From antigravity-awesome-skills
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...
npx claudepluginhub absjaded/antigravity-awesome-skillsThis skill uses the workspace's default tool permissions.
Generate real audio narratives from text content using Azure OpenAI's Realtime API.
Verifies tests pass on completed feature branch, presents options to merge locally, create GitHub PR, keep as-is or discard; executes choice and cleans up worktree.
Guides root cause investigation for bugs, test failures, unexpected behavior, performance issues, and build failures before proposing fixes.
Writes implementation plans from specs for multi-step tasks, mapping files and breaking into TDD bite-sized steps before coding.
Generate real audio narratives from text content using Azure OpenAI's Realtime API.
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.
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)
// 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 | Character |
|---|---|
| alloy | Neutral |
| echo | Warm |
| fable | Expressive |
| onyx | Deep |
| nova | Friendly |
| shimmer | Clear |
response.output_audio.delta - Base64 audio chunkresponse.output_audio_transcript.delta - Transcript textresponse.done - Generation completeerror - Handle with event.error.messageThis skill is applicable to execute the workflow or actions described in the overview.