Buy a Hindi conversational speech dataset

Two speakers hold an unscripted conversation seeded with a scenario, recorded on separate channels so overlap and turn-taking survive into the delivered files.

A 45–60 minute paired session with both speakers on isolated microphones, either in adjacent treated rooms or split-mic in one room with bleed measured and logged.

55–65% of recorded time is accepted. Overlap regions, crosstalk bleed and one-sided stretches all cost delivered time. The lowest-yield studio style we run, and priced accordingly.

• 16-bit PCM WAV audio, captured at 48 kHz / 24-bit and delivered at your target rate
• Verbatim Devanagari transcripts with utterance-level timestamps
• Speaker-turn segmentation with start and end timestamps
• Overlap regions marked with participating speaker IDs
• Backchannel and interruption markers
• Per-pair relationship metadata: familiar or stranger
• Per-speaker metadata: age band, gender, region, dialect, education band, device
• Consent records mapped to speaker IDs and retained for audit
• Speaker-disjoint train / dev / test splits

The failure modes this style is prone to: crosstalk bleed that makes clean per-speaker training targets impossible to recover afterwards

• Channel bleed measured on every session and rejected above the agreed threshold
• Diarisation labels verified against the isolated channels rather than inferred from the mix
• Turn boundaries and overlap regions checked by a native listener
• Speaking-time balance per pair audited, so a dominant speaker does not silently halve the session's value

Bands, not a price list. A signed specification converts them into one fixed figure, and staged delivery lets you start training before the full corpus lands.

Public Hindi corpora skew heavily towards read newspaper text from educated urban speakers in Delhi and NCR. Rural Bihar and eastern UP speech, elderly speakers, and low-literacy speakers reading prompts aloud are largely absent.

Urban Hindi speech is Hinglish in practice. Expect 15-40% English tokens in spontaneous speech: numbers, brands, technology terms, and whole clause switches. Any Hindi corpus that excludes English tokens will not match production traffic.

• Inconsistent Devanagari vs romanised spelling for the same English loan word
• Nukta characters (क़ ख़ ग़ ज़ फ़) applied inconsistently by transcribers
• Numerals: whether to write digits, Devanagari numerals, or spelled-out words must be fixed in the style guide up front
• Honorific verb forms create long agreement chains that annotators shorten unless the guide forbids it

• Perpetual, transferable licence covering model training, derivative models and commercial deployment
• Written informed consent from every speaker, retained and auditable
• Compliant with India's DPDP Act 2023 handling requirements and mapped to GDPR obligations for EU buyers
• No scraped, resold or re-licensed third-party audio in any delivery

• Send your specification or use the standard one above
• Receive a fixed scope, price and delivery schedule within one working day
• Optional paid pilot of 10-20 hours against the same spec
• Recruitment and recording begin on signature, with staged batches
• Acceptance testing per batch, then final delivery with the full manifest