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// Stage 7 · Multimodal & Generative AI

Speech, Video & World Models

Whisper, video diffusion (Sora-style DiT), 3-D reconstruction, world models, Dreamer and Genie.

about 8 hours10 algorithms4 paperslesson text in progress

01Goals

Do these first: Diffusion, Flow Matching & Image Generation · Reinforcement Learning Basics

02Algorithms to master

Each card has the original paper, prerequisites and resources.

03Must-read papers

04Courses & videos

05Hands-on: project, code & exercises

Free, official or author-published pages. Most run in the browser or on a free Colab GPU.

PROJECT

Use Whisper to generate time-stamped subtitles for a 10-minute Chinese video and measure the error rate.

  1. 1
    huggingface.co ↗Courses

    Do the ASR and TTS hands-on units of the Hugging Face audio course.

  2. 2
    github.com ↗Reference code

    Transcribe Chinese audio with Whisper and compare error rates by model size.

  3. 3
    Nerfstudio ↗Official docs

    Train your first NeRF / 3D Gaussian scene from a phone video.

06Self-test

Answer in your head, then open to check.

What input features does Whisper use?

Log-Mel spectrograms.

In the Sora technical report, what is a visual “token”?

A spacetime latent patch.

How do world models help reinforcement learning?

Agents can train in imagination, improving sample efficiency.

Data updated: AI algorithms & curriculum(UTC+8)