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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: 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
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