RRepoGEO

REPOGEO REPORT · LITE

wenet-e2e/WenetSpeech

Default branch main · commit d293df83 · scanned 6/6/2026, 7:47:44 AM

GitHub: 614 stars · 56 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 wenet-e2e/WenetSpeech, 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
  • hightopics#1
    Add descriptive topics to improve categorization

    Why:

    COPY-PASTE FIX
    speech-recognition, asr, chinese-speech, speech-corpus, dataset, mandarin, deep-learning
  • highreadme#2
    Emphasize WenetSpeech's scale and diversity in the README's opening

    Why:

    CURRENT
    A 10000+ Hours Multi-domain Chinese Corpus for Speech Recognition
    COPY-PASTE FIX
    WenetSpeech is the largest publicly available multi-domain Chinese speech corpus, offering over 10,000 hours of diverse, real-world audio for advanced ASR development.
  • mediumcomparison#3
    Add a brief comparison section to the README

    Why:

    COPY-PASTE FIX
    ## Comparison with other Chinese ASR datasets
WenetSpeech stands out with its unprecedented scale of over 10,000 hours, significantly larger and more diverse than datasets like AISHELL-1/2/3, DataBaker, or MagicData-RAMC, making it ideal for training robust, real-world Chinese ASR models.

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 wenet-e2e/WenetSpeech
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
AISHELL-1
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. AISHELL-1 · recommended 2×
  2. AISHELL-2 · recommended 2×
  3. AISHELL-3 · recommended 2×
  4. DataBaker · recommended 2×
  5. MagicData-RAMC · recommended 2×
  • CATEGORY QUERY
    Where can I find a massive dataset for training Chinese automatic speech recognition models?
    you: not recommended
    AI recommended (in order):
    1. AISHELL-1
    2. AISHELL-2
    3. AISHELL-3
    4. Common Voice (Mandarin Chinese)
    5. LibriSpeech (Chinese/Mandarin)
    6. DataBaker
    7. THCHS-30
    8. MagicData-RAMC

    AI recommended 8 alternatives but never named wenet-e2e/WenetSpeech. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the largest multi-domain Chinese speech corpora available for ASR development?
    you: not recommended
    AI recommended (in order):
    1. AISHELL-1
    2. AISHELL-2
    3. AISHELL-3
    4. Common Voice Chinese (Mandarin) (mozilla/common-voice)
    5. DataBaker
    6. LDC (Linguistic Data Consortium)
    7. THCHS-30 (thu-spmi/THCHS-30)
    8. MagicData-RAMC

    AI recommended 8 alternatives but never named wenet-e2e/WenetSpeech. 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 wenet-e2e/WenetSpeech?
    pass
    AI named wenet-e2e/WenetSpeech explicitly

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

  • If a team adopts wenet-e2e/WenetSpeech in production, what risks or prerequisites should they evaluate first?
    pass
    AI named wenet-e2e/WenetSpeech 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 wenet-e2e/WenetSpeech solve, and who is the primary audience?
    pass
    AI named wenet-e2e/WenetSpeech 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 wenet-e2e/WenetSpeech. 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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wenet-e2e/WenetSpeech — 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