Labels tied to model behaviour.
- verbatim transcript
- normalized transcript
- language and code-switch tags
- speaker cohort metadata
- device and environment
- noise and overlap events
- utterance intent where applicable
Speech and voice AI · Audio
Indian Multilingual Speech
A multilingual speech collection concept spanning Indian English, Hindi and regional-language cohorts across devices and acoustic environments.
Catalogue statusIllustrative custom specification
Reference volumeIllustrative 4,200-hour specification
Delivery modelScoped pilot → versioned production
Designed for
ASR, voice-agent and conversational-AI teams that need representative Indian speech rather than studio-only audio.
Every specification is adapted to the buyer's model, production environment, data rights and measurable acceptance criteria.
Primary use cases
- Automatic speech recognition
- Voice-assistant command understanding
- Conversational-agent evaluation
- Accent and code-switching robustness
Variation before volume.
- Indian English and selected regional languages
- Urban, semi-urban and rural cohorts
- Phone, headset and laptop microphones
- Quiet, traffic, household and workplace noise
- Read, prompted and conversational speech
Quality target
Target: ≥98% transcript acceptance on audited segments
Targets are agreed during scoping and reported only after measurement on the applicable delivery.
Quality controls
- Transcriber qualification by language
- Second-pass transcript review
- Audio-text alignment checks
- Speaker and cohort distribution audits
- WER-oriented benchmark subsets
Delivery format
16 kHz WAV or buyer-defined audio plus JSONL manifests
Buyer-specific schema and storage requirements can be incorporated before production.
Example file package
01
audio/02
manifest.jsonl03
speaker_metadata.csv04
language_map.csv05
dataset_card.md06
qa_benchmark.jsonlEvidence travels with the files.
- Consent and contributor-notice summary
- Language and cohort distribution
- Transcription and normalization guide
- Acoustic-condition taxonomy
- Quality report and benchmark methodology
What buyers should understand.
- Language list and cohort targets are project-specific
- Accent labels are descriptive metadata, not identity conclusions
- Consent scope determines permitted model use
Build from this pattern
Turn your requirement into a pilot.
Share the model objective, target environment, approximate volume and delivery constraints. We will respond with the questions needed to scope a credible dataset.
Request this dataset pattern ↗