---
base_model: facebook/wav2vec2-base
datasets:
- common_voice_13_0
license: apache-2.0
metrics:
- wer
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-large-xls-r-vi-colab
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: common_voice_13_0
      type: common_voice_13_0
      config: vi
      split: test[:50%]
      args: vi
    metrics:
    - type: wer
      value: 0.9155054191550542
      name: Wer
---

<!-- 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-vi-colab

This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the common_voice_13_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0995
- Wer: 0.9155
- Cer: 0.4345

## 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: 3e-05
- 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: 400
- num_epochs: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer    | Cer    |
|:-------------:|:-------:|:----:|:---------------:|:------:|:------:|
| 14.8797       | 4.4444  | 200  | 4.6129          | 1.0    | 1.0    |
| 3.9436        | 8.8889  | 400  | 3.5521          | 1.0    | 1.0    |
| 3.4845        | 13.3333 | 600  | 3.4997          | 1.0    | 1.0    |
| 3.1358        | 17.7778 | 800  | 2.7899          | 1.0011 | 0.7023 |
| 2.0727        | 22.2222 | 1000 | 2.2606          | 0.9600 | 0.4680 |
| 1.5218        | 26.6667 | 1200 | 2.0995          | 0.9155 | 0.4345 |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1