Generate AI-powered podcast-style audio narratives using Azure OpenAI's GPT Realtime Mini model via WebSocket. Use when building text-to-speech features, audio narrative generation, podcast creation from content, or integrating with Azure OpenAI Realtime API for real audio output. Covers full-stack implementation from React frontend to Python FastAPI backend with WebSocket streaming.
From microsoft-azure-skillsnpx claudepluginhub jadecli/jadecli-claude-plugins --plugin microsoft-azure-skillsThis skill uses the workspace's default tool permissions.
references/acceptance-criteria.mdreferences/architecture.mdreferences/code-examples.mdscripts/pcm_to_wav.pyGuides Next.js Cache Components and Partial Prerendering (PPR) with cacheComponents enabled. Implements 'use cache', cacheLife(), cacheTag(), revalidateTag(), static/dynamic optimization, and cache debugging.
Migrates code, prompts, and API calls from Claude Sonnet 4.0/4.5 or Opus 4.1 to Opus 4.5, updating model strings on Anthropic, AWS, GCP, Azure platforms.
Orchestrates subagents to execute phased plans: deploys for implementation, verification, anti-pattern checks, code quality review, and commits only after passing checks.
Generate real audio narratives from text content using Azure OpenAI's Realtime API.
AZURE_OPENAI_AUDIO_API_KEY=your_realtime_api_key
AZURE_OPENAI_AUDIO_ENDPOINT=https://your-resource.cognitiveservices.azure.com
AZURE_OPENAI_AUDIO_DEPLOYMENT=gpt-realtime-mini
Note: Endpoint should NOT include /openai/v1/ - just the base URL.
from openai import AsyncOpenAI
import base64
# Convert HTTPS endpoint to WebSocket URL
ws_url = endpoint.replace("https://", "wss://") + "/openai/v1"
client = AsyncOpenAI(
websocket_base_url=ws_url,
api_key=api_key
)
audio_chunks = []
transcript_parts = []
async with client.realtime.connect(model="gpt-realtime-mini") as conn:
# Configure for audio-only output
await conn.session.update(session={
"output_modalities": ["audio"],
"instructions": "You are a narrator. Speak naturally."
})
# Send text to narrate
await conn.conversation.item.create(item={
"type": "message",
"role": "user",
"content": [{"type": "input_text", "text": prompt}]
})
await conn.response.create()
# Collect streaming events
async for event in conn:
if event.type == "response.output_audio.delta":
audio_chunks.append(base64.b64decode(event.delta))
elif event.type == "response.output_audio_transcript.delta":
transcript_parts.append(event.delta)
elif event.type == "response.done":
break
# Convert PCM to WAV (see scripts/pcm_to_wav.py)
pcm_audio = b''.join(audio_chunks)
wav_audio = pcm_to_wav(pcm_audio, sample_rate=24000)
// Convert base64 WAV to playable blob
const base64ToBlob = (base64, mimeType) => {
const bytes = atob(base64);
const arr = new Uint8Array(bytes.length);
for (let i = 0; i < bytes.length; i++) arr[i] = bytes.charCodeAt(i);
return new Blob([arr], { type: mimeType });
};
const audioBlob = base64ToBlob(response.audio_data, 'audio/wav');
const audioUrl = URL.createObjectURL(audioBlob);
new Audio(audioUrl).play();
| Voice | Character |
|---|---|
| alloy | Neutral |
| echo | Warm |
| fable | Expressive |
| onyx | Deep |
| nova | Friendly |
| shimmer | Clear |
response.output_audio.delta - Base64 audio chunkresponse.output_audio_transcript.delta - Transcript textresponse.done - Generation completeerror - Handle with event.error.message