Sarvam AI Speech-to-Text

Overview

Sarvam AI offers two real-time speech recognition services for Indian languages:

SarvamSTTService: Uses Sarvam’s transcription WebSocket API with VAD-based segmentation and multiple audio formats (saaras:v3 model)
SarvamRealtimeSTTService: Uses Sarvam’s realtime WebSocket endpoint (saaras:v3-realtime model) with server-side endpointing, in-band configuration updates, and lower latency

Sarvam STT API Reference

Pipecat’s API methods for Sarvam STT integration

SarvamSTTService Example

Complete example with VAD-based turn detection

SarvamRealtimeSTTService Example

Realtime STT with manual endpointing

Sarvam Documentation

Official Sarvam AI STT documentation and features

Sarvam AI Platform

Access API keys and speech models

Installation

To use Sarvam services, install the required dependency:

uv add "pipecat-ai[sarvam]"

Prerequisites

Sarvam AI Account Setup

Before using Sarvam STT services, you need:

Sarvam AI Account: Sign up at Sarvam AI
API Key: Generate an API key from your account dashboard
Model Access:
- SarvamSTTService: Access to Saarika (STT) or Saaras (STT-Translate) models, including the saaras:v3 model with support for multiple modes (transcribe, translate, verbatim, translit, codemix)
- SarvamRealtimeSTTService: Access to the saaras:v3-realtime model

Required Environment Variables

SARVAM_API_KEY: Your Sarvam AI API key for authentication

Configuration

SarvamSTTService

str

required

Sarvam API key for authentication.

str

default:"saaras:v3"

deprecated

Sarvam model to use. Allowed values: "saarika:v2.5" (standard STT), "saaras:v2.5" (STT-Translate, auto-detects language), "saaras:v3" (advanced, supports mode and fine-grained VAD). Deprecated in v0.0.105. Use settings=SarvamSTTService.Settings(...) instead.

int

default:"None"

Audio sample rate in Hz. Defaults to 16000 if not specified.

Literal['transcribe', 'translate', 'verbatim', 'translit', 'codemix']

default:"None"

Mode of operation. Only applicable to models that support it (e.g., saaras:v3). Defaults to the model’s default mode.

str

default:"wav"

Audio codec/format of the input file.

SarvamSTTService.InputParams

default:"None"

deprecated

Configuration parameters for Sarvam STT service. Deprecated in v0.0.105. Use settings=SarvamSTTService.Settings(...) instead.

SarvamSTTService.Settings

default:"None"

Runtime-configurable settings for the STT service. See Settings below.

float

default:"None"

Seconds of no audio before sending silence to keep the connection alive. None disables keepalive.

float

default:"SARVAM_TTFS_P99"

P99 latency from speech end to final transcript in seconds. Override for your deployment. See stt-benchmark.

float

default:"5.0"

Seconds between idle checks when keepalive is enabled.

Settings

Runtime-configurable settings passed via the settings constructor argument using SarvamSTTService.Settings(...). These can be updated mid-conversation with STTUpdateSettingsFrame. See Service Settings for details.

