🎁 Get the FREE AI Skills Starter Guide β€” Subscribe β†’
BytesAgainBytesAgain
πŸ¦€ ClawHub

Podcast Generation with Microsoft Foundry

by @thegovind

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.

Versionv0.1.0
Downloads3,454
Stars⭐ 3
TERMINAL
clawhub install podcast-generation

πŸ“– About This Skill


name: podcast-generation description: 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.

Podcast Generation with GPT Realtime Mini

Generate real audio narratives from text content using Azure OpenAI's Realtime API.

Quick Start

1. Configure environment variables for Realtime API 2. Connect via WebSocket to Azure OpenAI Realtime endpoint 3. Send text prompt, collect PCM audio chunks + transcript 4. Convert PCM to WAV format 5. Return base64-encoded audio to frontend for playback

Environment Configuration

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.

Core Workflow

Backend Audio Generation

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)

Frontend Audio Playback

// 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 Options

| Voice | Character | |-------|-----------| | alloy | Neutral | | echo | Warm | | fable | Expressive | | onyx | Deep | | nova | Friendly | | shimmer | Clear |

Realtime API Events

  • response.output_audio.delta - Base64 audio chunk
  • response.output_audio_transcript.delta - Transcript text
  • response.done - Generation complete
  • error - Handle with event.error.message
  • Audio Format

  • Input: Text prompt
  • Output: PCM audio (24kHz, 16-bit, mono)
  • Storage: Base64-encoded WAV
  • References

  • Full architecture: See references/architecture.md for complete stack design
  • Code examples: See references/code-examples.md for production patterns
  • PCM conversion: Use scripts/pcm_to_wav.py for audio format conversion
  • πŸ’‘ Examples

    1. Configure environment variables for Realtime API 2. Connect via WebSocket to Azure OpenAI Realtime endpoint 3. Send text prompt, collect PCM audio chunks + transcript 4. Convert PCM to WAV format 5. Return base64-encoded audio to frontend for playback