RRepoGEO

REPOGEO REPORT · LITE

wenet-e2e/wenet

Default branch main · commit 51b57728 · scanned 5/25/2026, 11:01:56 PM

GitHub: 5,124 stars · 1,183 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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/wenet, 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 core value proposition in README intro

    Why:

    CURRENT
    The text immediately following `# WeNet` is a series of badges/links and then a navigation bar. The core value proposition is currently within the `## Highlights` section.
    COPY-PASTE FIX
    Insert the following sentence immediately after the initial badges/links, before the "Roadmap" or "Highlights" section:
`WeNet is a production-first and production-ready end-to-end speech recognition toolkit, offering full-stack solutions for building and deploying accurate, lightweight ASR systems.`
  • mediumcomparison#2
    Add a "Why WeNet?" or "Comparison" section to README

    Why:

    COPY-PASTE FIX
    Add a new section to the README, for example:
```markdown
## Why WeNet?

WeNet stands out as a unified, production-ready, and efficient end-to-end streaming Automatic Speech Recognition (ASR) toolkit. Unlike some alternatives, WeNet prioritizes:
- **Production-grade deployment:** Optimized for real-world applications with efficient runtime and full-stack solutions.
- **Streaming ASR:** Designed for low-latency, real-time transcription.
- **Lightweight and easy to use:** Simple installation and clear APIs for both command-line and Python programming.
- **State-of-the-art models:** Integrates advanced models like Conformer, Transformer, and Paraformer for high accuracy.
```
  • lowabout#3
    Expand GitHub "About" description

    Why:

    CURRENT
    Production First and Production Ready End-to-End Speech Recognition Toolkit
    COPY-PASTE FIX
    WeNet is a production-first and production-ready end-to-end speech recognition toolkit, offering full-stack solutions for building and deploying accurate, lightweight ASR systems with state-of-the-art models like Conformer, Transformer, and Paraformer.

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/wenet
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Mozilla DeepSpeech
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Mozilla DeepSpeech · recommended 2×
  2. NVIDIA NeMo · recommended 1×
  3. ESPnet · recommended 1×
  4. Kaldi · recommended 1×
  5. SpeechBrain · recommended 1×
  • CATEGORY QUERY
    What are the best open-source toolkits for production-grade automatic speech recognition?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA NeMo
    2. ESPnet
    3. Kaldi
    4. Mozilla DeepSpeech
    5. SpeechBrain

    AI recommended 5 alternatives but never named wenet-e2e/wenet. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a lightweight Python library for accurate end-to-end speech-to-text transcription.
    you: not recommended
    AI recommended (in order):
    1. OpenAI Whisper
    2. Vosk
    3. Mozilla DeepSpeech
    4. SpeechRecognition library
    5. CMU Sphinx
    6. Google Cloud Speech-to-Text

    AI recommended 6 alternatives but never named wenet-e2e/wenet. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • 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/wenet?
    pass
    AI named wenet-e2e/wenet 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/wenet in production, what risks or prerequisites should they evaluate first?
    pass
    AI named wenet-e2e/wenet 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/wenet solve, and who is the primary audience?
    pass
    AI named wenet-e2e/wenet explicitly

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

Embed your GEO score

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wenet-e2e/wenet — 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