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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: 0.5725
- Wer: 0.3413
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 |
|---|---|---|---|---|
| 3.508 | 1.0 | 500 | 1.9315 | 0.9962 |
| 0.8832 | 2.01 | 1000 | 0.5552 | 0.5191 |
| 0.4381 | 3.01 | 1500 | 0.4451 | 0.4574 |
| 0.2983 | 4.02 | 2000 | 0.4096 | 0.4265 |
| 0.2232 | 5.02 | 2500 | 0.4280 | 0.4083 |
| 0.1811 | 6.02 | 3000 | 0.4307 | 0.3942 |
| 0.1548 | 7.03 | 3500 | 0.4453 | 0.3889 |
| 0.1367 | 8.03 | 4000 | 0.5043 | 0.4138 |
| 0.1238 | 9.04 | 4500 | 0.4530 | 0.3807 |
| 0.1072 | 10.04 | 5000 | 0.4435 | 0.3660 |
| 0.0978 | 11.04 | 5500 | 0.4739 | 0.3676 |
| 0.0887 | 12.05 | 6000 | 0.5052 | 0.3761 |
| 0.0813 | 13.05 | 6500 | 0.5098 | 0.3619 |
| 0.0741 | 14.06 | 7000 | 0.4666 | 0.3602 |
| 0.0654 | 15.06 | 7500 | 0.5642 | 0.3657 |
| 0.0589 | 16.06 | 8000 | 0.5489 | 0.3638 |
| 0.0559 | 17.07 | 8500 | 0.5260 | 0.3598 |
| 0.0562 | 18.07 | 9000 | 0.5250 | 0.3640 |
| 0.0448 | 19.08 | 9500 | 0.5215 | 0.3569 |
| 0.0436 | 20.08 | 10000 | 0.5117 | 0.3560 |
| 0.0412 | 21.08 | 10500 | 0.4910 | 0.3570 |
| 0.0336 | 22.09 | 11000 | 0.5221 | 0.3524 |
| 0.031 | 23.09 | 11500 | 0.5278 | 0.3480 |
| 0.0339 | 24.1 | 12000 | 0.5353 | 0.3486 |
| 0.0278 | 25.1 | 12500 | 0.5342 | 0.3462 |
| 0.0251 | 26.1 | 13000 | 0.5399 | 0.3439 |
| 0.0242 | 27.11 | 13500 | 0.5626 | 0.3431 |
| 0.0214 | 28.11 | 14000 | 0.5749 | 0.3408 |
| 0.0216 | 29.12 | 14500 | 0.5725 | 0.3413 |
Framework versions
- Transformers 4.17.0
- Pytorch 1.12.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
