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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-lv-v05
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-large-xls-r-300m-lv-v05
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3862
- Wer: 0.2588
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.8836 | 2.81 | 400 | 0.8722 | 0.7244 |
| 0.5365 | 5.63 | 800 | 0.4622 | 0.4812 |
| 0.277 | 8.45 | 1200 | 0.4348 | 0.4056 |
| 0.1947 | 11.27 | 1600 | 0.4223 | 0.3636 |
| 0.1655 | 14.08 | 2000 | 0.4084 | 0.3465 |
| 0.1441 | 16.9 | 2400 | 0.4329 | 0.3497 |
| 0.121 | 19.72 | 2800 | 0.4371 | 0.3324 |
| 0.1062 | 22.53 | 3200 | 0.4202 | 0.3198 |
| 0.0937 | 25.35 | 3600 | 0.4063 | 0.3265 |
| 0.0871 | 28.17 | 4000 | 0.4253 | 0.3255 |
| 0.0755 | 30.98 | 4400 | 0.4368 | 0.3194 |
| 0.0627 | 33.8 | 4800 | 0.4067 | 0.2908 |
| 0.0595 | 36.62 | 5200 | 0.3929 | 0.2973 |
| 0.0523 | 39.44 | 5600 | 0.3748 | 0.2817 |
| 0.0434 | 42.25 | 6000 | 0.3769 | 0.2711 |
| 0.0391 | 45.07 | 6400 | 0.3901 | 0.2653 |
| 0.0319 | 47.88 | 6800 | 0.3862 | 0.2588 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
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