Gujarati AI Training Data Collection

Gujarati is not a variant of a language your model already handles. The specific properties below are the ones that show up as errors in production.

• 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
• Frequent final-vowel deletion in fast speech

Gujarati has 5 varieties that matter for data collection. Collecting only the standard variety produces a model that works in the capital and fails everywhere else.

• Standard (Amdavadi)
• Surti
• Kathiyawadi
• Kachchhi-influenced
• Charotari

Business and trade vocabulary is heavily English; Gujarati diaspora speech adds further English structure. Specify whether diaspora speakers are in or out of scope.

Specify your expected English-token ratio in the brief. It is cheaper to collect the right mix than to filter the wrong one afterwards.

Very little spontaneous Gujarati audio exists publicly; nearly all of it is Ahmedabad read speech.

This is the practical reason to commission collection rather than assemble open corpora: the gap in the public data is exactly the part your users occupy.

These are the Gujarati-specific decisions that go into the style guide before any transcriber starts work.

• Breathy vowels have no consistent orthographic marking
• Kathiyawadi lexical items replaced with standard equivalents
• Numerals and currency in trade speech written inconsistently

• Trade, retail and logistics domain conversation
• Read prompts covering murmured vowels
• Surti and Kathiyawadi dialect sets

Surat and Rajkot recruitment is essential for dialect coverage; Ahmedabad-only cohorts sound uniform.

Gujarati sessions run in Ahmedabad, Surat, Vadodara, Rajkot, Bhavnagar. Multi-city fielding is the default because single-city cohorts carry an audible accent signature.

• 100% of delivered files pass automated technical QA for SNR, clipping, duration and silence
• 5-25% of files pass a second native-speaker content review, stratified by city, dialect and transcriber, and escalating to 100% on any batch that fails the agreed threshold
• Accepted yield runs 85-90% for scripted speech, 60-70% for spontaneous, 55-65% for conversational and 50-60% for telephony
• Default cohort quotas: 50/50 gender, with age bands at 30% (18-25), 40% (26-40) and 30% (41-60)
• 48 kHz / 24-bit capture, delivered as 16-bit PCM WAV, with studio sessions held below a -50 dBFS noise floor
• First response within one working day; a scoped, fixed quote within two to three

These are the figures a delivery is measured against, not aspirations. A batch that misses them is re-recorded at our cost rather than repaired.