RRepoGEO

REPOGEO REPORT · LITE

AutoArk/GPA

Default branch main · commit 3ec1efb4 · scanned 6/5/2026, 9:03:30 AM

GitHub: 535 stars · 46 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 AutoArk/GPA, 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
    Add a clear, domain-specific opening sentence to the README

    Why:

    COPY-PASTE FIX
    GPA is a cutting-edge audio processing model for Automatic Speech Recognition (ASR), Text-to-Speech (TTS), and Voice Conversion (VC), all unified in one compact architecture.
  • mediumreadme#2
    Add a dedicated 'What is GPA?' section to the README

    Why:

    COPY-PASTE FIX
    ## What is GPA?
GPA (General Purpose Audio) is a unified, compact deep learning model designed for a wide range of speech tasks, including Automatic Speech Recognition (ASR), Text-to-Speech (TTS), and Voice Conversion (VC). It aims to provide high performance with a minimal footprint, making it suitable for both research and edge deployments.
  • lowreadme#3
    Consolidate key features into a dedicated README section

    Why:

    COPY-PASTE FIX
    ## Key Features
- **Unified Model:** Performs ASR, TTS, and Voice Conversion with a single compact model.
- **High Performance:** Delivers near-SOTA results for both ASR and TTS.
- **Edge-Ready:** Optimized with INT8/INT4 quantization for efficient deployment on resource-constrained devices.
- **Voice Cloning:** Supports voice cloning capabilities for personalized speech synthesis.
- **ONNX Runtime:** Provides flexible deployment options via ONNX CLI, FastAPI, and browser UI.

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 AutoArk/GPA
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Tacotron 2
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Tacotron 2 · recommended 2×
  2. Fairseq S2UT · recommended 1×
  3. SeamlessM4T · recommended 1×
  4. ESPnet · recommended 1×
  5. VITS · recommended 1×
  • CATEGORY QUERY
    What's a unified model for both automatic speech recognition and text-to-speech synthesis?
    you: not recommended
    AI recommended (in order):
    1. Fairseq S2UT
    2. SeamlessM4T
    3. ESPnet
    4. VITS
    5. SpeechBrain
    6. Tacotron 2
    7. Conformer

    AI recommended 7 alternatives but never named AutoArk/GPA. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking an efficient, small model for real-time voice conversion and speech synthesis on edge devices.
    you: not recommended
    AI recommended (in order):
    1. Tacotron 2
    2. WaveGlow
    3. MelGAN
    4. FastSpeech 2
    5. Hifi-GAN
    6. Glow-TTS
    7. Mozilla TTS (Coqui TTS)
    8. ESPnet (End-to-End Speech Processing Toolkit)
    9. PicoVoice Cheetah/Rhino/Porcupine
    10. Google's TensorFlow Lite models

    AI recommended 10 alternatives but never named AutoArk/GPA. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • 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 AutoArk/GPA?
    pass
    AI named AutoArk/GPA explicitly

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

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

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

Embed your GEO score

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MARKDOWN (README)
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AutoArk/GPA — 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