podcast-generation

name podcast-generation description 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. 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