Service
Voice Recording for AI Training in India
Studio voice recording built for model training rather than broadcast: controlled acoustics, consistent mic distance, and reproducible session parameters across every speaker.
- Turnaround
- 2-5 weeks depending on speaker count and city spread.
- Languages
- 14 Indian languages + Indian English
- Delivery
- Unprocessed WAV masters
What you get
- Unprocessed WAV masters
- Session logs
- Take-level metadata
- Speaker consent records
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 |
|---|---|
| Sample rate | 48 kHz |
| Bit depth | 24-bit |
| Microphone | Large-diaphragm condenser, fixed distance, pop filter |
| Room | Treated booth, RT60 under 0.3s |
| Processing | None. No compression, EQ, or noise reduction on delivered audio |
| Session length | 30-90 minutes per speaker, with breaks logged |
How the work runs
- Session template definition so every studio in the network records identically
- Speaker briefing and pronunciation calibration
- Take-level monitoring with immediate re-record on defect
- Per-session technical report
- Delivery with unprocessed masters
Quality control
Delivered audio is never processed. Training pipelines should see the raw capture so augmentation stays under your control.
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
Voice talent and ordinary speakers both available; specify which, because they produce very different acoustic distributions.
Consent is captured per participant and mapped to file IDs, so provenance survives an external audit of your training data.
Timeline
2-5 weeks depending on speaker count and city spread.
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
- TTS voice builds
- Voice cloning research
- Prompt-based speech models
Frequently asked
What is the minimum volume for voice recording?
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 voice recording for ai training
Send the specification you already have, or the rough shape of it, and you get a scoped quote with a timeline.