ASR
Speech-to-Text
- SOTA WER on Indian-language speech
- Robust to accents, dialects & noisy telephony
- Native code-mixed (Hinglish etc.) handling
- Punctuation, numerals & timestamps
- Domain & vocabulary adaptation
Indic Speech AI · ASR · TTS · Streaming
Hear and speak every Indian language.
Proprietary speech recognition and synthesis models trained natively on Indian languages, regional accents, noisy real-world audio and Code-mixingSwitching languages within one sentence, like Hinglish: “mujhe ticket book karni hai”. speech, delivering state-of-the-art Word Error Rate (WER)The share of words a speech recogniser gets wrong. Lower is better. on an inference stack engineered for speed and low cost.
- Hindiनमस्ते“Hello” · namaste
- Tamilவணக்கம்“Hello” · vanakkam
- Teluguనమస్కారం“Hello” · namaskaram
- Kannadaನಮಸ್ಕಾರ“Hello” · namaskara
- Bengaliনমস্কার“Hello” · nomoshkar
- Marathiनमस्कार“Hello” · namaskar
- Gujaratiનમસ્તે“Hello” · namaste
- Malayalamനമസ്കാരം“Hello” · namaskaram
- Punjabiਸਤ ਸ੍ਰੀ ਅਕਾਲ“Hello” · sat sri akal
- Odiaନମସ୍କାର“Hello” · namaskar
- English · Hinglish
Streaming ASR: text appears as people speakLive · low latency
नमस्ते,
मुझे
ticket
बुक
करनी है
Streaming
Real-Time Speech
- Low-latency streaming ASRAutomatic speech recognition: turning spoken audio into text. with partial results
- Streaming TTSText-to-speech: turning text into a natural-sounding voice.: audio starts instantly
- Voice-activity detectionDetecting when someone starts and stops speaking, so the system knows when to listen and when to reply. & endpointing
- WebSocket / gRPC streaming APIs
- Built for live captions & broadcasts
TTS
Text-to-Speech
- Natural, expressive Indian voices
- Accurate prosody & pronunciation
- Multiple languages, voices & styles
- Handles numbers, dates & abbreviations
- Custom voice creation
Accuracy you can measure
We benchmark on Word Error Rate, the share of words the system gets wrong, on real Indian audio, not just clean studio data.
WER = (Substitutions + Deletions + Insertions) ÷ Words
Optimised inference
- QuantisationStoring model weights with fewer bits (e.g. 8 or 4 instead of 32), cutting memory and speeding up inference with little loss in accuracy. & DistillationTraining a small “student” model to imitate a large “teacher”, keeping most of the accuracy at a fraction of the size. modelsSmaller footprint with accuracy preserved.
- 85× faster than real timeLow Real-time factorProcessing time divided by audio length. A factor below 1 means faster than the audio plays., with streaming TTS at 250 ms Time-to-first-audioHow long after a request the voice starts playing. It decides whether a conversation feels instant. (p9090th percentile: 9 out of 10 requests are at least this fast.).
- High concurrencyBatchingProcessing many requests together on the same hardware. Continuous batching lets new requests join mid-flight, so GPUs stay busy. for thousands of streams.
See Indic Speech AI on your own data.
Sovereign · Scalable · Sustainable. Deployed on your infrastructure.