Buy a Hindi scripted speech dataset

Speakers read prompts from a phonetically balanced script shown one line at a time, with retakes on any flubbed or misread line.

A 45-minute booth session yields roughly 300–400 prompts. One file per utterance, cut at the prompt boundary, so alignment is exact before any forced-alignment pass runs.

85–90% of recorded time is accepted. Prompt-level retakes catch problems inside the session, so very little is discarded afterwards. This is the highest-yield style we run.

• 16-bit PCM WAV audio, captured at 48 kHz / 24-bit and delivered at your target rate
• Verbatim Devanagari transcripts with utterance-level timestamps
• Prompt ID mapped to every utterance
• Verbatim deviation flags where the speaker departed from the script
• Per-utterance SNR and duration in the manifest
• 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: reading voice: flat prosody and unnatural stress that trains a model on speech nobody actually produces

• Prompt-to-audio match verified by a native reviewer; a misread line is a rejected file, not an edited transcript
• Hyperarticulation flagged — a speaker over-enunciating because they are reading produces audio that does not match deployment
• Clipping and truncation checked at both utterance boundaries
• Per-speaker prompt coverage confirmed, so no speaker silently skips a block

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