RRepoGEO

REPOGEO REPORT · LITE

yeyupiaoling/MASR

Default branch develop · commit 03c92400 · scanned 6/5/2026, 1:38:49 AM

GitHub: 723 stars · 116 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/MASR, 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 H1 and opening paragraph to clarify framework purpose

    Why:

    CURRENT
    # MASR流式与非流式语音识别项目

MASR是一款基于Pytorch实现的自动语音识别框架，MASR全称是神奇的自动语音识别框架（Magical 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。MASR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
    # MASR: Pytorch-based Streaming & Non-Streaming ASR Framework

MASR (Magical Automatic Speech Recognition) is a flexible, Pytorch-based framework for building both streaming and non-streaming automatic speech recognition systems. It supports a variety of modern deep learning models like Conformer, Squeezeformer, and DeepSpeech2, along with multiple decoders and extensive data augmentation methods. Designed for simplicity and practicality, MASR is deployable on servers, Nvidia Jetson devices, and is planned for future mobile support.
  • highhomepage#2
    Add a homepage URL to the repository settings

    Why:

    COPY-PASTE FIX
    https://github.com/yeyupiaoling/MASR
  • mediumtopics#3
    Add more specific topics to improve categorization

    Why:

    CURRENT
    asr, conformer, deep-learning, deepspeech, pytorch, speech, speech-recognition, speech-to-text, squeezeformer
    COPY-PASTE FIX
    asr, conformer, deep-learning, deepspeech, pytorch, speech, speech-recognition, speech-to-text, squeezeformer, asr-framework, speech-recognition-toolkit, streaming-asr, offline-asr, pytorch-asr

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/MASR
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
NVIDIA/NeMo
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. NVIDIA/NeMo · recommended 2×
  2. huggingface/transformers · recommended 2×
  3. mozilla/DeepSpeech · recommended 2×
  4. kaldi-asr/kaldi · recommended 2×
  5. openai/whisper · recommended 1×
  • CATEGORY QUERY
    How to build a real-time speech-to-text system using modern deep learning models?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA NeMo (NVIDIA/NeMo)
    2. Hugging Face Transformers (huggingface/transformers)
    3. OpenAI Whisper (openai/whisper)
    4. Google Cloud Speech-to-Text API
    5. Amazon Transcribe
    6. Mozilla DeepSpeech (mozilla/DeepSpeech)
    7. Kaldi (kaldi-asr/kaldi)

    AI recommended 7 alternatives but never named yeyupiaoling/MASR. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What framework provides flexible ASR with streaming, various decoders, and data augmentation for multiple languages?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA NeMo (NVIDIA/NeMo)
    2. ESPnet (espnet/espnet)
    3. Hugging Face Transformers (huggingface/transformers)
    4. Google Speech Recognition API
    5. Mozilla DeepSpeech (mozilla/DeepSpeech)
    6. Kaldi (kaldi-asr/kaldi)

    AI recommended 6 alternatives but never named yeyupiaoling/MASR. 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/MASR?
    pass
    AI named yeyupiaoling/MASR 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/MASR in production, what risks or prerequisites should they evaluate first?
    pass
    AI named yeyupiaoling/MASR 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/MASR solve, and who is the primary audience?
    pass
    AI named yeyupiaoling/MASR 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/MASR — 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