or {} if not data: return jsonify({"error": "invalid request body"}), 400 title = ("title", "Untitled") text = ("text", "") voice = ("voice", DEFAULT_VOICE) if not text: return jsonify({"error": "Provide 'text' to narrate"}), 400 book_id = f"book-{uuid.uuid4().hex[:8]}" # Step 1: AI chunks text into chapters with narration cues try: chunking_result = inference([ {"role": "system", "content": "You are an audiobook production assistant..."}, {"role": "user", "content": text[:15000]} except : # Fallback: split by paragraphs paragraphs = [ () for p in ("\n\n") if ()] chapters = [{ "chapter_title": f"Chapter {i + 1}", "tone": "warm", "pacing": "moderate" } for i, chunk in enumerate(paragraphs)] # Step 2 + 3: TTS per chapter → Cloud Storage for chapter in chapters: audio = tts_generate(chapter["narration_text"], voice=voice) key = f"{book_id}/chapter-{chapter['chapter_number']:02d}.mp3" url = upload_to_storage(BUCKET_NAME, key, audio) books[book_id]["storage_urls"].append(url) return jsonify({ "book_id": book_id, "title": title, "status": "complete", "chapters": len(books[book_id]["chapters"]), "storage_urls": books[book_id]["storage_urls"] }), 201 Run It git clone cd telnyx-code-examples/ai-audiobook-narrator-python cp . .env # add TELNYX_API_KEY, optionally tune AI_MODEL, TTS_MODEL, BUCKET_NAME pip install -r python The server starts on . Create a bucket named audiobooks in the Telnyx Portal (or set BUCKET_NAME to an existing bucket) and submit a chunk of text: curl -X POST \ -H "Content-Type: application/json" \ -d '{ "title": "The Future of Infrastructure", "text": "Chapter 1: The shift from cloud-edge to carrier-edge compute is changing where latency-sensitive workloads run...", "voice": "nova" }' The response includes a book_id and a list of presigned storage_urls , one per chapter: { "title": "The Future of Infrastructure", "status": "complete", "chapters": 3, "voice": "nova", " " " ] } Open any storage_url in a browser or stream it from a client to hear the narrated chapter. The app exposes five narrator voices out of the box: List them at any time: curl Pass any voice identifier in the voice field of the /books/narrate request. This example uses an in-memory dict for book metadata, which is fine for local testing but loses state on restart. For production: Do I need an existing Cloud Storage bucket before running this? Yes. Create a bucket in the Telnyx Portal and set BUCKET_NAME in .env to match. The app does not create the bucket for you. What happens if the AI returns malformed JSON for the chapter split? The app catches and falls back to splitting the text by double-newline paragraphs. Each paragraph becomes a chapter with default tone: warm and pacing: moderate . Narration still completes — you just lose the AI's tone and pacing direction. Which AI model is used by default? The default is moonshotai/Kimi-K2.6 (set in . and overridable via AI_MODEL ). Any model available on Telnyx AI Inference works — see the models list for options. Can I use a different TTS voice per chapter? Not in this example — the voice is set once per request and used for every chapter. To vary voices per chapter, extend the narrate_book loop to read chapter["voice"] from the AI's response and pass it to tts_generate . How long do the presigned URLs last? One hour ( ExpiresIn=3600 ). The stored MP3s persist in the bucket; only the URL expires. Generate a fresh presigned URL with s3.generate_presigned_url whenever you need a new one. Is the

uploaded audio public? No. The bucket is private by default. Only the presigned URL grants time-limited read access. To make audio publicly accessible (e.g., for public-domain audiobooks), set a public read policy on the bucket in the Telnyx Portal. What is the maximum input text length? The chunking step truncates input to 15,000 characters per request. For longer manuscripts, split the source into sections on the client and submit each as a separate /books/narrate request, then stitch the resulting storage_urls together. Can I narrate in languages other than English? Yes — Telnyx TTS supports multiple languages. The AI Inference prompt is in English, but you can edit the system message in to chunk in another language, and pass a language-appropriate voice to tts_generate .