Sub-Saharan Africa · Latin / Ajami
Hausa Voice Assistant Dataset
Hausa voice assistant sourced to your specification — no inventory, no fixed listing. This page covers what is specifically hard about this pairing, and which fields your specification needs to pin down.
Why voice assistant in Hausa is its own problem
Intent work for a Hausa voice assistant meets the same two-language reality as the rest of the region: users give the same intent in Hausa or in English, and which one they reach for depends on the domain, with device and app terms tending to come out in English. The intent definitions themselves are usually written in plain Boko, where tone and vowel length are absent, so an annotator matching an utterance to a definition has less information than the speaker had.
The field to pin down first: Decide whether the Hausa and English phrasings of one intent form a single class or separate classes, and whether intent definitions carry tone and length marking so annotators can match what was actually said.
At a glance
| Language | Hausa |
|---|---|
| Primary region | Sub-Saharan Africa |
| Writing system | Latin / Ajami |
| Category | Voice Assistant |
| Specification field to settle first | Decide whether the Hausa and English phrasings of one intent form a single class or separate classes, and whether intent definitions carry tone and length marking so annotators can match what was actually said. |
| Delivery | Sourced to order, pilot batch before the full run |
What voice assistant data is
Command and dialogue data for voice assistants, usually with intent annotation, covering the full chain from wake word to understanding to response.
What buyers get wrong about it
What buyers actually need is intent plus slot annotation, not just audio and transcription. The intent taxonomy has to align with the schema the buyer already runs.
The specification field that decides the quote
Whether the intent schema comes from the buyer or is designed by us — this is the dividing line in the quote.
The language side: what Hausa demands
Hausa is written in two scripts at once: Latin (Boko) dominates, while Arabic script (Ajami) is used in religious contexts. Running both in parallel means the script-to-speech mapping has to be defined separately for each.
What we can put in this delivery
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Read and conversational speech
Scripted recording for TTS, and unscripted conversation for recognition. Specified separately because they need different speaker pools.
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Transcription to your convention
Orthographic or phonetic, with the guideline written before production and shared with you for review.
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Speaker metadata
Age band, gender, region and dialect background per file, so you can slice the dataset rather than take it whole.
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Consent documentation
Signed speaker consent covering the intended use, plus collection methodology and the annotation guideline.
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Pilot batch
A small batch first, which you can reject. Misalignments surface after a few hours rather than at delivery.
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Delivery in your format
Audio format, sampling rate, segmentation length and metadata schema set to your pipeline's requirements.
What arrives in a delivery
| Component | What it is |
|---|---|
| Audio files | Format, sampling rate and segmentation length set to your pipeline. Named to a convention you specify. |
| Transcription | Orthographic or phonetic, produced under a guideline you review before production starts. |
| Speaker metadata | Age band, gender, region and dialect background per file, plus a speaker identifier so the dataset can be sliced. |
| Recording conditions | Environment, device and, where relevant, measured signal-to-noise ratio per file. |
| Annotation guideline | The document the annotators worked from, so you can reproduce the conventions on your own data. |
| Consent records | Signed speaker consent covering your intended use, with the transfer mechanism addressed where required. |
| Collection methodology | How speakers were recruited, screened and scheduled — the part that tells you how biased the pool is. |
| Quality report | Pilot outcome, re-work log, and the annotator agreement figures where the task supports measuring them. |
Questions we get about Hausa Voice Assistant
How many distinct speakers can you provide for Hausa Voice Assistant?
It depends on the specification and the timeline, and we will give a real number rather than a target. For this pairing, speaker recruitment is usually the step that sets the schedule. Decide whether the Hausa and English phrasings of one intent form a single class or separate classes, and whether intent definitions carry tone and length marking so annotators can match what was actually said.
Can you annotate to our own guideline instead of the default?
Yes, and for this pairing we would recommend it. Intent work for a Hausa voice assistant meets the same two-language reality as the rest of the region: users give the same intent in Hausa or in English, and which one they reach for depends on the domain, with device and app terms tending to come out in English. That is exactly the kind of decision a default guideline leaves open, and it is where annotators diverge. Send us your guideline, or we will draft one and you can review it before production starts.
Do you offer a sample before we commit to a full run?
Yes. Select a free sample in the request form and describe what you need. A pilot batch is the cheapest way to establish whether the quality bar is reachable for Hausa Voice Assistant before committing to the full volume.
Is the data licensed or owned outright?
Licensing terms are set per project, so tell us how the model will be used and whether it will be distributed. Consent documentation travels with the data either way, and we do not handle medical or clinical data or recorded telephone calls.
Other datasets in Hausa
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Hausa Speech Commands
Targeted recordings of short command words or phrases, usually with many speakers reading each entry several times over, used for wake words and on-device command recognition.
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Hausa Read Speech
Recordings of speakers reading specified text, with clear pronunciation and known text, the base material for TTS and ASR.
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Hausa Podcast Speech
Long-form podcast and interview audio, either solo monologue or two-person conversation, used for long-form speech recognition and speaker modeling.
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Hausa Multilingual Speech
Speech data covering multiple languages within one project, used for multilingual ASR, cross-lingual transfer, and language identification.
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Hausa Noisy Speech
Speech collected under background noise — street, in-car, restaurant, office and other real environments — used for noise-robust models and speech enhancement.
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Hausa Code-Switching Speech
Speech that mixes two or more languages inside a single utterance — Hinglish, Spanglish, Taglish — used for recognition in real spoken settings.
Voice Assistant in other languages
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Amharic Voice Assistant
Sub-Saharan Africa
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Ukrainian Voice Assistant
Europe
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Swahili Voice Assistant
Sub-Saharan Africa
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Kurdish Voice Assistant
Middle East & North Africa
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Uzbek Voice Assistant
Central Asia & Caucasus
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Hindi Voice Assistant
South Asia
Request Hausa Voice Assistant
Tell us the language, the hours, and what the data needs to look like. You will get a real number and a real timeline — not a range. If we cannot source it well, we will tell you that instead.
- Pilot batch before the full run, so problems surface early.
- Consent documentation delivered with the data.
- No medical or clinical data. No recorded telephone calls.