---
base_model: daila/wav2vec2-large-xls-r-300m-vi-colab
tags:
- generated_from_trainer
datasets:
- common_voice_16_1
metrics:
- wer
model-index:
- name: wav2vec2-large-xls-r-300m-vi-colab
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_16_1
      type: common_voice_16_1
      config: vi
      split: test
      args: vi
    metrics:
    - name: Wer
      type: wer
      value: 0.5894672631150875
---

<!-- 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-vi-colab

This model is a fine-tuned version of [daila/wav2vec2-large-xls-r-300m-vi-colab](https://huggingface.co/daila/wav2vec2-large-xls-r-300m-vi-colab) on the common_voice_16_1 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6432
- Wer: 0.5895

## 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: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0916        | 4.52  | 400  | 1.5440          | 0.6357 |
| 0.1344        | 9.04  | 800  | 1.6043          | 0.6543 |
| 0.0926        | 13.56 | 1200 | 1.7226          | 0.6365 |
| 0.0703        | 18.08 | 1600 | 1.5989          | 0.6048 |
| 0.0557        | 22.6  | 2000 | 1.6714          | 0.6001 |
| 0.051         | 27.12 | 2400 | 1.6432          | 0.5895 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1