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Overview

Cartesia provides two TTS service implementations:
  • CartesiaTTSService: WebSocket-based with streaming and word timestamps
  • CartesiaHttpTTSService: HTTP-based for simpler synthesis
CartesiaTTSService is recommended for real-time applications.

API Reference

Complete API documentation and method details

Cartesia Docs

Official Cartesia documentation and features

Example Code

Working example with interruption handling

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Installation

To use Cartesia services, install the required dependencies: 
pip install "pipecat-ai[cartesia]"
You’ll also need to set up your Cartesia API key as an environment variable: CARTESIA_API_KEY.
Get your API key by signing up at Cartesia.

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Frames

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Input

  • TextFrame - Text content to synthesize into speech
  • TTSSpeakFrame - Text that the TTS service should speak
  • TTSUpdateSettingsFrame - Runtime configuration updates (e.g., voice)
  • LLMFullResponseStartFrame / LLMFullResponseEndFrame - LLM response boundaries

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Output

  • TTSStartedFrame - Signals start of synthesis
  • TTSAudioRawFrame - Generated audio data chunks
  • TTSStoppedFrame - Signals completion of synthesis
  • ErrorFrame - Connection or processing errors

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Service Comparison

FeatureCartesiaTTSService (WebSocket)CartesiaHttpTTSService (HTTP)
Streaming✅ Real-time chunks❌ Single audio block
Word Timestamps✅ Precise timing❌ Not available
Interruption✅ Advanced handling⚠️ Basic support
Latency🚀 Low📈 Higher
Best ForInteractive appsBatch processing

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Language Support

Supports multiple languages through the Language enum:
Language CodeDescriptionService Code
Language.DEGermande
Language.ENEnglishen
Language.ESSpanishes
Language.FRFrenchfr
Language.HIHindihi
Language.ITItalianit
Language.JAJapaneseja
Language.KOKoreanko
Language.NLDutchnl
Language.PLPolishpl
Language.PTPortuguesept
Language.RURussianru
Language.SVSwedishsv
Language.TRTurkishtr
Language.ZHChinese (Mandarin)zh

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Usage Example

Initialize the WebSocket service with your API key and desired voice: 
from pipecat.services.cartesia.tts import CartesiaTTSService
from pipecat.transcriptions.language import Language
import os

# Configure WebSocket service
tts = CartesiaTTSService(
    api_key=os.getenv("CARTESIA_API_KEY"),
    voice_id="your-voice-id",
    model="sonic-2",
    params=CartesiaTTSService.InputParams(
        language=Language.EN,
        speed="normal"
    )
)

# Use in pipeline
pipeline = Pipeline([
    transport.input(),
    stt,
    llm,
    tts,  # Word timestamps enable precise context updates
    transport.output()
])

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HTTP Service

Initialize the HTTP service and use it in a pipeline: 
# For simpler, non-streaming use cases
http_tts = CartesiaHttpTTSService(
    api_key=os.getenv("CARTESIA_API_KEY"),
    voice_id="your-voice-id",
    model="sonic-2",
    params=CartesiaHttpTTSService.InputParams(
        language=Language.EN
    )
)

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Dynamic Configuration

Make settings updates by pushing a TTSUpdateSettingsFrame for the CartesiaTTSService: 
from pipecat.frames.frames import TTSUpdateSettingsFrame

await task.queue_frame(TTSUpdateSettingsFrame(
    voice="your-new-voice-id",
    speed="fast"
))

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Metrics

Both services provide:
  • Time to First Byte (TTFB) - Latency from text input to first audio
  • Processing Duration - Total synthesis time
  • Usage Metrics - Character count and synthesis statistics
Learn how to enable Metrics in your Pipeline.

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Additional Notes

  • WebSocket Recommended: Use CartesiaTTSService for low-latency streaming and accurate context updates with word timestamps
  • Connection Management: WebSocket lifecycle is handled automatically with reconnection support
  • Sample Rate: Set globally in PipelineParams rather than per-service for consistency