azure-ai-voicelive-py

name	azure-ai-voicelive-py
description	ALWAYS use this when the request matches Azure AI Voicelive PY: Build real-time voice AI applications with bidirectional WebSocket communication.

Azure AI Voice Live SDK

Selective Reading Rule

Start with:

• references/senior-master-standard.md
• references/usage-routing.md
• references/quality-checklist.md

Then load only the inherited docs, scripts, assets, or examples that match the user's actual task.

Build real-time voice AI applications with bidirectional WebSocket communication.

Installation

pip install azure-ai-voicelive aiohttp azure-identity

Environment Variables

AZURE_COGNITIVE_SERVICES_ENDPOINT=https://<region>.api.cognitive.microsoft.com
# For API key auth (not recommended for production)
AZURE_COGNITIVE_SERVICES_KEY=<api-key>

Authentication

DefaultAzureCredential (preferred):

from azure.ai.voicelive.aio import connect
from azure.identity.aio import DefaultAzureCredential

async with connect(
    endpoint=os.environ["AZURE_COGNITIVE_SERVICES_ENDPOINT"],
    credential=DefaultAzureCredential(),
    model="gpt-4o-realtime-preview",
    credential_scopes=["https://cognitiveservices.azure.com/.default"]
) as conn:
    ...

API Key:

from azure.ai.voicelive.aio import connect
from azure.core.credentials import AzureKeyCredential

async with connect(
    endpoint=os.environ["AZURE_COGNITIVE_SERVICES_ENDPOINT"],
    credential=AzureKeyCredential(os.environ["AZURE_COGNITIVE_SERVICES_KEY"]),
    model="gpt-4o-realtime-preview"
) as conn:
    ...

Quick Start

import asyncio
import os
from azure.ai.voicelive.aio import connect
from azure.identity.aio import DefaultAzureCredential

async def main():
    async with connect(
        endpoint=os.environ["AZURE_COGNITIVE_SERVICES_ENDPOINT"],
        credential=DefaultAzureCredential(),
        model="gpt-4o-realtime-preview",
        credential_scopes=["https://cognitiveservices.azure.com/.default"]
    ) as conn:
        # Update session with instructions
        await conn.session.update(session={
            "instructions": "You are a helpful assistant.",
            "modalities": ["text", "audio"],
            "voice": "alloy"
        })
        
        # Listen for events
        async for event in conn:
            print(f"Event: {event.type}")
            if event.type == "response.audio_transcript.done":
                print(f"Transcript: {event.transcript}")
            elif event.type == "response.done":
                break

asyncio.run(main())

Core Architecture

Connection Resources

The VoiceLiveConnection exposes these resources:

Resource	Purpose	Key Methods
conn.session	Session configuration	update(session=...)
conn.response	Model responses	create(), cancel()
conn.input_audio_buffer	Audio input	append(), commit(), clear()
conn.output_audio_buffer	Audio output	clear()
conn.conversation	Conversation state	item.create(), item.delete(), item.truncate()
conn.transcription_session	Transcription config	update(session=...)

Session Configuration

from azure.ai.voicelive.models import RequestSession, FunctionTool

await conn.session.update(session=RequestSession(
    instructions="You are a helpful voice assistant.",
    modalities=["text", "audio"],
    voice="alloy",  # or "echo", "shimmer", "sage", etc.
    input_audio_format="pcm16",
    output_audio_format="pcm16",
    turn_detection={
        "type": "server_vad",
        "threshold": 0.5,
        "prefix_padding_ms": 300,
        "silence_duration_ms": 500
    },
    tools=[
        FunctionTool(
            type="function",
            name="get_weather",
            description="Get current weather",
            parameters={
                "type": "object",
                "properties": {
                    "location": {"type": "string"}
                },
                "required": ["location"]
            }
        )
    ]
))

Audio Streaming

Send Audio (Base64 PCM16)

import base64

# Read audio chunk (16-bit PCM, 24kHz mono)
audio_chunk = await read_audio_from_microphone()
b64_audio = base64.b64encode(audio_chunk).decode()

await conn.input_audio_buffer.append(audio=b64_audio)

