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---
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
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