RRepoGEO

REPOGEO REPORT · LITE

syhw/wer_are_we

Default branch master · commit a5d4a301 · scanned 5/21/2026, 5:52:52 AM

GitHub: 1,863 stars · 225 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
22 /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
1 / 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 syhw/wer_are_we, 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
    Clarify the README's opening statement to explicitly state the repo's purpose

    Why:

    CURRENT
    # wer_are_we
WER are we? An attempt at tracking states of the art(s) and recent results on speech recognition. *Feel free to correct!*
    COPY-PASTE FIX
    Replace the first paragraph of the README with: "# wer_are_we: Tracking State-of-the-Art Benchmarks and Results in Speech Recognition
This repository tracks and compiles state-of-the-art (SOTA) results and benchmarks for speech recognition, focusing on Word Error Rate (WER) across key datasets like LibriSpeech. It serves as a living bibliography for researchers to quickly reference recent advancements."
  • mediumlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Add a LICENSE file to the repository root, choosing an appropriate open-source license (e.g., MIT, Apache-2.0, GPL-3.0) that reflects your intentions for contributions and usage.
  • mediumhomepage#3
    Add a homepage URL to the repository settings

    Why:

    COPY-PASTE FIX
    Add a homepage URL to the repository settings, linking to a relevant project page, documentation, or even the repository itself if no external site exists.

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 syhw/wer_are_we
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LibriSpeech
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. LibriSpeech · recommended 1×
  2. Switchboard (SWBD) · recommended 1×
  3. CallHome (CH) · recommended 1×
  4. Common Voice · recommended 1×
  5. AMI Meeting Corpus · recommended 1×
  • CATEGORY QUERY
    What are the current state-of-the-art benchmarks for deep learning speech recognition?
    you: not recommended
    AI recommended (in order):
    1. LibriSpeech
    2. Switchboard (SWBD)
    3. CallHome (CH)
    4. Common Voice
    5. AMI Meeting Corpus
    6. CHiME-6
    7. CHiME-7
    8. Earnings-21
    9. VoxPopuli

    AI recommended 9 alternatives but never named syhw/wer_are_we. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How do various neural network models perform on common speech recognition datasets?
    you: not recommended
    AI recommended (in order):
    1. Whisper
    2. Conformer
    3. RNN-T / Transformer Transducer
    4. DeepSpeech
    5. Kaldi
    6. Wav2Vec 2.0

    AI recommended 6 alternatives but never named syhw/wer_are_we. 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 syhw/wer_are_we?
    pass
    AI did not name syhw/wer_are_we — likely talking about a different project

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

  • If a team adopts syhw/wer_are_we in production, what risks or prerequisites should they evaluate first?
    pass
    AI named syhw/wer_are_we 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 syhw/wer_are_we solve, and who is the primary audience?
    pass
    AI did not name syhw/wer_are_we — likely talking about a different project

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

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syhw/wer_are_we — 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
syhw/wer_are_we — RepoGEO report