---
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: 55.78806767586821
---

<!-- 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: 0.2310
- Wer: 55.7881

## 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: 0.0003
- 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: 0.2
- num_epochs: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.7755        | 1.0   | 143  | 0.3657          | 92.8317 |
| 0.2954        | 2.0   | 286  | 0.2669          | 85.6189 |
| 0.2483        | 3.0   | 429  | 0.2830          | 82.7248 |
| 0.2332        | 4.0   | 572  | 0.2922          | 83.3927 |
| 0.204         | 5.0   | 715  | 0.2338          | 78.8068 |
| 0.1612        | 6.0   | 858  | 0.1876          | 74.8442 |
| 0.1203        | 7.0   | 1001 | 0.1940          | 72.1728 |
| 0.0919        | 8.0   | 1144 | 0.1639          | 65.8504 |
| 0.0663        | 9.0   | 1287 | 0.1610          | 62.5557 |
| 0.0461        | 10.0  | 1430 | 0.1633          | 63.2235 |
| 0.0336        | 11.0  | 1573 | 0.1830          | 62.8228 |
| 0.0238        | 12.0  | 1716 | 0.1777          | 60.5521 |
| 0.0153        | 13.0  | 1859 | 0.1783          | 59.4835 |
| 0.0099        | 14.0  | 2002 | 0.1945          | 58.2369 |
| 0.0066        | 15.0  | 2145 | 0.2002          | 57.1683 |
| 0.003         | 16.0  | 2288 | 0.2148          | 57.1683 |
| 0.0015        | 17.0  | 2431 | 0.2241          | 55.9662 |
| 0.0006        | 18.0  | 2574 | 0.2286          | 56.2778 |
| 0.0003        | 19.0  | 2717 | 0.2296          | 55.8771 |
| 0.0001        | 20.0  | 2860 | 0.2310          | 55.7881 |


### Framework versions

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