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xlsr-wav2vec2-2
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.5884
- Wer: 0.4301
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: 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: 800
- num_epochs: 60
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 6.6058 | 1.38 | 400 | 3.1894 | 1.0 |
| 2.3145 | 2.76 | 800 | 0.7193 | 0.7976 |
| 0.6737 | 4.14 | 1200 | 0.5338 | 0.6056 |
| 0.4651 | 5.52 | 1600 | 0.5699 | 0.6007 |
| 0.3968 | 6.9 | 2000 | 0.4608 | 0.5221 |
| 0.3281 | 8.28 | 2400 | 0.5264 | 0.5209 |
| 0.2937 | 9.65 | 2800 | 0.5366 | 0.5096 |
| 0.2619 | 11.03 | 3200 | 0.4902 | 0.5021 |
| 0.2394 | 12.41 | 3600 | 0.4706 | 0.4908 |
| 0.2139 | 13.79 | 4000 | 0.5526 | 0.4871 |
| 0.2034 | 15.17 | 4400 | 0.5396 | 0.5108 |
| 0.1946 | 16.55 | 4800 | 0.4959 | 0.4866 |
| 0.1873 | 17.93 | 5200 | 0.4898 | 0.4877 |
| 0.1751 | 19.31 | 5600 | 0.5488 | 0.4932 |
| 0.1668 | 20.69 | 6000 | 0.5645 | 0.4986 |
| 0.1638 | 22.07 | 6400 | 0.5367 | 0.4946 |
| 0.1564 | 23.45 | 6800 | 0.5282 | 0.4898 |
| 0.1566 | 24.83 | 7200 | 0.5489 | 0.4841 |
| 0.1522 | 26.21 | 7600 | 0.5439 | 0.4821 |
| 0.1378 | 27.59 | 8000 | 0.5796 | 0.4866 |
| 0.1459 | 28.96 | 8400 | 0.5603 | 0.4875 |
| 0.1406 | 30.34 | 8800 | 0.6773 | 0.5005 |
| 0.1298 | 31.72 | 9200 | 0.5858 | 0.4827 |
| 0.1268 | 33.1 | 9600 | 0.6007 | 0.4790 |
| 0.1204 | 34.48 | 10000 | 0.5716 | 0.4734 |
| 0.113 | 35.86 | 10400 | 0.5866 | 0.4748 |
| 0.1088 | 37.24 | 10800 | 0.5790 | 0.4752 |
| 0.1074 | 38.62 | 11200 | 0.5966 | 0.4721 |
| 0.1018 | 40.0 | 11600 | 0.5720 | 0.4668 |
| 0.0968 | 41.38 | 12000 | 0.5826 | 0.4698 |
| 0.0874 | 42.76 | 12400 | 0.5937 | 0.4634 |
| 0.0843 | 44.14 | 12800 | 0.6056 | 0.4640 |
| 0.0822 | 45.52 | 13200 | 0.5531 | 0.4569 |
| 0.0806 | 46.9 | 13600 | 0.5669 | 0.4484 |
| 0.072 | 48.28 | 14000 | 0.5683 | 0.4484 |
| 0.0734 | 49.65 | 14400 | 0.5735 | 0.4437 |
| 0.0671 | 51.03 | 14800 | 0.5455 | 0.4394 |
| 0.0617 | 52.41 | 15200 | 0.5838 | 0.4365 |
| 0.0607 | 53.79 | 15600 | 0.6233 | 0.4397 |
| 0.0593 | 55.17 | 16000 | 0.5649 | 0.4340 |
| 0.0551 | 56.55 | 16400 | 0.5923 | 0.4392 |
| 0.0503 | 57.93 | 16800 | 0.5858 | 0.4325 |
| 0.0496 | 59.31 | 17200 | 0.5884 | 0.4301 |
Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
