Whosper-large-v2 is a cutting-edge speech recognition model tailored for Wolof, Senegal's primary language. Built on OpenAI's Whisper-large-v2, it advances African language processing with notable improvements in Word Error Rate (WER) and Character Error Rate (CER). Whether you're transcribing conversations, building language learning tools, or conducting research, this model is designed for researchers, developers, and students working with Wolof speech data.
Key Strengths
Superior Code-Switching: Handles natural Wolof-French/English mixing, mirroring real-world speech patterns
Multilingual: Performs well in French and English in addition to Wolof
Production-Ready: Thoroughly tested and optimized for deployment
Open Source: Released under the apache-2.0 license, perfect for research and development
African NLP Focus: Contributing to the broader goal of comprehensive African language support
Performance Metrics
WER: 0.2345
CER: 0.1101
Lower values mean better accuracy—ideal for practical applications!
1from whosper import WhosperTranscriber
23# Initialize the transcriber4transcriber = WhosperTranscriber(model_id="CAYTU/whosper-large-v2")56# Transcribe an audio file7result = transcriber.transcribe_audio("path/to/your/audio.wav")8print(result)
Training Results
Training Loss
Epoch
Step
Validation Loss
0.7575
0.9998
2354
0.7068
0.6429
1.9998
4708
0.6073
0.5468
2.9998
7062
0.5428
0.4439
3.9998
9416
0.4935
0.3208
4.9998
11770
0.4600
0.2394
5.9998
14124
0.4490
Framework Versions
PEFT: 0.14.1.dev0
Transformers: 4.49.0.dev0
PyTorch: 2.5.1+cu124
Datasets: 3.2.0
Tokenizers: 0.21.0
Contributing to African NLP
Whosper-large-v2 embodies our commitment to open science and the advancement of African language technologies. We believe that by making cutting-edge speech recognition models freely available, we can accelerate NLP development across Africa.
Join our mission to democratize AI technology:
Open Science: Use and build upon our research - all code, models, and documentation are open source
Data Contribution: Share your Wolof speech datasets to help improve model performance
Research Collaboration: Integrate Whosper into your research projects and share your findings
Community Building: Help us create resources for African language processing
Educational Impact: Use Whosper in educational settings to train the next generation of African AI researchers
Together, we can ensure African languages are well-represented in the future of AI technology. Whether you're a researcher, developer, educator, or language enthusiast, your contributions can help bridge the technological divide.
This model is released under the Apache 2.0 license to encourage research, commercial use, and innovation in African language technologies while ensuring proper attribution and patent protection. You are free to:
Use the model commercially
Modify and distribute the model
Create derivative works
Use the model for patent purposes
Choosing Apache 2.0 aligns with our goals of open science and advancing African NLP while providing necessary protections for the community.
Citation
bibtex
1@misc{whosper2025,
2 title={Whosper-large: A Multilingual ASR Model for Wolof with Enhanced Code-Switching Capabilities},
3 author={Seydou DIALLO},
4 year={2025},
5 publisher={Hugging Face},
6 url={https://huggingface.co/CAYTU/whosper-large},
7 version={1.0}
8}
Acknowledgments
Developed by Seydou DIALLO at Caytu Robotics's AI Department, building on OpenAI's Whisper-large-v2. Special thanks to the Wolof-speaking community and contributors advancing African language technology.
