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b20-wav2vec2-large-xls-r-romansh-colab
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice_13_0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.3738
- Wer: 0.3181
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: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 30
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 9.6653 | 0.76 | 100 | 3.2423 | 1.0 |
| 3.0224 | 1.52 | 200 | 3.0321 | 1.0 |
| 2.969 | 2.29 | 300 | 3.0174 | 1.0 |
| 2.964 | 3.05 | 400 | 2.9531 | 1.0 |
| 2.9488 | 3.81 | 500 | 2.9441 | 1.0 |
| 2.962 | 4.58 | 600 | 2.9383 | 1.0 |
| 2.9646 | 5.34 | 700 | 2.9377 | 1.0 |
| 2.9411 | 6.11 | 800 | 2.9303 | 1.0 |
| 2.9313 | 6.87 | 900 | 2.9264 | 1.0 |
| 2.9327 | 7.63 | 1000 | 2.9211 | 1.0 |
| 2.9574 | 8.4 | 1100 | 2.9145 | 1.0 |
| 2.9227 | 9.16 | 1200 | 2.9034 | 1.0 |
| 2.8916 | 9.92 | 1300 | 2.8764 | 1.0 |
| 2.8311 | 10.68 | 1400 | 2.5611 | 0.9995 |
| 2.0497 | 11.45 | 1500 | 1.1256 | 0.8784 |
| 1.2359 | 12.21 | 1600 | 0.7668 | 0.7143 |
| 0.9607 | 12.97 | 1700 | 0.6340 | 0.6388 |
| 0.804 | 13.74 | 1800 | 0.5658 | 0.5806 |
| 0.693 | 14.5 | 1900 | 0.5147 | 0.5389 |
| 0.6403 | 15.27 | 2000 | 0.4711 | 0.4797 |
| 0.5716 | 16.03 | 2100 | 0.4298 | 0.4520 |
| 0.5124 | 16.79 | 2200 | 0.4353 | 0.4313 |
| 0.5104 | 17.56 | 2300 | 0.3991 | 0.3952 |
| 0.4416 | 18.32 | 2400 | 0.4012 | 0.3933 |
| 0.4419 | 19.08 | 2500 | 0.3945 | 0.3687 |
| 0.406 | 19.84 | 2600 | 0.4003 | 0.3675 |
| 0.3946 | 20.61 | 2700 | 0.3901 | 0.3579 |
| 0.379 | 21.37 | 2800 | 0.3963 | 0.3537 |
| 0.3663 | 22.14 | 2900 | 0.3826 | 0.3435 |
| 0.3425 | 22.9 | 3000 | 0.3850 | 0.3435 |
| 0.3396 | 23.66 | 3100 | 0.3852 | 0.3405 |
| 0.3041 | 24.43 | 3200 | 0.3771 | 0.3265 |
| 0.3194 | 25.19 | 3300 | 0.3796 | 0.3265 |
| 0.312 | 25.95 | 3400 | 0.3734 | 0.3228 |
| 0.313 | 26.71 | 3500 | 0.3864 | 0.3270 |
| 0.3039 | 27.48 | 3600 | 0.3734 | 0.3149 |
| 0.2929 | 28.24 | 3700 | 0.3785 | 0.3223 |
| 0.2884 | 29.01 | 3800 | 0.3734 | 0.3160 |
| 0.2812 | 29.77 | 3900 | 0.3738 | 0.3181 |
Framework versions
- Transformers 4.26.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
