---
library_name: transformers
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- generated_from_trainer
datasets:
- common_voice_17_0
metrics:
- wer
model-index:
- name: w2v-bert-2.0-Vietnamese-colab-CV17.0
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_17_0
      type: common_voice_17_0
      config: vi
      split: test
      args: vi
    metrics:
    - name: Wer
      type: wer
      value: 0.2728716645489199
---

<!-- 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. -->

# w2v-bert-2.0-Vietnamese-colab-CV17.0

This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on the common_voice_17_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0607
- Wer: 0.2729

## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- 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    |
|:-------------:|:-------:|:----:|:---------------:|:------:|
| 2.8799        | 3.2609  | 300  | 0.7434          | 0.3899 |
| 0.1626        | 6.5217  | 600  | 0.8157          | 0.3578 |
| 0.0823        | 9.7826  | 900  | 0.8759          | 0.3704 |
| 0.04          | 13.0435 | 1200 | 0.9129          | 0.3195 |
| 0.0169        | 16.3043 | 1500 | 0.9113          | 0.2904 |
| 0.0056        | 19.5652 | 1800 | 0.9906          | 0.2809 |
| 0.0016        | 22.8261 | 2100 | 1.0506          | 0.2848 |
| 0.0005        | 26.0870 | 2400 | 1.0502          | 0.2730 |
| 0.0002        | 29.3478 | 2700 | 1.0607          | 0.2729 |


### Framework versions

- Transformers 4.50.0
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1