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wav2vec2-large-xls-r-300m-pt-colab
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice_9_0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.2975
- Wer: 0.1736
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 |
|---|---|---|---|---|
| 5.179 | 0.49 | 400 | 1.4554 | 0.9349 |
| 0.7545 | 0.98 | 800 | 0.5594 | 0.5174 |
| 0.4485 | 1.47 | 1200 | 0.3964 | 0.3749 |
| 0.4118 | 1.96 | 1600 | 0.3547 | 0.3172 |
| 0.3282 | 2.45 | 2000 | 0.3372 | 0.3061 |
| 0.3199 | 2.94 | 2400 | 0.3466 | 0.2910 |
| 0.2847 | 3.44 | 2800 | 0.3651 | 0.3310 |
| 0.2713 | 3.93 | 3200 | 0.3509 | 0.3016 |
| 0.2414 | 4.42 | 3600 | 0.3451 | 0.2908 |
| 0.2473 | 4.91 | 4000 | 0.3253 | 0.2747 |
| 0.2168 | 5.4 | 4400 | 0.3243 | 0.2680 |
| 0.219 | 5.89 | 4800 | 0.3067 | 0.2540 |
| 0.196 | 6.38 | 5200 | 0.3268 | 0.2824 |
| 0.1934 | 6.87 | 5600 | 0.3252 | 0.2736 |
| 0.1808 | 7.36 | 6000 | 0.3422 | 0.2737 |
| 0.177 | 7.85 | 6400 | 0.3292 | 0.2707 |
| 0.1626 | 8.34 | 6800 | 0.3089 | 0.2524 |
| 0.1605 | 8.83 | 7200 | 0.3062 | 0.2471 |
| 0.1505 | 9.32 | 7600 | 0.3229 | 0.2474 |
| 0.1491 | 9.82 | 8000 | 0.3098 | 0.2491 |
| 0.1433 | 10.31 | 8400 | 0.3449 | 0.2681 |
| 0.1431 | 10.8 | 8800 | 0.3439 | 0.2532 |
| 0.1349 | 11.29 | 9200 | 0.3112 | 0.2413 |
| 0.1236 | 11.78 | 9600 | 0.3248 | 0.2378 |
| 0.1253 | 12.27 | 10000 | 0.3393 | 0.2394 |
| 0.1195 | 12.76 | 10400 | 0.3050 | 0.2336 |
| 0.1194 | 13.25 | 10800 | 0.3494 | 0.2550 |
| 0.1125 | 13.74 | 11200 | 0.3332 | 0.2395 |
| 0.1063 | 14.23 | 11600 | 0.3134 | 0.2365 |
| 0.1044 | 14.72 | 12000 | 0.3101 | 0.2303 |
| 0.0999 | 15.21 | 12400 | 0.3162 | 0.2248 |
| 0.0986 | 15.71 | 12800 | 0.3183 | 0.2260 |
| 0.0958 | 16.2 | 13200 | 0.3300 | 0.2279 |
| 0.0907 | 16.69 | 13600 | 0.3136 | 0.2260 |
| 0.0875 | 17.18 | 14000 | 0.3492 | 0.2203 |
| 0.0823 | 17.67 | 14400 | 0.3214 | 0.2259 |
| 0.0839 | 18.16 | 14800 | 0.3194 | 0.2145 |
| 0.0783 | 18.65 | 15200 | 0.3122 | 0.2180 |
| 0.0789 | 19.14 | 15600 | 0.3158 | 0.2127 |
| 0.0732 | 19.63 | 16000 | 0.3076 | 0.2109 |
| 0.0715 | 20.12 | 16400 | 0.3216 | 0.2150 |
| 0.0649 | 20.61 | 16800 | 0.2958 | 0.2051 |
| 0.0647 | 21.1 | 17200 | 0.3022 | 0.2014 |
| 0.0649 | 21.59 | 17600 | 0.3045 | 0.2033 |
| 0.0621 | 22.09 | 18000 | 0.3194 | 0.2035 |
| 0.0561 | 22.58 | 18400 | 0.3197 | 0.2022 |
| 0.0582 | 23.07 | 18800 | 0.3109 | 0.1978 |
| 0.0533 | 23.56 | 19200 | 0.3121 | 0.1932 |
| 0.0515 | 24.05 | 19600 | 0.3125 | 0.1939 |
| 0.0484 | 24.54 | 20000 | 0.3081 | 0.1908 |
| 0.0485 | 25.03 | 20400 | 0.3042 | 0.1896 |
| 0.0444 | 25.52 | 20800 | 0.3038 | 0.1886 |
| 0.0426 | 26.01 | 21200 | 0.2985 | 0.1868 |
| 0.0415 | 26.5 | 21600 | 0.3066 | 0.1858 |
| 0.0398 | 26.99 | 22000 | 0.3117 | 0.1828 |
| 0.0397 | 27.48 | 22400 | 0.2980 | 0.1795 |
| 0.0394 | 27.97 | 22800 | 0.2950 | 0.1791 |
| 0.0364 | 28.47 | 23200 | 0.3025 | 0.1773 |
| 0.0365 | 28.96 | 23600 | 0.3022 | 0.1747 |
| 0.0376 | 29.45 | 24000 | 0.2978 | 0.1738 |
| 0.0344 | 29.94 | 24400 | 0.2975 | 0.1736 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.10.0+cu111
- Datasets 2.3.2
- Tokenizers 0.12.1
