Commercial-grade African speech data for voice AI models
Consented, speaker-verified speech in Luganda, Ewe, Fulfulde, Hausa and more, with clean commercial licensing and an auditable provenance trail, to train and evaluate your speech and multilingual models.
Is your language covered?
Search the catalogue. If we don't ship it yet, we'll scope a collection mission for it.
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languages
26 licensed corpora available today, and we run collection missions on demand for any other language
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F1 macro
human-verified annotation quality on Wolof sentiment 2x the best frontier LLM we benchmarked
African languages are the gap in today's voice AI models
Scraped data won't clear legal
Unclear consent and licensing means IP, data-protection and AI-Act exposure for every model trained on it.
Too few hours, speakers and dialects
Most African languages lack the volume and speaker diversity that pretraining and fine-tuning need.
No ground truth to measure against
Without native-speaker gold standards, tone and code-switching errors go unmeasured until users hit them.
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Ways to work with Afriklang
From ready-to-license corpora to large collection programs, annotation and evaluation sets: pick the level of support your model team needs.
Ready-made datasets
License our speaker-verified corpora in Luganda, Ewe, Fulfulde, Hausa & more shipping today with volume scaling now.
Custom collection missions
Scope a collection in any African language, with the speaker, dialect and domain mix your model needs, through our Fair-Trade native-speaker network.
Data pipeline integration
Plug our capture, QA and delivery pipeline into your data infrastructure, in your format and schema.
Annotation & transcription
Native-speaker labeling and timestamped transcripts delivered to your schema and quality bar.
Evaluation & benchmarking
Native-speaker gold-standard test sets to measure your models in African languages against frontier baselines.
Advisory & scoping
Get expert guidance on languages, dialects, volume, format and compliance before you commit.
Built for teams training voice AI models
If your speech, multilingual or multimodal models need to work in African languages, you need training and evaluation data that is consented, commercially licensed and tonally accurate, not scraped from the internet.
Speech recognition & synthesis
Train ASR and TTS that hold up on tone, accents and dialects across African languages.
Multilingual & multimodal models
Add natural African speech, code-switching included, to your pretraining and fine-tuning mix.
Evaluation & safety
Measure model quality in African languages on native-speaker validated test sets.
From catalogue to production in three steps
- 01
Browse catalogue
Pick language, volume, format.
- 02
License your dataset
Commercial license + SLA: guaranteed delivery windows, defined annotation accuracy, and free re-delivery on any QA failure plus a provenance trail your compliance team can audit.
- 03
Receive via API or S3
Integrate directly into your pipeline.
Why our data is different
Image-prompted elicitation
Speakers describe images, not read scripts. Captures natural speech and code-switching.
AI quality filtering at capture
Noisy audio is discarded automatically before reaching human reviewers.
Fair-Trade & responsibly sourced
Every speaker gives documented consent and is paid fairly through our points-based micro-work system: provenance you can stand behind.
Inter-annotator agreement >80%
Every annotation is cross-checked against our linguistic quality standard.
Frontier LLMs can't read Wolof. Our data can.
We benchmarked GPT-4o, Claude, Gemini, Llama and Phi-4 on real Wolof social-media comments. The best reaches 45% F1 zero-shot. Data annotated and validated through our pipeline reaches 90.0% the quality moat under every dataset we sell.
F1 macro, higher is better. LLMs evaluated zero-shot at temperature 0 on a 1,000-comment Wolof gold standard; Afriklang measured as native-speaker inter-annotator agreement on the same 1,000 examples.
+0.0 pts
ahead of the best frontier LLM on Wolof sentiment 90.0% vs 45.0% for Microsoft Phi-4
+0.0 pts
ahead on 7-class emotion detection 85.0% vs 12.9% for GPT-4o
Latest articles
WER vs. CER vs. FER: Why African Language ASR Teams Need Better Evaluation Metrics
Word Error Rate lies about African language ASR performance. Feature Error Rate reveals what WER hides—and changes how you evaluate models.
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Full Fine-Tuning vs. PEFT for African Language ASR: When 10 Hours of Data Is Enough
New research shows LoRA matches full fine-tuning on Whisper-Small with just 10 hours of Hausa, Yoruba, and Igbo data—at 10% of the compute cost.
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Synthetic Speech Data for African Languages: What CLEAR Global's 18-Month Study Means for Your ASR Budget
CLEAR Global's Gates-funded study found synthetic voice data improved low-resource African ASR by only ~6% WER—insufficient for production. Here's what that means for your data budget.
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