TEN VAD

Community-maintained integration. This service is built and maintained by rahulsolanki001. Pipecat does not test or officially support it. Please report issues and request changes on the source repository. Learn more about community integrations.

Overview

TenVadAnalyzer is a Pipecat VADAnalyzer implementation backed by TEN VAD, a low-latency, high-performance voice activity detector for real-time voice agents. It analyzes incoming audio and reports voice activity confidence, letting Pipecat detect when a user starts and stops speaking. It is a drop-in alternative to Pipecat’s built-in SileroVADAnalyzer.

Source Repository

Source code, examples, and issues for the TEN VAD integration

PyPI Package

The pipecat-ten-vad package on PyPI

The upstream TEN VAD project and supported platforms

Installation

This is a community-maintained package distributed separately from pipecat-ai. First install the TEN VAD backend (per the source repository, install it directly from GitHub):

uv add git+https://github.com/TEN-framework/ten-vad.git

Then install the integration:

uv add pipecat-ten-vad

Prerequisites

No API key is required to use the VAD analyzer itself. You need:

TEN VAD backend: Installed from GitHub as shown above.
16 kHz audio: TEN VAD requires a 16000 Hz sample rate.

Supported platforms (per the source repository): Linux x64, macOS (arm64 + x64), and Windows x64.

Configuration

Constructor parameters for TenVadAnalyzer:

int

default:"None"

Audio sample rate in Hz. Must be 16000 if provided. Passing any other value raises a ValueError.

float

default:"0.6"

Detection threshold (0.0–1.0) used by TEN VAD. Higher values require a stronger voice signal to trigger.

VADParams

default:"None"

Pipecat VAD smoothing parameters (e.g. stop_secs). See VADParams below.

VADParams

Standard Pipecat VAD smoothing parameters, passed via the params argument using VADParams(...). A commonly tuned field:

Parameter	Type	Default	Description
`stop_secs`	`float`	`0.3`	Seconds of silence before end-of-turn is triggered. Lower is more responsive; higher cuts fewer turns short.

Usage

Pass TenVadAnalyzer to LLMUserAggregatorParams via vad_analyzer, the same way you would use SileroVADAnalyzer:

from pipecat.audio.vad.vad_analyzer import VADParams
from pipecat.processors.aggregators.llm_response_universal import (
    LLMContextAggregatorPair,
    LLMUserAggregatorParams,
)
from pipecat_ten_vad import TenVadAnalyzer

user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
    context,
    user_params=LLMUserAggregatorParams(
        vad_analyzer=TenVadAnalyzer(
            sample_rate=16000,
            threshold=0.6,
            params=VADParams(stop_secs=0.3),
        ),
    ),
)

Compatibility

Tested with Pipecat v0.0.106. Check the source repository for the latest tested version and changelog.

Get Started

Standards

Pipecat Server

Client SDKs

Pipecat Flows

Pipecat Cloud

CLI

Pipecat Context Hub

TEN VAD

Overview

Source Repository

PyPI Package

TEN VAD

Installation

Prerequisites

Configuration

VADParams

Usage

Compatibility

​Overview

Source Repository

PyPI Package

TEN VAD

​Installation

​Prerequisites

​Configuration

​VADParams

​Usage

​Compatibility

Overview

Installation

Prerequisites

Configuration

VADParams

Usage

Compatibility