RRepoGEO

REPOGEO REPORT · LITE

jaywalnut310/vits

Default branch main · commit 2e561ba5 · scanned 5/23/2026, 12:38:01 PM

GitHub: 7,848 stars · 1,389 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
52 /100
Needs work
Category recall
1 / 2
Avg rank #6.0 when recommended
Rule findings
2 pass · 0 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 jaywalnut310/vits, 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 README's opening to clearly state the project's purpose for developers

    Why:

    CURRENT
    In our recent paper, we propose VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech.
    COPY-PASTE FIX
    VITS is an open-source PyTorch implementation of a Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech. This repository provides the code and pretrained models for researchers and developers to generate high-quality, natural-sounding speech directly from text.
  • mediumtopics#2
    Add more specific topics to clarify the project's nature as a model/library

    Why:

    CURRENT
    deep-learning, pytorch, speech-synthesis, text-to-speech, tts
    COPY-PASTE FIX
    deep-learning, pytorch, speech-synthesis, text-to-speech, tts, text-to-speech-model, end-to-end-tts, speech-generation
  • lowreadme#3
    Add a 'Why VITS?' or 'Comparison' section to the README

    Why:

    COPY-PASTE FIX
    ## Why VITS?
Unlike commercial text-to-speech APIs, VITS provides an open-source, end-to-end solution for researchers and developers seeking full control over their speech synthesis models. It offers state-of-the-art quality with diverse rhythm generation, making it ideal for custom applications and academic research.

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
1 / 2
50% of queries surface jaywalnut310/vits
Avg rank
#6.0
Lower is better. #1 = top recommendation.
Share of voice
7%
Of all named tools, what % are you?
Top rival
Google Cloud Text-to-Speech
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Google Cloud Text-to-Speech · recommended 1×
  2. Amazon Polly · recommended 1×
  3. Microsoft Azure Text to Speech · recommended 1×
  4. ElevenLabs · recommended 1×
  5. coqui-ai/TTS · recommended 1×
  • CATEGORY QUERY
    How to generate natural-sounding speech directly from text using deep learning?
    you: not recommended
    AI recommended (in order):
    1. Google Cloud Text-to-Speech
    2. Amazon Polly
    3. Microsoft Azure Text to Speech
    4. ElevenLabs
    5. Coqui TTS (coqui-ai/TTS)
    6. Meta AI's Voicebox

    AI recommended 6 alternatives but never named jaywalnut310/vits. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What PyTorch library can synthesize diverse speech rhythms from text input?
    you: #6
    AI recommended (in order):
    1. Tacotron 2
    2. WaveGlow
    3. HiFi-GAN
    4. FastSpeech 2
    5. FastSpeech 2s
    6. VITS ← you
    7. Grad-TTS
    8. Glow-TTS
    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 jaywalnut310/vits?
    pass
    AI named jaywalnut310/vits explicitly

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

  • If a team adopts jaywalnut310/vits in production, what risks or prerequisites should they evaluate first?
    pass
    AI named jaywalnut310/vits 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 jaywalnut310/vits solve, and who is the primary audience?
    pass
    AI did not name jaywalnut310/vits — 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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jaywalnut310/vits — 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