paper-talker

Automate academic video production by using an AI pipeline that researches topics via NotebookLM, generates subtitles with faster-whisper, processes video through imageio-ffmpeg, and uploads to Bilibili.

Automated subtitle generation for AI content creation works by using faster-whisper to transcribe the video audio, which is then processed by imageio-ffmpeg to burn the subtitles directly into the final video file.

You can automatically publish generated videos to Bilibili and WeChat Channels. The pipeline uses the biliup dependency and platform-specific APIs to handle the automated upload after video processing completes.

This video pipeline requires API credentials and environment variables for NotebookLM and Bilibili. It relies on python-dotenv to manage these configurations securely for automated research and multi-platform uploads.

The best way to turn a research topic into a subtitled academic video is using an end-to-end AI pipeline, which handles deep research, automated video generation, transcription, and multi-platform publishing seamlessly.

When using faster-whisper for academic video transcription, limitations may arise with highly technical terminology or overlapping speech, requiring manual review to ensure subtitle accuracy before final video processing and upload.