RRepoGEO

REPOGEO REPORT · LITE

yeyupiaoling/Whisper-Finetune

Default branch master · commit cb4b6016 · scanned 6/30/2026, 3:53:27 PM

GitHub: 1,216 stars · 219 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

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 yeyupiaoling/Whisper-Finetune, 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 highlight unique features

    Why:

    CURRENT
    OpenAI open-sourced the Whisper project... The main purpose of this project is to fine-tune the Whisper model using Lora, supporting training without timestamp data, training with timestamp data, and training without speech data. ... Supports Windows desktop applications, Android applications and server deployment.
    COPY-PASTE FIX
    This project provides a comprehensive solution for fine-tuning OpenAI's Whisper model, uniquely supporting training **without timestamp data, with timestamp data, or even without speech data**. It also offers **accelerated inference** and versatile deployment options for **Web, Windows desktop, and Android applications**, making advanced speech recognition adaptable and accessible for diverse use cases.
  • hightopics#2
    Add specific solution-oriented topics

    Why:

    CURRENT
    android, asr, chinese, ctranslate2, huggingface, lora, pytorch, speech-recognition, transformers, web, whisper
    COPY-PASTE FIX
    android, asr, chinese, ctranslate2, huggingface, lora, pytorch, speech-recognition, transformers, web, whisper, **whisper-finetuning, asr-deployment, speech-to-text-deployment**
  • mediumreadme#3
    Add a dedicated section for unique training modes

    Why:

    CURRENT
    The '微调模型' (Fine-tune Model) section currently contains '单卡训练' (Single Card Training) and '多卡训练' (Multi-Card Training).
    COPY-PASTE FIX
    Under the '微调模型' (Fine-tune Model) section, add a new subsection titled '支持特殊数据训练模式 (Training with Special Data Modes)' that explicitly details the steps and considerations for '无时间戳数据训练 (training without timestamp data)', '有时间戳数据训练 (training with timestamp data)', and '无语音数据训练 (training without speech data)'.

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/Whisper-Finetune
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
huggingface/transformers
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 1×
  2. GPT-2 · recommended 1×
  3. T5 · recommended 1×
  4. BART · recommended 1×
  5. Wav2Vec 2.0 · recommended 1×
  • CATEGORY QUERY
    How to fine-tune a speech recognition model without timestamp or speech data?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. GPT-2
    3. T5
    4. BART
    5. Wav2Vec 2.0
    6. HuBERT
    7. Kaldi (kaldi-asr/kaldi)
    8. SRILM
    9. KenLM (kpu/kenlm)
    10. Vosk API (alphacep/vosk-api)
    11. Mozilla Common Voice (mozilla/common-voice)
    12. Tacotron 2
    13. FastSpeech 2
    14. ESPnet (espnet/espnet)
    15. Coqui TTS (coqui-ai/TTS)

    AI recommended 15 alternatives but never named yeyupiaoling/Whisper-Finetune. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a fast speech-to-text solution with web, desktop, and Android deployment.
    you: not recommended
    AI recommended (in order):
    1. Google Cloud Speech-to-Text
    2. AWS Transcribe
    3. AssemblyAI
    4. Deepgram
    5. Microsoft Azure Cognitive Services Speech
    6. OpenAI Whisper

    AI recommended 6 alternatives but never named yeyupiaoling/Whisper-Finetune. 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/Whisper-Finetune?
    pass
    AI named yeyupiaoling/Whisper-Finetune 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/Whisper-Finetune in production, what risks or prerequisites should they evaluate first?
    pass
    AI named yeyupiaoling/Whisper-Finetune 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/Whisper-Finetune solve, and who is the primary audience?
    pass
    AI did not name yeyupiaoling/Whisper-Finetune — 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?

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yeyupiaoling/Whisper-Finetune — 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