Building in Public: Turning Unstructured Data into Structured Data
This update discusses the project's aim to convert any form of unstructured data into structured data, which can then be used with Large Language Models (LLMs), applications, or services. The ultimate goal is to enable the creation of blog posts, transcriptions, and subtitles from various data sources.
Video Transcription and Subtitle Generation
The demonstration focuses on processing recorded video. The process involves transcribing the video and generating subtitles. This utilizes an SDK, sharing similarities with Cursor IDE products, that is designed for AI-native generation.
Agile Development on the Cloud
The key feature is the ability to spin up ephemeral or permanent resources based on user-defined configurations. This facilitates an agile and rapid development cycle on the cloud, similar to a local development environment. This allows for quicker experimentation and iteration.
Speed and Efficiency
The demonstration highlighted the speed at which large Whisper models are loaded for transcription. Compared to building Docker containers or images, this process is significantly faster.
The result of the process is both the text transcription and the subtitles, which can be readily uploaded to platforms like YouTube. Moreover, the subtitles can be translated into other languages, such as Mandarin for Chinese subtitles, or any other language.
Demonstration of Generated Subtitles
The speaker includes a snippet of the original video with generated subtitles: "hello world I'm in Tibet right now um why am I in Tibet I I guess it's just one of those places where for some reason you kind of have to go as a Chinese young person as for why I mean that's some".
Workflow and Cloud Integration
The process involves uploading audio to the cloud, generating transcriptions, and saving the data in remote storage. This data can then be used as captions when making API calls to YouTube and other services. The fact that everything is taking place on the cloud allows for building additional features and functionalities on top of the existing infrastructure.
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Audio uploaded to the cloud
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Transcription generated
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Data saved in remote storage
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Data used as captions for API calls
Data as an Asset
The resulting data is considered an asset that can be programmed and utilized. The speaker expresses excitement about future updates and developments.