Parameter	Type	Default	Description
`model`	`str`	`None`	STT model identifier. (Inherited from base STT settings.)
`language`	`Language \| str`	`None`	Target language for transcription. (Inherited from base STT settings.) Behavior varies by model: `saarika:v2.5` defaults to “unknown” (auto-detect), `saaras:v2.5` ignores this (auto-detects), `saaras:v3` defaults to “en-IN”.
`prompt`	`str`	`None`	Optional prompt to guide transcription/translation style. Only applicable to `saaras:v2.5`.
`vad_signals`	`bool`	`None`	Enable VAD signals in responses. When enabled, the service broadcasts `UserStartedSpeakingFrame` and `UserStoppedSpeakingFrame` from the server.
`high_vad_sensitivity`	`bool`	`None`	Enable high VAD sensitivity for more responsive speech detection.
`positive_speech_threshold`	`float`	`None`	VAD probability threshold (0.0-1.0) above which a frame is considered speech. Only for `saaras:v3`.
`negative_speech_threshold`	`float`	`None`	VAD probability threshold (0.0-1.0) below which a frame is considered silence. Only for `saaras:v3`.
`min_speech_frames`	`int`	`None`	Minimum consecutive speech frames to start a speech segment. Only for `saaras:v3`.
`first_turn_min_speech_frames`	`int`	`None`	Minimum speech frames for the first user turn. Only for `saaras:v3`.
`negative_frames_count`	`int`	`None`	Number of silence frames within the window to end a speech segment. Only for `saaras:v3`.
`negative_frames_window`	`int`	`None`	Sliding window size (in frames) for counting negative frames. Only for `saaras:v3`.
`start_speech_volume_threshold`	`float`	`None`	Volume level (dB) below which audio is too quiet to be speech. Only for `saaras:v3`.
`interrupt_min_speech_frames`	`int`	`None`	Minimum speech frames to register a barge-in/interruption. Only for `saaras:v3`.
`pre_speech_pad_frames`	`int`	`None`	Number of audio frames to prepend before detected speech onset. Only for `saaras:v3`.
`num_initial_ignored_frames`	`int`	`None`	Number of leading audio frames to skip at connection start. Only for `saaras:v3`.

SarvamRealtimeSTTService

str

required

Sarvam API key for authentication.

str

default:"wss://api.sarvam.ai/speech-to-text-realtime/ws"

Realtime STT websocket endpoint.

Literal['vad', 'manual']

default:"vad"

Which side detects turn boundaries: vad for Sarvam’s own detection, or manual for the pipeline’s. Decides the turn strategies this service asks the user aggregator to run. Defaults to vad.

int

default:"None"

Declared input audio sample rate, 8000 or 16000. None adopts the pipeline’s input rate.

bool

default:"False"

Whether final transcripts should include segment offsets.

int

default:"None"

Optional VAD prefix padding, used only under endpointing="vad".

SarvamRealtimeSTTService.Settings

default:"None"

Runtime-updatable realtime settings. See Realtime Settings below.

bool

default:"True"

Whether the bot should be interrupted when Sarvam detects user speech. Passed to the user turn strategies this service recommends.

float

default:"SARVAM_REALTIME_TTFS_P99"

P99 latency from speech end to final transcript in seconds. Override for your deployment.

Realtime Settings

Runtime-configurable settings passed via the settings constructor argument using SarvamRealtimeSTTService.Settings(...). These can be updated mid-conversation via config.update() or STTUpdateSettingsFrame.

Parameter	Type	Default	Description
`model`	`str`	`saaras:v3-realtime`	STT model identifier. (Fixed for realtime service.)
`language`	`Language \| str`	`None`	Target language for transcription. (Inherited from base STT settings.)
`language_code`	`str`	`en-IN`	Sarvam realtime language code or `auto` for automatic detection.
`stream_type`	`Literal['fast', 'balanced', 'simulated']`	`balanced`	Streaming cadence: `fast` for lowest latency, `balanced` for moderate latency, or `simulated` for testing.
`mode`	`Literal['transcribe', 'translate', 'verbatim', 'translit', 'codemix']`	`transcribe`	Realtime STT task mode.
`prompt`	`str \| None`	`None`	Optional decoding prompt to guide transcription style.
`threshold`	`float \| None`	`None`	Optional VAD sensitivity threshold (only applies under `endpointing="vad"`).
`silence_duration_ms`	`int \| None`	`None`	Optional silence duration for end-of-speech detection (only applies under `endpointing="vad"`).
`min_speech_duration_ms`	`int \| None`	`None`	Optional minimum speech duration (only applies under `endpointing="vad"`).

