This model is a fine-tuned version of KBLab/wav2vec2-large-voxrex on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - SV-SE dataset. It achieves the following results on the evaluation set:
- Loss: 0.2201
- Wer: 0.1778
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: 7.5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- 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: 50.0
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 3.1522 | 1.45 | 500 | 3.1290 | 1.0 |
| 2.9576 | 2.91 | 1000 | 2.9633 | 1.0 |
| 1.9853 | 4.36 | 1500 | 0.8902 | 0.6104 |
| 1.5867 | 5.81 | 2000 | 0.4793 | 0.3664 |
| 1.4608 | 7.27 | 2500 | 0.3816 | 0.3095 |
| 1.3496 | 8.72 | 3000 | 0.3415 | 0.2783 |
| 1.3058 | 10.17 | 3500 | 0.3072 | 0.2519 |
| 1.2533 | 11.63 | 4000 | 0.2877 | 0.2381 |
| 1.2535 | 13.08 | 4500 | 0.2791 | 0.2320 |
| 1.2273 | 14.53 | 5000 | 0.2726 | 0.2282 |
| 1.2083 | 15.99 | 5500 | 0.2638 | 0.2212 |
| 1.1606 | 17.44 | 6000 | 0.2531 | 0.2174 |
| 1.1545 | 18.89 | 6500 | 0.2468 | 0.2109 |
| 1.1344 | 20.35 | 7000 | 0.2494 | 0.2050 |
| 1.1173 | 21.8 | 7500 | 0.2447 | 0.1980 |
| 1.1081 | 23.26 | 8000 | 0.2428 | 0.1998 |
| 1.1023 | 24.71 | 8500 | 0.2329 | 0.1951 |
| 1.0923 | 26.16 | 9000 | 0.2388 | 0.1962 |
| 1.0798 | 27.61 | 9500 | 0.2363 | 0.1944 |
| 1.0769 | 29.07 | 10000 | 0.2342 | 0.1913 |
| 1.0672 | 30.52 | 10500 | 0.2250 | 0.1875 |
| 1.0735 | 31.97 | 11000 | 0.2305 | 0.1874 |
| 1.0628 | 33.43 | 11500 | 0.2291 | 0.1851 |
| 1.0451 | 34.88 | 12000 | 0.2263 | 0.1856 |
| 1.0299 | 36.34 | 12500 | 0.2257 | 0.1834 |
| 1.0368 | 37.79 | 13000 | 0.2230 | 0.1808 |
| 1.0322 | 39.24 | 13500 | 0.2231 | 0.1833 |
| 1.0451 | 40.7 | 14000 | 0.2197 | 0.1817 |
| 1.0304 | 42.15 | 14500 | 0.2241 | 0.1813 |
| 1.0102 | 43.6 | 15000 | 0.2233 | 0.1795 |
| 1.0135 | 45.06 | 15500 | 0.2200 | 0.1794 |
| 1.014 | 46.51 | 16000 | 0.2207 | 0.1779 |
| 1.0071 | 47.96 | 16500 | 0.2205 | 0.1784 |
| 0.9729 | 49.42 | 17000 | 0.2204 | 0.1777 |
Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0