Voice Agents — Sarvam AI

One-shot in-chat voice/dub → sarvam-mcp (sarvam_tools_voice / _dub). This skill = LiveKit/Pipecat agent code.

[!IMPORTANT] Auth: api-subscription-key header — NOT Authorization: Bearer. Env var: SARVAM_API_KEY

LiveKit Quick Start

pip install "livekit-agents[sarvam,silero]" python-dotenv

from livekit.agents import JobContext, WorkerOptions, cli
from livekit.agents.voice import Agent, AgentSession
from livekit.plugins import sarvam

class VoiceAgent(Agent):
    def __init__(self) -> None:
        super().__init__(
            instructions="You are a helpful voice assistant. Be friendly, concise, and conversational.",
            stt=sarvam.STT(
                language="unknown",   # auto-detect; or "hi-IN", "ta-IN", etc.
                model="saaras:v3",
                mode="transcribe",
                flush_signal=True     # emits speech start/end events for turn-taking
            ),
            llm=sarvam.LLM(model="sarvam-105b"),
            tts=sarvam.TTS(
                target_language_code="en-IN",
                model="bulbul:v4-flash",
                speaker="simran_en_customer",
            ),
        )

    async def on_enter(self):
        self.session.generate_reply()

async def entrypoint(ctx: JobContext):
    # Do NOT pass vad= — VAD is handled internally by the Sarvam plugin
    session = AgentSession(
        turn_detection="stt",       # let Sarvam STT handle turn detection
        min_endpointing_delay=0.07  # ~70ms matches Sarvam STT processing latency
    )
    await session.start(agent=VoiceAgent(), room=ctx.room)

if __name__ == "__main__":
    cli.run_app(WorkerOptions(entrypoint_fnc=entrypoint))

Run with python agent.py dev, test with python agent.py console.

Pipecat Quick Start

pip install "pipecat-ai[daily,sarvam]" python-dotenv loguru

import os
from pipecat.pipeline.pipeline import Pipeline
from pipecat.processors.aggregators.llm_context import LLMContext
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
from pipecat.services.sarvam.stt import SarvamSTTService
from pipecat.services.sarvam.tts import SarvamTTSService
from pipecat.services.sarvam.llm import SarvamLLMService

stt = SarvamSTTService(
    api_key=os.getenv("SARVAM_API_KEY"),
    language="unknown",   # auto-detect; or "hi-IN", "ta-IN", etc.
    model="saaras:v3",
    mode="transcribe"     # or "translate" for speech-to-English
)
tts = SarvamTTSService(
    api_key=os.getenv("SARVAM_API_KEY"),
    settings=SarvamTTSService.Settings(
        model="bulbul:v4-flash",
        voice="simran_en_customer",  # persona ID — not a v3 short name
        language="en-IN",
        pace=1.0,
    ),
)
llm = SarvamLLMService(
    api_key=os.getenv("SARVAM_API_KEY"),
    settings=SarvamLLMService.Settings(model="sarvam-105b"),
)

messages = [{"role": "system", "content": "You are a friendly AI assistant. Keep responses brief."}]
context_aggregator = LLMContextAggregatorPair(LLMContext(messages))

pipeline = Pipeline([
    transport.input(),            # Daily or WebRTC transport
    stt,
    context_aggregator.user(),
    llm,
    tts,
    transport.output(),
    context_aggregator.assistant(),
])

JavaScript/TypeScript Note

LiveKit and Pipecat agents are Python-only. For JS/TS voice pipelines, use the individual SDK methods directly:

import { SarvamAIClient } from "sarvamai";
const client = new SarvamAIClient({ apiSubscriptionKey: "YOUR_SARVAM_API_KEY" });

// STT: client.speechToText.transcribe({...})
// TTS: client.textToSpeech.convertStream({...})  // returns BinaryResponse
// LLM: client.chat.completions({...})

Gotchas

Full Docs

Fetch framework integration guides, environment setup, and advanced patterns from:

• https://docs.sarvam.ai/llms.txt — comprehensive docs index
• LiveKit Guide
• Pipecat Guide
• Bulbul v4 Flash voices
• Rate Limits