1,000 hours of Hinglish Telephony Speech

Speech captured over a real telephony path — a genuine call through the network, not studio audio downsampled afterwards to imitate one.

Scenario-driven calls between a caller and an agent or IVR flow, each leg recorded separately, across a deliberate spread of handsets and network conditions.

Yield at this style: 50–60% of recorded time is accepted. Dropped calls, network artefacts beyond tolerance and unusable legs are discarded. Real network conditions are the point of the style and also its main cost.

• Call scenarios drawn from real contact-centre intents: balance enquiry, complaint, booking change, escalation
• IVR flows scripted with deliberate mis-entry and barge-in paths, since those are where deployed systems fail
• Handset spread specified up front — low-end Android, feature phone, landline — because handset variance is a real acoustic axis
• Background conditions varied on purpose: street, vehicle, indoor, since real callers are rarely in quiet rooms
• Built against Hinglish specifically: Intra-sentential switching means English words carry Indian phonology, so English acoustic models mis-transcribe them
• Code-mixing handled explicitly rather than edited out — Hinglish is the code-mixing case itself. Typical urban customer-support speech is 30-60% English tokens embedded in Hindi grammar, with switching several times per utterance.

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.

Recruit by switching behaviour, not by language proficiency. Screening recordings are used to confirm speakers switch naturally rather than performing one language.

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.

Recruitment must be stratified by handset and carrier as well as by dialect, which adds a screening axis the other styles do not have.

• Intra-sentential switching means English words carry Indian phonology, so English acoustic models mis-transcribe them
• Hinglish is the code-mixing case itself. Typical urban customer-support speech is 30-60% English tokens embedded in Hindi grammar, with switching several times per utterance.
• Almost no public corpus contains genuine intra-sentential Hindi-English switching with per-token language tags. This is the highest-value gap for anyone building Indian conversational AI.

• Codec and sample rate verified per file, rejecting any studio audio that has been downsampled to fake a telephony path
• Echo and double-talk checked on both legs
• DTMF events verified against the call log
• Level normalisation applied per leg, since handset output levels vary far more than studio microphones do
• Whether English tokens are written in Latin or transliterated into Devanagari must be fixed by rule, not left to annotators
• Language-ID tagging per token is required for training but is skipped by most vendors

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.

• Studio audio downsampled to 8 kHz and passed off as telephony, which has none of the codec or packet-loss characteristics that matter
• Echo and double-talk that make the agent leg unusable
• Carrier and handset monoculture, producing a corpus that only represents one acoustic path
• Over-clean recordings from participants who move somewhere quiet to take the call, defeating the purpose

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
• Hinglish carries 4 recognised varieties across Delhi NCR, Mumbai, Bengaluru, so the quota matrix is wider than the headline volume suggests
• Almost no public corpus contains genuine intra-sentential Hindi-English switching with per-token language tags. This is the highest-value gap for anyone building Indian conversational AI.

• WAV audio to your naming convention, with the per-file manifest
• Verbatim Devanagari + Latin transcripts with utterance-level timestamps
• Call metadata: duration, codec, handset class, carrier, packet-loss events
• Intent label per call and per turn
• DTMF and hold, transfer and barge-in events
• Agent and caller leg identifiers
• 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

• Contact-centre and IVR ASR
• Voice bots operating over the phone network
• Intent classification on narrowband audio
• Robustness to codec and packet loss

Narrowband telephony audio is the wrong input for TTS or any wideband model — the frequency content simply is not there. Use it for models that will be deployed on a phone line and nothing else.