RRepoGEO

REPOGEO REPORT · LITE

jishengpeng/WavTokenizer

Default branch main · commit 5cf440d9 · scanned 5/22/2026, 2:44:23 PM

GitHub: 1,295 stars · 110 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
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 jishengpeng/WavTokenizer, 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 key value propositions in README

    Why:

    CURRENT
    [](https://arxiv.org/abs/2408.16532)
[](https://wavtokenizer.github.io/)
[](https://huggingface.co/novateur/WavTokenizer)

### 🎉🎉 with WavTokenizer, you can represent speech, music, and audio with only 40 tokens per second!
### 🎉🎉 with WavTokenizer, You can get strong reconstruction results.
### 🎉🎉 WavTokenizer owns rich semantic information and is build for audio language models such as GPT-4o.
    COPY-PASTE FIX
    ### 🎉🎉 with WavTokenizer, you can represent speech, music, and audio with only 40 tokens per second!
### 🎉🎉 with WavTokenizer, You can get strong reconstruction results.
### 🎉🎉 WavTokenizer owns rich semantic information and is build for audio language models such as GPT-4o.

[](https://arxiv.org/abs/2408.16532)
[](https://wavtokenizer.github.io/)
[](https://huggingface.co/novateur/WavTokenizer)
  • mediumabout#2
    Add homepage URL to About section

    Why:

    COPY-PASTE FIX
    https://wavtokenizer.github.io/
  • mediumtopics#3
    Refine topics for better specificity and recall

    Why:

    CURRENT
    acoustic, audio-representation, codec, dac, encodec, gpt4o, music-representation-learning, semantic, soundstream, speech-language-model, speech-representation, text-to-speech
    COPY-PASTE FIX
    acoustic, audio-representation, audio-llm, audio-tokenization, codec, dac, encodec, gpt4o, low-bitrate-audio, music-representation-learning, semantic, soundstream, speech-language-model, speech-representation, text-to-speech, vector-quantization

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 jishengpeng/WavTokenizer
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Audio Spectrogram Transformers (AST)
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Audio Spectrogram Transformers (AST) · recommended 1×
  • CATEGORY QUERY
    How to efficiently represent audio for large language models with minimal tokens?
    you: not recommended
    AI recommended (in order):
    1. Audio Spectrogram Transformers (AST)

    AI recommended 1 alternative but never named jishengpeng/WavTokenizer. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Need a discrete audio codec for high-quality speech and music representation.
    you: not recommended
    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 jishengpeng/WavTokenizer?
    pass
    AI named jishengpeng/WavTokenizer explicitly

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

  • If a team adopts jishengpeng/WavTokenizer in production, what risks or prerequisites should they evaluate first?
    pass
    AI named jishengpeng/WavTokenizer 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 jishengpeng/WavTokenizer solve, and who is the primary audience?
    pass
    AI named jishengpeng/WavTokenizer explicitly

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

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jishengpeng/WavTokenizer — 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