---
language:
- vi
base_model: openai/whisper-tiny-vi-v1
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Tiny Vi - Anh Phuong
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: vi 500
      type: mozilla-foundation/common_voice_11_0
      args: 'config: hi, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 17.927542787107694
---

<!-- 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 Tiny Vi - Anh Phuong

This model is a fine-tuned version of [openai/whisper-tiny-vi-v1](https://huggingface.co/openai/whisper-tiny-vi-v1) on the vi 500 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3071
- Wer: 17.9275

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.4594        | 0.16  | 1000 | 0.4406          | 24.6174 |
| 0.3731        | 0.32  | 2000 | 0.3586          | 20.4809 |
| 0.3199        | 0.48  | 3000 | 0.3223          | 18.8015 |
| 0.3026        | 0.64  | 4000 | 0.3071          | 17.9275 |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1