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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: 0.5501
- Wer: 0.3424
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 |
|---|---|---|---|---|
| 3.5448 | 1.0 | 500 | 2.5044 | 1.0 |
| 1.0167 | 2.01 | 1000 | 0.5435 | 0.5278 |
| 0.4453 | 3.01 | 1500 | 0.4450 | 0.4534 |
| 0.3 | 4.02 | 2000 | 0.4401 | 0.4245 |
| 0.2304 | 5.02 | 2500 | 0.4146 | 0.4022 |
| 0.1889 | 6.02 | 3000 | 0.4241 | 0.3927 |
| 0.1573 | 7.03 | 3500 | 0.4545 | 0.3878 |
| 0.1363 | 8.03 | 4000 | 0.4936 | 0.3940 |
| 0.1213 | 9.04 | 4500 | 0.4964 | 0.3806 |
| 0.108 | 10.04 | 5000 | 0.4931 | 0.3826 |
| 0.0982 | 11.04 | 5500 | 0.5373 | 0.3778 |
| 0.0883 | 12.05 | 6000 | 0.4978 | 0.3733 |
| 0.0835 | 13.05 | 6500 | 0.5189 | 0.3728 |
| 0.0748 | 14.06 | 7000 | 0.4608 | 0.3692 |
| 0.068 | 15.06 | 7500 | 0.4827 | 0.3608 |
| 0.0596 | 16.06 | 8000 | 0.5022 | 0.3661 |
| 0.056 | 17.07 | 8500 | 0.5482 | 0.3646 |
| 0.0565 | 18.07 | 9000 | 0.5158 | 0.3573 |
| 0.0487 | 19.08 | 9500 | 0.4910 | 0.3513 |
| 0.0444 | 20.08 | 10000 | 0.5771 | 0.3580 |
| 0.045 | 21.08 | 10500 | 0.5160 | 0.3539 |
| 0.0363 | 22.09 | 11000 | 0.5367 | 0.3503 |
| 0.0313 | 23.09 | 11500 | 0.5773 | 0.3500 |
| 0.0329 | 24.1 | 12000 | 0.5683 | 0.3508 |
| 0.0297 | 25.1 | 12500 | 0.5355 | 0.3464 |
| 0.0272 | 26.1 | 13000 | 0.5317 | 0.3450 |
| 0.0256 | 27.11 | 13500 | 0.5602 | 0.3443 |
| 0.0242 | 28.11 | 14000 | 0.5586 | 0.3419 |
| 0.0239 | 29.12 | 14500 | 0.5501 | 0.3424 |
Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
