Whisper fine-tuned on FluencyBank — openai/whisper-large-v3
This model is a fine-tuned version of openai/whisper-large-v3 on the FluencyBank Timestamped dataset. It achieves the following results on the evaluation set:
- Loss: 1.8275
- Wer: 9.7561
- Cer: 5.7991
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 |
|---|---|---|---|---|---|
| 1.4541 | 11.6279 | 250 | 1.6985 | 11.6678 | 7.8383 |
| 1.4269 | 23.2558 | 500 | 1.7426 | 9.6902 | 5.6261 |
| 1.4251 | 34.8837 | 750 | 1.7460 | 9.7781 | 5.7536 |
| 1.42 | 46.5116 | 1000 | 1.7811 | 9.6243 | 5.5760 |
| 1.4199 | 58.1395 | 1250 | 1.7827 | 9.8220 | 5.7991 |
| 1.418 | 69.7674 | 1500 | 1.8085 | 9.5803 | 5.6580 |
| 1.4176 | 81.3953 | 1750 | 1.8191 | 9.8000 | 5.7354 |
| 1.4173 | 93.0233 | 2000 | 1.8252 | 9.7341 | 5.7445 |
| 1.4173 | 104.6512 | 2250 | 1.8272 | 9.8220 | 5.8446 |
| 1.4172 | 116.2791 | 2500 | 1.8275 | 9.7561 | 5.7991 |
Framework versions
- Transformers 4.45.2
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.20.3