RRepoGEO

REPOGEO REPORT · LITE

hkchengrex/MMAudio

Default branch main · commit 974010a0 · scanned 6/19/2026, 11:47:29 AM

GitHub: 2,211 stars · 259 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface hkchengrex/MMAudio, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highabout#1
    Refine the 'About' description to explicitly state it's a research project/model

    Why:

    CURRENT
    [CVPR 2025] MMAudio: Taming Multimodal Joint Training for High-Quality Video-to-Audio Synthesis
    COPY-PASTE FIX
    MMAudio: A CVPR 2025 research project for high-quality video-to-audio synthesis using multimodal joint training.
  • highreadme#2
    Add a clear introductory sentence to the README to position the project as a research model

    Why:

    CURRENT
    ## Taming Multimodal Joint Training for High-Quality Video-to-Audio Synthesis
    COPY-PASTE FIX
    This repository presents the official PyTorch implementation for MMAudio, a CVPR 2025 research project on high-quality video-to-audio synthesis. 

## Taming Multimodal Joint Training for High-Quality Video-to-Audio Synthesis
  • mediumtopics#3
    Expand topics to include research, implementation, and conference context

    Why:

    CURRENT
    audio, audio-synthesis, computer-vision, deep-learning, text-to-audio, video-to-audio
    COPY-PASTE FIX
    audio, audio-synthesis, computer-vision, deep-learning, text-to-audio, video-to-audio, pytorch-implementation, generative-ai-model, cvpr-2025, multimodal-ai-research

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface hkchengrex/MMAudio
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Adobe Audition
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Adobe Audition · recommended 2×
  2. DaVinci Resolve · recommended 2×
  3. Descript · recommended 2×
  4. iZotope RX · recommended 1×
  5. Audacity · recommended 1×
  • CATEGORY QUERY
    How to generate high-quality, synchronized audio from video content?
    you: not recommended
    AI recommended (in order):
    1. Adobe Audition
    2. DaVinci Resolve
    3. iZotope RX
    4. Audacity
    5. Descript
    6. FFmpeg (FFmpeg/FFmpeg)

    AI recommended 6 alternatives but never named hkchengrex/MMAudio. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools allow synthesizing audio from both video and text prompts?
    you: not recommended
    AI recommended (in order):
    1. Descript
    2. Adobe Premiere Pro
    3. Adobe Audition
    4. WellSaid Labs
    5. ElevenLabs
    6. DaVinci Resolve
    7. Fairlight
    8. Murf.ai
    9. PlayHT
    10. Kapwing
    11. Synthesys Studio

    AI recommended 11 alternatives but never named hkchengrex/MMAudio. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of hkchengrex/MMAudio?
    pass
    AI named hkchengrex/MMAudio explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts hkchengrex/MMAudio in production, what risks or prerequisites should they evaluate first?
    pass
    AI named hkchengrex/MMAudio explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo hkchengrex/MMAudio solve, and who is the primary audience?
    pass
    AI named hkchengrex/MMAudio explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

Drop this badge into the README of hkchengrex/MMAudio. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/hkchengrex/MMAudio.svg)](https://repogeo.com/en/r/hkchengrex/MMAudio)
HTML
<a href="https://repogeo.com/en/r/hkchengrex/MMAudio"><img src="https://repogeo.com/badge/hkchengrex/MMAudio.svg" alt="RepoGEO" /></a>
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hkchengrex/MMAudio — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite