RRepoGEO

REPOGEO REPORT · LITE

xzf-thu/Mega-ASR

Default branch main · commit bd75877b · scanned 6/3/2026, 7:13:09 AM

GitHub: 905 stars · 60 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 xzf-thu/Mega-ASR, 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
  • highlicense#1
    Add a LICENSE file to the repository

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root with the chosen open-source license (e.g., MIT, Apache-2.0, GPL-3.0).
  • highreadme#2
    Explicitly state Mega-ASR's nature as an open-source foundation model in the README

    Why:

    CURRENT
    We introduce **MEGA-ASR**, the first foundation ASR model to target **full-scenario robust speech recognition in the wild** through systematic training on **7 atomic acoustic conditions** and **54 compound acoustic scenarios**.
    COPY-PASTE FIX
    We introduce **MEGA-ASR**, the first **open-source foundation ASR model** designed for **full-scenario robust speech recognition in the wild**, built for researchers and developers to integrate and build upon. It achieves this through systematic training on **7 atomic acoustic conditions** and **54 compound acoustic scenarios**.
  • mediumtopics#3
    Expand repository topics for better categorization

    Why:

    CURRENT
    asr, robust
    COPY-PASTE FIX
    asr, robust-asr, speech-recognition, foundation-model, deep-learning, acoustic-modeling, real-world-ai, machine-learning

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 xzf-thu/Mega-ASR
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Google Cloud Speech-to-Text
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Google Cloud Speech-to-Text · recommended 2×
  2. AssemblyAI · recommended 2×
  3. openai/whisper · recommended 1×
  4. AWS Transcribe · recommended 1×
  5. Picovoice · recommended 1×
  • CATEGORY QUERY
    What's the best speech recognition model for noisy, real-world audio environments?
    you: not recommended
    AI recommended (in order):
    1. Whisper (openai/whisper)
    2. Google Cloud Speech-to-Text
    3. AssemblyAI
    4. AWS Transcribe
    5. Picovoice
    6. Mozilla DeepSpeech (mozilla/DeepSpeech)
    7. Kaldi (kaldi-asr/kaldi)

    AI recommended 7 alternatives but never named xzf-thu/Mega-ASR. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Need an advanced automatic speech recognition system that performs reliably in diverse acoustic conditions.
    you: not recommended
    AI recommended (in order):
    1. Google Cloud Speech-to-Text
    2. AWS Amazon Transcribe
    3. Microsoft Azure Cognitive Services Speech
    4. Deepgram
    5. AssemblyAI
    6. OpenAI Whisper

    AI recommended 6 alternatives but never named xzf-thu/Mega-ASR. 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 xzf-thu/Mega-ASR?
    pass
    AI named xzf-thu/Mega-ASR explicitly

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

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