---
language:
- vi
base_model: openai/whisper-small-vi-v2
tags:
- generated_from_trainer
datasets:
- vi_500/80k
metrics:
- wer
model-index:
- name: Whisper Small Vi - Anh Phuong
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: vi 500
      type: vi_500/80k
      args: 'config: hi, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 5.828968294497862
---

<!-- 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. -->

# Whisper Small Vi - Anh Phuong

This model is a fine-tuned version of [openai/whisper-small-vi-v2](https://huggingface.co/openai/whisper-small-vi-v2) on the vi 500 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1002
- Wer: 5.8290

## 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.1638        | 0.2   | 1000 | 0.1707          | 9.6824 |
| 0.1233        | 0.4   | 2000 | 0.1302          | 7.4792 |
| 0.1063        | 0.6   | 3000 | 0.1097          | 6.4330 |
| 0.0962        | 0.8   | 4000 | 0.1002          | 5.8290 |


### Framework versions

- Transformers 4.43.3
- Pytorch 2.4.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1