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xlsr-syntesized-turkish-4-hour-hlr
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1491
- Wer: 0.1312
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: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 20
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss | Wer |
---|---|---|---|---|
6.3817 | 0.52 | 100 | 3.6970 | 1.0 |
3.1563 | 1.04 | 200 | 3.0338 | 1.0 |
2.0046 | 1.56 | 300 | 0.7680 | 0.6355 |
0.5563 | 2.08 | 400 | 0.3654 | 0.3463 |
0.3678 | 2.6 | 500 | 0.3055 | 0.3413 |
0.3173 | 3.12 | 600 | 0.2524 | 0.3021 |
0.2374 | 3.65 | 700 | 0.2030 | 0.2492 |
0.2194 | 4.17 | 800 | 0.1930 | 0.2411 |
0.1806 | 4.69 | 900 | 0.1653 | 0.1985 |
0.1603 | 5.21 | 1000 | 0.1562 | 0.2021 |
0.142 | 5.73 | 1100 | 0.1491 | 0.1820 |
0.1261 | 6.25 | 1200 | 0.1541 | 0.1787 |
0.1146 | 6.77 | 1300 | 0.1425 | 0.1784 |
0.1049 | 7.29 | 1400 | 0.1364 | 0.1803 |
0.095 | 7.81 | 1500 | 0.1331 | 0.1675 |
0.0887 | 8.33 | 1600 | 0.1349 | 0.1563 |
0.0772 | 8.85 | 1700 | 0.1330 | 0.1679 |
0.0732 | 9.38 | 1800 | 0.1354 | 0.1513 |
0.0735 | 9.9 | 1900 | 0.1390 | 0.1516 |
0.0701 | 10.42 | 2000 | 0.1530 | 0.1480 |
0.0642 | 10.94 | 2100 | 0.1428 | 0.1461 |
0.0611 | 11.46 | 2200 | 0.1366 | 0.1443 |
0.0584 | 11.98 | 2300 | 0.1485 | 0.1429 |
0.0587 | 12.5 | 2400 | 0.1554 | 0.1448 |
0.0559 | 13.02 | 2500 | 0.1430 | 0.1446 |
0.0502 | 13.54 | 2600 | 0.1448 | 0.1389 |
0.0519 | 14.06 | 2700 | 0.1397 | 0.1415 |
0.047 | 14.58 | 2800 | 0.1484 | 0.1403 |
0.0459 | 15.1 | 2900 | 0.1469 | 0.1437 |
0.0436 | 15.62 | 3000 | 0.1491 | 0.1340 |
0.039 | 16.15 | 3100 | 0.1521 | 0.1395 |
0.0406 | 16.67 | 3200 | 0.1513 | 0.1345 |
0.0391 | 17.19 | 3300 | 0.1483 | 0.1359 |
0.0376 | 17.71 | 3400 | 0.1487 | 0.1363 |
0.0383 | 18.23 | 3500 | 0.1478 | 0.1329 |
0.0394 | 18.75 | 3600 | 0.1486 | 0.1347 |
0.0361 | 19.27 | 3700 | 0.1480 | 0.1337 |
0.0342 | 19.79 | 3800 | 0.1491 | 0.1312 |
Framework versions
- Transformers 4.26.0
- Pytorch 2.0.1+cu118
- Datasets 2.9.0
- Tokenizers 0.13.3