ASR datasets · हिन्दी
Hindi 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 Hindi, where four-way stop contrast (voiced/voiceless x aspirated/unaspirated) that collapses in models trained on english-first acoustic units.
- Language
- Hindi (hi-IN)
- Dialects covered
- 7
- Typical programme
- 500-2,000 hours
- Cities
- Delhi, Lucknow, Jaipur
What changes when the language is Hindi
The service specification stays constant across languages; the linguistics do not. For Hindi, three things drive the design of a asr datasets programme.
- Four-way stop contrast (voiced/voiceless x aspirated/unaspirated) that collapses in models trained on English-first acoustic units
- Dialect spread: Khari Boli, Awadhi, Braj, Bhojpuri-influenced Hindi
- Urban Hindi speech is Hinglish in practice. Expect 15-40% English tokens in spontaneous speech: numbers, brands, technology terms, and whole clause switches. Any Hindi corpus that excludes English tokens will not match production traffic.
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 |
Hindi cohort design
Largest recruitment pool in the network. A 1,000-speaker Hindi cohort with balanced gender and 18-45 age bands is typically fielded across four cities to avoid a single-city accent bias.
| Dimension | Typical split | Why it matters for Hindi |
|---|---|---|
| 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 Hindi forms that younger urban speakers have lost |
| Region | Uttar Pradesh / Bihar / Madhya Pradesh and others | Dialect spread across 7 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
Hindi-specific quality rules
- Inconsistent Devanagari vs romanised spelling for the same English loan word
- Nukta characters (क़ ख़ ग़ ज़ फ़) applied inconsistently by transcribers
- Numerals: whether to write digits, Devanagari numerals, or spelled-out words must be fixed in the style guide up front
- Honorific verb forms create long agreement chains that annotators shorten unless the guide forbids it
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 Hindi asr datasets engagement: 500 hours from 1,000 speakers, 50/50 gender, ages 18-45, spread across Delhi, Lucknow, Jaipur, 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
Public Hindi corpora skew heavily towards read newspaper text from educated urban speakers in Delhi and NCR. Rural Bihar and eastern UP speech, elderly speakers, and low-literacy speakers reading prompts aloud are largely absent.
Frequently asked
How much does Hindi asr datasets cost?
Priced per delivered hour or unit against a written spec. The cost drivers for Hindi are dialect spread, demographic narrowness and recording condition, in that order.
Which Hindi dialects are included?
By default Khari Boli, Awadhi, Braj, Bhojpuri-influenced Hindi and others, tagged per speaker. You can also commission a single-dialect corpus if you are targeting one region.
Can you deliver Hindi 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 Hindi programme take?
Transcription adds roughly 1-2 weeks per 100 hours after recording, per language.
Request a Hindi asr datasets quote
Hours, speakers, dialects, deadline. Send what you have.