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wav2vec2-xls-r-1b-npsc
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the NbAiLab/NPSC (16K_mp3_bokmaal) dataset. It achieves the following results on the evaluation set:
- Loss: 0.1598
- WER: 0.0966
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 |
|---|---|---|---|---|
| 0.8361 | 0.32 | 500 | 0.6304 | 0.4970 |
| 0.5703 | 0.64 | 1000 | 0.3195 | 0.2775 |
| 0.5451 | 0.97 | 1500 | 0.2700 | 0.2246 |
| 0.47 | 1.29 | 2000 | 0.2564 | 0.2329 |
| 0.4063 | 1.61 | 2500 | 0.2459 | 0.2099 |
| 0.374 | 1.93 | 3000 | 0.2175 | 0.1894 |
| 0.3297 | 2.26 | 3500 | 0.2036 | 0.1755 |
| 0.3145 | 2.58 | 4000 | 0.1957 | 0.1757 |
| 0.3989 | 2.9 | 4500 | 0.1923 | 0.1723 |
| 0.271 | 3.22 | 5000 | 0.1889 | 0.1649 |
| 0.2758 | 3.55 | 5500 | 0.1768 | 0.1588 |
| 0.2683 | 3.87 | 6000 | 0.1720 | 0.1534 |
| 0.2341 | 4.19 | 6500 | 0.1689 | 0.1471 |
| 0.2316 | 4.51 | 7000 | 0.1706 | 0.1405 |
| 0.2383 | 4.84 | 7500 | 0.1637 | 0.1426 |
| 0.2148 | 5.16 | 8000 | 0.1584 | 0.1347 |
| 0.2085 | 5.48 | 8500 | 0.1601 | 0.1387 |
| 0.2944 | 5.8 | 9000 | 0.1566 | 0.1294 |
| 0.1944 | 6.13 | 9500 | 0.1494 | 0.1271 |
| 0.1853 | 6.45 | 10000 | 0.1561 | 0.1247 |
| 0.235 | 6.77 | 10500 | 0.1461 | 0.1215 |
| 0.2286 | 7.09 | 11000 | 0.1447 | 0.1167 |
| 0.1781 | 7.41 | 11500 | 0.1502 | 0.1199 |
| 0.1714 | 7.74 | 12000 | 0.1425 | 0.1179 |
| 0.1725 | 8.06 | 12500 | 0.1427 | 0.1173 |
| 0.143 | 8.38 | 13000 | 0.1448 | 0.1142 |
| 0.154 | 8.7 | 13500 | 0.1392 | 0.1104 |
| 0.1447 | 9.03 | 14000 | 0.1404 | 0.1094 |
| 0.1471 | 9.35 | 14500 | 0.1404 | 0.1088 |
| 0.1479 | 9.67 | 15000 | 0.1414 | 0.1133 |
| 0.1607 | 9.99 | 15500 | 0.1458 | 0.1171 |
| 0.166 | 10.32 | 16000 | 0.1652 | 0.1264 |
| 0.188 | 10.64 | 16500 | 0.1713 | 0.1322 |
| 0.1461 | 10.96 | 17000 | 0.1423 | 0.1111 |
| 0.1289 | 11.28 | 17500 | 0.1388 | 0.1097 |
| 0.1273 | 11.61 | 18000 | 0.1438 | 0.1074 |
| 0.1317 | 11.93 | 18500 | 0.1312 | 0.1066 |
| 0.1448 | 12.25 | 19000 | 0.1446 | 0.1042 |
| 0.1424 | 12.57 | 19500 | 0.1386 | 0.1015 |
| 0.1392 | 12.89 | 20000 | 0.1379 | 0.1005 |
| 0.1408 | 13.22 | 20500 | 0.1408 | 0.0992 |
| 0.1239 | 13.54 | 21000 | 0.1338 | 0.0968 |
| 0.1244 | 13.86 | 21500 | 0.1335 | 0.0957 |
| 0.1254 | 14.18 | 22000 | 0.1382 | 0.0950 |
| 0.1597 | 14.51 | 22500 | 0.1544 | 0.0970 |
| 0.1566 | 14.83 | 23000 | 0.1589 | 0.0963 |
Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu113
- Datasets 1.18.3.dev0
- Tokenizers 0.11.0
