mcpbeat

Podcast Generation

microsoft/podcast-generation

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.

This is a copy. The original lives at comeonoliver/podcast-generation.

4k tokens
context cost
the whole folder, loaded on every use
4
files
ships runnable scripts
2
copies elsewhere
how many repositories repackaged it
106 d ago
last touched
this folder, not the whole repository

Install

one command, takes just this skill from the repository
npx skills add https://github.com/microsoft/skills --skill podcast-generation

What comes with it

13 327 bytes besides the instruction
references/architecture.md
references/code-examples.md
scripts/pcm_to_wav.py

The instruction itself

10 sections, as written by the author

Podcast Generation with GPT Realtime Mini

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

Quick Start

  • Configure environment variables for Realtime API
  • Connect via WebSocket to Azure OpenAI Realtime endpoint
  • Send text prompt, collect PCM audio chunks + transcript
  • Convert PCM to WAV format
  • 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

Repackaged in 2 other repositories

same content, different owner
ComeOnOliver/skillshub open on GitHub →

How to use it

Copy the folder

Take microsoft/podcast-generation from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.