---
library_name: transformers
language:
- vi
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Small vn - pbl4
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 11.0
      type: mozilla-foundation/common_voice_11_0
      config: vi
      split: None
      args: 'config: vi, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 27.821033008005262
---

<!-- 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 vn - pbl4

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7314
- Wer: 27.8210

## 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: Use 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: 500
- training_steps: 4000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer     |
|:-------------:|:-------:|:----:|:---------------:|:-------:|
| 0.0252        | 5.7471  | 1000 | 0.6066          | 28.1171 |
| 0.0006        | 11.4943 | 2000 | 0.6882          | 27.6017 |
| 0.0003        | 17.2414 | 3000 | 0.7211          | 27.9088 |
| 0.0002        | 22.9885 | 4000 | 0.7314          | 27.8210 |


### Framework versions

- Transformers 4.47.1
- Pytorch 2.4.0
- Datasets 3.2.0
- Tokenizers 0.21.0