Azure Ai Voicelive Py
Build real-time voice AI applications using Azure AI Voice Live SDK (azure-ai-voicelive). Use this skill when creating Python applications that need real-time bidirectional audio c…
thegovind
@thegovind
What This Skill Does
Python SDK for building real-time bidirectional voice AI applications with Azure AI Voice Live. Handles WebSocket-based audio streaming, server VAD, turn-based conversation, function calling, and avatar integration.
Replaces building custom WebSocket audio pipelines and VAD logic by providing a managed SDK for real-time speech-to-speech interaction with Azure AI models.
When to Use It
- Build a voice assistant that listens and responds in real-time
- Create a voice-enabled chatbot with turn-based conversation
- Implement real-time speech-to-speech translation in a Python app
- Develop a voice-driven avatar that speaks and reacts live
- Stream audio to Azure AI for transcription with server-side VAD
- Add function calling to a voice AI application using MCP tools
Install
$ openclaw skills install @thegovind/azure-ai-voicelive-pyAzure AI Voice Live SDK
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=True)
case "response.audio.delta":
audio = base64.b64decode(event.delta)
case "response.done":
print(f"Response complete: {event.response.status}")
# Function calls
case "response.function_call_arguments.done":
result = handle_function(event.name, event.arguments)
await conn.conversation.item.create(item={
"type": "function_call_output",
"call_id": event.call_id,
"output": json.dumps(result)
})
await conn.response.create()
# Errors
case "error":
print(f"Error: {event.error.message}")
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
Top skills in this category
Humanizer
@biostartechnologyRemove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases.
Answer Overflow
@rhyssullivanSearch indexed Discord community discussions via Answer Overflow. Find solutions to coding problems, library issues, and community Q&A that only exist in Discord conversations.
gmail
@byungkyuGmail API integration with managed OAuth. Read, send, and manage emails, threads, labels, and drafts. Use this skill when users want to interact with Gmail. For other third party apps, use the api-gateway skill (https://clawhub.ai/byungkyu/api-gateway). Calls run through the `maton` CLI with OAuth login, or over raw HTTP with a Maton API key where the CLI cannot be installed. Every call is authenticated as the user's connection and reaches only what that connection's authorization allows, which the provider enforces on every request; the endpoints documented here are the ones this skill uses, and any other endpoint of this app needs the user to ask for it by name. Default to read and list calls, and confirm every write or new connection with the user. This file also documents the three constructs that turn a Gmail connection into automation, in the order they are used: the connection (the first step), a hosted function that runs a Gmail action through the Maton SDK, and a trigger that calls that function on a schedule or on an event. Those sections are the platform's own reference text, shared with the api-gateway skill, with Gmail examples; they add no Gmail capability - Gmail is itself an event source, so its own events are in `references/google-mail/triggers.md`, and the files under `references/<source>/triggers.md` are the platform's event catalogues for the sources Maton offers (time, Calendly, GitHub, Gmail, HubSpot, Linear, Notion, Slack, Stripe).
WhatsApp Business
@byungkyuWhatsApp Business API integration with managed OAuth. Send messages, manage templates, and handle conversations. Use this skill when users want to interact with WhatsApp Business. For other third party apps, use the api-gateway skill (https://clawhub.ai/byungkyu/api-gateway). Calls run through the `maton` CLI with OAuth login, or over raw HTTP with a Maton API key where the CLI cannot be installed. Every call is authenticated as the user's connection and reaches only what that connection's authorization allows, which the provider enforces on every request; the endpoints documented here are the ones this skill uses, and any other endpoint of this app needs the user to ask for it by name. Default to read and list calls, and confirm every write or new connection with the user. This file also documents the three constructs that turn a WhatsApp Business connection into automation, in the order they are used: the connection (the first step), a hosted function that runs a WhatsApp Business action through the Maton SDK, and a trigger that calls that function on a schedule or on an event. Those sections are the platform's own reference text, shared with the api-gateway skill, with WhatsApp Business examples; they add no WhatsApp Business capability - WhatsApp Business is not an event source, a trigger cannot read WhatsApp Business data, and the files under `references/<source>/triggers.md` are the platform's event catalogues for the sources Maton offers (time, Calendly, GitHub, Gmail, HubSpot, Linear, Notion, Slack, Stripe).
GitHub
@byungkyuGitHub API integration with managed OAuth. Access repositories, issues, pull requests, commits, branches, and users. Use this skill when users want to interact with GitHub repositories, manage issues and PRs, search code, or automate workflows. For other third party apps, use the api-gateway skill (https://clawhub.ai/byungkyu/api-gateway). Calls run through the `maton` CLI with OAuth login, or over raw HTTP with a Maton API key where the CLI cannot be installed. Every call is authenticated as the user's connection and reaches only what that connection's authorization allows, which the provider enforces on every request; the endpoints documented here are the ones this skill uses, and any other endpoint of this app needs the user to ask for it by name. Default to read and list calls, and confirm every write or new connection with the user. This file also documents the three constructs that turn a GitHub connection into automation, in the order they are used: the connection (the first step), a hosted function that runs a GitHub action through the Maton SDK, and a trigger that calls that function on a schedule or on an event. Those sections are the platform's own reference text, shared with the api-gateway skill, with GitHub examples; they add no GitHub capability - GitHub is itself an event source, so its own events are in `references/github/triggers.md`, and the files under `references/<source>/triggers.md` are the platform's event catalogues for the sources Maton offers (time, Calendly, GitHub, Gmail, HubSpot, Linear, Notion, Slack, Stripe).