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wav2vec2-xls-r-pt-cv7-from-bp400h
This model is a fine-tuned version of lgris/bp_400h_xlsr2_300M on the common_voice dataset. It achieves the following results on the evaluation set:
- Loss: 0.1535
- Wer: 0.1254
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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 5000
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.4991 | 0.13 | 100 | 0.1774 | 0.1464 |
| 0.4655 | 0.26 | 200 | 0.1884 | 0.1568 |
| 0.4689 | 0.39 | 300 | 0.2282 | 0.1672 |
| 0.4662 | 0.52 | 400 | 0.1997 | 0.1584 |
| 0.4592 | 0.65 | 500 | 0.1989 | 0.1663 |
| 0.4533 | 0.78 | 600 | 0.2004 | 0.1698 |
| 0.4391 | 0.91 | 700 | 0.1888 | 0.1642 |
| 0.4655 | 1.04 | 800 | 0.1921 | 0.1624 |
| 0.4138 | 1.17 | 900 | 0.1950 | 0.1602 |
| 0.374 | 1.3 | 1000 | 0.2077 | 0.1658 |
| 0.4064 | 1.43 | 1100 | 0.1945 | 0.1596 |
| 0.3922 | 1.56 | 1200 | 0.2069 | 0.1665 |
| 0.4226 | 1.69 | 1300 | 0.1962 | 0.1573 |
| 0.3974 | 1.82 | 1400 | 0.1919 | 0.1553 |
| 0.3631 | 1.95 | 1500 | 0.1854 | 0.1573 |
| 0.3797 | 2.08 | 1600 | 0.1902 | 0.1550 |
| 0.3287 | 2.21 | 1700 | 0.1926 | 0.1598 |
| 0.3568 | 2.34 | 1800 | 0.1888 | 0.1534 |
| 0.3415 | 2.47 | 1900 | 0.1834 | 0.1502 |
| 0.3545 | 2.6 | 2000 | 0.1906 | 0.1560 |
| 0.3344 | 2.73 | 2100 | 0.1804 | 0.1524 |
| 0.3308 | 2.86 | 2200 | 0.1741 | 0.1485 |
| 0.344 | 2.99 | 2300 | 0.1787 | 0.1455 |
| 0.309 | 3.12 | 2400 | 0.1773 | 0.1448 |
| 0.312 | 3.25 | 2500 | 0.1738 | 0.1440 |
| 0.3066 | 3.38 | 2600 | 0.1727 | 0.1417 |
| 0.2999 | 3.51 | 2700 | 0.1692 | 0.1436 |
| 0.2985 | 3.64 | 2800 | 0.1732 | 0.1430 |
| 0.3058 | 3.77 | 2900 | 0.1754 | 0.1402 |
| 0.2943 | 3.9 | 3000 | 0.1691 | 0.1379 |
| 0.2813 | 4.03 | 3100 | 0.1754 | 0.1376 |
| 0.2733 | 4.16 | 3200 | 0.1639 | 0.1363 |
| 0.2592 | 4.29 | 3300 | 0.1675 | 0.1349 |
| 0.2697 | 4.42 | 3400 | 0.1618 | 0.1360 |
| 0.2538 | 4.55 | 3500 | 0.1658 | 0.1348 |
| 0.2746 | 4.67 | 3600 | 0.1674 | 0.1325 |
| 0.2655 | 4.8 | 3700 | 0.1655 | 0.1319 |
| 0.2745 | 4.93 | 3800 | 0.1665 | 0.1316 |
| 0.2617 | 5.06 | 3900 | 0.1600 | 0.1311 |
| 0.2674 | 5.19 | 4000 | 0.1623 | 0.1311 |
| 0.237 | 5.32 | 4100 | 0.1591 | 0.1315 |
| 0.2669 | 5.45 | 4200 | 0.1584 | 0.1295 |
| 0.2476 | 5.58 | 4300 | 0.1572 | 0.1285 |
| 0.2445 | 5.71 | 4400 | 0.1580 | 0.1271 |
| 0.2207 | 5.84 | 4500 | 0.1567 | 0.1269 |
| 0.2289 | 5.97 | 4600 | 0.1536 | 0.1260 |
| 0.2438 | 6.1 | 4700 | 0.1530 | 0.1260 |
| 0.227 | 6.23 | 4800 | 0.1544 | 0.1249 |
| 0.2256 | 6.36 | 4900 | 0.1543 | 0.1254 |
| 0.2184 | 6.49 | 5000 | 0.1535 | 0.1254 |
Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.0
- Tokenizers 0.10.3
