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wav2vec2-dataset-vios
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the vivos_dataset dataset. It achieves the following results on the evaluation set:
- Loss: 0.5423
- Wer: 0.4075
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: 30
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 5.0963 | 1.1 | 400 | 1.1336 | 0.7374 |
| 0.6576 | 2.2 | 800 | 0.4716 | 0.3727 |
| 0.4099 | 3.3 | 1200 | 0.3907 | 0.3100 |
| 0.3332 | 4.4 | 1600 | 0.3735 | 0.2766 |
| 0.2976 | 5.49 | 2000 | 0.3932 | 0.2801 |
| 0.2645 | 6.59 | 2400 | 0.3628 | 0.2542 |
| 0.2395 | 7.69 | 2800 | 0.3702 | 0.2734 |
| 0.2208 | 8.79 | 3200 | 0.3667 | 0.2467 |
| 0.1974 | 9.89 | 3600 | 0.3688 | 0.2398 |
| 0.1772 | 10.99 | 4000 | 0.3819 | 0.2457 |
| 0.1695 | 12.09 | 4400 | 0.3840 | 0.2451 |
| 0.319 | 13.19 | 4800 | 0.6531 | 0.4084 |
| 0.7305 | 14.29 | 5200 | 0.9883 | 0.6348 |
| 0.5787 | 15.38 | 5600 | 0.5260 | 0.3063 |
| 0.8558 | 16.48 | 6000 | 1.2870 | 0.7692 |
| 1.155 | 17.58 | 6400 | 1.0568 | 0.6353 |
| 0.8393 | 18.68 | 6800 | 0.7360 | 0.4486 |
| 0.6094 | 19.78 | 7200 | 0.6072 | 0.4108 |
| 0.5346 | 20.88 | 7600 | 0.5749 | 0.4095 |
| 0.5073 | 21.98 | 8000 | 0.5588 | 0.4056 |
| 0.4859 | 23.08 | 8400 | 0.5475 | 0.4015 |
| 0.475 | 24.18 | 8800 | 0.5430 | 0.4011 |
| 0.4683 | 25.27 | 9200 | 0.5400 | 0.3990 |
| 0.4673 | 26.37 | 9600 | 0.5407 | 0.4011 |
| 0.4665 | 27.47 | 10000 | 0.5408 | 0.3992 |
| 0.4703 | 28.57 | 10400 | 0.5420 | 0.4070 |
| 0.4709 | 29.67 | 10800 | 0.5423 | 0.4075 |
Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