Receive Audio

async for event in conn:
    if event.type == "response.audio.delta":
        audio_bytes = base64.b64decode(event.delta)
        await play_audio(audio_bytes)
    elif event.type == "response.audio.done":
        print("Audio complete")

Event Handling

async for event in conn:
    match event.type:
        # Session events
        case "session.created":
            print(f"Session: {event.session}")
        case "session.updated":
            print("Session updated")
        
        # Audio input events
        case "input_audio_buffer.speech_started":
            print(f"Speech started at {event.audio_start_ms}ms")
        case "input_audio_buffer.speech_stopped":
            print(f"Speech stopped at {event.audio_end_ms}ms")
        
        # Transcription events
        case "conversation.item.input_audio_transcription.completed":
            print(f"User said: {event.transcript}")
        case "conversation.item.input_audio_transcription.delta":
            print(f"Partial: {event.delta}")
        
        # Response events
        case "response.created":
            print(f"Response started: {event.response.id}")
        case "response.audio_transcript.delta":
            print(event.delta, end=, flush=)
         :
            audio = base64.b64decode(event.delta)
         :
            ()
        
        
         :
            result = handle_function(event.name, event.arguments)
             conn.conversation.item.create(item={
                : ,
                : event.call_id,
                : json.dumps(result)
            })
             conn.response.create()
        
        
         :
            ()

Common Patterns

Manual Turn Mode (No VAD)

await conn.session.update(session={"turn_detection": None})

# Manually control turns
await conn.input_audio_buffer.append(audio=b64_audio)
await conn.input_audio_buffer.commit()  # End of user turn
await conn.response.create()  # Trigger response

Interrupt Handling

async for event in conn:
    if event.type == "input_audio_buffer.speech_started":
        # User interrupted - cancel current response
        await conn.response.cancel()
        await conn.output_audio_buffer.clear()

Conversation History

# Add system message
await conn.conversation.item.create(item={
    "type": "message",
    "role": "system",
    "content": [{"type": "input_text", "text": "Be concise."}]
})

# Add user message
await conn.conversation.item.create(item={
    "type": "message",
    "role": "user", 
    "content": [{"type": "input_text", "text": "Hello!"}]
})

await conn.response.create()

Voice Options

Voice	Description
alloy	Neutral, balanced
echo	Warm, conversational
shimmer	Clear, professional
sage	Calm, authoritative
coral	Friendly, upbeat
ash	Deep, measured
ballad	Expressive
verse	Storytelling

Azure voices: Use AzureStandardVoice, AzureCustomVoice, or AzurePersonalVoice models.

Audio Formats

Format	Sample Rate	Use Case
pcm16	24kHz	Default, high quality
pcm16-8000hz	8kHz	Telephony
pcm16-16000hz	16kHz	Voice assistants
g711_ulaw	8kHz	Telephony (US)
g711_alaw	8kHz	Telephony (EU)

Turn Detection Options

# Server VAD (default)
{"type": "server_vad", "threshold": 0.5, "silence_duration_ms": 500}

# Azure Semantic VAD (smarter detection)
{"type": "azure_semantic_vad"}
{"type": "azure_semantic_vad_en"}  # English optimized
{"type": "azure_semantic_vad_multilingual"}

Error Handling

from azure.ai.voicelive.aio import ConnectionError, ConnectionClosed

try:
    async with connect(...) as conn:
        async for event in conn:
            if event.type == "error":
                print(f"API Error: {event.error.code} - {event.error.message}")
except ConnectionClosed as e:
    print(f"Connection closed: {e.code} - {e.reason}")
except ConnectionError as e:
    print(f"Connection error: {e}")

References

• Detailed API Reference: See references/api-reference.md
• Complete Examples: See references/examples.md
• All Models & Types: See references/models.md

When to Use

This skill is applicable to execute the workflow or actions described in the overview.

Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.