RRepoGEO

REPOGEO REPORT · LITE

OpenMOSS/MOSS-Audio

Default branch main · commit da7b350a · scanned 6/1/2026, 10:53:13 PM

GitHub: 511 stars · 36 forks

AI VISIBILITY SCORE
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 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 OpenMOSS/MOSS-Audio, 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

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

OVERALL DIRECTION
  • highlicense#1
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root with the chosen open-source license (e.g., Apache-2.0, MIT, GPL-3.0).
  • highreadme#2
    Strengthen the README's opening sentence to emphasize its unique category

    Why:

    CURRENT
    MOSS-Audio is an open-source **audio understanding model** from MOSI.AI, the OpenMOSS team, and Shanghai Innovation Institute. It performs unified modeling over complex real-world audio, supporting **speech understanding, environmental sound understanding, music understanding, audio captioning, time-aware QA, and complex reasoning**.
    COPY-PASTE FIX
    MOSS-Audio is a pioneering open-source **unified audio-language foundation model** designed for **complex reasoning and understanding across diverse real-world audio scenarios**, including speech, environmental sounds, and music. Developed by MOSI.AI, the OpenMOSS team, and Shanghai Innovation Institute, it enables advanced applications like audio captioning and time-aware QA.

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 OpenMOSS/MOSS-Audio
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Whisper
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Whisper · recommended 1×
  2. AudioMAE · recommended 1×
  3. CLAP · recommended 1×
  4. PANNs · recommended 1×
  5. HTS-AT · recommended 1×
  • CATEGORY QUERY
    What open-source models provide unified understanding for speech, music, and environmental sounds?
    you: not recommended
    AI recommended (in order):
    1. Whisper
    2. AudioMAE
    3. CLAP
    4. PANNs
    5. HTS-AT

    AI recommended 5 alternatives but never named OpenMOSS/MOSS-Audio. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to implement audio captioning, question answering, and complex reasoning from sound?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. Audio Spectrogram Transformer (AST)
    3. PyTorch Audio (torchaudio)
    4. Librosa
    5. Wav2Vec 2.0
    6. BART
    7. T5
    8. Fairseq
    9. CLIP
    10. ALIGN
    11. PyTorch
    12. TensorFlow
    13. BERT
    14. RoBERTa
    15. DeepMind's Perceiver IO
    16. Graph Neural Networks (GNNs)
    17. PyTorch Geometric
    18. Deep Graph Library - DGL

    AI recommended 18 alternatives but never named OpenMOSS/MOSS-Audio. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    warn

    Suggestion:

  • 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 OpenMOSS/MOSS-Audio?
    pass
    AI named OpenMOSS/MOSS-Audio explicitly

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

  • If a team adopts OpenMOSS/MOSS-Audio in production, what risks or prerequisites should they evaluate first?
    pass
    AI named OpenMOSS/MOSS-Audio 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 OpenMOSS/MOSS-Audio solve, and who is the primary audience?
    pass
    AI named OpenMOSS/MOSS-Audio 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 OpenMOSS/MOSS-Audio. 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)
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OpenMOSS/MOSS-Audio — 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