Usage

Basic Setup (SarvamSTTService)

from pipecat.services.sarvam.stt import SarvamSTTService

stt = SarvamSTTService(
    api_key=os.getenv("SARVAM_API_KEY"),
)

With Language and Model Configuration

from pipecat.services.sarvam.stt import SarvamSTTService
from pipecat.transcriptions.language import Language

stt = SarvamSTTService(
    api_key=os.getenv("SARVAM_API_KEY"),
    mode="transcribe",
    settings=SarvamSTTService.Settings(
        model="saaras:v3",
        language=Language.HI_IN,
        prompt="Transcribe Hindi conversation about technology.",
    ),
)

With Server-Side VAD

from pipecat.services.sarvam.stt import SarvamSTTService

stt = SarvamSTTService(
    api_key=os.getenv("SARVAM_API_KEY"),
    settings=SarvamSTTService.Settings(
        vad_signals=True,
        high_vad_sensitivity=True,
    ),
)

Basic Realtime Setup

from pipecat.services.sarvam.stt import SarvamRealtimeSTTService

stt = SarvamRealtimeSTTService(
    api_key=os.getenv("SARVAM_API_KEY"),
)

Realtime with Language and Stream Type

from pipecat.services.sarvam.stt import SarvamRealtimeSTTService
from pipecat.transcriptions.language import Language

stt = SarvamRealtimeSTTService(
    api_key=os.getenv("SARVAM_API_KEY"),
    settings=SarvamRealtimeSTTService.Settings(
        language_code="hi-IN",
        stream_type="fast",
        mode="transcribe",
    ),
)

Realtime with Manual Endpointing

from pipecat.services.sarvam.stt import SarvamRealtimeSTTService
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.processors.aggregators.llm_response_universal import (
    LLMContextAggregatorPair,
    LLMUserAggregatorParams,
)

# With manual endpointing, the pipeline drives turn boundaries
stt = SarvamRealtimeSTTService(
    api_key=os.getenv("SARVAM_API_KEY"),
    endpointing="manual",
    settings=SarvamRealtimeSTTService.Settings(
        language_code="en-IN",
        stream_type="balanced",
    ),
)

# A VAD analyzer is required to mark turn boundaries
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
    context,
    user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
)

Updating Realtime Configuration

# Update settings mid-conversation without reconnecting
await stt.update_config(
    language_code="ta-IN",
    stream_type="fast",
    threshold=0.5,
)

Notes

SarvamSTTService

Default model changed: As of this update, the default model is saaras:v3 (previously saarika:v2.5). Applications that relied on the previous default should set settings=SarvamSTTService.Settings(model="saarika:v2.5") explicitly.
Supported languages: Bengali (bn-IN), Gujarati (gu-IN), Hindi (hi-IN), Kannada (kn-IN), Malayalam (ml-IN), Marathi (mr-IN), Tamil (ta-IN), Telugu (te-IN), Punjabi (pa-IN), Odia (od-IN), English (en-IN), and Assamese (as-IN).
Model-specific parameter validation: The service validates that parameters are compatible with the selected model. For example, prompt is only supported with saaras:v2.5, language is not supported with saaras:v2.5 (which auto-detects language), and the fine-grained VAD parameters are only supported with saaras:v3.
Fine-grained VAD tuning (saaras:v3 only): The saaras:v3 model supports server-side VAD with 10 tuning parameters for speech detection thresholds, frame-count controls, pre-speech padding, interruption sensitivity, and initial-frame skipping. These parameters are only available with the saaras:v3 model.
VAD modes: When vad_signals=False (default), the service relies on Pipecat’s local VAD and flushes the server buffer on VADUserStoppedSpeakingFrame. When vad_signals=True, the service uses Sarvam’s server-side VAD and broadcasts speaking frames from the server.

