Whisper fine-tuned on FluencyBank — openai/whisper-medium
This model is a fine-tuned version of openai/whisper-medium on the FluencyBank Timestamped dataset. It achieves the following results on the evaluation set:
- Loss: 1.8983
- Wer: 15.9086
- Cer: 10.9154
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: 8e-06
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 2500
- label_smoothing_factor: 0.1
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|---|---|---|---|---|---|
| 1.4549 | 11.6279 | 250 | 1.7186 | 11.7776 | 6.6503 |
| 1.4261 | 23.2558 | 500 | 1.7611 | 10.8548 | 6.2588 |
| 1.4204 | 34.8837 | 750 | 1.8104 | 10.7888 | 6.2679 |
| 1.4216 | 46.5116 | 1000 | 1.7901 | 10.9207 | 6.4819 |
| 1.4179 | 58.1395 | 1250 | 1.8390 | 10.9426 | 6.4637 |
| 1.4168 | 69.7674 | 1500 | 1.8682 | 15.7328 | 10.7515 |
| 1.4164 | 81.3953 | 1750 | 1.8841 | 15.9086 | 10.8517 |
| 1.4161 | 93.0233 | 2000 | 1.8941 | 15.8207 | 10.8790 |
| 1.416 | 104.6512 | 2250 | 1.8984 | 15.9525 | 10.9882 |
| 1.416 | 116.2791 | 2500 | 1.8983 | 15.9086 | 10.9154 |
Framework versions
- Transformers 4.45.2
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.20.3