---
license: apache-2.0
base_model: facebook/wav2vec2-large-xlsr-53
tags:
- generated_from_trainer
datasets:
- common_voice_16_1
metrics:
- wer
model-index:
- name: wav2vec2-common-voice-16_1_vi
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_16_1
      type: common_voice_16_1
      config: vi
      split: None
      args: vi
    metrics:
    - name: Wer
      type: wer
      value: 0.8004270028466857
---

<!-- 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-common-voice-16_1_vi

This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice_16_1 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4999
- Wer: 0.8004

## 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    |
|:-------------:|:-------:|:----:|:---------------:|:------:|
| 16.2607       | 4.2373  | 500  | 3.5522          | 0.9999 |
| 3.4445        | 8.4746  | 1000 | 3.3740          | 0.9999 |
| 2.2065        | 12.7119 | 1500 | 1.5781          | 0.9709 |
| 0.7589        | 16.9492 | 2000 | 1.4173          | 0.8542 |
| 0.4407        | 21.1864 | 2500 | 1.4711          | 0.8281 |
| 0.3235        | 25.4237 | 3000 | 1.4836          | 0.8097 |
| 0.2751        | 29.6610 | 3500 | 1.4999          | 0.8004 |


### Framework versions

- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1