---
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: 0.7611
- Wer: 77.7127

## 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      |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 2.7389        | 0.2915 | 200  | 2.6665          | 373.6628 |
| 2.2233        | 0.5831 | 400  | 2.2426          | 189.4549 |
| 1.8164        | 0.8746 | 600  | 1.8990          | 131.6353 |
| 1.5731        | 1.1662 | 800  | 1.6678          | 124.3760 |
| 1.4459        | 1.4577 | 1000 | 1.4828          | 95.8227  |
| 1.3009        | 1.7493 | 1200 | 1.3453          | 96.9689  |
| 1.0242        | 2.0408 | 1400 | 1.2264          | 89.9898  |
| 0.9227        | 2.3324 | 1600 | 1.1492          | 80.0815  |
| 0.9111        | 2.6239 | 1800 | 1.0539          | 83.2399  |
| 0.8831        | 2.9155 | 2000 | 0.9899          | 88.1814  |
| 0.5906        | 3.2070 | 2200 | 0.9452          | 84.5899  |
| 0.54          | 3.4985 | 2400 | 0.9017          | 79.6740  |
| 0.542         | 3.7901 | 2600 | 0.8713          | 72.2364  |
| 0.4606        | 4.0816 | 2800 | 0.8320          | 72.9241  |
| 0.4879        | 4.3732 | 3000 | 0.8172          | 75.4712  |
| 0.4033        | 4.6647 | 3200 | 0.7940          | 75.9552  |
| 0.4235        | 4.9563 | 3400 | 0.7737          | 73.2552  |
| 0.3638        | 5.2478 | 3600 | 0.7704          | 79.2155  |
| 0.383         | 5.5394 | 3800 | 0.7641          | 77.7382  |
| 0.3714        | 5.8309 | 4000 | 0.7611          | 77.7127  |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.4.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1