> ## Documentation Index
> Fetch the complete documentation index at: https://daily-mb-reorg-api-reference-docs.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Krisp VIVA Turn

> Turn detection using Krisp VIVA SDK

## Overview

`KrispVivaTurn` is a turn analyzer that uses Krisp's VIVA SDK turn detection (Tt) API to determine when a user has finished speaking. Unlike the [Smart Turn model](/api-reference/server/utilities/turn-detection/smart-turn-overview) which analyzes audio in batches when VAD detects a pause, `KrispVivaTurn` processes audio frame-by-frame in real time using Krisp's streaming model.

<CardGroup cols={2}>
  <Card title="Krisp VIVA Example" icon="play" href="https://github.com/pipecat-ai/pipecat/blob/main/examples/voice/voice-krisp-viva.py">
    Complete example with Krisp VIVA voice isolation and turn detection
  </Card>

  <Card title="Krisp Developers" icon="globe" href="https://krisp.ai/developers">
    Get the Krisp SDK and API key
  </Card>
</CardGroup>

## Installation

`KrispVivaTurn` requires the Krisp Python SDK. See the [Krisp VIVA guide](/pipecat/features/krisp-viva) for installation instructions.

## Environment Variables

You need to provide the path to the Krisp turn detection model file (.kef extension). This can either be done by setting the `KRISP_VIVA_TURN_MODEL_PATH` environment variable or by passing `model_path` to the constructor.

For SDK v1.6.1+, you also need to provide a Krisp API key via the `api_key` constructor parameter or the `KRISP_VIVA_API_KEY` environment variable.

```bash theme={null}
KRISP_VIVA_TURN_MODEL_PATH=/path/to/krisp-viva-tt-v2.kef
KRISP_VIVA_API_KEY=your_api_key_here
```

## Configuration

The `KrispTurnParams` class configures turn detection behavior:

<ParamField path="threshold" type="float" default="0.5">
  Probability threshold for turn completion (0.0 to 1.0). Higher values require
  more confidence before marking a turn as complete.
</ParamField>

<ParamField path="frame_duration_ms" type="int" default="20">
  Frame duration in milliseconds for turn detection. Supported values: 10, 15,
  20, 30, 32.
</ParamField>

## Constructor Parameters

<ParamField path="model_path" type="Optional[str]" default="None">
  Path to the Krisp turn detection model file (.kef extension). If not provided,
  falls back to the `KRISP_VIVA_TURN_MODEL_PATH` environment variable.
</ParamField>

<ParamField path="sample_rate" type="Optional[int]" default="None">
  Audio sample rate (will be set by the transport if not provided).
</ParamField>

<ParamField path="params" type="KrispTurnParams" default="KrispTurnParams()">
  Configuration parameters for turn detection.
</ParamField>

<ParamField path="api_key" type="str" default="&#x22;&#x22;">
  Krisp SDK API key for licensing (required for SDK v1.6.1+). If empty, falls
  back to the `KRISP_VIVA_API_KEY` environment variable.
</ParamField>

## Example

```python theme={null}
from pipecat.audio.turn.krisp_viva_turn import KrispVivaTurn
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.processors.aggregators.llm_response_universal import (
    LLMContextAggregatorPair,
    LLMUserAggregatorParams,
)
from pipecat.turns.user_stop import TurnAnalyzerUserTurnStopStrategy
from pipecat.turns.user_turn_strategies import UserTurnStrategies

# Configure Krisp turn detection via user turn strategies
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
    context,
    user_params=LLMUserAggregatorParams(
        user_turn_strategies=UserTurnStrategies(
            stop=[TurnAnalyzerUserTurnStopStrategy(
                turn_analyzer=KrispVivaTurn()
            )]
        ),
        vad_analyzer=SileroVADAnalyzer(),
    ),
)
```

## How It Works

`KrispVivaTurn` processes audio as a streaming model, analyzing each audio frame in real time:

1. **Frame-by-frame processing**: Each incoming audio frame is processed by the Krisp turn detection model, which outputs a probability that the user's turn is complete.
2. **Speech tracking**: VAD signals are used to track when speech starts and stops.
3. **Threshold crossing**: When the model's probability exceeds the configured `threshold` after speech has been detected, the turn is marked as complete.

This differs from the [Smart Turn model](/api-reference/server/utilities/turn-detection/smart-turn-overview) which buffers audio and runs batch inference when VAD detects a pause. `KrispVivaTurn` makes its decision continuously as audio flows through, which can result in faster turn detection.

## Notes

* Requires a valid Krisp SDK license and turn detection model file
* Works with any VAD analyzer (Silero is recommended)
* Emits `TurnMetricsData` with end-to-end processing time, measuring the interval from VAD speech-to-silence transition to the model crossing the probability threshold