Datasets:

StephaneBah
/

AfroRadVoice-FR

dataset_info:
  features:
    - name: audio
      dtype:
        audio:
          sampling_rate: 16000
          decode: false
    - name: text
      dtype: string
    - name: file
      dtype: string
  splits:
    - name: train
      num_bytes: 500389302
      num_examples: 487
    - name: test
      num_bytes: 38797145
      num_examples: 75
  download_size: 500843329
  dataset_size: 539186447
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: test
        path: data/test-*
license: cc-by-4.0
task_categories:
  - automatic-speech-recognition
language:
  - fr
tags:
  - medical

AfroRadVoice-FR

Dataset Description

AfroRadVoice-FR is a French speech dataset composed of radiology report recordings, designed to support research in Automatic Speech Recognition (ASR) for African-accented French in medical contexts.

The dataset combines real recordings, synthetic speech, and augmented audio to address data scarcity and improve acoustic diversity in a specialized domain.

Motivation

Current ASR systems show strong performance in high-resource settings but fail to generalize effectively to African-accented French, particularly in specialized domains such as medical reporting.

This dataset aims to:

Improve ASR robustness to African accents
Enable research in low-resource medical speech recognition
Support domain adaptation for clinical transcription systems

Dataset Composition

Total samples: 562 audio recordings
Total duration: ~4.54 hours
Speakers: 26 (real + synthetic voices)

Data Types

Real recordings: 177 samples (~76 minutes)
Synthetic recordings: 317 samples
Augmented recordings: 68 samples

Domain Coverage

Radiology reports including:

Mammography
Brain imaging
Fractures
Ultrasound
Pediatric radiology
etc

Splits

Train/Validation: 487 samples
Test: 75 samples

Data Fields

Each sample contains:

file: relative path to audio file
audio: waveform (Hugging Face Audio feature)
text: normalized transcription

Data Collection Process

Text Source

The dataset is based on 150 radiology report conclusions collected from medical contexts.

Audio Collection

Real recordings were collected through a web-based recording platform
Speakers read predefined radiology reports
Recording conditions were semi-controlled

Synthetic Data

Synthetic speech was generated using a neural text-to-speech system with voices adapted to African French accents.

Data Augmentation

To improve balance and robustness:

Pitch shifting
Time stretching
Noise injection

were applied to underrepresented speakers.

Preprocessing

Audio

Resampled to 16 kHz
Normalized amplitude

Text

Lowercased
Cleaned (basic normalization)
Removal of irrelevant formatting

Anonymization and Privacy

No personal or demographic data is collected
No identifiable patient information is present
Text content consists only of generic radiology report conclusions

Synthetic samples are identifiable via filename prefixes (e.g., synth_).

Intended Uses

ASR model training and evaluation
Research on African-accented French speech
Domain adaptation for medical transcription

Limitations

Limited dataset size (~4.5 hours)
Restricted to radiology domain
Limited number of real speakers
Partial reliance on synthetic data
Does not represent all African French accents

Ethical Considerations

This dataset is intended for research purposes only.

It should not be used in clinical decision-making systems without proper validation and regulatory compliance.

Licensing and Access

License: Creative Commons Attributions 4.0
Access: Gated dataset (controlled access for usage tracking)

Future Work

Expansion of real speaker data
Inclusion of broader medical domains
Improved accent diversity
Detailed metadata documentation

Citation

If you use this dataset in your research, please cite:

@dataset{afroradvoice_fr,
  author = {Aholou-Bah, Stéphane},
  title = {AfroRadVoice-FR: A French Radiology Speech Dataset for African-Accented ASR},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/StephaneBah/AfricaVoice_Radiology_FR}
}

Acknowledgments

This dataset was developed as part of research efforts at AdjibolaTech, focusing on improving speech technologies for African contexts.