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This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the /WORKSPACE/DATA/UK/COMPOSED_DATASET/ - NA dataset. It achieves the following results on the evaluation set:
- Loss: 0.1092
- Wer: 0.1752
- Cer: 0.0323
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: 5e-05
- train_batch_size: 16
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 12000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|---|---|---|---|---|---|
| 1.7005 | 1.61 | 500 | 0.4082 | 0.5584 | 0.1164 |
| 1.1555 | 3.22 | 1000 | 0.2020 | 0.2953 | 0.0557 |
| 1.0927 | 4.82 | 1500 | 0.1708 | 0.2584 | 0.0480 |
| 1.0707 | 6.43 | 2000 | 0.1563 | 0.2405 | 0.0450 |
| 1.0728 | 8.04 | 2500 | 0.1620 | 0.2442 | 0.0463 |
| 1.0268 | 9.65 | 3000 | 0.1588 | 0.2378 | 0.0458 |
| 1.0328 | 11.25 | 3500 | 0.1466 | 0.2352 | 0.0442 |
| 1.0249 | 12.86 | 4000 | 0.1552 | 0.2341 | 0.0449 |
| 1.016 | 14.47 | 4500 | 0.1602 | 0.2435 | 0.0473 |
| 1.0164 | 16.08 | 5000 | 0.1491 | 0.2337 | 0.0444 |
| 0.9935 | 17.68 | 5500 | 0.1539 | 0.2373 | 0.0458 |
| 0.9626 | 19.29 | 6000 | 0.1458 | 0.2305 | 0.0434 |
| 0.9505 | 20.9 | 6500 | 0.1368 | 0.2157 | 0.0407 |
| 0.9389 | 22.51 | 7000 | 0.1437 | 0.2231 | 0.0426 |
| 0.9129 | 24.12 | 7500 | 0.1313 | 0.2076 | 0.0394 |
| 0.9118 | 25.72 | 8000 | 0.1292 | 0.2040 | 0.0384 |
| 0.8848 | 27.33 | 8500 | 0.1299 | 0.2028 | 0.0384 |
| 0.8667 | 28.94 | 9000 | 0.1228 | 0.1945 | 0.0367 |
| 0.8641 | 30.55 | 9500 | 0.1223 | 0.1939 | 0.0364 |
| 0.8516 | 32.15 | 10000 | 0.1184 | 0.1876 | 0.0349 |
| 0.8379 | 33.76 | 10500 | 0.1137 | 0.1821 | 0.0338 |
| 0.8235 | 35.37 | 11000 | 0.1127 | 0.1779 | 0.0331 |
| 0.8112 | 36.98 | 11500 | 0.1103 | 0.1766 | 0.0327 |
| 0.8069 | 38.59 | 12000 | 0.1092 | 0.1752 | 0.0323 |
Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2
- Datasets 1.18.4.dev0
- Tokenizers 0.11.0
