Pipecat

This guide walks you through integrating Smallest AI TTS and STT into a Pipecat voice pipeline. Pipecat is an open-source Python framework for building real-time voice and multimodal conversational AI agents using a frame-based architecture.

Code Example

The complete runnable example lives in the Pipecat repository:

Pipecat Example - Smallest AI TTS + STT

Setup

1. Create a Virtual Environment

$
python3.11 -m venv .venv

Activate it:

• On Linux/Mac:

  $
  source .venv/bin/activate
• On Windows:

  $
  .venv\Scripts\activate

2. Install Pipecat with Smallest AI support

The smallest extra installs both the TTS and STT services for Smallest AI:

$
pip install "pipecat-ai[smallest]"

To run the full voice agent example, you also need:

• daily - Daily transport, which the bot uses to manage audio rooms and connect participants
• openai - OpenAI LLM service for the language model
• silero - Silero VAD for voice activity detection and interruption handling
• runner - Pipecat development runner that creates Daily rooms automatically and serves the bot locally

$
pip install "pipecat-ai[smallest,daily,openai,silero,runner]"

3. Create a .env file

$
SMALLEST_API_KEY=...
$
DAILY_API_KEY=...
$
OPENAI_API_KEY=...

DAILY_API_KEY is required - the Pipecat runner creates a Daily room automatically at startup. If you want to reuse an existing room instead of creating a new one each run, set the optional DAILY_ROOM_URL variable.

Services

SmallestSTTService

1
from pipecat.services.smallest.stt import SmallestSTTService
2
from pipecat.transcriptions.language import Language
3
4
stt = SmallestSTTService(
5
    api_key=os.getenv("SMALLEST_API_KEY"),
6
    settings=SmallestSTTService.Settings(
7
        language=Language.EN,
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        word_timestamps=True,
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    ),
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)

Constructor parameters:

Settings (SmallestSTTService.Settings):

The STT service connects to the Waves v4 STT endpoint (wss://api.smallest.ai/waves/v1/stt/live?model=pulse) and streams audio frames from the pipeline, returning transcriptions with 64ms TTFT. Pipecat’s VAD triggers a finalize message on each end-of-utterance to flush the transcript while keeping the WebSocket session open for the next utterance.

SmallestTTSService

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from pipecat.services.smallest.tts import SmallestTTSService, SmallestTTSModel
2
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tts = SmallestTTSService(
4
    api_key=os.getenv("SMALLEST_API_KEY"),
5
    output_format="pcm",
6
    settings=SmallestTTSService.Settings(
7
        model=SmallestTTSModel.LIGHTNING_V3_1_PRO,
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        voice="meher",
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    ),
10
)

Constructor parameters:

Settings (SmallestTTSService.Settings):

The TTS service connects to wss://api.smallest.ai/waves/v1/tts/live and uses WebSocket streaming for low-latency, real-time audio delivery. The model field is sent per-message, so switching models takes effect on the next utterance without reconnecting.

Running the Example

Clone the Pipecat repository and navigate to the examples directory:

$
git clone https://github.com/pipecat-ai/pipecat.git
$
cd pipecat/examples/voice

Create a .env file with the keys listed in the Setup section above, then run:

Daily transport - server mode (recommended):

$
python voice-smallest.py -t daily

Open http://localhost:7860 in your browser. The runner creates a Daily room automatically and redirects you to it.

Daily transport - direct mode (no web server, for quick testing):

$
python voice-smallest.py -d

The room URL is printed in the terminal. Open it in your browser to join.

The full source for voice-smallest.py is at examples/voice/voice-smallest.py. It sets up a complete interruptible voice bot using Smallest AI STT + TTS, OpenAI for the LLM, Silero VAD for interruptions, and Daily as the transport - all wired together with the Pipecat runner.

Notes

• The pipeline is interruptible: if a user speaks while the bot is talking, audio stops immediately and the pipeline re-engages - no custom logic needed.
• For any issues or questions, open an issue in the Pipecat repository or contact us on Discord.