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---
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
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