whisper-small-en
This model is a fine-tuned version of openai/whisper-small on the librispeech_asr dataset. It achieves the following results on the evaluation set:
- Loss: 6.7832
- Wer: 124.5115
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: 0.0005
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-06
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2
- training_steps: 100
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 9.6259 | 1.57 | 5 | 10.7408 | 1127.3535 |
| 11.5288 | 3.29 | 10 | 9.2534 | 100.0 |
| 10.9249 | 4.86 | 15 | 7.8357 | 100.0 |
| 7.0442 | 6.57 | 20 | 6.9971 | 595.3819 |
| 8.6762 | 8.29 | 25 | 5.6135 | 312.2558 |
| 5.4239 | 9.86 | 30 | 5.4885 | 97.1581 |
| 4.986 | 11.57 | 35 | 5.2888 | 628.7744 |
| 6.708 | 13.29 | 40 | 4.9665 | 277.6199 |
| 3.9096 | 14.86 | 45 | 5.0861 | 631.9716 |
| 3.2326 | 16.57 | 50 | 5.0090 | 279.7513 |
| 3.9691 | 18.29 | 55 | 5.0804 | 133.2149 |
| 1.8661 | 19.86 | 60 | 5.4423 | 317.5844 |
| 1.1588 | 21.57 | 65 | 5.7955 | 119.5382 |
| 1.0355 | 23.29 | 70 | 6.0458 | 190.2309 |
| 0.3455 | 24.86 | 75 | 6.3057 | 106.7496 |
| 0.142 | 26.57 | 80 | 6.5767 | 209.9467 |
| 0.1722 | 28.29 | 85 | 6.5937 | 101.4210 |
| 0.0816 | 29.86 | 90 | 6.7679 | 149.7336 |
| 0.079 | 31.57 | 95 | 6.8008 | 133.5702 |
| 0.1007 | 33.29 | 100 | 6.7832 | 124.5115 |
Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
- Downloads last month
- 12
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.