RRepoGEO

REPOGEO REPORT · LITE

Aratako/Irodori-TTS

Default branch main · commit eaf74d6a · scanned 6/17/2026, 12:22:53 AM

GitHub: 950 stars · 109 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 Aratako/Irodori-TTS, 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 open-source nature and emoji-driven style control

    Why:

    CURRENT
    Training and inference code for **Irodori-TTS**, a Flow Matching-based Text-to-Speech model. The architecture and training design largely follow Echo-TTS, using DACVAE continuous latents as the generation target.
    COPY-PASTE FIX
    Irodori-TTS is an open-source Flow Matching-based Text-to-Speech model, providing training and inference code for developers and researchers. It features advanced emoji-driven style control and zero-shot voice cloning, building on the Echo-TTS architecture with DACVAE continuous latents.
  • mediumtopics#2
    Add specific topics for key features

    Why:

    CURRENT
    diffusion-models, flow-matching, python, speech-synthesis, text-to-speech, tts, voice-cloning
    COPY-PASTE FIX
    diffusion-models, flow-matching, python, speech-synthesis, text-to-speech, tts, voice-cloning, emoji-style-control, zero-shot-voice-cloning
  • mediumreadme#3
    Add a comparison section to differentiate from commercial APIs and older models

    Why:

    COPY-PASTE FIX
    ## Comparison

Unlike commercial Text-to-Speech APIs (e.g., ElevenLabs, Google Cloud TTS, Amazon Polly), Irodori-TTS provides a fully open-source codebase for training and inference, giving researchers and developers complete control and flexibility. Compared to older open-source models like Tacotron2, Irodori-TTS leverages modern Flow Matching and Diffusion Transformer architectures for higher quality and more controllable speech synthesis, including unique emoji-driven style control.

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 Aratako/Irodori-TTS
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 2 of 2 queries
COMPETITOR LEADERBOARD
  1. NVIDIA/tacotron2 · recommended 2×
  2. ElevenLabs · recommended 1×
  3. Google Cloud Text-to-Speech · recommended 1×
  4. Amazon Polly · recommended 1×
  5. Microsoft Azure Text to Speech · recommended 1×
  • CATEGORY QUERY
    How to generate natural-sounding speech with expressive style control using emojis?
    you: not recommended
    AI recommended (in order):
    1. ElevenLabs
    2. Google Cloud Text-to-Speech
    3. Amazon Polly
    4. Microsoft Azure Text to Speech
    5. OpenAI TTS
    6. Meta Voicebox

    AI recommended 6 alternatives but never named Aratako/Irodori-TTS. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a Python library to synthesize speech using flow matching models and clone voices.
    you: not recommended
    AI recommended (in order):
    1. Coqui TTS
    2. ESPnet
    3. Hugging Face `transformers`
    4. `diffusers`
    5. NVIDIA NeMo
    6. Tacotron 2 (NVIDIA/tacotron2)
    7. WaveGlow (NVIDIA/tacotron2)

    AI recommended 7 alternatives but never named Aratako/Irodori-TTS. 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 Aratako/Irodori-TTS?
    pass
    AI named Aratako/Irodori-TTS explicitly

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

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

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

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  • Brand-free category queries5 vs 2 in Lite
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