Language · hi-IN
Hindi speech data for AI
Demand for Hindi sits in the mid tier: large demand with notable code-switching that models struggle on.
Speakers & regions
Hindi is one of the most spoken languages on earth — roughly 600 million people speak it as a first or second language, the large majority in northern and central India, with communities in Nepal, Fiji, Mauritius and across the Gulf diaspora. It anchors the Hindi belt — Uttar Pradesh, Bihar, Madhya Pradesh, Rajasthan, Delhi — and works as a lingua franca well beyond it. Voice interfaces matter disproportionately here: for many Hindi speakers, talking to a phone is easier than typing Devanagari.
Dialects, accents & what a corpus should cover
Two things dominate Hindi corpus design. First, register: everyday spoken Hindi sits between Sanskritized formal Hindi and Persian-influenced Urdu vocabulary, so news-broadcast speech does not sound like a kirana-store call. Second, Hinglish: Hindi–English code-switching is the default urban register — English nouns and whole phrases dropped into Hindi grammar, sometimes flipping language mid-sentence. ASR that cannot follow the switch fails on exactly the users most likely to use voice products. Regional accent variation — Bhojpuri-, Rajasthani- or Haryanvi-influenced Hindi — adds a third axis worth declaring in the brief.
Where Hindi speech data gets used
Typical buyer applications for Hindi audio:
- Voice assistants for Hindi-first users on mobile
- Call-center analytics for Indian telecom, banking and delivery apps
- Media localization and subtitle QC for streaming
- Voice payments and commerce IVR
- Hinglish ASR and language-ID evaluation sets
Most briefs pair conversational speech for ASR robustness with read speech for controlled phonetic and vocabulary coverage.
What fiund sources
fiund sources conversational Hindi — spontaneous two-party dialogue, telephony calls, real environments — and read speech against Devanagari prompts where a brief needs controlled coverage. Hinglish code-switched conversation is collected and transcribed to an agreed script convention, with speaker metadata covering region, gender and age band. AI-training consent is signed per speaker before anything ships. Deliverables can pair verbatim transcripts with per-span language tags, so Hinglish segments train code-switch-aware models instead of polluting a monolingual corpus, and diarization is available for two-party calls.
Rights are cleared before anything moves: every asset ships under a signed licence with explicit AI-training rights, and speaker consent is on file wherever voices are identifiable.
Frequently asked questions
What accents should a Hindi corpus cover?
Start with the Hindi belt’s spread: Delhi and UP standard speech plus speakers whose Hindi carries Bhojpuri, Rajasthani, Haryanvi or Punjabi influence, and urban speakers from Mumbai or Bengaluru who mix English heavily. Balance region and rural/urban background — the acoustic differences are large enough to move WER.
How should Hinglish code-switching be transcribed?
Pick a convention before collection starts: Devanagari throughout, Latin script for English spans, or dual-script with language tags. Consistency matters more than the choice — mixed conventions poison training data. fiund scopes the convention with the buyer and applies it across the corpus.
Does Hindi speech data transfer to Urdu?
Partially. Spoken Hindi and Urdu overlap heavily in everyday registers, so acoustic models benefit — but the scripts differ (Devanagari vs Perso-Arabic) and formal vocabularies diverge, so text and language-model layers do not transfer cleanly. Treat Urdu as a separate deliverable if it is in scope.
How much does Hindi speech data cost?
Licensed per hour; price moves with channel, code-switching density, transcription convention and speaker spread. Hindi sits in the mid tier — very large demand plus the code-switching problem models struggle on. Send a brief for a quote.
Related speech data
Need Hindi speech data?
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