
| 0.5788 | 1.3477 | 1500 | 0.9254 | 0.4439 |
| 0.5308 | 1.7969 | 2000 | 0.9195 | 0.4464 |
| 0.4003 | 2.2462 | 2500 | 0.9081 | 0.4373 |
| 0.3703 | 2.6954 | 3000 | 0.9046 | 0.4275 |
| 0.2858 | 3.1447 | 3500 | 0.9070 | 0.4381 |
| 0.2963 | 3.5939 | 4000 | 0.9081 | 0.4346 |
| 0.2686 | 4.0431 | 4500 | 0.9119 | 0.4346 |
| 0.2597 | 4.4924 | 5000 | 0.9134 | 0.4361 |
Framework versions
- Transformers 4.48.3
- Pytorch 2.6.0
- Datasets 3.3.0
- Tokenizers 0.21.0