African Speech, Audio and Audio-Visual Data for AI
FYI Africa collects authentic African datasets for model training, testing, evaluation, localisation and research — capturing real languages, accents, code-switching patterns and recording environments.
African voices remain underrepresented in global AI datasets
AI systems often struggle across African markets because they are not trained or evaluated on enough authentic local speech, accents, languages, code-switching and real-world audio conditions.
FYI Africa closes this gap by collecting African datasets built around how people actually speak, sound and interact.
Local languages
Coverage across African languages and market-specific speech realities.
Regional accents
Datasets structured around real accent and pronunciation variation.
Code-switching
Capturing how African speakers naturally move between languages.
Audio conditions
Clean, mobile, telephony and real-world recording environments.
Low-resource languages
Support for language contexts often missing from standard datasets.
Interaction context
Speech, behaviour and visual context for real-world evaluation.
What We Collect
FYI Africa collects and processes African speech, audio and audio-visual datasets for AI training, testing, evaluation, localisation and research.
Speech Data
Read speech, spontaneous speech, conversational speech, call-centre-style audio, command phrases, wake words and code-switching datasets.
Audio Data
Human voice recordings, multi-speaker audio, interviews, group discussions, mobile recordings, noisy-environment audio and real-world acoustic datasets.
Audio-Visual Data
Video interviews, speaker videos, product interactions, customer service simulations, UX recordings and multimodal datasets.
Dataset Processing
Transcription, translation, annotation, metadata structuring, consent tracking, QC reporting and structured delivery.
Built for African language and accent diversity
African speech and audio data is not only about language. It is about accent, region, code-switching, device conditions, recording environment and real-world usage.
FYI Africa structures datasets around the details that affect AI performance in African markets.
From brief to usable dataset
A managed workflow designed to transform requirements into structured, rights-cleared and quality-checked datasets.
Scope the data requirement
Define sample design and data structure
Design prompts, scripts, scenarios or tasks
Coordinate contributors according to the dataset specification
Collect recordings
Capture consent and usage rights
Transcribe, translate, annotate and label
Structure metadata and files
Quality-check the dataset
Deliver files, transcripts, metadata and QC reporting
Built for AI training, testing and evaluation
ASR
Speech data for recognition across African languages and accents.
Conversational AI
Natural and scenario-based speech for dialogue systems.
Voice Assistants
Commands, wake words and short prompts.
Call-Centre AI
Telephony-style and customer service recordings.
Multimodal AI
Audio-visual datasets with context and interaction data.
Localisation Testing
Speech and UX data for African market relevance.
UX Research
Task-based audio and video recordings.
Model Benchmarking
Structured datasets for testing performance.
Need authentic African data for your AI models?
Start with a focused pilot dataset to validate quality, workflow and delivery before scaling.