Tencent SkillHub · Developer Tools

Podcast Generation with Microsoft Foundry

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.

skill openclawclawhub Free

0 Downloads

0 Stars

0 Installs

0 Score

High Signal

⬇ 0 downloads ★ 0 stars Unverified but indexed

Install for OpenClaw

Quick setup

Download the package from Yavira.
Extract the archive and review SKILL.md first.
Import or place the package into your OpenClaw setup.

Requirements

Target platform: OpenClaw
Install method: Manual import
Extraction: Extract archive
Prerequisites: OpenClaw
Primary doc: SKILL.md

Package facts

Download mode: Yavira redirect
Package format: ZIP package
Source platform: Tencent SkillHub
What's included: SKILL.md, references/acceptance-criteria.md, references/architecture.md, references/code-examples.md, scripts/pcm_to_wav.py

Validation

Use the Yavira download entry.
Review SKILL.md after the package is downloaded.
Confirm the extracted package contains the expected setup assets.

Install with your agent

Agent handoff

Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.

Download the package from Yavira.
Extract it into a folder your agent can access.
Paste one of the prompts below and point your agent at the extracted folder.

New install

I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.

Upgrade existing

I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.

Open Send to Agent page Open JSON manifest Open Markdown brief

Trust & source

Release facts

Source: Tencent SkillHub
Verification: Indexed source record
Version: 0.1.0

Provenance

Publisher: thegovind
Source page: View original listing
Canonical URL: Open canonical page

Documentation

ClawHub primary doc Primary doc: SKILL.md 9 sections Open source page

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.

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

VoiceCharacteralloyNeutralechoWarmfableExpressiveonyxDeepnovaFriendlyshimmerClear

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

Category context

Code helpers, APIs, CLIs, browser automation, testing, and developer operations.

Source: Tencent SkillHub

Largest current source with strong distribution and engagement signals.

Package contents

Included in package

4 Docs1 Scripts

SKILL.md Primary doc
references/acceptance-criteria.md Docs
references/architecture.md Docs
references/code-examples.md Docs
scripts/pcm_to_wav.py Scripts