RRepoGEO

REPOGEO REPORT · LITE

SpeechColab/Leaderboard

Default branch master · commit 678f55a7 · scanned 6/12/2026, 8:27:58 PM

GitHub: 546 stars · 73 forks

AI VISIBILITY SCORE
28 /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
2 / 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 SpeechColab/Leaderboard, 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 README's opening to emphasize 'comparison platform'

    Why:

    CURRENT
    SpeechIO leaderboard serves as an ASR benchmarking platform by providing 3 components:
    COPY-PASTE FIX
    SpeechColab Leaderboard is a comprehensive, open-source platform designed for robustly comparing and benchmarking Automatic Speech Recognition (ASR) models across a wide range of test sets.
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the root directory of the repository, containing the text for the MIT License (or the appropriate license for the project).
  • mediumhomepage#3
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    Add a URL to the repository's About section, such as 'https://speechcolab.github.io/Leaderboard' (if this is the project's official site or GitHub Pages URL).

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 SpeechColab/Leaderboard
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
FFmpeg
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. FFmpeg · recommended 1×
  2. `re` · recommended 1×
  3. `sclite` · recommended 1×
  4. `pywer` · recommended 1×
  5. jitsi/jiwer · recommended 1×
  • CATEGORY QUERY
    How can I accurately compare the performance of different automatic speech recognition models?
    you: not recommended
    AI recommended (in order):
    1. FFmpeg
    2. `re`
    3. `sclite`
    4. `pywer`
    5. `jiwer` (jitsi/jiwer)
    6. Hugging Face `evaluate` library (huggingface/evaluate)
    7. OpenAI Whisper (openai/whisper)
    8. Google Cloud Speech-to-Text
    9. Amazon Transcribe
    10. AssemblyAI
    11. Deepgram
    12. Kaldi (kaldi-asr/kaldi)
    13. NVIDIA NeMo (NVIDIA/NeMo)

    AI recommended 13 alternatives but never named SpeechColab/Leaderboard. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help evaluate speech-to-text system accuracy on a wide range of test sets?
    you: not recommended
    AI recommended (in order):
    1. SpeechBrain
    2. pyannote.metrics
    3. Kaldi
    4. DeepSpeech
    5. Google Cloud Speech-to-Text API
    6. AWS Transcribe
    7. Azure Speech
    8. ESPRESSO

    AI recommended 8 alternatives but never named SpeechColab/Leaderboard. 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 SpeechColab/Leaderboard?
    pass
    AI did not name SpeechColab/Leaderboard — 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 SpeechColab/Leaderboard in production, what risks or prerequisites should they evaluate first?
    pass
    AI named SpeechColab/Leaderboard 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 SpeechColab/Leaderboard solve, and who is the primary audience?
    pass
    AI named SpeechColab/Leaderboard explicitly

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

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SpeechColab/Leaderboard — 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