RRepoGEO

REPOGEO REPORT · LITE

liusongxiang/Large-Audio-Models

Default branch main · commit bf2761c2 · scanned 6/1/2026, 6:38:13 PM

GitHub: 511 stars · 31 forks

AI VISIBILITY SCORE
17 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 liusongxiang/Large-Audio-Models, 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
  • hightopics#1
    Add specific topics to improve categorization

    Why:

    COPY-PASTE FIX
    audio, large-language-models, llm, speech-processing, music-generation, audio-synthesis, model-list, research-papers, deep-learning, machine-learning, curated-list
  • highreadme#2
    Reposition the README's opening to clarify its purpose as a curated list

    Why:

    CURRENT
    # Large-Audio-Models

We keep track of something big in the audio domain, including speech, singing, music etc.
    COPY-PASTE FIX
    # Large-Audio-Models: A Curated List of Foundation Models in Audio

This repository serves as a comprehensive, up-to-date tracker and curated list of significant large models in the audio domain, encompassing speech, singing, music, and more. It's designed for researchers and developers to easily find and explore cutting-edge foundation models.
  • highlicense#3
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root. For example, if you intend an MIT license, the file should contain the standard MIT license text.

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 liusongxiang/Large-Audio-Models
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Bark
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Bark · recommended 2×
  2. Hugging Face Hub · recommended 1×
  3. Whisper · recommended 1×
  4. BART · recommended 1×
  5. Wav2Vec2 · recommended 1×
  • CATEGORY QUERY
    Where can I find a comprehensive list of large language models for audio processing?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Hub
    2. Whisper
    3. BART
    4. Wav2Vec2
    5. MMS (Massively Multilingual Speech)
    6. SpeechT5
    7. Bark
    8. AudioGPT
    9. Papers With Code
    10. arXiv
    11. GitHub Trending Repositories (github.com/trending)
    12. Google Scholar
    13. Semantic Scholar
    14. VALL-E
    15. AudioLM
    16. MusicGen
    17. Encodec

    AI recommended 17 alternatives but never named liusongxiang/Large-Audio-Models. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the leading open-source models for advanced speech and music generation?
    you: not recommended
    AI recommended (in order):
    1. Meta AudioCraft
    2. Google Magenta
    3. Riffusion
    4. OpenAI Jukebox
    5. Bark
    6. Tacotron 2
    7. WaveNet

    AI recommended 7 alternatives but never named liusongxiang/Large-Audio-Models. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    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 liusongxiang/Large-Audio-Models?
    pass
    AI named liusongxiang/Large-Audio-Models explicitly

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

  • If a team adopts liusongxiang/Large-Audio-Models in production, what risks or prerequisites should they evaluate first?
    pass
    AI did not name liusongxiang/Large-Audio-Models — 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?

  • In one sentence, what problem does the repo liusongxiang/Large-Audio-Models solve, and who is the primary audience?
    pass
    AI did not name liusongxiang/Large-Audio-Models — 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?

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liusongxiang/Large-Audio-Models — 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