---
library_name: transformers
language:
- my
license: mit
base_model: openai/whisper-large-v3-turbo
tags:
- generated_from_trainer
datasets:
- chuuhtetnaing/myanmar-speech-dataset-openslr-80
metrics:
- wer
model-index:
- name: Whisper Large V3 Turbo Burmese Finetune
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Myanmar Speech Dataset (OpenSLR-80)
      type: chuuhtetnaing/myanmar-speech-dataset-openslr-80
      args: 'config: my, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 47.10596616206589
---

<!-- 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 Large V3 Turbo Burmese Finetune

This model is a fine-tuned version of [openai/whisper-large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo) on the Myanmar Speech Dataset (OpenSLR-80) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1727
- Wer: 47.1060
- Cer: 15.6324

## 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: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- 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_ratio: 0.1
- num_epochs: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     | Cer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
| 0.8922        | 1.0   | 143  | 0.4413          | 95.9484 | 48.4730 |
| 0.2576        | 2.0   | 286  | 0.1971          | 83.8379 | 26.9627 |
| 0.1481        | 3.0   | 429  | 0.1505          | 66.4292 | 22.9769 |
| 0.0996        | 4.0   | 572  | 0.1315          | 62.0214 | 20.5786 |
| 0.0697        | 5.0   | 715  | 0.1344          | 60.8638 | 20.5786 |
| 0.0507        | 6.0   | 858  | 0.1249          | 57.3464 | 19.3075 |
| 0.038         | 7.0   | 1001 | 0.1273          | 55.2538 | 18.4391 |
| 0.0279        | 8.0   | 1144 | 0.1257          | 54.4524 | 18.4908 |
| 0.02          | 9.0   | 1287 | 0.1374          | 53.3838 | 17.9559 |
| 0.0147        | 10.0  | 1430 | 0.1422          | 53.3393 | 17.9847 |
| 0.0101        | 11.0  | 1573 | 0.1530          | 53.8736 | 17.9674 |
| 0.0066        | 12.0  | 1716 | 0.1512          | 50.8905 | 16.8344 |
| 0.0043        | 13.0  | 1859 | 0.1526          | 49.5993 | 16.2708 |
| 0.0026        | 14.0  | 2002 | 0.1594          | 49.9110 | 16.4261 |
| 0.0017        | 15.0  | 2145 | 0.1612          | 49.0205 | 16.2248 |
| 0.0008        | 16.0  | 2288 | 0.1646          | 48.7088 | 15.9027 |
| 0.0003        | 17.0  | 2431 | 0.1676          | 47.8629 | 15.9429 |
| 0.0001        | 18.0  | 2574 | 0.1707          | 47.5512 | 15.6209 |
| 0.0001        | 19.0  | 2717 | 0.1721          | 47.3731 | 15.6439 |
| 0.0           | 20.0  | 2860 | 0.1727          | 47.1060 | 15.6324 |


### Framework versions

- Transformers 4.46.2
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3