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wav2vec2-base-timit-demo-google-colab
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.5253
- Wer: 0.3406
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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 3.4884 | 1.0 | 500 | 1.6139 | 1.0293 |
| 0.8373 | 2.01 | 1000 | 0.5286 | 0.5266 |
| 0.4394 | 3.01 | 1500 | 0.4933 | 0.4678 |
| 0.2974 | 4.02 | 2000 | 0.4159 | 0.4268 |
| 0.2268 | 5.02 | 2500 | 0.4288 | 0.4074 |
| 0.1901 | 6.02 | 3000 | 0.4407 | 0.3852 |
| 0.1627 | 7.03 | 3500 | 0.4599 | 0.3849 |
| 0.1397 | 8.03 | 4000 | 0.4330 | 0.3803 |
| 0.1342 | 9.04 | 4500 | 0.4661 | 0.3785 |
| 0.1165 | 10.04 | 5000 | 0.4518 | 0.3745 |
| 0.1 | 11.04 | 5500 | 0.4714 | 0.3899 |
| 0.0881 | 12.05 | 6000 | 0.4985 | 0.3848 |
| 0.0794 | 13.05 | 6500 | 0.5074 | 0.3672 |
| 0.0707 | 14.06 | 7000 | 0.5692 | 0.3681 |
| 0.0669 | 15.06 | 7500 | 0.4722 | 0.3814 |
| 0.0589 | 16.06 | 8000 | 0.5738 | 0.3784 |
| 0.0562 | 17.07 | 8500 | 0.5183 | 0.3696 |
| 0.0578 | 18.07 | 9000 | 0.5473 | 0.3841 |
| 0.0473 | 19.08 | 9500 | 0.4918 | 0.3655 |
| 0.0411 | 20.08 | 10000 | 0.5258 | 0.3517 |
| 0.0419 | 21.08 | 10500 | 0.5256 | 0.3501 |
| 0.0348 | 22.09 | 11000 | 0.5511 | 0.3597 |
| 0.0328 | 23.09 | 11500 | 0.5054 | 0.3560 |
| 0.0314 | 24.1 | 12000 | 0.5327 | 0.3537 |
| 0.0296 | 25.1 | 12500 | 0.5142 | 0.3446 |
| 0.0251 | 26.1 | 13000 | 0.5155 | 0.3411 |
| 0.0249 | 27.11 | 13500 | 0.5344 | 0.3414 |
| 0.0225 | 28.11 | 14000 | 0.5193 | 0.3408 |
| 0.0226 | 29.12 | 14500 | 0.5253 | 0.3406 |
Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
