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Overview

LMNT provides real-time text-to-speech synthesis through a WebSocket-based streaming API optimized for conversational AI. The service offers ultra-low latency with high-quality voice models and supports multiple languages with automatic interruption handling.

API Reference

Complete API documentation and method details

LMNT Speech Docs

Official LMNT streaming speech API documentation

Example Code

Working example with voice synthesis

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Installation

To use LMNT services, install the required dependencies: 
pip install "pipecat-ai[lmnt]"
You’ll also need to set up your LMNT API key as an environment variable: LMNT_API_KEY.
Get your API key from the LMNT Console.

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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 (streaming PCM)
  • TTSStoppedFrame - Signals completion of synthesis
  • ErrorFrame - WebSocket or API errors

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Language Support

Most common languages supported:
  • Language.EN - English
  • Language.ES - Spanish
  • Language.FR - French
  • Language.DE - German
  • Language.ZH - Chinese
  • Language.JA - Japanese

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Usage Example

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Basic Configuration

Initialize the LmntTTSService and use it in a pipeline: 
from pipecat.services.lmnt.tts import LmntTTSService
from pipecat.transcriptions.language import Language
import os

# Configure service
tts = LmntTTSService(
    api_key=os.getenv("LMNT_API_KEY"),
    voice_id="morgan",
    model="aurora",
    language=Language.EN,
    sample_rate=24000
)

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

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Dynamic Configuration

Make settings updates by pushing a TTSUpdateSettingsFrame for the LmntTTSService: 
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 real-time metrics:
  • Time to First Byte (TTFB) - Latency from text input to first audio
  • Processing Duration - Total synthesis time
  • Character Usage - Text processed for billing
Learn how to enable Metrics in your Pipeline.

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Additional Notes

  • WebSocket Streaming: Uses persistent WebSocket connection for ultra-low latency
  • Custom Voices: Supports custom voice creation through LMNT dashboard
  • Language Detection: Automatically handles language variants and fallbacks