whisper-large-v3-verbatim-1
This model is a fine-tuned version of openai/whisper-large-v3 on the JacobLinCool/ami-disfluent dataset. It achieves the following results on the evaluation set:
- Loss: 0.1300
- Wer: 32.3225
- Cer: 45.5147
- Decode Runtime: 141.5643
- Wer Runtime: 0.1227
- Cer Runtime: 0.2049
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: 5e-05
- train_batch_size: 4
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 1000
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer | Decode Runtime | Wer Runtime | Cer Runtime |
|---|---|---|---|---|---|---|---|---|
| No log | 0 | 0 | 1.8283 | 63.2783 | 251.8035 | 164.5307 | 0.1838 | 0.3386 |
| 0.2617 | 0.1 | 100 | 0.2189 | 49.6995 | 178.3721 | 161.1098 | 0.1397 | 0.4071 |
| 0.1291 | 0.2 | 200 | 0.1452 | 50.3383 | 95.5275 | 143.0863 | 0.1342 | 0.2932 |
| 0.1418 | 0.3 | 300 | 0.1387 | 29.9186 | 74.6491 | 150.1053 | 0.0780 | 0.1514 |
| 0.1273 | 1.088 | 400 | 0.1372 | 30.8218 | 91.1134 | 166.0178 | 0.1252 | 0.2728 |
| 0.1139 | 1.188 | 500 | 0.1335 | 29.9117 | 101.9003 | 144.2796 | 0.1318 | 0.2934 |
| 0.1663 | 1.288 | 600 | 0.1306 | 31.8418 | 83.0183 | 149.9060 | 0.0826 | 0.1679 |
| 0.1275 | 2.076 | 700 | 0.1311 | 24.9665 | 29.6191 | 143.2151 | 0.0781 | 0.1135 |
| 0.1077 | 2.176 | 800 | 0.1304 | 25.9109 | 36.6217 | 143.4620 | 0.0770 | 0.1227 |
| 0.1711 | 2.276 | 900 | 0.1298 | 35.1729 | 45.0300 | 145.3294 | 0.0786 | 0.1310 |
| 0.0994 | 3.064 | 1000 | 0.1300 | 32.3225 | 45.5147 | 141.5643 | 0.1227 | 0.2049 |
Framework versions
- PEFT 0.14.0
- Transformers 4.48.0
- Pytorch 2.4.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0