Whosper-large-v2

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, 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!

Performance Comparison

Metric	Whosper-large-v2	Whosper-large	Improvement
WER	0.2345	0.2423	3.2% better
CER	0.1101	0.1135	3.0% better

Key Features

Improved WER and CER compared to whosper-large
Optimized for Wolof and French recognition
Enhanced performance on bilingual content

Limitations

Reduced performance on English compared to whosper-large
Less effective for general multilingual content compared to 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

Quick Start Guide

Installation

pip install git+https://github.com/sudoping01/whosper.git@v1.0.0

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)

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.

License

Apache 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 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.

Contact US

For any question or support contact us

Email : sdiallo@caytu.com

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