---
library_name: transformers
language:
- vi
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- capleaf/viVoice
metrics:
- wer
model-index:
- name: Whisper Small Vi - finetune viVoice - 70000
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: viVoice
      type: capleaf/viVoice
      config: default
      split: test
      args: 'split: train'
    metrics:
    - name: Wer
      type: wer
      value: 14.076664076664077
---

<!-- 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 - finetune viVoice - 70000

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the viVoice dataset.
It achieves the following results on the evaluation set:
- Loss: 5.7260
- Wer: 14.0767

## 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: 1.25e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 64
- total_eval_batch_size: 16
- 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: 1000
- training_steps: 80000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Wer     |
|:-------------:|:------:|:-----:|:---------------:|:-------:|
| 0.1892        | 0.05   | 4000  | 3.5308          | 18.7775 |
| 0.1551        | 0.1    | 8000  | 4.2465          | 18.1171 |
| 0.1444        | 0.15   | 12000 | 4.4830          | 16.9775 |
| 0.1097        | 1.0266 | 16000 | 4.4955          | 16.1357 |
| 0.0966        | 1.0766 | 20000 | 4.8873          | 15.6825 |
| 0.0915        | 1.1266 | 24000 | 4.8408          | 15.6177 |
| 0.0853        | 2.0032 | 28000 | 5.0293          | 15.1904 |
| 0.065         | 2.0532 | 32000 | 5.0290          | 15.8120 |
| 0.0644        | 2.1032 | 36000 | 5.1940          | 14.5299 |
| 0.0584        | 2.1532 | 40000 | 5.3418          | 15.1515 |
| 0.0466        | 3.0298 | 44000 | 5.2564          | 15.2422 |
| 0.0405        | 3.0798 | 48000 | 5.4065          | 14.7112 |
| 0.0412        | 3.1298 | 52000 | 5.5395          | 14.1414 |
| 0.0344        | 4.0064 | 56000 | 5.6079          | 14.5947 |
| 0.0288        | 4.0564 | 60000 | 5.5141          | 14.4911 |
| 0.0257        | 4.1064 | 64000 | 5.6983          | 14.7242 |
| 0.0249        | 4.1564 | 68000 | 5.7079          | 14.0378 |
| 0.0209        | 5.033  | 72000 | 5.5744          | 13.8177 |
| 0.0192        | 5.083  | 76000 | 5.7272          | 14.1803 |
| 0.0185        | 5.133  | 80000 | 5.7260          | 14.0767 |


### Framework versions

- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0