| --- |
| 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. |
| <!-- (Details on source provenance may be extended in future versions.) --> |
|
|
| ### 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. |
|
|
| <!-- (Specific provider details can be disclosed if needed for reproducibility.) --> |
|
|
| ### 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: |
|
|
| ```bibtex |
| @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. |
|
|
| --- |
