RRepoGEO

REPOGEO REPORT · LITE

Vaibhavs10/fast-whisper-finetuning

Default branch main · commit 0515e112 · scanned 6/11/2026, 10:28:18 PM

GitHub: 561 stars · 46 forks

AI VISIBILITY SCORE
23 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 Vaibhavs10/fast-whisper-finetuning, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highabout#1
    Add a concise description to the repository's About section

    Why:

    COPY-PASTE FIX
    A step-by-step guide and tools to fine-tune Whisper (large) up to 5x faster on consumer GPUs with less than 8GB VRAM using LoRA and 🤗 PEFT, achieving comparable performance to full-finetuning.
  • highlicense#2
    Add a LICENSE file and specify the project's license

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the root of the repository. If the project is intended to be open source, choose a standard open-source license (e.g., MIT, Apache-2.0, GPL-3.0) and add its text to the file. Then, update the repository settings to reflect the chosen license.

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 Vaibhavs10/fast-whisper-finetuning
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
FlashAttention
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. FlashAttention · recommended 2×
  2. QLoRA · recommended 1×
  3. TimDettmers/bitsandbytes · recommended 1×
  4. huggingface/transformers · recommended 1×
  5. LoRA · recommended 1×
  • CATEGORY QUERY
    How to quickly fine-tune large AI models on consumer GPUs with limited VRAM?
    you: not recommended
    AI recommended (in order):
    1. QLoRA
    2. bitsandbytes (TimDettmers/bitsandbytes)
    3. Hugging Face Transformers (huggingface/transformers)
    4. LoRA
    5. Hugging Face PEFT (huggingface/peft)
    6. DeepSpeed (microsoft/DeepSpeed)
    7. ZeRO-Offload
    8. ZeRO-2
    9. ZeRO-3
    10. PyTorch FSDP
    11. PyTorch (pytorch/pytorch)
    12. FlashAttention
    13. xFormers (facebookresearch/xformers)
    14. ONNX Runtime (microsoft/onnxruntime)
    15. TensorRT (NVIDIA/TensorRT)

    AI recommended 15 alternatives but never named Vaibhavs10/fast-whisper-finetuning. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are efficient methods for adapting large pre-trained models with minimal computational resources?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face PEFT
    2. Microsoft DeepSpeed
    3. PyTorch
    4. TensorFlow
    5. NVIDIA APEX
    6. PyTorch `torch.cuda.amp`
    7. TensorFlow Lite
    8. PyTorch `torch.quantization`
    9. Hugging Face `transformers`
    10. OpenAI SparseGPT
    11. NVIDIA SparseML
    12. FlashAttention
    13. xFormers

    AI recommended 13 alternatives but never named Vaibhavs10/fast-whisper-finetuning. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    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 Vaibhavs10/fast-whisper-finetuning?
    pass
    AI named Vaibhavs10/fast-whisper-finetuning explicitly

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

  • If a team adopts Vaibhavs10/fast-whisper-finetuning in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Vaibhavs10/fast-whisper-finetuning 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 Vaibhavs10/fast-whisper-finetuning solve, and who is the primary audience?
    pass
    AI did not name Vaibhavs10/fast-whisper-finetuning — 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?

Embed your GEO score

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Vaibhavs10/fast-whisper-finetuning — 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
Vaibhavs10/fast-whisper-finetuning — RepoGEO report