RRepoGEO

REPOGEO REPORT · LITE

Yuan-ManX/ai-audio-datasets

Default branch main · commit 5277bfc9 · scanned 6/3/2026, 12:22:33 PM

GitHub: 942 stars · 92 forks

AI VISIBILITY SCORE
27 /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
1 / 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 Yuan-ManX/ai-audio-datasets, 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
  • highreadme#1
    Reposition the README's opening paragraph to emphasize its role as a curated index

    Why:

    CURRENT
    AI Audio Datasets (AI-ADS) 🎵, including Speech, Music, and Sound Effects, which can provide training data for Generative AI, AIGC, AI model training, intelligent audio tool development, and audio applications.
    COPY-PASTE FIX
    AI Audio Datasets (AI-ADS) 🎵 is a **curated, centralized index of high-quality audio datasets** for Generative AI, AIGC, AI model training, intelligent audio tool development, and audio applications. It includes comprehensive collections for Speech, Music, and Sound Effects, providing a single source to discover training data.
  • hightopics#2
    Add more specific topics to highlight its function as a dataset collection/index

    Why:

    CURRENT
    aigc, artificial-intelligence, audio, audio-effect, audio-generation, datasets, deep-learning, machine-learning, music-generation
    COPY-PASTE FIX
    aigc, artificial-intelligence, audio, audio-effect, audio-generation, datasets, deep-learning, machine-learning, music-generation, dataset-collection, audio-resources, ai-datasets-index, curated-list
  • mediumreadme#3
    Add a dedicated section highlighting the value proposition as a centralized index

    Why:

    COPY-PASTE FIX
    ## Why Choose AI-ADS?
AI-ADS serves as a comprehensive, categorized index, making it easy to discover and access diverse audio datasets for your AI projects. Instead of searching multiple sources, find all your speech, music, and sound effect data needs in one place.

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 Yuan-ManX/ai-audio-datasets
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
MusiCubes
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. MusiCubes · recommended 1×
  2. MAESTRO · recommended 1×
  3. Lakh MIDI Dataset · recommended 1×
  4. NSynth · recommended 1×
  5. FMA · recommended 1×
  • CATEGORY QUERY
    Where can I find diverse audio datasets to train a generative AI music model?
    you: not recommended
    AI recommended (in order):
    1. MusiCubes
    2. MAESTRO
    3. Lakh MIDI Dataset
    4. NSynth
    5. FMA
    6. AudioSet
    7. OpenMIC-2018

    AI recommended 7 alternatives but never named Yuan-ManX/ai-audio-datasets. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Need high-quality speech and sound effect datasets for developing intelligent audio applications.
    you: not recommended
    AI recommended (in order):
    1. LibriSpeech
    2. Common Voice (Mozilla)
    3. AudioSet (Google)
    4. Freesound
    5. ESC-50 (Environmental Sound Classification)
    6. UrbanSound8K

    AI recommended 6 alternatives but never named Yuan-ManX/ai-audio-datasets. 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 Yuan-ManX/ai-audio-datasets?
    pass
    AI did not name Yuan-ManX/ai-audio-datasets — likely talking about a different project

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

  • If a team adopts Yuan-ManX/ai-audio-datasets in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Yuan-ManX/ai-audio-datasets 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 Yuan-ManX/ai-audio-datasets solve, and who is the primary audience?
    pass
    AI did not name Yuan-ManX/ai-audio-datasets — likely talking about a different project

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

Embed your GEO score

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Yuan-ManX/ai-audio-datasets — 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