250119-centralized_learning
This model is a fine-tuned version of facebook/wav2vec2-base-960h on the None dataset. It achieves the following results on the evaluation set:
- Loss: 3624.4978
- Wer: 1.0
- Cer: 0.9981
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: 0.001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|---|---|---|---|---|---|
| 3768.9383 | 1.0 | 2900 | 3624.4978 | 1.0 | 0.9981 |
| 3664.7982 | 2.0 | 5800 | 3623.8293 | 1.0 | 0.9981 |
| 3628.479 | 3.0 | 8700 | 3623.8401 | 1.0 | 0.9981 |
| 3629.406 | 4.0 | 11600 | 3623.7019 | 1.0 | 0.9981 |
| 3625.2683 | 5.0 | 14500 | 3623.6763 | 1.0 | 0.9981 |
| 3624.6528 | 6.0 | 17400 | 3623.6826 | 1.0 | 0.9981 |
| 3624.5862 | 7.0 | 20300 | 3623.6912 | 1.0 | 0.9981 |
| 3624.5 | 8.0 | 23200 | 3623.6826 | 1.0 | 0.9981 |
| 3623.9722 | 9.0 | 26100 | 3623.6606 | 1.0 | 0.9981 |
| 3625.5082 | 10.0 | 29000 | 3623.7019 | 1.0 | 0.9981 |
| 3624.1595 | 11.0 | 31900 | 3623.6472 | 1.0 | 0.9981 |
| 3625.175 | 12.0 | 34800 | 3623.6831 | 1.0 | 0.9981 |
| 3625.192 | 13.0 | 37700 | 3623.6731 | 1.0 | 0.9981 |
| 3623.9662 | 14.0 | 40600 | 3623.6777 | 1.0 | 0.9981 |
| 3623.9175 | 15.0 | 43500 | 3623.6653 | 1.0 | 0.9981 |
| 3623.9675 | 16.0 | 46400 | 3623.6497 | 1.0 | 0.9981 |
| 3624.471 | 17.0 | 49300 | 3623.6682 | 1.0 | 0.9981 |
| 3623.9983 | 18.0 | 52200 | 3623.6560 | 1.0 | 0.9981 |
| 3624.317 | 19.0 | 55100 | 3623.6587 | 1.0 | 0.9981 |
| 3623.9965 | 20.0 | 58000 | 3623.6743 | 1.0 | 0.9981 |
| 3623.9733 | 21.0 | 60900 | 3623.6799 | 1.0 | 0.9981 |
| 3623.9163 | 22.0 | 63800 | 3623.6455 | 1.0 | 0.9981 |
| 3623.817 | 23.0 | 66700 | 3623.6487 | 1.0 | 0.9981 |
| 3623.8545 | 24.0 | 69600 | 3623.6912 | 1.0 | 0.9981 |
| 3623.9572 | 25.0 | 72500 | 3623.6763 | 1.0 | 0.9981 |
| 3624.0855 | 26.0 | 75400 | 3623.6682 | 1.0 | 0.9981 |
| 3623.9163 | 27.0 | 78300 | 3623.6519 | 1.0 | 0.9981 |
| 3623.813 | 28.0 | 81200 | 3623.6863 | 1.0 | 0.9981 |
| 3624.0905 | 29.0 | 84100 | 3623.6797 | 1.0 | 0.9981 |
| 3623.847 | 30.0 | 87000 | 3623.6743 | 1.0 | 0.9981 |
| 3624.7505 | 31.0 | 89900 | 3623.6768 | 1.0 | 0.9981 |
| 3623.794 | 32.0 | 92800 | 3623.6716 | 1.0 | 0.9981 |
| 3623.775 | 33.0 | 95700 | 3623.6409 | 1.0 | 0.9981 |
| 3624.021 | 34.0 | 98600 | 3623.6431 | 1.0 | 0.9981 |
| 3623.7987 | 35.0 | 101500 | 3623.6394 | 1.0 | 0.9981 |
| 3623.8223 | 36.0 | 104400 | 3623.6831 | 1.0 | 0.9981 |
| 3623.979 | 37.0 | 107300 | 3623.6785 | 1.0 | 0.9981 |
| 3623.809 | 38.0 | 110200 | 3623.6826 | 1.0 | 0.9981 |
| 3623.857 | 39.0 | 113100 | 3623.6643 | 1.0 | 0.9981 |
| 3623.8252 | 40.0 | 116000 | 3623.6731 | 1.0 | 0.9981 |
| 3623.916 | 41.0 | 118900 | 3623.6838 | 1.0 | 0.9981 |
| 3623.9533 | 42.0 | 121800 | 3623.6694 | 1.0 | 0.9981 |
