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Overview

Azure Cognitive Services provides high-quality text-to-speech synthesis with two implementations:
  • AzureTTSService (WebSocket-based streaming)
  • AzureHttpTTSService (HTTP-based batch synthesis).
AzureTTSService is recommended for real-time applications requiring low latency and streaming capabilities.

API Reference

Complete API documentation and method details

Azure Speech Docs

Official Azure Speech Services documentation

Example Code

Working example with streaming synthesis

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Installation

To use Azure services, install the required dependencies: 
pip install "pipecat-ai[azure]"
You’ll also need to set up your Azure credentials as environment variables:
  • AZURE_API_KEY (or AZURE_SPEECH_API_KEY)
  • AZURE_REGION (or AZURE_SPEECH_REGION)
Get your API key and region from the Azure Portal under Cognitive Services > Speech.

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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 (PCM format)
  • TTSStoppedFrame - Signals completion of synthesis
  • ErrorFrame - Azure API or processing errors

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Service Comparison

FeatureAzureTTSService (Streaming)AzureHttpTTSService (HTTP)
Streaming✅ Real-time chunks❌ Single audio block
Latency🚀 Low📈 Higher
Complexity⚠️ WebSocket management✅ Simple HTTP
ConnectionWebSocket-basedHTTP-based

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Language Support

Common languages supported include:
  • Language.EN_US - English (US)
  • Language.EN_GB - English (UK)
  • Language.FR - French
  • Language.DE - German
  • Language.ES - Spanish
  • Language.IT - Italian

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Supported Sample Rates

Azure supports multiple sample rates with automatic format selection:
  • 8000 Hz: Raw8Khz16BitMonoPcm
  • 16000 Hz: Raw16Khz16BitMonoPcm
  • 22050 Hz: Raw22050Hz16BitMonoPcm
  • 24000 Hz: Raw24Khz16BitMonoPcm (default)
  • 44100 Hz: Raw44100Hz16BitMonoPcm
  • 48000 Hz: Raw48Khz16BitMonoPcm

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Usage Example

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

# Configure streaming service
tts = AzureTTSService(
    api_key=os.getenv("AZURE_SPEECH_API_KEY"),
    region=os.getenv("AZURE_SPEECH_REGION"),
    voice="en-US-JennyNeural",
    params=AzureTTSService.InputParams(
        language=Language.EN_US,
        rate="1.1",
        style="cheerful",
        style_degree="1.5"
    )
)

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

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HTTP Service

Initialize the AzureHttpTTSService and use it in a pipeline: 
from pipecat.services.azure.tts import AzureHttpTTSService

# For simpler, non-streaming use cases
http_tts = AzureHttpTTSService(
    api_key=os.getenv("AZURE_SPEECH_API_KEY"),
    region=os.getenv("AZURE_SPEECH_REGION"),
    voice="en-US-AriaNeural",
    params=AzureHttpTTSService.InputParams(
        language=Language.EN_US,
        rate="1.05"
    )
)

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SSML Features

Azure TTS supports rich SSML customization through parameters: 
# Advanced SSML configuration
params = AzureTTSService.InputParams(
    language=Language.EN_US,
    style="cheerful",           # Speaking style
    style_degree="2.0",         # Style intensity (0.01-2.0)
    role="YoungAdultFemale",    # Voice role
    rate="1.2",                 # Speech rate
    pitch="+2st",               # Pitch adjustment
    volume="loud",              # Volume level
    emphasis="strong"           # Text emphasis
)

tts = AzureTTSService(
    api_key=os.getenv("AZURE_SPEECH_API_KEY"),
    region="eastus",
    voice="en-US-JennyNeural",
    params=params
)

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Dynamic Configuration

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

await task.queue_frame(TTSUpdateSettingsFrame(
    voice="en-US-AriaNeural",
  )
)

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Metrics

Both services provide comprehensive 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

  • Neural Voices: Use neural voices (ending in “Neural”) for highest quality
  • Regional Availability: Some voices and features may be region-specific
  • SSML Automatic: Service automatically constructs SSML based on parameters
  • Audio Format: Automatic format selection based on sample rate
  • Voice Matching: Ensure voice selection matches the specified language
  • Streaming Recommended: Use AzureTTSService for real-time applications requiring low latency
  • Connection Management: WebSocket lifecycle handled automatically in streaming service