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---
library_name: transformers
language:
- ug
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- THUGY20
metrics:
- cer
- wer
model-index:
- name: Whisper Small Fine-tuned with THUYG20 Uyghur Dataset
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: 'THUGY20: A free Uyghur speech database'
type: THUGY20
metrics:
- name: Cer
type: cer
value: 4.927369689396644
- name: Wer
type: wer
value: 17.940071709066075
---
<!-- 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 Small Fine-tuned with THUYG20 Uyghur Dataset
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the THUGY20: A free Uyghur speech database dataset.
It achieves the following results on the test set of THUGY20:
- Loss: 0.7473
- Wer Ortho: 18.0908
- Wer: 17.9401
- Cer: 4.9274
## Training procedure
Finetuning code avaiblable in https://github.com/ixxan/ug-speech
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- 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_steps: 50
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer | Cer |
|:-------------:|:------:|:----:|:---------------:|:---------:|:-------:|:-------:|
| 0.3815 | 0.8058 | 500 | 0.7944 | 34.8819 | 34.7960 | 10.4265 |
| 0.1343 | 1.6116 | 1000 | 0.7441 | 28.3393 | 28.3550 | 8.3051 |
| 0.0646 | 2.4174 | 1500 | 0.7396 | 27.7378 | 27.5653 | 8.5366 |
| 0.0311 | 3.2232 | 2000 | 0.6984 | 25.1910 | 24.9445 | 7.5643 |
| 0.0176 | 4.0290 | 2500 | 0.6934 | 21.3709 | 21.2523 | 5.8316 |
| 0.0075 | 4.8348 | 3000 | 0.7654 | 20.5541 | 20.3603 | 5.7519 |
| 0.0023 | 5.6406 | 3500 | 0.7686 | 18.7582 | 18.5846 | 5.1923 |
| 0.0004 | 6.4464 | 4000 | 0.7473 | 18.0908 | 17.9401 | 4.9274 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3 |