1,000 hours of Telugu Scripted Speech

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.

Yield at this style: 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.

• Script built for triphone coverage rather than word coverage, so rare phone contexts appear often enough to train on
• Digit strings, dates, currency and person names blocked separately, because these are where deployed ASR actually fails
• Domain lexicon injected from your product vocabulary when you supply one
• Sentence-length distribution spread deliberately, since all-short prompts produce a model that cannot handle long utterances
• Built against Telugu specifically: Vowel-length contrasts are phonemic and short/long confusion changes meaning outright
• Code-mixing handled explicitly rather than edited out — Hyderabad speech mixes Telugu, Urdu/Deccani, Hindi and English. A Telugu dataset for Hyderabad deployment must include Urdu-origin vocabulary.

Twelve to sixteen weeks. This is a programme with its own governance rather than a project — weekly demographic reconciliation, a standing protocol review and a named counterpart on your side.

Ten to twelve batches, fortnightly, with a formal acceptance test per batch. Rejected batches are re-recorded rather than patched, so schedule contingency is built into the plan.

Split cohorts explicitly between Telangana and Andhra Pradesh and tag every speaker; models trained without the tag cannot be evaluated per region.

At 1,000 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.

Requires literate speakers comfortable reading aloud in the target script, which is the main constraint on cohort breadth and has to be actively counterweighted.

• Vowel-length contrasts are phonemic and short/long confusion changes meaning outright
• Hyderabad speech mixes Telugu, Urdu/Deccani, Hindi and English. A Telugu dataset for Hyderabad deployment must include Urdu-origin vocabulary.
• Coastal Andhra read speech dominates. Telangana rural and Rayalaseema speech is thin, despite Hyderabad being the largest deployment market.

• 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
• Telangana forms normalised to Coastal Andhra standard
• Long/short vowel marking errors under time pressure

100% technical QA, 7% content QA stratified across every axis, plus a blind 1% re-transcription audit measuring inter-annotator agreement across the whole programme.

• Reading voice: flat prosody and unnatural stress that trains a model on speech nobody actually produces
• Prompt fatigue in the back half of long sessions, where accuracy drops and pace flattens
• Speakers who are not fluent readers, which quietly biases the cohort toward higher education bands
• Script leakage across speakers, producing a corpus that memorises sentences instead of covering sounds

Cost at this band is driven by: Programme management overhead, which is real at this scale and should be quoted explicitly rather than hidden in the hourly rate; The tail of rare dialect and demographic quotas.

• Transcriber consistency across a 45-person team is the dominant quality risk and needs continuous calibration, not a one-time briefing
• Speaker pool exhaustion in smaller cities, where the genuinely available cohort is finite
• Specification drift over three months as your model team learns what it actually needs — build a change-control step in rather than pretending it will not happen
• Telugu carries 4 recognised varieties across Andhra Pradesh, Telangana, parts of Karnataka and Odisha, so the quota matrix is wider than the headline volume suggests
• Coastal Andhra read speech dominates. Telangana rural and Rayalaseema speech is thin, despite Hyderabad being the largest deployment market.

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

• ASR acoustic model baselines
• TTS voice building where a single speaker is recorded at depth
• Pronunciation lexicon and G2P validation
• Forced-alignment and phone-boundary work

Scripted audio contains no disfluencies, no false starts and no natural turn-taking. A model trained on it alone degrades sharply on real spontaneous input, so it is a baseline layer rather than a complete training set.