RRepoGEO

REPOGEO REPORT · LITE

QwenLM/Qwen2-Audio

Default branch main · commit 595360e8 · scanned 6/22/2026, 9:12:52 PM

GitHub: 2,082 stars · 165 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
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

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

OVERALL DIRECTION
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    audio-language-model, llm, multimodal-ai, speech-to-text, audio-analysis, voice-chat, generative-ai, qwen, alibaba-cloud
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the root directory of the repository, for example, with the Apache-2.0 license text.
  • mediumhomepage#3
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    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
Whisper API
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Whisper API · recommended 2×
  2. Google Cloud Speech-to-Text · recommended 2×
  3. AssemblyAI · recommended 2×
  4. Cohere Command · recommended 2×
  5. OpenAI API · recommended 2×
  • CATEGORY QUERY
    Need a large language model to process diverse audio inputs and generate contextual text responses.
    you: not recommended
    AI recommended (in order):
    1. OpenAI GPT-4
    2. Whisper API
    3. Google Gemini
    4. Google Cloud Speech-to-Text
    5. Anthropic Claude 3
    6. AssemblyAI
    7. Deepgram
    8. Meta Llama 3
    9. NVIDIA Riva
    10. Whisper
    11. Wav2Vec2
    12. Cohere Command
    13. Rev.ai
    14. Voci

    AI recommended 14 alternatives but never named QwenLM/Qwen2-Audio. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I build an AI assistant that understands audio and responds via text chat?
    you: not recommended
    AI recommended (in order):
    1. OpenAI API
    2. Whisper API
    3. GPT-4
    4. GPT-3.5 Turbo
    5. Google Cloud Speech-to-Text
    6. Google Gemini API
    7. PaLM 2
    8. AssemblyAI
    9. Anthropic Claude
    10. Cohere Command
    11. Hugging Face Transformers (huggingface/transformers)
    12. Whisper models (openai/whisper)
    13. Wav2Vec2 (facebookresearch/fairseq)
    14. OpenAI API
    15. GPT-3.5 Turbo
    16. Mozilla DeepSpeech (mozilla/DeepSpeech)
    17. Llama 3 (meta-llama/llama3)
    18. Mistral Large
    19. Mistral AI
    20. OpenAI TTS API
    21. Google Cloud Text-to-Speech
    22. ElevenLabs

    AI recommended 22 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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MARKDOWN (README)
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QwenLM/Qwen2-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