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wav2vec2-large-voxrex-npsc
This model is a fine-tuned version of KBLab/wav2vec2-large-voxrex on the NBAILAB/NPSC - 16K_MP3 dataset. It achieves the following results on the evaluation set:
- Loss: nan
- Wer: 1.0
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: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- 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: 2000
- num_epochs: 15.0
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 2.9728 | 0.32 | 500 | 2.9449 | 1.0 |
| 2.5099 | 0.64 | 1000 | 1.8492 | 0.9910 |
| 0.7872 | 0.97 | 1500 | 0.4467 | 0.3774 |
| 0.5993 | 1.29 | 2000 | 0.3181 | 0.2819 |
| 0.5134 | 1.61 | 2500 | 0.2638 | 0.2401 |
| 0.4544 | 1.93 | 3000 | 0.2287 | 0.2091 |
| 0.4085 | 2.26 | 3500 | 0.2153 | 0.1918 |
| 0.3921 | 2.58 | 4000 | 0.2004 | 0.1804 |
| 0.4613 | 2.9 | 4500 | 0.1905 | 0.1732 |
| 0.3402 | 3.22 | 5000 | 0.1778 | 0.1659 |
| 0.3258 | 3.55 | 5500 | 0.1732 | 0.1571 |
| 0.3044 | 3.87 | 6000 | 0.1677 | 0.1497 |
| 0.2914 | 4.19 | 6500 | 0.1597 | 0.1420 |
| 0.278 | 4.51 | 7000 | 0.1574 | 0.1386 |
| 0.2858 | 4.84 | 7500 | 0.1552 | 0.1300 |
| 0.2585 | 5.16 | 8000 | 0.1523 | 0.1276 |
| 0.2827 | 5.48 | 8500 | 0.1448 | 0.1265 |
| 0.3365 | 5.8 | 9000 | 0.1411 | 0.1232 |
| 0.2488 | 6.13 | 9500 | 0.1456 | 0.1195 |
| 0.2406 | 6.45 | 10000 | 0.1414 | 0.1194 |
| 0.2488 | 6.77 | 10500 | 0.1393 | 0.1173 |
| 0.3084 | 7.09 | 11000 | 0.1379 | 0.1164 |
| 0.2365 | 7.41 | 11500 | 0.1387 | 0.1165 |
| 0.2217 | 7.74 | 12000 | 0.1381 | 0.1132 |
| 0.2381 | 8.06 | 12500 | 0.1360 | 0.1126 |
| 0.2329 | 8.38 | 13000 | 0.1357 | 0.1124 |
| 0.2103 | 8.7 | 13500 | 0.1335 | 0.1087 |
| 0.2366 | 9.03 | 14000 | 0.1388 | 0.1105 |
| 0.2289 | 9.35 | 14500 | 0.1383 | 0.1098 |
| 0.2486 | 9.67 | 15000 | 0.1386 | 0.1087 |
| 0.2772 | 9.99 | 15500 | 0.1598 | 0.1093 |
| 0.2728 | 10.32 | 16000 | 0.1814 | 0.1110 |
| 0.3437 | 10.64 | 16500 | 0.2505 | 0.1124 |
| 0.431 | 10.96 | 17000 | 0.2828 | 0.1143 |
| 0.3929 | 11.28 | 17500 | 0.2977 | 0.1149 |
| 0.4396 | 11.61 | 18000 | 0.3198 | 0.1170 |
| 0.59 | 11.93 | 18500 | 0.4158 | 0.1315 |
| 0.7813 | 12.25 | 19000 | 0.6123 | 0.2208 |
| 0.9345 | 12.57 | 19500 | 0.6815 | 0.2885 |
| 0.998 | 12.89 | 20000 | 0.7587 | 0.1991 |
| 1.0493 | 13.22 | 20500 | 0.7583 | 0.1996 |
| 1.438 | 13.54 | 21000 | nan | 1.0 |
| 0.0 | 13.86 | 21500 | nan | 1.0 |
| 0.0 | 14.18 | 22000 | nan | 1.0 |
| 0.0 | 14.51 | 22500 | nan | 1.0 |
| 0.0 | 14.83 | 23000 | nan | 1.0 |
Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu113
- Datasets 1.18.3.dev0
- Tokenizers 0.11.0
