Service
Audio Annotation in India
Labelling of existing audio: speaker diarisation, emotion, intent, events, language identification and segment-level quality tagging, against your label schema.
- Turnaround
- Scoped per label complexity; simple diarisation runs at roughly 3-5x real time.
- Languages
- 14 Indian languages + Indian English
- Delivery
- Labelled data in your schema
What you get
- Labelled data in your schema
- Gold set and calibration results
- Per-label agreement statistics
- Edge-case log
Technical specification
Every parameter below is written into the statement of work before recording begins. If your pipeline needs different values, they replace ours rather than being converted after delivery.
| Parameter | Standard |
|---|---|
| Label types | Diarisation, emotion, intent, events, language ID, quality |
| Granularity | Segment, utterance, or frame-level boundaries |
| Schema | Yours, or authored with you before work starts |
| Agreement | Multi-annotator overlap on a defined percentage |
| Tooling | Client tooling supported; otherwise our annotation workflow |
How the work runs
- Schema definition and edge-case documentation
- Annotator training and gold-set calibration
- Production annotation with gold items seeded in
- Adjudication of disagreements by a senior reviewer
- Delivery with per-label agreement statistics
Quality control
Gold items are seeded throughout production so drift is caught during the run, not at delivery.
QA failures are remedied by re-collection, not by editing the delivered files. Repaired audio introduces artefacts that survive into your model.
Speaker and contributor sourcing
Annotators are native speakers with domain briefing, not generic crowd workers.
Consent is captured per participant and mapped to file IDs, so provenance survives an external audit of your training data.
Timeline
Scoped per label complexity; simple diarisation runs at roughly 3-5x real time.
Staged delivery is available: first batches ship while later batches are still recording, so training can start early.
The numbers we hold ourselves to
- 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.
Commonly used for
- Diarisation
- Emotion AI
- Intent classification
- Data cleaning
Frequently asked
What is the minimum volume for audio annotation?
Programmes typically start around 50 hours or equivalent units per language. Smaller pilots are accepted when they lead into a larger build, because most of the setup cost is in specification and recruitment rather than recording time.
Can you work to our schema instead of yours?
Yes. Manifest fields, file naming, directory structure and label schema are set by you. Working to your schema from the start avoids a conversion pass that usually loses metadata.
Who owns the delivered data?
You do. Deliverables come with a perpetual, transferable licence and participant consent that covers model training and distribution of the resulting model.
How is pricing structured?
Per delivered hour or per unit, quoted against a written specification. Quotas, recording conditions and QA thresholds all move the price, which is why we quote from a spec rather than from a price list.
Get a quote for audio annotation
Send the specification you already have, or the rough shape of it, and you get a scoped quote with a timeline.