RRepoGEO

REPOGEO REPORT · LITE

yeyupiaoling/PPASR

Default branch develop · commit f24d9f9e · scanned 6/15/2026, 1:32:48 AM

GitHub: 873 stars · 130 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 yeyupiaoling/PPASR, 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's opening to emphasize 'framework' and 'production-ready'

    Why:

    CURRENT
    # PPASR流式与非流式语音识别项目
    
    PPASR是一款基于PaddlePaddle实现的自动语音识别框架,PPASR中文名称PaddlePaddle中文语音识别(PaddlePaddle Automatic Speech Recognition),当前为V3版本,与V2版本不兼容,如果想使用V2版本,请在这个分支V2。PPASR致力于简单,实用的语音识别项目。可部署在服务器,Nvidia Jetson设备,未来还计划支持Android等移动设备。
    COPY-PASTE FIX
    # PPASR:基于PaddlePaddle的生产级中文语音识别框架 (End-to-End Chinese ASR Toolkit)
    
    PPASR (PaddlePaddle Automatic Speech Recognition) 是一个功能强大、易于使用的开源框架,专为端到端中文语音识别设计。它提供从入门到实战的完整解决方案,支持流式与非流式识别,并集成了DeepSpeech2、Conformer、Squeezeformer等当前最流行的模型。PPASR致力于构建超实用的企业级项目,可轻松部署于服务器、Nvidia Jetson设备,并计划未来支持Android等移动设备,是AI/ML工程师和研究人员构建高性能ASR应用的理想选择。
  • mediumhomepage#2
    Add a homepage URL to the repository settings

    Why:

    COPY-PASTE FIX
    Add the official project website or a stable public demo URL to the 'Homepage' field in the repository settings.
  • lowcomparison#3
    Add a 'Comparison with Alternatives' section to the README

    Why:

    COPY-PASTE FIX
    ## 与同类项目的比较 (Comparison with Alternatives)
    
    PPASR在众多中文语音识别框架中独树一帜,其核心优势在于:
    
    - **纯PaddlePaddle生态**:与主流基于PyTorch或TensorFlow的框架(如ESPnet、WeNet)不同,PPASR完全基于PaddlePaddle构建,为PaddlePaddle用户提供无缝体验。
    - **企业级与易用性**:PPASR致力于提供从入门到实战的解决方案,特别优化了部署流程,使其在服务器、边缘设备(如Nvidia Jetson)上部署更为简便,适合企业级应用。
    - **模型与功能全面**:全面支持DeepSpeech2、Conformer、Squeezeformer等主流流式与非流式模型,并提供丰富的解码器和数据增强方法,满足多样化的ASR需求。
    - **专注于中文ASR**:针对中文语音识别进行了深度优化,提供高性能和高准确率。

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 yeyupiaoling/PPASR
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ESPnet
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. ESPnet · recommended 2×
  2. SpeechBrain · recommended 2×
  3. WeNet · recommended 1×
  4. FunASR · recommended 1×
  5. Kaldi · recommended 1×
  • CATEGORY QUERY
    What are reliable open-source frameworks for building end-to-end Chinese speech recognition systems?
    you: not recommended
    AI recommended (in order):
    1. WeNet
    2. ESPnet
    3. FunASR
    4. Kaldi
    5. NeMo
    6. SpeechBrain

    AI recommended 6 alternatives but never named yeyupiaoling/PPASR. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a robust Python library for streaming Chinese ASR with Conformer or DeepSpeech2 models.
    you: not recommended
    AI recommended (in order):
    1. Nemo (NVIDIA NeMo)
    2. ESPnet
    3. PaddleSpeech
    4. WeNet (Production-first and Production-ready End-to-End Speech Recognition Toolkit)
    5. SpeechBrain

    AI recommended 5 alternatives but never named yeyupiaoling/PPASR. 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 yeyupiaoling/PPASR?
    pass
    AI named yeyupiaoling/PPASR explicitly

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

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

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

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yeyupiaoling/PPASR — 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