u/sai_teja_

I’ve been exploring this idea for a while:

You type something like “Explain how neural networks work for a beginner” and get an actual video back instead of another wall of AI-generated text.

That became SarasFlow.

It generates the script, narration, visuals and subtitles and combines them into a video. The longer-term idea is to make video creation accessible for education, training and knowledge sharing without needing editing experience.

I decided to open-source the project while continuing to build the hosted version.

Would love to know what people here think about the idea and where you would actually use something like this.

https://sarasflow.com

https://github.com/SaiTejaMummadi/sarasflow

I’m building an open-source project called SarasFlow for creating educational videos in multiple languages.

The current videos use generated images with motion effects and an audio narration layered over them. The voice stage uses multilingual TTS, followed by Whisper timing so the captions stay synchronized with the narration.

It isn’t a real-time voice agent or a lip-sync system. Right now I’m mainly working through issues such as pronunciation, pacing, pauses, sentence splitting, voice consistency, and evaluating less-common languages.

Website: https://sarasflow.com
GitHub: https://github.com/SaiTejaMummadi/sarasflow

I’d genuinely appreciate advice from people working deeply with voice AI. I’m also looking for contributors interested in TTS provider integrations, pronunciation controls, and automated voice-quality evaluation.

SarasFlow — an open-source platform for multilingual educational videos

I recently made SarasFlow open source.

It takes a topic and creates an educational or storytelling video by generating the script, narration, visuals, subtitles, and final assembled video. It supports multiple languages, narrative formats, visual styles, and different image providers.

I open-sourced it because I wanted the complete product to be inspectable—not only a small demo that stops after calling a model. The repository includes the frontend, backend, prompt files, media-processing code, authentication, storage, billing, and testing setup.

Website: https://sarasflow.com
Repository: https://github.com/SaiTejaMummadi/sarasflow

Contributions are welcome, especially around additional languages, TTS providers, caption styles, model integrations, batch generation, and publishing integrations.

Please consider starring the repo if you find it useful. It would help the project reach more open-source developers.

I’ve been experimenting with an end-to-end AI video workflow: topic → script → scene breakdown → image prompts → narration → video assembly → word-timed captions.

The hardest part wasn’t generating individual assets. It was keeping everything consistent across the pipeline.

A few things I noticed:

• Better scripts don’t automatically produce better visuals. Each scene needs a separate, visually concrete prompt.
• Character and style consistency becomes difficult as soon as multiple images are generated independently.
• TTS pacing affects the entire edit. Even a good voice can make scenes feel awkward if the duration wasn’t considered earlier.
• Captions generated from the final audio worked better than estimating timings from the script.
• Different image models were better at different styles, so using one provider for everything wasn’t always the best choice.
• Supporting multiple languages changes pacing, subtitle length, and sometimes the number of scenes needed.

I ended up building a pipeline around these problems and recently open-sourced the code:

Links in comments.

For people building similar workflows: which part causes you the most trouble—visual consistency, motion, prompt generation, voice synchronization, or editing everything together?

I’ve been building SubAgent, an open-source localization system that assists with translating and editing subtitles while keeping the entire workflow on infrastructure controlled by the user.

SubAgent imports SRT or RTF files, breaks the content into subtitle cues, generates multiple translation suggestions, supports human review and editing, synchronizes cues with video playback, and exports the completed subtitles.

It currently supports 5 languages.

The translation model runs locally through vLLM, with GoVarnam handling Romanized-to-native-script transliteration. The system rejects public inference endpoints, so subtitle text cannot be accidentally routed to a hosted model.

This is the first open-source release from Hyper Latent, an AI research and product company focused on reliable, privacy-first systems.

I’d appreciate feedback on two questions:

1. Which parts of this workflow would benefit most from greater agent autonomy?
2. Would you be willing to test the entire pipeline on my infrastructure?

Love it