---
language:
- vi
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- common_voice_17_0
metrics:
- wer
model-index:
- name: Whisper small vi - Ox
  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: 31.26665341022072
---

<!-- 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 - Ox

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the common_voice_17_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0138
- Wer: 31.2667

## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer     |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|
| 0.2276        | 0.08  | 1000  | 0.7506          | 29.8509 |
| 0.1768        | 0.16  | 2000  | 0.8114          | 31.2189 |
| 0.1828        | 0.24  | 3000  | 0.8569          | 31.2985 |
| 0.1632        | 0.32  | 4000  | 0.8523          | 31.9268 |
| 0.1566        | 0.4   | 5000  | 0.9062          | 31.9149 |
| 0.1532        | 0.48  | 6000  | 0.8914          | 31.4496 |
| 0.1593        | 0.56  | 7000  | 0.9342          | 31.9825 |
| 0.1411        | 0.64  | 8000  | 0.9412          | 32.0302 |
| 0.1531        | 0.72  | 9000  | 0.9456          | 31.6206 |
| 0.1246        | 0.8   | 10000 | 0.9452          | 31.7240 |
| 0.1336        | 0.88  | 11000 | 0.9622          | 31.1195 |
| 0.1392        | 0.96  | 12000 | 0.9638          | 31.3939 |
| 0.0725        | 1.04  | 13000 | 1.0032          | 31.5649 |
| 0.0838        | 1.12  | 14000 | 1.0346          | 31.7916 |
| 0.0766        | 1.2   | 15000 | 1.0138          | 31.2667 |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.4.1
- Datasets 3.0.1
- Tokenizers 0.15.2