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Dataset specification · Flagship scale

2,000 hours of Odia Conversational Speech

A flagship scale build of 2,000 hours of Odia conversational speech. Foundation-model input, or a multi-year corpus intended to be the reference dataset for a language. Two speakers hold an unscripted conversation seeded with a scenario, recorded on separate channels so overlap and turn-taking survive into the delivered files.

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Two speakers recording natural conversational speech data — 2,000 hours of Odia Conversational Speech
Volume
2,000 hours
Scale
Flagship scale
Per speaker
20–30 minutes of accepted audio per speaker, in pairs
Accepted yield
55–65% of recorded time is accepted
01

How Odia conversational speech is captured

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.

Flagship scale — what the build is actually made ofCitiesTwelve or more, includi…StudiosFourteen rooms plus six…RecruitersSixteen coordinators, t…Speakers~4,000–5,000Sessions per day120–150 nationallyTeam1 programme director, 3…
02

The specification

FieldValue
LanguageOdia (or-IN, Odia)
Volume2,000 hours
Equivalent4,000 speakers at 30 minutes each, or 2,000 speakers at one hour each
Speech typeConversational Speech
Per speaker20–30 minutes of accepted audio per speaker, in pairs
ChannelsTwo, one per speaker, never mixed down before delivery
Channel isolationBleed measured per session and logged; sessions over threshold are re-recorded
OverlapPreserved, timestamped and labelled rather than edited out
File granularityPer-channel session WAV plus a turn-level manifest with speaker IDs
DialectsCuttack-Bhubaneswar standard, Sambalpuri (Kosli), Ganjami, Baleswari
TranscriptionVerbatim, native-speaker, second-pass reviewed
Studio-grade voice recording session for text-to-speech training data — supporting 2,000 hours of odia conversational speech
Studio-grade voice recording session for text-to-speech training data
03

Designing the scenarios for Odia

  • 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 Odia specifically: Retains a distinct retroflex ଳ and a full retroflex series
  • Code-mixing handled explicitly rather than edited out — Urban Odia mixes Hindi and English; western Odisha mixes Chhattisgarhi and Sambalpuri forms.
04

Running a flagship scale Odia build

Twenty to twenty-eight weeks, run in two or three waves. Wave one establishes the protocol and the transcription convention; later waves scale against a proven baseline rather than an assumed one.

Continuous fortnightly delivery from week four, roughly fourteen batches, each with acceptance testing and a rolling demographic reconciliation against the target quota matrix.

Western Odisha recruitment requires local field partners; remote-only recruitment yields an all-coastal cohort.

ParameterAt this volume
CitiesTwelve or more, including tier-2 and rural catchments — Bhubaneswar, Cuttack, Sambalpur, Berhampur, Rourkela
StudiosFourteen rooms plus six mobile rigs
RecruitersSixteen coordinators, three regional managers, one programme director
Speakers~4,000–5,000
Sessions per day120–150 nationally
Team1 programme director, 3 regional managers, 16 coordinators, 30 engineers, 80 transcribers, 8 QA leads
05

Cohort design

At 2,000 hours 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.

DimensionTypical splitWhy it matters for Odia
Gender50 / 50Pitch range differences change acoustic model behaviour; unbalanced cohorts bias recognition
Age18-25: 30%, 26-40: 40%, 41-60: 30%Older speakers retain conservative Odia forms that younger urban speakers have lost
RegionOdisha / parts of Jharkhand, West Bengal, Chhattisgarh and Andhra Pradesh and othersDialect spread across 5 recognised varieties
EducationMixed, including below-graduatePrompt-reading fluency correlates with education and skews prosody
ConditionStudio / quiet room / fieldMatch the noise profile of your deployment
06

Odia-specific considerations

  • Retains a distinct retroflex ଳ and a full retroflex series
  • Urban Odia mixes Hindi and English; western Odisha mixes Chhattisgarhi and Sambalpuri forms.
  • Odia is one of the least-resourced major Indian languages. Sambalpuri and Ganjami are effectively absent from public data.
07

Quality gates for conversational 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
  • Sambalpuri normalised into coastal Odia
  • Unicode confusables between Odia and Bengali characters when transcribers reuse tooling

100% technical QA, 5% content QA stratified across every axis, a blind 2% re-transcription audit, and a monthly cross-wave consistency review comparing early and late batches for drift.

08

What goes wrong on conversational speech sessions

  • 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
09

Risks at flagship scale in Odia

Cost at this band is driven by: Wave structure and the programme governance it requires; Long-tail demographic and dialect quotas, which dominate the final third of the build.

  • Drift between waves is the defining risk: audio recorded in month one and month six must be indistinguishable in convention, or the corpus splits into two datasets
  • Speaker pool exhaustion is a live constraint in all but the largest languages and shapes which cities are used
  • Staff turnover across twenty-eight weeks is a certainty, so handover documentation is part of the deliverable rather than an afterthought
  • Storage, transfer and manifest integrity become engineering problems in their own right at this size
  • Odia carries 5 recognised varieties across Odisha, parts of Jharkhand, West Bengal, Chhattisgarh and Andhra Pradesh, so the quota matrix is wider than the headline volume suggests
  • Odia is one of the least-resourced major Indian languages. Sambalpuri and Ganjami are effectively absent from public data.
10

Deliverables

  • WAV audio to your naming convention, with the per-file manifest
  • Verbatim Odia 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
11

What this trains, and what it does not

  • 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.

Frequently asked

Is 2,000 hours of Odia enough?

Enough for foundation-model pre-training input in one language, or a reference corpus intended to outlive the model that prompted it.

Why conversational speech rather than another speech type?

Speaker diarisation, Meeting and multi-party ASR, Turn-taking and endpointing for voice agents are what this style is the right input for. Two-party conversation does not generalise to multi-party meetings with four or more speakers, where overlap statistics change substantially. Specify that case separately.

How long does a flagship scale Odia build take?

Twenty to twenty-eight weeks, run in two or three waves. Wave one establishes the protocol and the transcription convention; later waves scale against a proven baseline rather than an assumed one. Continuous fortnightly delivery from week four, roughly fourteen batches, each with acceptance testing and a rolling demographic reconciliation against the target quota matrix.

What does 2,000 hours of Odia conversational speech cost?

Quoted per delivered hour against this specification. At this band the drivers are wave structure and the programme governance it requires and long-tail demographic and dialect quotas, which dominate the final third of the build. Send the spec and you get one fixed figure.

How much QA is applied at this volume?

100% technical QA, 5% content QA stratified across every axis, a blind 2% re-transcription audit, and a monthly cross-wave consistency review comparing early and late batches for drift.

Quote this Odia dataset

2,000 hours, conversational speech, Odia — flagship scale. Adjust anything and send it.

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