SarvamRealtimeSTTService

Supported sample rates: Only 8000 Hz and 16000 Hz are supported. The service validates the sample rate at initialization.
Supported languages: auto, Bengali (bn-IN), Gujarati (gu-IN), Hindi (hi-IN), Kannada (kn-IN), Malayalam (ml-IN), Marathi (mr-IN), Tamil (ta-IN), Telugu (te-IN), Punjabi (pa-IN), Odia (or-IN), English (en-IN), Assamese (as-IN), Urdu (ur-IN), Nepali (ne-IN), Konkani (kok-IN), Kashmiri (ks-IN), Sindhi (sd-IN), Sanskrit (sa-IN), Santali (sat-IN), Manipuri (mni-IN), Bodo (brx-IN), Maithili (mai-IN), and Dogri (doi-IN).
Endpointing modes:
- endpointing="vad" (default): Sarvam’s server-side VAD decides turn boundaries. The service proposes turn frames via ProposedUserStartedSpeakingFrame and ProposedUserStoppedSpeakingFrame.
- endpointing="manual": The pipeline drives turn boundaries via VADUserStartedSpeakingFrame and VADUserStoppedSpeakingFrame. Requires a VAD analyzer on the user aggregator.
VAD analyzer required: A VAD analyzer is required in either endpointing mode. Under vad it times transcription latency; under manual it also marks the turn for Sarvam.
In-band configuration updates: Settings can be updated mid-conversation via update_config() without reconnecting. Connection-only values (sample_rate, return_timestamps, prefix_padding_ms, endpointing) cannot be updated at runtime.
Stream types: fast provides lowest latency, balanced provides moderate latency with better quality, and simulated is for testing. The stream type controls server-side flush cadence; the client always sends audio in 50ms chunks.

The InputParams / params= pattern is deprecated as of v0.0.105. Use Settings / settings= instead. See the Service Settings guide for migration details.

Event Handlers

In addition to the standard service connection events (on_connected, on_disconnected, on_connection_error), Sarvam STT provides:

Event	Description
`on_speech_started`	Speech detected in the audio stream
`on_speech_stopped`	Speech stopped
`on_utterance_end`	End of utterance detected

@stt.event_handler("on_speech_started")
async def on_speech_started(service):
    print("User started speaking")

@stt.event_handler("on_utterance_end")
async def on_utterance_end(service):
    print("Utterance ended")

Get Started

Pipecat Server

Client SDKs

Pipecat Flows

Pipecat Cloud

CLI

Pipecat Context Hub

Sarvam AI Speech-to-Text

Overview

Sarvam STT API Reference

SarvamSTTService Example

SarvamRealtimeSTTService Example

Sarvam Documentation

Sarvam AI Platform

Installation

Prerequisites

Sarvam AI Account Setup

Required Environment Variables

Configuration

SarvamSTTService

Settings

SarvamRealtimeSTTService

Realtime Settings

Usage

Basic Setup (SarvamSTTService)

With Language and Model Configuration

With Server-Side VAD

Basic Realtime Setup

Realtime with Language and Stream Type

Realtime with Manual Endpointing

Updating Realtime Configuration

Notes

SarvamSTTService

SarvamRealtimeSTTService

Event Handlers

​Overview

Sarvam STT API Reference

SarvamSTTService Example

SarvamRealtimeSTTService Example

Sarvam Documentation

Sarvam AI Platform

​Installation

​Prerequisites

​Sarvam AI Account Setup

​Required Environment Variables

​Configuration

​SarvamSTTService

​Settings

​SarvamRealtimeSTTService

​Realtime Settings

​Usage

​Basic Setup (SarvamSTTService)

​With Language and Model Configuration

​With Server-Side VAD

​Basic Realtime Setup

​Realtime with Language and Stream Type

​Realtime with Manual Endpointing

​Updating Realtime Configuration

​Notes

​SarvamSTTService

​SarvamRealtimeSTTService

​Event Handlers

Overview

Installation

Prerequisites

Sarvam AI Account Setup

Required Environment Variables

Configuration

SarvamSTTService

Settings

SarvamRealtimeSTTService

Realtime Settings

Usage

Basic Setup (SarvamSTTService)

With Language and Model Configuration

With Server-Side VAD

Basic Realtime Setup

Realtime with Language and Stream Type

Realtime with Manual Endpointing

Updating Realtime Configuration

Notes

SarvamSTTService

SarvamRealtimeSTTService

Event Handlers