metadata
library_name: transformers
language:
- kh
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- generated_from_trainer
datasets:
- S-Sethisak/KhmerAsrDataset
metrics:
- wer
model-index:
- name: Whisper large-v3 kh - Sethisak San
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: KhmerAsrDataset
type: S-Sethisak/KhmerAsrDataset
args: 'config: kh, split: test'
metrics:
- name: Wer
type: wer
value: 81.8018018018018
Whisper large-v3 kh - Sethisak San
This model is a fine-tuned version of openai/whisper-large-v3 on the KhmerAsrDataset dataset. It achieves the following results on the evaluation set:
- Loss: 0.0830
- Wer: 81.8018
- Cer: 20.7231
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: 5e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.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_steps: 500
- training_steps: 10000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Cer | Validation Loss | Wer |
|---|---|---|---|---|---|
| 0.0232 | 1.6086 | 1000 | 24.9919 | 0.1027 | 92.9730 |
| 0.0044 | 3.2157 | 2000 | 22.4125 | 0.0933 | 88.8288 |
| 0.0015 | 4.8243 | 3000 | 22.8354 | 0.0882 | 89.5495 |
| 0.0001 | 6.4315 | 4000 | 21.4830 | 0.0924 | 86.6667 |
| 0.2061 | 8.0499 | 5000 | 0.1793 | 97.1171 | 33.1714 |
| 0.1139 | 9.6585 | 6000 | 0.1184 | 93.3333 | 25.9980 |
| 0.0739 | 11.2656 | 7000 | 0.0932 | 87.5676 | 22.9656 |
| 0.0515 | 12.8742 | 8000 | 0.0838 | 86.4865 | 22.1825 |
| 0.0384 | 14.4814 | 9000 | 0.0823 | 81.4414 | 21.5597 |
| 0.0131 | 16.0885 | 10000 | 0.0830 | 81.8018 | 20.7231 |
Framework versions
- Transformers 4.48.0
- Pytorch 2.8.0+cu126
- Datasets 2.14.7
- Tokenizers 0.21.4