ASR Model Training · ગુજરાતી
Gujarati Data for ASR Model Training
Building or fine-tuning speech recognition for Indian languages from scratch or from a multilingual base model. In Gujarati, the binding constraint is usually dialect coverage and code-mixing, not raw hours.
- Language
- Gujarati
- Primary metric
- Word error rate overall and per dialect
- Typical volume
- 250-1,000 hours
Data profile required
- Hundreds to thousands of hours of verbatim-transcribed speech
- Wide speaker diversity: age, gender, region, education, recording condition
- Speaker-disjoint train/dev/test splits
What Gujarati adds to the requirement
- Murmured (breathy-voiced) vowels are phonemic in Gujarati and are absent from most shared Indic acoustic models
- Surti speech has distinctive intonation and vowel quality that mismatches Ahmedabad-trained models
- Dialects to cover: Standard (Amdavadi), Surti, Kathiyawadi, Kachchhi-influenced
- Business and trade vocabulary is heavily English; Gujarati diaspora speech adds further English structure. Specify whether diaspora speakers are in or out of scope.
Metrics to track
- Word error rate overall and per dialect
- Entity error rate on names and numbers
- Code-switch token accuracy
Failure modes
- Read-speech-only corpora that do not transfer to spontaneous audio
- Speaker leakage across splits inflating reported accuracy
- Normalised-only transcripts with the raw text discarded
For Gujarati specifically: Very little spontaneous Gujarati audio exists publicly; nearly all of it is Ahmedabad read speech.
Recommended cohort
Surat and Rajkot recruitment is essential for dialect coverage; Ahmedabad-only cohorts sound uniform.
| Dimension | Typical split | Why it matters for Gujarati |
|---|---|---|
| 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 Gujarati forms that younger urban speakers have lost |
| Region | Gujarat / Daman & Diu / Dadra & Nagar Haveli and others | Dialect spread across 5 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 |
Suggested programme shape
Start with an evaluation set of 100 speakers spread across every Gujarati dialect in scope, collected before training data. Then field 250-1,000 hours of training data from disjoint speakers.
This ordering is what makes the improvement measurable rather than assumed.
Frequently asked
Is there usable public Gujarati data for asr model training?
Very little spontaneous Gujarati audio exists publicly; nearly all of it is Ahmedabad read speech.
How many Gujarati speakers do we need?
500-1,500 speakers for a training corpus, plus a disjoint evaluation cohort covering each dialect. Speaker count matters more than hours for generalisation.
Can you run this across multiple languages at once?
Yes. Multi-language programmes run to one master specification so per-language results stay comparable.
Scope Gujarati data for asr model training
Send the target metric and the languages in scope.