RRepoGEO

REPOGEO REPORT · LITE

snakers4/silero-models

Default branch master · commit 468d83ce · scanned 5/10/2026, 11:57:37 AM

GitHub: 5,914 stars · 365 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 snakers4/silero-models, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • mediumhomepage#1
    Add a homepage URL to the repository's 'About' section

    Why:

    COPY-PASTE FIX
    https://silero.ai
  • mediumlicense#2
    Clarify the existing license(s) in the README's 'Licence' section

    Why:

    COPY-PASTE FIX
    This project is licensed under [SPECIFIC LICENSE NAME(S) HERE]. Please refer to the LICENSE file for complete terms and conditions.

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 snakers4/silero-models
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
NVIDIA/tacotron2
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. NVIDIA/tacotron2 · recommended 1×
  2. NVIDIA/waveglow · recommended 1×
  3. espnet/espnet · recommended 1×
  4. FastSpeech 2 · recommended 1×
  5. coqui-ai/TTS · recommended 1×
  • CATEGORY QUERY
    What are some easy-to-use pre-trained text-to-speech models for PyTorch?
    you: not recommended
    AI recommended (in order):
    1. Tacotron 2 (NVIDIA/tacotron2)
    2. WaveGlow (NVIDIA/waveglow)
    3. ESPnet (espnet/espnet)
    4. FastSpeech 2
    5. Coqui TTS (coqui-ai/TTS)
    6. VITS

    AI recommended 6 alternatives but never named snakers4/silero-models. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for speech synthesis models supporting multiple Cyrillic and Central Asian languages.
    you: not recommended
    AI recommended (in order):
    1. Google Cloud Text-to-Speech (TTS)
    2. Microsoft Azure Cognitive Services Speech (Text-to-Speech)
    3. Yandex SpeechKit
    4. Amazon Polly
    5. Mozilla Common Voice
    6. Coqui TTS
    7. DeepMind Tacotron 2
    8. WaveNet

    AI recommended 8 alternatives but never named snakers4/silero-models. 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 snakers4/silero-models?
    pass
    AI did not name snakers4/silero-models — 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 snakers4/silero-models in production, what risks or prerequisites should they evaluate first?
    pass
    AI named snakers4/silero-models 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 snakers4/silero-models solve, and who is the primary audience?
    pass
    AI named snakers4/silero-models explicitly

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

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  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite