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---
base_model: facebook/wav2vec2-xls-r-300m
license: apache-2.0
metrics:
- wer
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-xls-r-300m-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-300m-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: 0.5156
- Wer: 0.3337
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 8.3411 | 0.66 | 500 | 3.5728 | 1.0 |
| 3.418 | 1.31 | 1000 | 3.1432 | 1.0 |
| 1.6726 | 1.97 | 1500 | 0.7995 | 0.7146 |
| 0.8244 | 2.62 | 2000 | 0.5569 | 0.5370 |
| 0.6392 | 3.28 | 2500 | 0.4773 | 0.4744 |
| 0.5537 | 3.94 | 3000 | 0.4592 | 0.4631 |
| 0.4956 | 4.59 | 3500 | 0.4649 | 0.4536 |
| 0.4539 | 5.25 | 4000 | 0.4345 | 0.4175 |
| 0.4144 | 5.91 | 4500 | 0.4291 | 0.4204 |
| 0.3899 | 6.56 | 5000 | 0.4325 | 0.4105 |
| 0.3748 | 7.22 | 5500 | 0.4151 | 0.3954 |
| 0.3543 | 7.87 | 6000 | 0.4320 | 0.4070 |
| 0.3335 | 8.53 | 6500 | 0.4061 | 0.3776 |
| 0.3266 | 9.19 | 7000 | 0.4307 | 0.3899 |
| 0.3107 | 9.84 | 7500 | 0.4404 | 0.3866 |
| 0.2886 | 10.5 | 8000 | 0.4528 | 0.3825 |
| 0.2897 | 11.15 | 8500 | 0.4027 | 0.3731 |
| 0.2757 | 11.81 | 9000 | 0.4423 | 0.3837 |
| 0.2582 | 12.47 | 9500 | 0.4412 | 0.3717 |
| 0.2598 | 13.12 | 10000 | 0.4410 | 0.3609 |
| 0.2421 | 13.78 | 10500 | 0.4398 | 0.3651 |
| 0.2414 | 14.44 | 11000 | 0.4488 | 0.3585 |
| 0.2259 | 15.09 | 11500 | 0.4528 | 0.3572 |
| 0.2269 | 15.75 | 12000 | 0.4613 | 0.3590 |
| 0.2109 | 16.4 | 12500 | 0.4492 | 0.3610 |
| 0.2097 | 17.06 | 13000 | 0.4468 | 0.3522 |
| 0.1992 | 17.72 | 13500 | 0.4520 | 0.3531 |
| 0.1949 | 18.37 | 14000 | 0.4782 | 0.3525 |
| 0.1924 | 19.03 | 14500 | 0.4643 | 0.3459 |
| 0.1906 | 19.69 | 15000 | 0.4839 | 0.3519 |
| 0.1837 | 20.34 | 15500 | 0.4891 | 0.3427 |
| 0.1744 | 21.0 | 16000 | 0.4905 | 0.3481 |
| 0.1705 | 21.65 | 16500 | 0.4758 | 0.3445 |
| 0.1697 | 22.31 | 17000 | 0.4765 | 0.3441 |
| 0.1657 | 22.97 | 17500 | 0.5059 | 0.3447 |
| 0.1582 | 23.62 | 18000 | 0.4941 | 0.3446 |
| 0.159 | 24.28 | 18500 | 0.4977 | 0.3469 |
| 0.1562 | 24.93 | 19000 | 0.4966 | 0.3415 |
| 0.1516 | 25.59 | 19500 | 0.5130 | 0.3403 |
| 0.144 | 26.25 | 20000 | 0.5049 | 0.3390 |
| 0.1429 | 26.9 | 20500 | 0.5130 | 0.3355 |
| 0.1378 | 27.56 | 21000 | 0.5140 | 0.3371 |
| 0.1436 | 28.22 | 21500 | 0.5172 | 0.3351 |
| 0.1363 | 28.87 | 22000 | 0.5215 | 0.3361 |
| 0.1332 | 29.53 | 22500 | 0.5156 | 0.3337 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
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