---
base_model: openai/whisper-base
language:
- vi
license: apache-2.0
metrics:
- wer
tags:
- hf-asr-leaderboard
- generated_from_trainer
model-index:
- name: Whisper Base Mnong
  results: []
---

<!-- 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 Base Mnong

This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the MnongAudio-v2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5864
- Wer: 73.4845

## 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
- training_steps: 4000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer      |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 2.7525        | 0.1421 | 200  | 2.6537          | 416.5818 |
| 2.2459        | 0.2843 | 400  | 2.2237          | 158.5838 |
| 1.8682        | 0.4264 | 600  | 1.8896          | 237.7483 |
| 1.7212        | 0.5686 | 800  | 1.6295          | 110.0866 |
| 1.4164        | 0.7107 | 1000 | 1.4443          | 108.9913 |
| 1.2698        | 0.8529 | 1200 | 1.3000          | 91.3653  |
| 1.1479        | 0.9950 | 1400 | 1.1657          | 102.3688 |
| 1.0034        | 1.1372 | 1600 | 1.0799          | 84.6918  |
| 0.945         | 1.2793 | 1800 | 0.9844          | 85.7106  |
| 0.8249        | 1.4215 | 2000 | 0.8974          | 87.1880  |
| 0.726         | 1.5636 | 2200 | 0.8412          | 92.9699  |
| 0.7561        | 1.7058 | 2400 | 0.7859          | 80.8202  |
| 0.6884        | 1.8479 | 2600 | 0.7328          | 85.3031  |
| 0.6329        | 1.9900 | 2800 | 0.6872          | 80.9985  |
| 0.5129        | 2.1322 | 3000 | 0.6672          | 76.4901  |
| 0.5361        | 2.2743 | 3200 | 0.6369          | 78.2985  |
| 0.482         | 2.4165 | 3400 | 0.6178          | 75.9042  |
| 0.5211        | 2.5586 | 3600 | 0.6030          | 79.3938  |
| 0.4749        | 2.7008 | 3800 | 0.5905          | 76.0316  |
| 0.4648        | 2.8429 | 4000 | 0.5864          | 73.4845  |


### Framework versions

- Transformers 4.43.4
- Pytorch 2.4.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1