<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->
wav2vec2-large-xls-r-300m-pt-colab
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset. It achieves the following results on the evaluation set:
- Loss: 0.3637
- Wer: 0.2982
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 |
|---|---|---|---|---|
| 4.591 | 1.15 | 400 | 0.9128 | 0.6517 |
| 0.5049 | 2.31 | 800 | 0.4596 | 0.4437 |
| 0.2871 | 3.46 | 1200 | 0.3964 | 0.3905 |
| 0.2077 | 4.61 | 1600 | 0.3958 | 0.3744 |
| 0.1695 | 5.76 | 2000 | 0.4040 | 0.3720 |
| 0.1478 | 6.92 | 2400 | 0.3866 | 0.3651 |
| 0.1282 | 8.07 | 2800 | 0.3987 | 0.3674 |
| 0.1134 | 9.22 | 3200 | 0.4128 | 0.3688 |
| 0.1048 | 10.37 | 3600 | 0.3928 | 0.3561 |
| 0.0938 | 11.53 | 4000 | 0.4048 | 0.3619 |
| 0.0848 | 12.68 | 4400 | 0.4229 | 0.3555 |
| 0.0798 | 13.83 | 4800 | 0.3974 | 0.3468 |
| 0.0688 | 14.98 | 5200 | 0.3870 | 0.3503 |
| 0.0658 | 16.14 | 5600 | 0.3875 | 0.3351 |
| 0.061 | 17.29 | 6000 | 0.4133 | 0.3417 |
| 0.0569 | 18.44 | 6400 | 0.3915 | 0.3414 |
| 0.0526 | 19.6 | 6800 | 0.3957 | 0.3231 |
| 0.0468 | 20.75 | 7200 | 0.4110 | 0.3301 |
| 0.0407 | 21.9 | 7600 | 0.3866 | 0.3186 |
| 0.0384 | 23.05 | 8000 | 0.3976 | 0.3193 |
| 0.0363 | 24.21 | 8400 | 0.3910 | 0.3177 |
| 0.0313 | 25.36 | 8800 | 0.3656 | 0.3109 |
| 0.0293 | 26.51 | 9200 | 0.3712 | 0.3092 |
| 0.0277 | 27.66 | 9600 | 0.3613 | 0.3054 |
| 0.0249 | 28.82 | 10000 | 0.3783 | 0.3015 |
| 0.0234 | 29.97 | 10400 | 0.3637 | 0.2982 |
Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu102
- Datasets 1.13.3
- Tokenizers 0.10.3
