|
--- |
|
license: apache-2.0 |
|
base_model: facebook/wav2vec2-xls-r-300m |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- wer |
|
model-index: |
|
- name: wav2vec2-xls-r-300-vivos |
|
results: [] |
|
--- |
|
|
|
<!-- 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-xls-r-300-vivos |
|
|
|
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on an unknown dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.5745 |
|
- Wer: 0.3214 |
|
|
|
## 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: 50 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Wer | |
|
|:-------------:|:-----:|:-----:|:---------------:|:------:| |
|
| 8.1425 | 0.66 | 500 | 3.5478 | 1.0 | |
|
| 3.4041 | 1.31 | 1000 | 2.9316 | 1.0001 | |
|
| 1.6144 | 1.97 | 1500 | 0.7917 | 0.6804 | |
|
| 0.8284 | 2.62 | 2000 | 0.5468 | 0.5401 | |
|
| 0.6356 | 3.28 | 2500 | 0.4703 | 0.4812 | |
|
| 0.553 | 3.94 | 3000 | 0.4371 | 0.4597 | |
|
| 0.4903 | 4.59 | 3500 | 0.4748 | 0.4622 | |
|
| 0.4524 | 5.25 | 4000 | 0.4442 | 0.4235 | |
|
| 0.4107 | 5.91 | 4500 | 0.4354 | 0.4219 | |
|
| 0.3869 | 6.56 | 5000 | 0.4204 | 0.4084 | |
|
| 0.3711 | 7.22 | 5500 | 0.4053 | 0.3917 | |
|
| 0.3507 | 7.87 | 6000 | 0.4134 | 0.3930 | |
|
| 0.3396 | 8.53 | 6500 | 0.4040 | 0.3834 | |
|
| 0.3284 | 9.19 | 7000 | 0.4278 | 0.3961 | |
|
| 0.3096 | 9.84 | 7500 | 0.4590 | 0.3877 | |
|
| 0.2878 | 10.5 | 8000 | 0.4369 | 0.3761 | |
|
| 0.2872 | 11.15 | 8500 | 0.4224 | 0.3759 | |
|
| 0.2756 | 11.81 | 9000 | 0.4442 | 0.3778 | |
|
| 0.2618 | 12.47 | 9500 | 0.4504 | 0.3832 | |
|
| 0.2658 | 13.12 | 10000 | 0.4431 | 0.3677 | |
|
| 0.245 | 13.78 | 10500 | 0.4491 | 0.3684 | |
|
| 0.2467 | 14.44 | 11000 | 0.4436 | 0.3553 | |
|
| 0.2289 | 15.09 | 11500 | 0.4655 | 0.3649 | |
|
| 0.2332 | 15.75 | 12000 | 0.4396 | 0.3530 | |
|
| 0.2205 | 16.4 | 12500 | 0.4577 | 0.3605 | |
|
| 0.2181 | 17.06 | 13000 | 0.4662 | 0.3544 | |
|
| 0.2081 | 17.72 | 13500 | 0.4979 | 0.3617 | |
|
| 0.2009 | 18.37 | 14000 | 0.4564 | 0.3598 | |
|
| 0.1997 | 19.03 | 14500 | 0.4696 | 0.3526 | |
|
| 0.1946 | 19.69 | 15000 | 0.5036 | 0.3590 | |
|
| 0.1937 | 20.34 | 15500 | 0.4763 | 0.3565 | |
|
| 0.1848 | 21.0 | 16000 | 0.5059 | 0.3564 | |
|
| 0.1821 | 21.65 | 16500 | 0.5048 | 0.3622 | |
|
| 0.1784 | 22.31 | 17000 | 0.5252 | 0.3588 | |
|
| 0.1758 | 22.97 | 17500 | 0.4968 | 0.3482 | |
|
| 0.1665 | 23.62 | 18000 | 0.5142 | 0.3511 | |
|
| 0.1661 | 24.28 | 18500 | 0.5230 | 0.3507 | |
|
| 0.1625 | 24.93 | 19000 | 0.5133 | 0.3476 | |
|
| 0.1601 | 25.59 | 19500 | 0.5045 | 0.3406 | |
|
| 0.1521 | 26.25 | 20000 | 0.5205 | 0.3472 | |
|
| 0.1474 | 26.9 | 20500 | 0.5262 | 0.3481 | |
|
| 0.1442 | 27.56 | 21000 | 0.5167 | 0.3393 | |
|
| 0.1487 | 28.22 | 21500 | 0.5420 | 0.3467 | |
|
| 0.1403 | 28.87 | 22000 | 0.5737 | 0.3548 | |
|
| 0.1365 | 29.53 | 22500 | 0.5168 | 0.3359 | |
|
| 0.133 | 30.18 | 23000 | 0.5551 | 0.3394 | |
|
| 0.1372 | 30.84 | 23500 | 0.5464 | 0.3471 | |
|
| 0.1313 | 31.5 | 24000 | 0.5537 | 0.3425 | |
|
| 0.1275 | 32.15 | 24500 | 0.5673 | 0.3366 | |
|
| 0.1177 | 32.81 | 25000 | 0.5440 | 0.3375 | |
|
| 0.1231 | 33.46 | 25500 | 0.5436 | 0.3353 | |
|
| 0.121 | 34.12 | 26000 | 0.5624 | 0.3333 | |
|
| 0.1152 | 34.78 | 26500 | 0.5686 | 0.3415 | |
|
| 0.117 | 35.43 | 27000 | 0.5517 | 0.3390 | |
|
| 0.1139 | 36.09 | 27500 | 0.5543 | 0.3304 | |
|
| 0.1089 | 36.75 | 28000 | 0.5630 | 0.3348 | |
|
| 0.1159 | 37.4 | 28500 | 0.5635 | 0.3366 | |
|
| 0.1115 | 38.06 | 29000 | 0.5657 | 0.3350 | |
|
| 0.1068 | 38.71 | 29500 | 0.5782 | 0.3348 | |
|
| 0.1026 | 39.37 | 30000 | 0.5721 | 0.3282 | |
|
| 0.1058 | 40.03 | 30500 | 0.5746 | 0.3339 | |
|
| 0.1017 | 40.68 | 31000 | 0.5727 | 0.3265 | |
|
| 0.099 | 41.34 | 31500 | 0.5721 | 0.3309 | |
|
| 0.1008 | 41.99 | 32000 | 0.5543 | 0.3274 | |
|
| 0.0957 | 42.65 | 32500 | 0.5642 | 0.3245 | |
|
| 0.0921 | 43.31 | 33000 | 0.5768 | 0.3239 | |
|
| 0.0941 | 43.96 | 33500 | 0.5649 | 0.3235 | |
|
| 0.0927 | 44.62 | 34000 | 0.5659 | 0.3250 | |
|
| 0.0899 | 45.28 | 34500 | 0.5680 | 0.3193 | |
|
| 0.0898 | 45.93 | 35000 | 0.5643 | 0.3212 | |
|
| 0.0864 | 46.59 | 35500 | 0.5769 | 0.3250 | |
|
| 0.0941 | 47.24 | 36000 | 0.5726 | 0.3247 | |
|
| 0.0882 | 47.9 | 36500 | 0.5804 | 0.3250 | |
|
| 0.086 | 48.56 | 37000 | 0.5762 | 0.3225 | |
|
| 0.0861 | 49.21 | 37500 | 0.5748 | 0.3234 | |
|
| 0.0842 | 49.87 | 38000 | 0.5745 | 0.3214 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.39.3 |
|
- Pytorch 2.1.2 |
|
- Datasets 2.18.0 |
|
- Tokenizers 0.15.2 |
|
|
