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Model: NUTN-KWS/Whisper-Taiwanese-model-v0.5
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---
library_name: transformers
license: cc-by-nc-4.0
language:
- en
- zh
metrics:
- cer
pipeline_tag: automatic-speech-recognition
base_model:
- openai/whisper-large-v3-turbo
---
[ [英文 README_EN.md](https://huggingface.co/NUTN-KWS/Whisper-Taiwanese-model-v0.5/blob/main/README_EN.md) ]
# 👳 Whisper-Taiwanese model V0.5 (Tv0.5)
這個模型是由國立臺南大學執行國科會產學合作計畫,使用 [openai/whisper-large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo) 微調的版本並執行國科會TAIDE台英語家庭先導計畫與真平出版社合作使用中小學教材內容及學生學習資料進行模型微調用於真平教材台語辨識。並與國研院國網中心合作運用國網中心算力以及TAIDE模型共同建構中小學台語AI學習模型。
示範網址: [https://kws.oaselab.org/taigitong/](https://kws.oaselab.org/taigitong/)
## 📝 Model Details
- **Base Model**: `openai/whisper-large-v3-turbo`
- **Fine-tuned for**: 台灣閩南語語音辨識 (ASR)
- **Fine-tuning Framework**: Hugging Face Transformers
- **Training Duration**: 使用兩片 V100大約 180 小時
- **Dataset**: 自訂資料集、教育部臺灣台語常用詞辭典,大約 90 小時的資料
- **Input Format**: 16kHz mono WAV
- **License**: CC BY-NC 4.0
## 🚀 Usage
### 安裝套件:
```bash
pip install torch torchvision torchaudio transformers
```
### 執行範例:
```python
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="./model/whisper-taiwanese", device=0)
result = pipe("audio.wav", generate_kwargs={"language": "zh", "task": "transcribe"})
print(result["text"])
```
## 👨‍🎓 Citation
### BibTeX:
```bibtex
@misc{taiwanesewhisperasr2025,
title={Taiwanese Whisper ASR},
author={KWS Center, National University of Tainan, Taiwan},
year={2025},
url={https://huggingface.co/NUTN-KWS/Whisper-Taiwanese-model-v0.5}
}
```
### APA:
- C. S. Lee, M. H. Wang, C. C. Yue, G. Y. Teseng, and Y. Nojima, "Fuzzy Estimation Agent with Knowledge Graph and Quantum Fuzzy Inference Engine for Taiwanese-English Co-Learning," 2025 IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS 2025), Banff, Alberta, Canada, Aug. 16-19, 2025.
- C. S. Lee, M. H. Wang, C. Y. Chen, S. C. Yang, M. Reformat, N. Kubota, and A. Pourabdollah, "Integrating quantum CI and generative AI for Taiwanese/English co-learning," Quantum Machine Intelligence, vol. 6, 64, pp. 1-19, 2024.
- C. S. Lee, M. H. Wang, C. Y. Chen, S. C. Yang, M. Reformat, N. Kubota, and A. Pourabdollah, "Quantum fuzzy inference engine with generative AI and TAIDE KG for Taiwanese/English co-learning," 2025 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2025), Reims, France, Jul. 6-9, 2025.

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---
library_name: transformers
license: cc-by-nc-4.0
language:
- en
- zh
metrics:
- cer
pipeline_tag: automatic-speech-recognition
base_model:
- openai/whisper-large-v3-turbo
---
[ [繁體中文 README.md](https://huggingface.co/NUTN-KWS/Whisper-Taiwanese-model-v0.5) ]
# 👳 Whisper-Taiwanese model V0.5 (Tv0.5)
This model is a fine-tuned version of OpenAIs [openai/whisper-large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo). It was developed by the National University of Tainan (NUTN), Taiwan, as part of a National Science and Technology Council (NSTC)-funded industry-academia collaboration project. We carried out the Taiwanese-English Co-Learning Pilot Project from September 2024 to June 2025 in collaboration with JEN-PIN ENTERPRISE CO., LTD. The model is trained for Taiwanese language recognition tasks using JEN-PIN educational materials generated through StudentMachine Co-Learning during the Fall 2024 semester. Additionally, the NUTN is collaborating with the National Center for High-performance Computing (NCHC) of the National Applied Research Laboratories (NARLabs) in Taiwan to provide computational and storage resources and co-develop an AI learning model for elementary and high school students.
Demo: [https://kws.oaselab.org/taigitong/](https://kws.oaselab.org/taigitong/)
## 📝 Model Details
- **Base Model**: `openai/whisper-large-v3-turbo`
- **Fine-tuned for**: Taiwanese Hokkien Automatic Speech Recognition (ASR)
- **Fine-tuning Framework**: Hugging Face Transformers
- **Training Duration**: Approximately 180 hours using two V100 GPUs
- **Dataset**: Custom dataset, including the Dictionary of Frequently-Used Taiwanese Taigi released by the Ministry of Education, Taiwan, totaling approximately 90 hours of audio data.
- **Input Format**: 16kHz mono WAV
- **License**: CC BY-NC 4.0
## 🚀 Usage
### Installing Packages:
```bash
pip install torch torchvision torchaudio transformers
```
### Example:
```python
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="./model/whisper-taiwanese", device=0)
result = pipe("audio.wav", generate_kwargs={"language": "zh", "task": "transcribe"})
print(result["text"])
```
## 👨‍🎓 Citation
### BibTeX:
```bibtex
@misc{taiwanesewhisperasr2025,
title={Taiwanese Whisper ASR},
author={KWS Center, National University of Tainan, Taiwan},
year={2025},
url={https://huggingface.co/NUTN-KWS/Whisper-Taiwanese-model-v0.5}
}
```
### APA:
- C. S. Lee, M. H. Wang, C. C. Yue, G. Y. Teseng, and Y. Nojima, "Fuzzy Estimation Agent with Knowledge Graph and Quantum Fuzzy Inference Engine for Taiwanese-English Co-Learning," 2025 IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS 2025), Banff, Alberta, Canada, Aug. 16-19, 2025.
- C. S. Lee, M. H. Wang, C. Y. Chen, S. C. Yang, M. Reformat, N. Kubota, and A. Pourabdollah, "Integrating quantum CI and generative AI for Taiwanese/English co-learning," Quantum Machine Intelligence, vol. 6, 64, pp. 1-19, 2024.
- C. S. Lee, M. H. Wang, C. Y. Chen, S. C. Yang, M. Reformat, N. Kubota, and A. Pourabdollah, "Quantum fuzzy inference engine with generative AI and TAIDE KG for Taiwanese/English co-learning," 2025 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2025), Reims, France, Jul. 6-9, 2025.

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