WhisperTiny30hCommonVoice0.0Augment
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: 0.8173
- Wer: 0.4007
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 |
|---|---|---|---|---|
| 0.7384 | 0.4405 | 500 | 0.9908 | 0.4775 |
| 0.6275 | 0.8811 | 1000 | 0.8960 | 0.4447 |
| 0.4919 | 1.3216 | 1500 | 0.8604 | 0.4225 |
| 0.4913 | 1.7621 | 2000 | 0.8450 | 0.4177 |
| 0.3667 | 2.2026 | 2500 | 0.8273 | 0.4090 |
| 0.3462 | 2.6432 | 3000 | 0.8362 | 0.4095 |
| 0.3239 | 3.0837 | 3500 | 0.8199 | 0.4061 |
| 0.3169 | 3.5242 | 4000 | 0.8202 | 0.4023 |
| 0.3123 | 3.9648 | 4500 | 0.8112 | 0.3996 |
| 0.2951 | 4.4053 | 5000 | 0.8173 | 0.4007 |
Framework versions
- Transformers 4.48.3
- Pytorch 2.6.0
- Datasets 3.3.0
- Tokenizers 0.21.0