RRepoGEO

REPOGEO REPORT · LITE

hexgrad/kokoro

Default branch main · commit dfb907a0 · scanned 5/29/2026, 3:18:29 AM

GitHub: 7,218 stars · 783 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 hexgrad/kokoro, 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
  • highreadme#1
    Reposition the README's opening to explicitly state its TTS purpose

    Why:

    CURRENT
    # kokoro

An inference library for Kokoro-82M.
    COPY-PASTE FIX
    # kokoro: A Python Inference Library for the Kokoro-82M Text-to-Speech Model

This library provides a lightweight and efficient way to perform inference with the Kokoro-82M open-weight Text-to-Speech (TTS) model.
  • mediumhomepage#2
    Set the Hugging Face model page as the repository homepage

    Why:

    COPY-PASTE FIX
    https://hf.co/hexgrad/Kokoro-82M

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 hexgrad/kokoro
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Mozilla TTS
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Mozilla TTS · recommended 2×
  2. ESPnet · recommended 2×
  3. Coqui TTS · recommended 2×
  4. OpenVINO · recommended 2×
  5. Piper · recommended 1×
  • CATEGORY QUERY
    What are some fast, lightweight text-to-speech models for Python projects?
    you: not recommended
    AI recommended (in order):
    1. Piper
    2. Mozilla TTS
    3. ESPnet
    4. Coqui TTS
    5. Bark
    6. OpenVINO

    AI recommended 6 alternatives but never named hexgrad/kokoro. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for an open-source, deployable TTS solution with good quality for production.
    you: not recommended
    AI recommended (in order):
    1. Mozilla TTS
    2. Coqui TTS
    3. VITS
    4. ESPnet
    5. OpenVINO
    6. MaryTTS

    AI recommended 6 alternatives but never named hexgrad/kokoro. 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 hexgrad/kokoro?
    pass
    AI named hexgrad/kokoro explicitly

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

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

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

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hexgrad/kokoro — 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