RRepoGEO

REPOGEO REPORT · LITE

Spr-Aachen/Easy-Voice-Toolkit

Default branch main · commit 1bde56a7 · scanned 5/31/2026, 10:36:49 AM

GitHub: 877 stars · 122 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 Spr-Aachen/Easy-Voice-Toolkit, 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 the README's opening line to clarify its nature as a user-friendly application

    Why:

    CURRENT
    A toolkit based on open source voice projects, which provides a variety of automated audio tools
    COPY-PASTE FIX
    A user-friendly desktop application and Python toolkit for voice recognition, transcription, conversion, and more, integrating state-of-the-art open-source models.
  • highhomepage#2
    Add a project homepage URL to the repository's 'About' section

    Why:

    COPY-PASTE FIX
    https://huggingface.co/SprAachen/Easy-Voice-Toolkit-Package
  • mediumtopics#3
    Expand repository topics to include terms describing its application nature

    Why:

    CURRENT
    asr, audio-denoising, tts, vpr
    COPY-PASTE FIX
    asr, audio-denoising, tts, vpr, voice-toolkit, speech-processing, audio-tools, desktop-application, python-application

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 Spr-Aachen/Easy-Voice-Toolkit
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Google Cloud Speech-to-Text
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Google Cloud Speech-to-Text · recommended 1×
  2. AssemblyAI · recommended 1×
  3. AWS Transcribe · recommended 1×
  4. OpenAI Whisper · recommended 1×
  5. Microsoft Azure Cognitive Services Speech · recommended 1×
  • CATEGORY QUERY
    Looking for a user-friendly toolkit to perform voice recognition, transcription, and conversion tasks.
    you: not recommended
    AI recommended (in order):
    1. Google Cloud Speech-to-Text
    2. AssemblyAI
    3. AWS Transcribe
    4. OpenAI Whisper
    5. Microsoft Azure Cognitive Services Speech
    6. Deepgram
    7. Vosk

    AI recommended 7 alternatives but never named Spr-Aachen/Easy-Voice-Toolkit. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are good options for automating audio processing, denoising, and voice model training?
    you: not recommended
    AI recommended (in order):
    1. Librosa
    2. Pydub
    3. SciPy
    4. SpeechBrain
    5. espnet
    6. Hugging Face Transformers
    7. TensorFlow
    8. PyTorch
    9. Audacity
    10. FFmpeg
    11. MATLAB
    12. SoX
    13. Google Cloud AI Platform
    14. AWS SageMaker
    15. Azure Machine Learning
    16. NVIDIA NeMo

    AI recommended 16 alternatives but never named Spr-Aachen/Easy-Voice-Toolkit. 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 Spr-Aachen/Easy-Voice-Toolkit?
    pass
    AI named Spr-Aachen/Easy-Voice-Toolkit explicitly

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

  • If a team adopts Spr-Aachen/Easy-Voice-Toolkit in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Spr-Aachen/Easy-Voice-Toolkit 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 Spr-Aachen/Easy-Voice-Toolkit solve, and who is the primary audience?
    pass
    AI named Spr-Aachen/Easy-Voice-Toolkit explicitly

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

Embed your GEO score

Drop this badge into the README of Spr-Aachen/Easy-Voice-Toolkit. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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