---
language:
- vi
base_model: openai/whisper-small-vi-v2
tags:
- generated_from_trainer
datasets:
- google/fleurs
metrics:
- wer
model-index:
- name: Whisper Small Vi - Anh Phuong
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Google fleurs
      type: google/fleurs
      config: vi_vn
      split: None
      args: 'config: hi, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 14.430800123571208
---

<!-- 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 Google fleurs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4633
- Wer: 14.4308

## 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: 6000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer     |
|:-------------:|:-------:|:----:|:---------------:|:-------:|
| 0.0133        | 4.7619  | 1000 | 0.3913          | 15.3383 |
| 0.0009        | 9.5238  | 2000 | 0.4180          | 14.3227 |
| 0.0006        | 14.2857 | 3000 | 0.4382          | 14.6162 |
| 0.0003        | 19.0476 | 4000 | 0.4496          | 14.4269 |
| 0.0002        | 23.8095 | 5000 | 0.4594          | 14.4578 |
| 0.0002        | 28.5714 | 6000 | 0.4633          | 14.4308 |


### Framework versions

- Transformers 4.42.3
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1