Podcast Generator

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Project Overview

This article introduces a workflow-driven intelligent podcast generator. The system combines large language models with text-to-speech services to automate every step from topic selection to audio output. A modular design supports asynchronous processing and state tracking, giving content creators a streamlined way to produce podcasts at scale.

Business Background

Traditional podcast production involves multiple stages—script writing, recording, and post-production—and often requires professional gear and expertise. Solo creators face a high entry barrier, and consistently publishing new episodes is time-consuming.

Our project dramatically simplifies the process. Users only need to provide a topic; the system then drafts the script and converts it into audio automatically. By automating the workflow we reduce production overhead while enabling reliable, repeatable output.

Technical Architecture

The solution is built on a workflow-centric architecture that breaks the podcast pipeline into discrete, orchestrated nodes. Core components include the DeepSeek-v3 large language model for script generation, Tencent Cloud TTS for audio synthesis, and Python utilities for data transformation and control logic.

Core Features

Parameter management

The workflow first extracts three required parameters: the Tencent Cloud API Secret ID, Secret Key, and the user-provided podcast topic.

Parameters are collected through a dedicated node that validates presence and type. Environment secrets are read from a secure store, while the topic is captured through user interaction and normalized for downstream tasks.

Model orchestration

The script-generation node calls DeepSeek-v3 with a carefully designed prompt that includes the topic, target duration, tone, and any optional hints. Responses are cached so the user can review or edit before committing to synthesis.

Asynchronous TTS processing

The TTS node submits a long-text synthesis job to Tencent Cloud. Because audio rendering is asynchronous, the workflow spawns a polling sub-workflow that checks task status at regular intervals. Results are appended to an array until a valid audio URL is returned.

The polling strategy increases wait intervals over time, applies a maximum retry limit, and handles transient API failures gracefully. The final URL is extracted from the aggregated polling history.

Error handling

The TTS node uses a progressive retry policy—up to three attempts with a 30-second backoff—to absorb transient outages. When all retries fail, the workflow returns a default response while logging detailed diagnostics for later review.

Any polling errors bubble up through a dedicated exception branch so operations teams can be alerted without interrupting the user experience.

Conclusion

By orchestrating script generation and long-form TTS inside a single workflow, the project validates how AI can accelerate content creation. Modular design, asynchronous control, and resilient error handling work together to deliver stable automation. With continuous iteration, the system will keep improving and unlock even more value for creators.