RRepoGEO

REPOGEO REPORT · LITE

kakaobrain/pororo

Default branch master · commit 7d05a75e · scanned 5/27/2026, 4:56:55 AM

GitHub: 1,307 stars · 219 forks

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 kakaobrain/pororo, 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 the README's opening to emphasize its unified platform nature

    Why:

    CURRENT
    `pororo` performs Natural Language Processing and Speech-related tasks. It is easy to solve various subtasks in the natural language and speech processing field by simply passing the task name.
    COPY-PASTE FIX
    `pororo` is a unified platform providing an easy-to-use interface for a wide range of Natural Language Processing and Speech-related tasks, including their combination. It allows solving various subtasks by simply passing the task name, leveraging a single model architecture for over 20 distinct NLP and speech functionalities.
  • mediumtopics#2
    Add topics that highlight multi-task and unified aspects

    Why:

    CURRENT
    automatic-speech-recognition, deep-learning, natural-language-processing, neural-models, speech-synthesis
    COPY-PASTE FIX
    automatic-speech-recognition, deep-learning, natural-language-processing, neural-models, speech-synthesis, multi-task-nlp, unified-api, language-models-platform, speech-ai
  • lowabout#3
    Refine the repository description to emphasize its platform nature

    Why:

    CURRENT
    PORORO: Platform Of neuRal mOdels for natuRal language prOcessing
    COPY-PASTE FIX
    PORORO: A unified platform of neural models for a wide range of Natural Language Processing and Speech-related tasks.

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 kakaobrain/pororo
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
huggingface/transformers
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 2×
  2. Wav2Vec2 · recommended 1×
  3. openai/whisper · recommended 1×
  4. Google USM · recommended 1×
  5. nltk/nltk · recommended 1×
  • CATEGORY QUERY
    What are good libraries for combining natural language processing and speech recognition tasks?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. Wav2Vec2
    3. OpenAI Whisper (openai/whisper)
    4. Google USM
    5. NLTK (nltk/nltk)
    6. spaCy (explosion/spaCy)
    7. Google Cloud Speech-to-Text API
    8. Google Cloud Natural Language API
    9. AssemblyAI API
    10. Mozilla DeepSpeech (mozilla/DeepSpeech)
    11. Microsoft Azure Cognitive Services
    12. Azure Speech Service
    13. Azure Text Analytics

    AI recommended 13 alternatives but never named kakaobrain/pororo. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for an easy-to-use deep learning framework for various language models and speech synthesis.
    you: not recommended
    AI recommended (in order):
    1. PyTorch (pytorch/pytorch)
    2. Hugging Face Transformers (huggingface/transformers)
    3. TensorFlow (tensorflow/tensorflow)
    4. Keras (keras-team/keras)
    5. fast.ai (fastai/fastai)

    AI recommended 5 alternatives but never named kakaobrain/pororo. 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 kakaobrain/pororo?
    pass
    AI did not name kakaobrain/pororo — 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 kakaobrain/pororo in production, what risks or prerequisites should they evaluate first?
    pass
    AI named kakaobrain/pororo 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 kakaobrain/pororo solve, and who is the primary audience?
    pass
    AI named kakaobrain/pororo 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 kakaobrain/pororo. 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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kakaobrain/pororo — 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