RRepoGEO

REPOGEO REPORT · LITE

zhenye234/LLaSA_training

Default branch main · commit 479acd52 · scanned 6/12/2026, 2:32:52 PM

GitHub: 661 stars · 50 forks

AI VISIBILITY SCORE
35 /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
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 zhenye234/LLaSA_training, 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 clarify project purpose

    Why:

    CURRENT
    [](https://arxiv.org/abs/2502.04128)

## Directly used on Hugging Face
    COPY-PASTE FIX
    This repository provides the official training framework for LLaSA, a novel approach to scaling train-time and inference-time compute for LLaMA-based speech synthesis. LLaSA enables the training of large language models (LLMs) using a multimodal approach that integrates both text and audio data, specifically designed for researchers and developers building advanced multilingual speech synthesis models.

[](https://arxiv.org/abs/2502.04128)

## Directly used on Hugging Face
  • mediumreadme#2
    Clarify the project's license in the README

    Why:

    COPY-PASTE FIX
    ## License
This project is licensed under [Specify License(s) here, e.g., a custom license, or a combination of licenses]. Please refer to the `LICENSE` file for full details.
  • lowhomepage#3
    Add the arXiv paper link as the repository homepage

    Why:

    COPY-PASTE FIX
    https://arxiv.org/abs/2502.04128

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 zhenye234/LLaSA_training
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Google Cloud Text-to-Speech
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Google Cloud Text-to-Speech · recommended 1×
  2. Google Cloud Vertex AI · recommended 1×
  3. Amazon Polly · recommended 1×
  4. Amazon SageMaker · recommended 1×
  5. Microsoft Azure AI Speech · recommended 1×
  • CATEGORY QUERY
    How to implement a scalable text-to-speech system powered by large language models?
    you: not recommended
    AI recommended (in order):
    1. Google Cloud Text-to-Speech
    2. Google Cloud Vertex AI
    3. Amazon Polly
    4. Amazon SageMaker
    5. Microsoft Azure AI Speech
    6. Azure OpenAI Service
    7. Azure Machine Learning
    8. ElevenLabs
    9. OpenAI API
    10. Hugging Face Transformers
    11. Coqui TTS
    12. ESPnet
    13. RunwayML
    14. Descript

    AI recommended 14 alternatives but never named zhenye234/LLaSA_training. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help train and optimize multilingual speech synthesis models with extensive datasets?
    you: not recommended
    AI recommended (in order):
    1. ESPnet (espnet/espnet)
    2. NVIDIA NeMo (NVIDIA/NeMo)
    3. Fairseq (facebookresearch/fairseq)
    4. Mozilla TTS (mozilla/TTS)
    5. TensorFlow TTS (TensorFlow/TTS)
    6. OpenVITS (OpenVITS/OpenVITS)

    AI recommended 6 alternatives but never named zhenye234/LLaSA_training. 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 zhenye234/LLaSA_training?
    pass
    AI named zhenye234/LLaSA_training explicitly

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

  • If a team adopts zhenye234/LLaSA_training in production, what risks or prerequisites should they evaluate first?
    pass
    AI named zhenye234/LLaSA_training 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 zhenye234/LLaSA_training solve, and who is the primary audience?
    pass
    AI named zhenye234/LLaSA_training 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 zhenye234/LLaSA_training. 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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zhenye234/LLaSA_training — 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