RRepoGEO

REPOGEO REPORT · LITE

aiola-lab/whisper-medusa

Default branch main · commit 19819c37 · scanned 6/12/2026, 8:13:21 PM

GitHub: 860 stars · 53 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 aiola-lab/whisper-medusa, 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 relevant topics to the repository

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    whisper, asr, speech-to-text, inference-optimization, speculative-decoding, medusa, transformer, deep-learning
  • mediumabout#2
    Expand the repository description

    Why:

    CURRENT
    Whisper with Medusa heads
    COPY-PASTE FIX
    Accelerate Whisper ASR inference using Medusa multi-head decoding for significant speed improvements in speech-to-text tasks.
  • mediumhomepage#3
    Add a homepage URL to the repository

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    https://medium.com/@sgl.yael/whisper-medusa-using-multiple-decoding-heads-to-achieve-1-5x-speedup-7344348ef89b

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 aiola-lab/whisper-medusa
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
NVIDIA TensorRT
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. NVIDIA TensorRT · recommended 1×
  2. OpenVINO · recommended 1×
  3. ONNX Runtime · recommended 1×
  4. PyTorch's `torch.quantization` module · recommended 1×
  5. NVIDIA Apex · recommended 1×
  • CATEGORY QUERY
    How to achieve faster inference speed for large transformer-based automatic speech recognition models?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA TensorRT
    2. OpenVINO
    3. ONNX Runtime
    4. PyTorch's `torch.quantization` module
    5. NVIDIA Apex
    6. TensorFlow Model Optimization Toolkit
    7. Hugging Face Transformers library
    8. PaddlePaddle PaddleSlim
    9. DeepSpeed
    10. FasterTransformer
    11. NVIDIA Triton Inference Server

    AI recommended 11 alternatives but never named aiola-lab/whisper-medusa. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for efficient multi-token prediction methods to accelerate speech-to-text decoding.
    you: not recommended
    AI recommended (in order):
    1. CTC (Connectionist Temporal Classification) with Beam Search
    2. RNN-T (Recurrent Neural Network Transducer)
    3. Transformer-based models with Speculative Decoding
    4. Neural Transducers (e.g., Conformer-Transducer)
    5. Hybrid CTC/Attention Models
    6. Weighted Finite-State Transducers (WFSTs) with N-gram Language Models

    AI recommended 6 alternatives but never named aiola-lab/whisper-medusa. 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 aiola-lab/whisper-medusa?
    pass
    AI named aiola-lab/whisper-medusa explicitly

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

  • If a team adopts aiola-lab/whisper-medusa in production, what risks or prerequisites should they evaluate first?
    pass
    AI named aiola-lab/whisper-medusa 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 aiola-lab/whisper-medusa solve, and who is the primary audience?
    pass
    AI named aiola-lab/whisper-medusa 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 aiola-lab/whisper-medusa. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/aiola-lab/whisper-medusa.svg)](https://repogeo.com/en/r/aiola-lab/whisper-medusa)
HTML
<a href="https://repogeo.com/en/r/aiola-lab/whisper-medusa"><img src="https://repogeo.com/badge/aiola-lab/whisper-medusa.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

aiola-lab/whisper-medusa — 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