Whosper Large V2

🚀 Whosper-large-v2

Whosper-large-v2 is a state - of - the - art speech recognition model designed for Wolof, with significant improvements in WER and CER, suitable for researchers, developers, and students working with Wolof speech data.

🚀 Quick Start

Installation

pip install git+https://github.com/sudoping01/whosper.git

Basic Usage

from whosper import WhosperTranscriber

# Initialize the transcriber
transcriber = WhosperTranscriber(model_id="CAYTU/whosper-large-v2") 

# Transcribe an audio file
result = transcriber.transcribe_audio("path/to/your/audio.wav")
print(result)

✨ Features

• 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](https://www.apache.org/licenses/LICENSE - 2.0) license, perfect for research and development.
• African NLP Focus: Contributing to the broader goal of comprehensive African language support.
• Improved WER and CER compared to [whosper - large](https://huggingface.co/sudoping01/whosper - large).
• Optimized for Wolof and French recognition.
• Enhanced performance on bilingual content.

📦 Installation

pip install git+https://github.com/sudoping01/whosper.git

💻 Usage Examples

Basic Usage

from whosper import WhosperTranscriber

# Initialize the transcriber
transcriber = WhosperTranscriber(model_id="CAYTU/whosper-large-v2") 

# Transcribe an audio file
result = transcriber.transcribe_audio("path/to/your/audio.wav")
print(result)

📚 Documentation

Model Overview

Whosper - large - v2 is a cutting - edge speech recognition model tailored for Wolof, Senegal's primary language. Built on OpenAI's [Whisper - large - v2](https://huggingface.co/openai/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.

Performance Metrics

• WER: 0.2345
• CER: 0.1101

Lower values mean better accuracy—ideal for practical applications!

Performance Comparison

Training Results

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

Model Information

Model Index

• Name: whosper - large - v2
  • Results:
    • Task:
      • Name: Automatic Speech Recognition
      • Type: automatic - speech - recognition
    • Dataset:
      • Name: Test Set
      • Type: custom
      • Split: test
      • Args:
        • Language: wo
    • Metrics:
      • Name: Test WER
      • Type: wer
      • Value: 23.45
      • Name: Test CER
      • Type: cer
      • Value: 11.01

Limitations

• Reduced performance on English compared to [whosper - large](https://huggingface.co/sudoping01/whosper - large).
• Less effective for general multilingual content compared to [whosper - large](https://huggingface.co/sudoping01/whosper - large).
• Low performances on very bad audios quality.

Training Data

Trained on diverse Wolof speech data:

• ALFFA Public Dataset
• FLEURS Dataset
• Bus Urbain Dataset
• Anta Women TTS Dataset
• Kallama Dataset

This diversity ensures the model excels across:

• Speaking styles and dialects
• Code - switching patterns
• Gender and age groups
• Recording conditions

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.

📄 License

[Apache License 2.0](https://www.apache.org/licenses/LICENSE - 2.0)

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

@misc{whosper2025,
  title={Whosper-large: A Multilingual ASR Model for Wolof with Enhanced Code-Switching Capabilities},
  author={Seydou DIALLO},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/CAYTU/whosper-large},
  version={1.0}
}

🙏 Acknowledgments

Developed by [Seydou DIALLO](https://www.linkedin.com/in/seydou - diallo - 08ab311ba) at Caytu Robotics's AI Department, building on OpenAI's [Whisper - large - v2](https://huggingface.co/openai/whisper - large - v2). Special thanks to the Wolof - speaking community and contributors advancing African language technology.

📞 Contact US

For any question or support contact us

Email : sdiallo@caytu.com