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6d 1h ago
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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 creatio...

.agents/skills/podcast-generation Python
PY
TY
JA
4+ layers Tracked stack
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Capabilities
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Phases
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References
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Scripts
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Architectural Overview

Skill Reading

"This module is grounded in ai engineering patterns and exposes 1 core capabilities across 1 execution phases."

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

When to Use

This skill is applicable to execute the workflow or actions described in the overview.

Primary Stack

Python

Tooling Surface

Guide only

Workspace Path

.agents/skills/podcast-generation

Operational Ecosystem

The complete hardware and software toolchain required.

This skill is mostly documentation-driven and does not expose extra scripts, references, examples, or templates.

Module Topology

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