Whisper-small-ru-v2
This model is a fine-tuned version of openai/whisper-small on an Russian part of the Common Voice 15 dataset. It achieves the following results on the evaluation set:
- Loss: 0.1329
- Wer: 12.6750
- Cer: 3.7305
- Learning Rate: 0.0000
Model description
Same as openai/whisper-small.
Intended uses & limitations
Same as openai/whisper-small
Training and evaluation data
Fine-tunned on an Russian part of the Common Voice 15 dataset.
Training procedure
According to the article "Fine-Tune Whisper For Multilingual ASR with 🤗 Transformers"
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-08
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 250
- training_steps: 15000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer | Rate |
|---|---|---|---|---|---|---|
| 0.0661 | 0.09 | 500 | 0.1358 | 12.9097 | 3.8217 | 0.0000 |
| 0.0616 | 0.17 | 1000 | 0.1357 | 12.9620 | 3.8949 | 0.0000 |
| 0.0601 | 0.26 | 1500 | 0.1357 | 12.8795 | 3.8225 | 0.0000 |
| 0.0666 | 0.35 | 2000 | 0.1353 | 12.9481 | 3.8871 | 0.0000 |
| 0.0669 | 0.43 | 2500 | 0.1352 | 12.8284 | 3.8283 | 0.0000 |
| 0.0665 | 0.52 | 3000 | 0.1351 | 12.8203 | 3.7833 | 0.0000 |
| 0.0649 | 0.61 | 3500 | 0.1349 | 12.8098 | 3.7824 | 0.0000 |
| 0.0607 | 0.69 | 4000 | 0.1347 | 12.8110 | 3.8105 | 0.0000 |
| 0.0636 | 0.78 | 4500 | 0.1345 | 12.7994 | 3.7893 | 0.0000 |
| 0.063 | 0.87 | 5000 | 0.1342 | 12.8319 | 3.8084 | 0.0000 |
| 0.0589 | 0.95 | 5500 | 0.1341 | 12.8807 | 3.8551 | 0.0000 |
| 0.0734 | 1.04 | 6000 | 0.1341 | 12.7691 | 3.7604 | 0.0000 |
| 0.0577 | 1.13 | 6500 | 0.1340 | 12.7645 | 3.7602 | 0.0000 |
| 0.052 | 1.21 | 7000 | 0.1340 | 12.7610 | 3.7655 | 0.0000 |
| 0.0626 | 1.3 | 7500 | 0.1339 | 12.7657 | 3.7593 | 0.0000 |
| 0.0617 | 1.39 | 8000 | 0.1338 | 12.7912 | 3.8268 | 0.0000 |
| 0.063 | 1.47 | 8500 | 0.1337 | 12.7343 | 3.7573 | 0.0000 |
| 0.0668 | 1.56 | 9000 | 0.1336 | 12.7308 | 3.7198 | 0.0000 |
| 0.0634 | 1.65 | 9500 | 0.1335 | 12.7215 | 3.7400 | 0.0000 |
| 0.0604 | 1.73 | 10000 | 0.1333 | 12.7192 | 3.7515 | 0.0000 |
| 0.0707 | 1.82 | 10500 | 0.1333 | 12.7052 | 3.7568 | 0.0000 |
| 0.0639 | 1.91 | 11000 | 0.1332 | 12.6983 | 3.7617 | 0.0000 |
| 0.0617 | 1.99 | 11500 | 0.1331 | 12.6936 | 3.7402 | 0.0000 |
| 0.0601 | 2.08 | 12000 | 0.1330 | 12.6901 | 3.7586 | 0.0000 |
| 0.0632 | 2.17 | 12500 | 0.1330 | 12.6785 | 3.7279 | 0.0000 |
| 0.0626 | 2.25 | 13000 | 0.1330 | 12.6808 | 3.7333 | 0.0000 |
| 0.066 | 2.34 | 13500 | 0.1329 | 12.6704 | 3.7512 | 0.0000 |
| 0.0674 | 2.42 | 14000 | 0.1329 | 12.6599 | 3.7384 | 0.0000 |
| 0.0637 | 2.51 | 14500 | 0.1329 | 12.6797 | 3.7428 | 0.0000 |
| 0.0641 | 2.6 | 15000 | 0.1329 | 12.6750 | 3.7305 | 0.0000 |
Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0