RRepoGEO

REPOGEO REPORT · LITE

QwenLM/Qwen2-Audio

Default branch main · commit 595360e8 · scanned 5/12/2026, 2:32:39 PM

GitHub: 2,068 stars · 165 forks

AI VISIBILITY SCORE
30 /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
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 QwenLM/Qwen2-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 appropriate license text (e.g., Apache-2.0, MIT, etc.).
  • mediumhomepage#2
    Set the repository homepage URL

    Why:

    COPY-PASTE FIX
    Set the repository homepage URL to `https://qwenlm.github.io/blog/qwen2-audio`.

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 QwenLM/Qwen2-Audio
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
openai/whisper
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. openai/whisper · recommended 1×
  2. Conformer-1 · recommended 1×
  3. Google Speech-to-Text · recommended 1×
  4. Azure Speech-to-Text · recommended 1×
  5. SeamlessM4T · recommended 1×
  • CATEGORY QUERY
    What are the best large audio language models for understanding spoken input?
    you: not recommended
    AI recommended (in order):
    1. Whisper (openai/whisper)
    2. Conformer-1
    3. Google Speech-to-Text
    4. Azure Speech-to-Text
    5. SeamlessM4T
    6. NVIDIA NeMo (NVIDIA/NeMo)

    AI recommended 6 alternatives but never named QwenLM/Qwen2-Audio. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a solution to perform audio analysis and generate text from speech.
    you: not recommended
    AI recommended (in order):
    1. Google Cloud Speech-to-Text
    2. AWS Transcribe
    3. Azure Cognitive Services Speech
    4. OpenAI Whisper
    5. AssemblyAI
    6. Deepgram

    AI recommended 6 alternatives but never named QwenLM/Qwen2-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
    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 QwenLM/Qwen2-Audio?
    pass
    AI named QwenLM/Qwen2-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 QwenLM/Qwen2-Audio in production, what risks or prerequisites should they evaluate first?
    pass
    AI named QwenLM/Qwen2-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 QwenLM/Qwen2-Audio solve, and who is the primary audience?
    pass
    AI named QwenLM/Qwen2-Audio explicitly

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

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