2,000 speakers of Gujarati Conversational Speech

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.

Yield at this style: 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.

• Scenario seeds rather than scripts — a disagreement to resolve, a plan to make, an experience to compare
• Pairing designed deliberately: familiar pairs produce natural interruption, stranger pairs produce polite turn-taking, and you need both
• Scenarios that invite disagreement, because agreeable conversation produces almost no overlap to train on
• Register mixed across pairs so the corpus is not uniformly formal
• Built against Gujarati specifically: Murmured (breathy-voiced) vowels are phonemic in Gujarati and are absent from most shared Indic acoustic models
• Code-mixing handled explicitly rather than edited out — Business and trade vocabulary is heavily English; Gujarati diaspora speech adds further English structure. Specify whether diaspora speakers are in or out of scope.

Sixteen to twenty-two weeks, run in waves so that recruitment in the harder catchments starts early rather than being left to the end.

Ten to twelve batches with rolling demographic reconciliation, since a quota gap discovered in week eighteen is very expensive to close.

Surat and Rajkot recruitment is essential for dialect coverage; Ahmedabad-only cohorts sound uniform.

At 2,000 speakers the quota matrix is enforced per cell, not in aggregate. Every dialect, age and gender combination carries its own target and is signed off individually before final acceptance, because an aggregate 50/50 split can hide a cell that was never filled at all.

Effectively doubles recruitment load, since speakers are booked in matched pairs and a single drop-out cancels the whole session. Plan on 20–25% over-recruitment.

• Murmured (breathy-voiced) vowels are phonemic in Gujarati and are absent from most shared Indic acoustic models
• Business and trade vocabulary is heavily English; Gujarati diaspora speech adds further English structure. Specify whether diaspora speakers are in or out of scope.
• Very little spontaneous Gujarati audio exists publicly; nearly all of it is Ahmedabad read speech.

• 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
• Breathy vowels have no consistent orthographic marking
• Kathiyawadi lexical items replaced with standard equivalents

100% technical QA, 7% content QA stratified across every axis, blind re-transcription audit, and per-cell demographic sign-off before final acceptance.

• Crosstalk bleed that makes clean per-speaker training targets impossible to recover afterwards
• One speaker dominating, leaving a pair that delivers half the expected audio
• Unnatural politeness between strangers, producing clean but unrepresentative turn-taking
• Scheduling attrition — both speakers have to show up, so no-show rates compound rather than add

Cost at this band is driven by: Long-tail demographic cells, which dominate both cost and schedule at this size.

• The final 15% of the cohort — the rarest demographic cells — routinely takes as long as the first half and should be scheduled first, not last
• Speaker pool exhaustion is a hard constraint in all but the largest languages
• Voice-based duplicate detection across 2,000 speakers is a genuine engineering task, not a spreadsheet check
• Gujarati carries 5 recognised varieties across Gujarat, Daman & Diu, Dadra & Nagar Haveli, so the quota matrix is wider than the headline volume suggests
• Very little spontaneous Gujarati audio exists publicly; nearly all of it is Ahmedabad read speech.

• WAV audio to your naming convention, with the per-file manifest
• Verbatim Gujarati 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
• Consent records mapped to speaker IDs
• QA report with pass rates, rejection reasons and agreement statistics
• Speaker-disjoint train / dev / test splits on request

• Speaker diarisation
• Meeting and multi-party ASR
• Turn-taking and endpointing for voice agents
• Speaker separation and target-speaker extraction

Two-party conversation does not generalise to multi-party meetings with four or more speakers, where overlap statistics change substantially. Specify that case separately.