RRepoGEO

REPOGEO REPORT · LITE

r9y9/wavenet_vocoder

Default branch master · commit a35fff76 · scanned 5/20/2026, 5:56:53 AM

GitHub: 2,374 stars · 493 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
33 /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
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 r9y9/wavenet_vocoder, 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 opening to highlight core differentiator

    Why:

    CURRENT
    The goal of the repository is to provide an implementation of the WaveNet vocoder, which can generate high quality raw speech samples conditioned on linguistic or acoustic features.
    COPY-PASTE FIX
    This repository provides a high-quality, faithful reference implementation of the original, autoregressive WaveNet vocoder, focused on generating raw speech samples conditioned on linguistic or acoustic features. It is a foundational tool for researchers and developers working with WaveNet-based speech synthesis.
  • mediumreadme#2
    Add a comparison point to 'Highlights' for context

    Why:

    COPY-PASTE FIX
    Add to the 'Highlights' section: "- Serves as a robust reference for the original WaveNet architecture, offering deep insights into autoregressive raw audio generation compared to newer, non-autoregressive models."
  • lowlicense#3
    Clarify license in README

    Why:

    COPY-PASTE FIX
    Add a section to the README, e.g., "## License\nThis project is licensed under the terms specified in the [LICENSE](LICENSE) file."

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 r9y9/wavenet_vocoder
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. WaveGlow · recommended 2×
  3. HiFi-GAN · recommended 1×
  4. VITS · recommended 1×
  5. Glow-TTS · recommended 1×
  • CATEGORY QUERY
    How to generate high-quality synthetic speech using deep learning architectures?
    you: not recommended
    AI recommended (in order):
    1. Tacotron 2
    2. WaveGlow
    3. HiFi-GAN
    4. VITS
    5. Glow-TTS
    6. FastSpeech 2
    7. YourTTS
    8. StyleTTS 2

    AI recommended 8 alternatives but never named r9y9/wavenet_vocoder. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best Python tools for raw audio waveform generation in speech synthesis?
    you: not recommended
    AI recommended (in order):
    1. Tacotron 2
    2. WaveNet
    3. WaveGlow
    4. NVIDIA NeMo
    5. ESPnet
    6. DiffSinger
    7. Hifi-GAN
    8. PyTorch
    9. TensorFlow
    10. Librosa

    AI recommended 10 alternatives but never named r9y9/wavenet_vocoder. 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 r9y9/wavenet_vocoder?
    pass
    AI did not name r9y9/wavenet_vocoder — 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 r9y9/wavenet_vocoder in production, what risks or prerequisites should they evaluate first?
    pass
    AI named r9y9/wavenet_vocoder 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 r9y9/wavenet_vocoder solve, and who is the primary audience?
    pass
    AI named r9y9/wavenet_vocoder 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 r9y9/wavenet_vocoder. 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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r9y9/wavenet_vocoder — 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