RRepoGEO

REPOGEO REPORT · LITE

declare-lab/tango

Default branch master · commit 34ecd388 · scanned 5/17/2026, 6:02:43 PM

GitHub: 1,236 stars · 105 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
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 declare-lab/tango, 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, concise introductory sentence to the README defining Tango's core purpose

    Why:

    COPY-PASTE FIX
    Add the sentence: "Tango is a cutting-edge system for text-to-audio generation, leveraging diffusion models and guided by Large Language Models (LLMs) to produce high-quality audio from text descriptions." immediately after the H1.
  • mediumlicense#2
    Clarify the project's license(s) directly in the README

    Why:

    COPY-PASTE FIX
    Add a section (e.g., "License") or a clear statement within the README specifying the exact license(s) that apply to the Tango project.
  • mediumtopics#3
    Update the project's topics to be more precise about the application of LLMs in text-to-audio generation

    Why:

    CURRENT
    audio-generation, diffusion, diffusion-models, language-models, large-language-models, text-to-audio
    COPY-PASTE FIX
    audio-generation, diffusion, diffusion-models, text-to-audio, llm-guided-audio, generative-ai-audio

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 declare-lab/tango
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ElevenLabs Text to Speech
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. ElevenLabs Text to Speech · recommended 1×
  2. Google Cloud Text-to-Speech · recommended 1×
  3. Microsoft Azure AI Speech · recommended 1×
  4. OpenAI's TTS API · recommended 1×
  5. Meta's Voicebox · recommended 1×
  • CATEGORY QUERY
    How can I generate realistic audio from text descriptions using AI models?
    you: not recommended
    AI recommended (in order):
    1. ElevenLabs Text to Speech
    2. Google Cloud Text-to-Speech
    3. Microsoft Azure AI Speech
    4. OpenAI's TTS API
    5. Meta's Voicebox
    6. Bark
    7. Tortoise-TTS

    AI recommended 7 alternatives but never named declare-lab/tango. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a fast text-to-audio generation solution utilizing diffusion models.
    you: not recommended
    AI recommended (in order):
    1. AudioGen
    2. AudioLDM
    3. Riffusion
    4. DiffSound
    5. Make-An-Audio

    AI recommended 5 alternatives but never named declare-lab/tango. 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 declare-lab/tango?
    pass
    AI named declare-lab/tango explicitly

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

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

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

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declare-lab/tango — 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