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Overview

Piper provides high-quality neural text-to-speech synthesis through a self-hosted HTTP server. The service offers complete privacy and control with no external API dependencies, making it ideal for on-premise deployments and applications requiring data sovereignty.

API Reference

Complete API documentation and method details

Piper TTS Docs

Official Piper TTS documentation and setup

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Installation

To use Piper services, no additional Pipecat dependencies are required: 
pip install "pipecat-ai"  # Base installation is sufficient
You’ll need to set up a running Piper TTS server following the HTTP server documentation.
Piper runs entirely locally, providing complete privacy and eliminating API key requirements.

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Frames

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Input

  • TextFrame - Text content to synthesize into speech
  • TTSSpeakFrame - Text that should be spoken immediately
  • TTSUpdateSettingsFrame - Runtime configuration updates
  • LLMFullResponseStartFrame / LLMFullResponseEndFrame - LLM response boundaries

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Output

  • TTSStartedFrame - Signals start of synthesis
  • TTSAudioRawFrame - Generated audio data chunks (WAV headers automatically removed)
  • TTSStoppedFrame - Signals completion of synthesis
  • ErrorFrame - HTTP server or processing errors

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Voice Models

Piper offers various pre-trained voice models with different qualities and languages:

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English Models

  • en_US-lessac-medium - Natural female voice, balanced quality
  • en_US-ryan-high - High-quality male voice
  • en_US-amy-medium - Clear female voice
  • en_GB-alan-medium - British male voice

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Quality Levels

  • low - Fastest, smallest file size
  • medium - Balanced quality and speed
  • high - Best quality, larger models
Check the Piper voices repository for the complete list of available models and languages.

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Supported Sample Rates

Piper supports multiple sample rates depending on the model quality:
  • Low quality: 16kHz
  • Medium quality: 22.05kHz
  • High quality: 24kHz

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Usage Example

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Basic Configuration

Initialize the Piper TTS service and use it in a pipeline: 
import aiohttp
from pipecat.services.piper.tts import PiperTTSService

# Create aiohttp session (reuse across requests)
session = aiohttp.ClientSession()

# Configure service
tts = PiperTTSService(
    base_url="http://localhost:5000/api/tts",
    aiohttp_session=session,
    sample_rate=22050  # Match your Piper model's sample rate
)

# Use in pipeline
pipeline = Pipeline([
    transport.input(),
    stt,
    context_aggregator.user(),
    llm,
    tts,
    transport.output(),
    context_aggregator.assistant()
])

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Dynamic Voice Switching

You can dynamically switch voices by updating the voice_id parameter: 
from pipecat.frames.frames import TTSUpdateSettingsFrame

await task.queue_frame(
    TTSUpdateSettingsFrame(settings={"voice": "your-new-voice-id"})
)

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Metrics

The service provides comprehensive metrics:
  • Time to First Byte (TTFB) - Latency from text input to first audio
  • Processing Duration - Total synthesis time
  • Character Usage - Text processed for monitoring
Learn how to enable Metrics in your Pipeline.

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Additional Notes

  • Self-Hosted: Complete control over TTS infrastructure and data privacy
  • No API Keys: No external service dependencies or API costs
  • Language Support: Multiple languages available through different voice models