podcast-generation

已同步 v1.1.0 许可证: MIT 来源: microsoft/skills/.github/skills/podcast-generation 提交: cde32f9 收录的最新变更: 2026-01-28

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.

摘要

用途: 说明如何使用 Azure OpenAI Realtime 将文字转为语音叙述，并准备供播放的音频。
使用时机: 当服务需要透过 WebSocket 将文字符串流转为旁白，并提供可在网页前端播放的音频时使用。
输出结果: 会收集 24 kHz、16 比特、单声道的 PCM 音频与逐字稿文字，再将音频转为 WAV，供 Base64 编码后在浏览器播放。

上游变更（2 条，最新收录 2026-01-28）

a2003b6 2026-01-28 docs(skills): standardize all skills to skill-creator guidelines 移除了 podcast-generation 的 SKILL.md 文件前后的 ```skill 围栏。

14224cc 2026-01-16 feat: Add skill creator scripts and templates for initializing and packaging skills 添加 podcast-generation 技能，说明透过 Azure OpenAI GPT Realtime Mini WebSocket 产生音频、PCM 转 WAV、浏览器播放、语音选项与 API 事件。

npx skills add "lettucebo/Skills#v2.2.0@podcast-generation" --full-depth

⚠ v2.2.0 发布后即可使用

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