Whisper-squeezeformer-N6SQU-full-per-norn
This model is a fine-tuned version of openai/whisper-small on the LibriSpeech dataset. It achieves the following results on the evaluation set:
- Loss: 0.1775
- Wer: 8.1539
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: 20
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2500
- training_steps: 30000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 4.7944 | 1.0 | 2500 | 3.7862 | 136.5585 |
| 3.8012 | 2.0 | 5000 | 2.9923 | 107.0298 |
| 0.7205 | 3.0 | 7500 | 0.2924 | 17.2474 |
| 0.1473 | 4.0 | 10000 | 0.2399 | 13.8276 |
| 0.0847 | 5.0 | 12500 | 0.2228 | 11.7677 |
| 0.0505 | 6.0 | 15000 | 0.2200 | 12.2071 |
| 0.1802 | 7.0 | 17500 | 0.1782 | 9.7554 |
| 0.107 | 8.0 | 20000 | 0.1681 | 9.1962 |
| 0.0693 | 9.0 | 22500 | 0.1669 | 8.2205 |
| 0.0462 | 10.0 | 25000 | 0.1700 | 8.9604 |
| 0.032 | 11.0 | 27500 | 0.1745 | 8.5952 |
| 0.024 | 12.0 | 30000 | 0.1775 | 8.1539 |
Framework versions
- Transformers 4.45.2
- Pytorch 2.4.0
- Datasets 3.1.0
- Tokenizers 0.20.0
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Base model
openai/whisper-small