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<@U07FRRZH2GN> best STT models for indian language...
# ask-ai
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@icy-policeman-64898 best STT models for indian languages
t
Thanks for reaching out, Abhishek. I'm an AI assistant and I'm here to help. Give me a moment while I research this topic.
Thanks — I’m responding to your question about the best STT models for Indian languages. Here are LiveKit-supported STT options that work well for Indian-language use cases, and when you might choose each: • Deepgram (Nova-3) ◦ Supports Hindi (
hi
) and multilingual transcription. ◦ Has a regional deployment in Mumbai to reduce latency for users in India, which helps real‑time apps. ◦ Good when you need low-latency realtime transcription and solid Hindi/multilingual coverage. • Sarvam (plugin) ◦ Supports Indian language codes like 
hi-IN
and other regional languages. ◦ Default model 
saarika:v2.5
. ◦ Good if you need models tuned specifically for Indian languages and want plugin-based flexibility. • ElevenLabs (Scribe V2 Realtime) ◦ Supports Indian English (
en-IN
). ◦ Useful when your primary need is high-quality Indian-accent English transcription in realtime. • AssemblyAI (Universal-Streaming-Multilingual) ◦ Supports Indian English (
en-IN
) and works for multilingual streaming scenarios. ◦ Good for streaming transcription across languages including Indian English. • Cartesia (Ink Whisper) ◦ Lists Hindi (
hi
) among many supported languages. ◦ Useful as an additional option if you want broad language coverage. Quick guidance to pick one: • If you need realtime, low-latency transcription inside India → try Deepgram Nova-3 (Mumbai regional endpoint helps). • If you need strong coverage for Hindi or other Indian languages specifically → try Sarvam. • If your focus is Indian-accent English → consider ElevenLabs Scribe V2 Realtime or AssemblyAI. If you can tell me: • Which specific Indian languages you need (Hindi, Tamil, Telugu, Bengali, etc.), and • Whether you need realtime streaming or batch transcription, and • Any accuracy or latency priorities (e.g., phone calls, meetings, broadcast), I can recommend a best-fit model and example configuration for LiveKit. Sources: Deepgram STT | LiveKit Documentation | ElevenLabs STT | LiveKit Documentation | Sarvam STT plugin guide | LiveKit Documentation | AssemblyAI STT | LiveKit Documentation | Speech-to-text (STT) models overview | LiveKit Documentation
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best one for turn detection
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Thanks — I’m responding to your question about the best STT model for turn detection in Indian languages. Short answer: For turn detection in Indian-language conversations, the best approach is to use a multilingual STT model (for accurate transcription and language detection) together with LiveKit’s turn detector plugin (which predicts end-of-turn semantically). The most recommended pairings are: • Deepgram Nova-3 + LiveKit turn detector plugin ◦ Deepgram Nova-3 supports Hindi and multilingual transcription (can be set to 
multi
), and Deepgram offers a Mumbai regional endpoint for lower latency in India. This makes it strong for realtime, mixed-language conversations. ◦ The LiveKit turn detector uses a multilingual model that predicts end-of-turn semantically, reducing false interruptions compared with basic VAD. • Sarvam (plugin) + LiveKit turn detector plugin ◦ Sarvam provides models tailored for Indian languages (e.g., 
hi-IN
) and can improve transcription accuracy for regional languages, which helps the turn detector perform better when language-specific cues matter. Other options to consider: • AssemblyAI (Universal-Streaming-Multilingual) or ElevenLabs (Scribe V2 Realtime) if your focus is Indian English (
en-IN
) for streaming use cases. • Cartesia (Ink Whisper) as an additional Hindi-capable option if you need broader model choices. Quick selection guidance: • If you need low-latency realtime turn detection inside India → choose Deepgram Nova-3 + LiveKit turn detector. • If you need the best possible transcription accuracy for specific Indian languages (Hindi, Tamil, Telugu, etc.) → try Sarvam + LiveKit turn detector. • If your use case is primarily Indian-accent English → consider ElevenLabs or AssemblyAI with the turn detector. To give a precise recommendation and example configuration, could you tell me: 1. Which specific Indian languages you need (Hindi only, or Tamil/Telugu/Bengali etc.)? 2. Do you need realtime streaming turn detection or offline/batch segmentation? 3. Any constraints on latency, CPU cost, or allowed cloud region (Mumbai preferred)? With that I’ll recommend the single best model + settings and a short example config for LiveKit. Sources: Building the all-in-one platform for voice AI agents | Deepgram STT | LiveKit Documentation | Sarvam STT plugin guide | LiveKit Documentation | LiveKit turn detector plugin | LiveKit Documentation | Improving voice AI&amp;#39;s turn detection with transformers
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