RRepoGEO

REPOGEO REPORT · LITE

kaituoxu/Speech-Transformer

Default branch master · commit 43d83eab · scanned 6/12/2026, 5:32:57 PM

GitHub: 810 stars · 196 forks

AI VISIBILITY SCORE
28 /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
2 / 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 kaituoxu/Speech-Transformer, 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 opening to emphasize ASR framework

    Why:

    CURRENT
    A PyTorch implementation of Speech Transformer [1], an end-to-end automatic speech recognition with Transformer network, which directly converts acoustic features to character sequence using a single nueral network.
    COPY-PASTE FIX
    This repository offers a complete, runnable PyTorch framework for building and experimenting with end-to-end automatic speech recognition (ASR) systems using the Transformer network, with a focus on Mandarin Chinese.
  • highreadme#2
    Add License Information to README

    Why:

    COPY-PASTE FIX
    ## License

This project is currently not explicitly licensed. Please add a `LICENSE` file to the repository root, choosing an appropriate open-source license (e.g., MIT, Apache 2.0, GPLv3) to clarify usage rights for contributors and users.
  • mediumreadme#3
    Add Project Homepage to README

    Why:

    COPY-PASTE FIX
    ## Project Homepage

For more details and the original research, please refer to the associated paper: [Speech Transformer Paper](YOUR_PAPER_URL_HERE)

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 kaituoxu/Speech-Transformer
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LibriSpeech
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. LibriSpeech · recommended 1×
  2. Common Voice · recommended 1×
  3. TED-LIUM Corpus · recommended 1×
  4. torchaudio · recommended 1×
  5. Librosa · recommended 1×
  • CATEGORY QUERY
    How to build an end-to-end automatic speech recognition system using transformer models?
    you: not recommended
    AI recommended (in order):
    1. LibriSpeech
    2. Common Voice
    3. TED-LIUM Corpus
    4. torchaudio
    5. Librosa
    6. FFmpeg
    7. Fairseq
    8. Hugging Face Transformers
    9. ESPnet
    10. Wav2Vec 2.0
    11. HuBERT
    12. PyTorch
    13. TensorFlow
    14. Keras
    15. Greedy Decoding
    16. Beam Search Decoding
    17. KenLM
    18. ONNX Runtime
    19. TorchScript
    20. TensorFlow Lite
    21. TensorFlow Serving
    22. Docker
    23. Kubernetes

    AI recommended 23 alternatives but never named kaituoxu/Speech-Transformer. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a PyTorch-based solution for Mandarin Chinese speech-to-text conversion.
    you: not recommended
    AI recommended (in order):
    1. Whisper (openai/whisper)
    2. NeMo (NVIDIA/NeMo)
    3. ESPnet (espnet/espnet)
    4. FunASR (alibaba-damo-academy/FunASR)
    5. SpeechBrain (speechbrain/speechbrain)

    AI recommended 5 alternatives but never named kaituoxu/Speech-Transformer. 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 kaituoxu/Speech-Transformer?
    pass
    AI did not name kaituoxu/Speech-Transformer — likely talking about a different project

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

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