RRepoGEO

REPOGEO REPORT · LITE

freewym/espresso

Default branch main · commit 043bd94a · scanned 6/15/2026, 1:37:42 AM

GitHub: 939 stars · 116 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 freewym/espresso, 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
    Strengthen README H1 to clarify project identity

    Why:

    CURRENT
    # Espresso
    COPY-PASTE FIX
    # Espresso: A Fast End-to-End Neural Speech Recognition Toolkit
  • mediumhomepage#2
    Add official project homepage URL

    Why:

    COPY-PASTE FIX
    https://freewym.github.io/espresso/docs
  • mediumreadme#3
    Clarify existing license(s) in README

    Why:

    COPY-PASTE FIX
    Espresso is licensed under [Specify License(s) Here, e.g., MIT License, Apache 2.0, or a custom license as detailed in the LICENSE file].

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 freewym/espresso
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ESPnet
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. ESPnet · recommended 2×
  2. NeMo · recommended 1×
  3. WeNet · recommended 1×
  4. OpenSeq2Seq · recommended 1×
  5. fairseq · recommended 1×
  • CATEGORY QUERY
    What are good open-source toolkits for end-to-end speech recognition using deep learning?
    you: not recommended
    AI recommended (in order):
    1. ESPnet
    2. NeMo
    3. WeNet
    4. OpenSeq2Seq
    5. fairseq
    6. DeepSpeech

    AI recommended 6 alternatives but never named freewym/espresso. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a fast, distributed toolkit for training custom neural speech recognition models.
    you: not recommended
    AI recommended (in order):
    1. NVIDIA NeMo
    2. ESPnet
    3. Fairseq
    4. PyTorch-Kaldi
    5. TensorFlow ASR

    AI recommended 5 alternatives but never named freewym/espresso. 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 freewym/espresso?
    pass
    AI named freewym/espresso explicitly

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

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