TTS datasets · Hinglish
Hinglish TTS Training Data
Single-speaker and multi-speaker text-to-speech corpora with phonetically balanced scripts, consistent prosody, and studio-grade capture suitable for neural TTS. This page covers how that works specifically for Hinglish, where intra-sentential switching means english words carry indian phonology, so english acoustic models mis-transcribe them.
- Language
- Hinglish (hi-Latn-IN)
- Dialects covered
- 4
- Typical programme
- 500-2,000 hours
- Cities
- Delhi, Gurugram, Noida
What changes when the language is Hinglish
The service specification stays constant across languages; the linguistics do not. For Hinglish, three things drive the design of a tts datasets programme.
- Intra-sentential switching means English words carry Indian phonology, so English acoustic models mis-transcribe them
- Dialect spread: Delhi corporate Hinglish, Mumbai Bambaiya, Call-centre register, Youth/social media register
- 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.
Technical specification
| Parameter | Standard |
|---|---|
| Sample rate | 48 kHz, 24-bit |
| Speaker consistency | Same booth, mic, distance and time-of-day banding across sessions |
| Script | Phonetically balanced, diphone-covering, domain-extended |
| Prosody | Neutral base set plus optional expressive styles |
| Alignment | Text-audio alignment verified per utterance |
| Silence | Leading/trailing silence trimmed to a fixed window |
Hinglish cohort design
Recruit by switching behaviour, not by language proficiency. Screening recordings are used to confirm speakers switch naturally rather than performing one language.
| Dimension | Typical split | Why it matters for Hinglish |
|---|---|---|
| Gender | 50 / 50 | Pitch range differences change acoustic model behaviour; unbalanced cohorts bias recognition |
| Age | 18-25: 30%, 26-40: 40%, 41-60: 30% | Older speakers retain conservative Hinglish forms that younger urban speakers have lost |
| Region | Delhi NCR / Mumbai / Bengaluru and others | Dialect spread across 4 recognised varieties |
| Education | Mixed, including below-graduate | Prompt-reading fluency correlates with education and skews prosody |
| Condition | Studio / quiet room / field | Match the noise profile of your deployment |
Process
- Script generation with phoneme and diphone coverage analysis
- Voice casting with client shortlisting from audition samples
- Multi-session recording with drift monitoring between sessions
- Alignment verification and mispronunciation review by a linguist
- Delivery with a coverage report
Hinglish-specific quality rules
- 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
- Ambiguous words shared by both languages need an explicit tie-break rule
Session drift is the main TTS killer. Every session is compared acoustically against the reference session and re-recorded if it drifts.
Deliverables
- Studio WAV per utterance
- Verified transcripts and pronunciation notes
- Phoneme coverage report
- Voice talent licence and consent documentation
Worked example
A representative Hinglish tts datasets engagement: 500 hours from 1,000 speakers, 50/50 gender, ages 18-45, spread across Delhi, Gurugram, Noida, recorded to the specification above and delivered in WAV with a per-utterance manifest.
Timeline: 4-8 weeks for a 20-40 hour single-speaker voice build including casting.
Where this data is missing today
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.
Frequently asked
How much does Hinglish tts datasets cost?
Priced per delivered hour or unit against a written spec. The cost drivers for Hinglish are dialect spread, demographic narrowness and recording condition, in that order.
Which Hinglish dialects are included?
By default Delhi corporate Hinglish, Mumbai Bambaiya, Call-centre register, Youth/social media register and others, tagged per speaker. You can also commission a single-dialect corpus if you are targeting one region.
Can you deliver Hinglish data in our format?
Yes. Studio WAV per utterance is the default, but naming, schema and directory structure follow your pipeline.
How long does a Hinglish programme take?
4-8 weeks for a 20-40 hour single-speaker voice build including casting.
Request a Hinglish tts datasets quote
Hours, speakers, dialects, deadline. Send what you have.