ASR datasets · Hinglish
Hinglish ASR Training Data
Transcribed speech corpora built to train and evaluate automatic speech recognition, with verbatim transcription, timestamps and per-token language tagging where code-mixing occurs. 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 asr 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 |
|---|---|
| Audio | 16 kHz or 48 kHz PCM WAV |
| Transcription | Verbatim, including disfluencies, false starts and fillers |
| Timestamps | Utterance level by default; word level on request |
| Tagging | Noise, overlap, unintelligible, foreign-language and code-switch tags |
| Normalisation | Raw and normalised text columns delivered separately |
| Split | Train/dev/test splits with no speaker leakage across splits |
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
- Style guide authored per language, covering numerals, loanwords, script and disfluency rules
- Transcriber calibration round with inter-annotator agreement measurement
- First-pass transcription
- Second-pass native review
- Automated consistency checks against the style guide
- Split generation with speaker-disjoint partitions
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
Agreement is measured, not assumed. We report word-level agreement on a held-out sample so you can judge label quality before training on it.
Deliverables
- Audio plus aligned transcripts (JSON/TSV, or your schema)
- Style guide as delivered documentation
- Inter-annotator agreement report
- Speaker-disjoint train/dev/test splits
Worked example
A representative Hinglish asr 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: Transcription adds roughly 1-2 weeks per 100 hours after recording, per language.
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 asr 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. Audio plus aligned transcripts (JSON/TSV, or your schema) is the default, but naming, schema and directory structure follow your pipeline.
How long does a Hinglish programme take?
Transcription adds roughly 1-2 weeks per 100 hours after recording, per language.
Request a Hinglish asr datasets quote
Hours, speakers, dialects, deadline. Send what you have.