| 3618.18 | 43.0 | 124700 | nan | 1.0 | 0.9981 |
| 0.0 | 44.0 | 127600 | nan | 1.0 | 0.9981 |
| 0.0 | 45.0 | 130500 | nan | 1.0 | 0.9981 |
| 0.0 | 46.0 | 133400 | nan | 1.0 | 0.9981 |
| 0.0 | 47.0 | 136300 | nan | 1.0 | 0.9981 |
| 0.0 | 48.0 | 139200 | nan | 1.0 | 0.9981 |
| 0.0 | 49.0 | 142100 | nan | 1.0 | 0.9981 |
| 0.0 | 50.0 | 145000 | nan | 1.0 | 0.9981 |
| 0.0 | 51.0 | 147900 | nan | 1.0 | 0.9981 |
| 0.0 | 52.0 | 150800 | nan | 1.0 | 0.9981 |
| 0.0 | 53.0 | 153700 | nan | 1.0 | 0.9981 |
| 0.0 | 54.0 | 156600 | nan | 1.0 | 0.9981 |
| 0.0 | 55.0 | 159500 | nan | 1.0 | 0.9981 |
| 0.0 | 56.0 | 162400 | nan | 1.0 | 0.9981 |
| 0.0 | 57.0 | 165300 | nan | 1.0 | 0.9981 |
| 0.0 | 58.0 | 168200 | nan | 1.0 | 0.9981 |
| 0.0 | 59.0 | 171100 | nan | 1.0 | 0.9981 |
| 0.0 | 60.0 | 174000 | nan | 1.0 | 0.9981 |
| 0.0 | 61.0 | 176900 | nan | 1.0 | 0.9981 |
| 0.0 | 62.0 | 179800 | nan | 1.0 | 0.9981 |
| 0.0 | 63.0 | 182700 | nan | 1.0 | 0.9981 |
| 0.0 | 64.0 | 185600 | nan | 1.0 | 0.9981 |
| 0.0 | 65.0 | 188500 | nan | 1.0 | 0.9981 |
| 0.0 | 66.0 | 191400 | nan | 1.0 | 0.9981 |
| 0.0 | 67.0 | 194300 | nan | 1.0 | 0.9981 |
| 0.0 | 68.0 | 197200 | nan | 1.0 | 0.9981 |
| 0.0 | 69.0 | 200100 | nan | 1.0 | 0.9981 |
| 0.0 | 70.0 | 203000 | nan | 1.0 | 0.9981 |
| 0.0 | 71.0 | 205900 | nan | 1.0 | 0.9981 |
| 0.0 | 72.0 | 208800 | nan | 1.0 | 0.9981 |
| 0.0 | 73.0 | 211700 | nan | 1.0 | 0.9981 |
| 0.0 | 74.0 | 214600 | nan | 1.0 | 0.9981 |
| 0.0 | 75.0 | 217500 | nan | 1.0 | 0.9981 |
| 0.0 | 76.0 | 220400 | nan | 1.0 | 0.9981 |
| 0.0 | 77.0 | 223300 | nan | 1.0 | 0.9981 |
| 0.0 | 78.0 | 226200 | nan | 1.0 | 0.9981 |
| 0.0 | 79.0 | 229100 | nan | 1.0 | 0.9981 |
| 0.0 | 80.0 | 232000 | nan | 1.0 | 0.9981 |
| 0.0 | 81.0 | 234900 | nan | 1.0 | 0.9981 |
| 0.0 | 82.0 | 237800 | nan | 1.0 | 0.9981 |
| 0.0 | 83.0 | 240700 | nan | 1.0 | 0.9981 |
| 0.0 | 84.0 | 243600 | nan | 1.0 | 0.9981 |
| 0.0 | 85.0 | 246500 | nan | 1.0 | 0.9981 |
| 0.0 | 86.0 | 249400 | nan | 1.0 | 0.9981 |
| 0.0 | 87.0 | 252300 | nan | 1.0 | 0.9981 |
| 0.0 | 88.0 | 255200 | nan | 1.0 | 0.9981 |
| 0.0 | 89.0 | 258100 | nan | 1.0 | 0.9981 |
| 0.0 | 90.0 | 261000 | nan | 1.0 | 0.9981 |
| 0.0 | 91.0 | 263900 | nan | 1.0 | 0.9981 |
| 0.0 | 92.0 | 266800 | nan | 1.0 | 0.9981 |
| 0.0 | 93.0 | 269700 | nan | 1.0 | 0.9981 |
| 0.0 | 94.0 | 272600 | nan | 1.0 | 0.9981 |
| 0.0 | 95.0 | 275500 | nan | 1.0 | 0.9981 |
| 0.0 | 96.0 | 278400 | nan | 1.0 | 0.9981 |
| 0.0 | 97.0 | 281300 | nan | 1.0 | 0.9981 |
| 0.0 | 98.0 | 284200 | nan | 1.0 | 0.9981 |
| 0.0 | 99.0 | 287100 | nan | 1.0 | 0.9981 |
| 0.0 | 100.0 | 290000 | nan | 1.0 | 0.9981 |
Framework versions
- Transformers 4.43.3
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.19.1