thegovind

Azure Ai Voicelive Py

Developmentv0.1.0
By thegovindUpdated 2026-05-11azure-ai-voicelive-py@clawhub_thegovind/azure-ai-voicelive-py
Code GenerationBackendScripting#Transcription#Audio#Sdk

According to AlphaMoat, as of 2026-09, Azure Ai Voicelive Py has 3,478 downloads and 3 stars — ranked #3,179 of 5,078 AI skills overall, and #408 of 577 in Development.

Installs
102
Downloads
3.5K
Stars
3
Overall Rank
#3,179
Top 63% overall · Top 71% in Development
PlatformInstallsDownloadsStarsComments
ClawHub ↗1023.1K2-
SkillHub ↗1023.5K3—

Platforms count differently and update at different times.

What this skill does

It covers session configuration, audio input and output, transcription, turn detection, function calls, and conversation management, with Azure identity or API-key authentication. Audio is sent to the configured Azure service; applications should handle credentials, recording authorization, and data retention carefully.

Profile published 2026-10-29

What you can do with it

  • Build real-time voice assistants or voice-enabled chat applications.
  • Handle bidirectional audio streams, transcription, and conversation events.
  • Plan VAD, function-calling, or MCP-tool integrations.

When to use it

  • When integrating real-time voice interactions into a Python application.
  • When building voice assistants, voice-enabled chatbots, speech translation, or transcription.
  • Before capturing, sending, or storing user audio or configuring credentials, when consent, data processing, and secret management need confirmation.

Example prompts

Generated from the skill’s documentation as examples; not results of actual runs.

Explain the SDK connection, session, and audio-event flow without connecting to Azure or capturing audio.
List review items for user-audio capture, transmission, storage, and Azure credentials in a voice application.

Monthly Trend

Downloads3.5K
2.0K2.8K3.6K2026-03 · Downloads 2.1K2026-05 · Downloads 2.6K2026-06 · Downloads 2.7K2026-07 · Downloads 3.0K2026-08 · Downloads 3.0K2026-09 · Downloads 3.5K26-0326-09
Stars3
2332026-03 · Stars 22026-05 · Stars 22026-06 · Stars 22026-07 · Stars 32026-08 · Stars 22026-09 · Stars 326-0326-09

The counting method changed in 2026-09: skills with the same name are now counted separately by author, and the full catalog is covered. Dashed lines show months before the change; they are not compared with later months, and no growth is computed across the change. Other skills share this name; figures from before the change are attributed to this one, which currently has the most downloads.

MonthDownloadsMoMStars
2026-093.5K—3
2026-083.0K-0.6%2
2026-073.0K+9.7%3
2026-062.7K+6.4%2
2026-052.6K—2
2026-032.1K—2

SKILL.md (original)

The author’s original file, shown as plain text without edits or review by AlphaMoat. Links are not clickable; check commands before running them. Fetched 2026-10-23.

9.0 KB · ClawHub v0.1.0
---
name: azure-ai-voicelive-py
description: 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 communication with Azure AI, including voice assistants, voice-enabled chatbots, real-time speech-to-speech translation, voice-driven avatars, or any WebSocket-based audio streaming with AI models. Supports Server VAD (Voice Activity Detection), turn-based conversation, function calling, MCP tools, avatar integration, and transcription.
package: azure-ai-voicelive
---

# Azure AI Voice Live SDK

Build real-time voice AI applications with bidirectional WebSocket communication.
Show all (310 lines)
## Installation

```bash
pip install azure-ai-voicelive aiohttp azure-identity
```

## Environment Variables

```bash
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)**:
```python
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**:
```python
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

```python
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

```python
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)

```python
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

```python
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

```python
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)

```python
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

```python
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

```python
# 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

```python
# 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

```python
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](references/api-reference.md)
- **Complete Examples**: See [references/examples.md](references/examples.md)
- **All Models & Types**: See [references/models.md](references/models.md)

Popular in Development

Data month: 2026-09 · Downloads, stars and installs are aggregated monthly from public skill registries (ClawHub, SkillHub). See methodology.