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wav2vec2-large-xls-r-300m-japanese-colab
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset. It achieves the following results on the evaluation set:
- Loss: 0.8060
- Wer: 0.1393
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: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- 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: 500
- num_epochs: 100
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss | Wer |
---|---|---|---|---|
No log | 2.44 | 100 | 3.7830 | 1.0 |
No log | 4.88 | 200 | 2.9520 | 0.9999 |
No log | 7.32 | 300 | 1.1938 | 0.2940 |
3.7558 | 9.76 | 400 | 0.7278 | 0.1977 |
3.7558 | 12.2 | 500 | 0.6456 | 0.1668 |
3.7558 | 14.63 | 600 | 0.6702 | 0.1530 |
3.7558 | 17.07 | 700 | 0.7131 | 0.1568 |
0.2503 | 19.51 | 800 | 0.7277 | 0.1488 |
0.2503 | 21.95 | 900 | 0.7558 | 0.1630 |
0.2503 | 24.39 | 1000 | 0.7611 | 0.1437 |
0.2503 | 26.83 | 1100 | 0.7501 | 0.1426 |
0.1316 | 29.27 | 1200 | 0.7635 | 0.1445 |
0.1316 | 31.71 | 1300 | 0.8348 | 0.1578 |
0.1316 | 34.15 | 1400 | 0.7285 | 0.1545 |
0.1316 | 36.59 | 1500 | 0.7949 | 0.1491 |
0.0974 | 39.02 | 1600 | 0.7706 | 0.1524 |
0.0974 | 41.46 | 1700 | 0.8180 | 0.1432 |
0.0974 | 43.9 | 1800 | 0.7718 | 0.1281 |
0.0974 | 46.34 | 1900 | 0.7915 | 0.1315 |
0.0731 | 48.78 | 2000 | 0.7905 | 0.1337 |
0.0731 | 51.22 | 2100 | 0.8401 | 0.1340 |
0.0731 | 53.66 | 2200 | 0.7810 | 0.1410 |
0.0731 | 56.1 | 2300 | 0.8034 | 0.1418 |
0.0569 | 58.54 | 2400 | 0.8219 | 0.1472 |
0.0569 | 60.98 | 2500 | 0.7661 | 0.1432 |
0.0569 | 63.41 | 2600 | 0.7989 | 0.1442 |
0.0569 | 65.85 | 2700 | 0.8212 | 0.1440 |
0.0456 | 68.29 | 2800 | 0.8029 | 0.1395 |
0.0456 | 70.73 | 2900 | 0.8113 | 0.1425 |
0.0456 | 73.17 | 3000 | 0.8298 | 0.1434 |
0.0456 | 75.61 | 3100 | 0.8131 | 0.1403 |
0.0343 | 78.05 | 3200 | 0.8313 | 0.1415 |
0.0343 | 80.49 | 3300 | 0.8395 | 0.1434 |
0.0343 | 82.93 | 3400 | 0.8048 | 0.1386 |
0.0343 | 85.37 | 3500 | 0.8126 | 0.1393 |
0.026 | 87.8 | 3600 | 0.7933 | 0.1378 |
0.026 | 90.24 | 3700 | 0.8317 | 0.1389 |
0.026 | 92.68 | 3800 | 0.8005 | 0.1378 |
0.026 | 95.12 | 3900 | 0.8059 | 0.1385 |
0.0204 | 97.56 | 4000 | 0.8071 | 0.1389 |
0.0204 | 100.0 | 4100 | 0.8060 | 0.1393 |
Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1