whisper-small-eng
This model is a fine-tuned version of openai/whisper-small on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.5746
- Wer: 24.4747
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: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- training_steps: 1000
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.7025 | 0.03 | 100 | 0.6855 | 36.9988 |
| 0.7478 | 0.07 | 200 | 0.8034 | 35.4196 |
| 0.7516 | 0.1 | 300 | 0.7854 | 31.8551 |
| 0.7175 | 0.13 | 400 | 0.7868 | 32.9444 |
| 0.6748 | 0.17 | 500 | 0.7239 | 31.1203 |
| 0.6739 | 0.2 | 600 | 0.7045 | 29.7473 |
| 0.6262 | 0.24 | 700 | 0.6620 | 27.1239 |
| 0.585 | 0.27 | 800 | 0.6254 | 26.6147 |
| 0.5305 | 0.3 | 900 | 0.5877 | 24.6552 |
| 0.5463 | 0.34 | 1000 | 0.5746 | 24.4747 |
Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
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Base model
openai/whisper-small