RRepoGEO

REPOGEO REPORT · LITE

sanchit-gandhi/whisper-jax

Default branch main · commit f983178a · scanned 6/20/2026, 5:07:55 PM

GitHub: 4,687 stars · 415 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 sanchit-gandhi/whisper-jax, 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 H1 to highlight speed and JAX

    Why:

    CURRENT
    # Whisper JAX
    COPY-PASTE FIX
    # Whisper JAX: 70x Faster Speech-to-Text with OpenAI's Whisper on JAX/TPU
  • mediumhomepage#2
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    https://huggingface.co/spaces/sanchit-gandhi/whisper-jax
  • lowcomparison#3
    Add a dedicated 'Why Whisper JAX?' comparison section to the README

    Why:

    COPY-PASTE FIX
    ## Why Whisper JAX?

Whisper JAX offers significant advantages over other Whisper implementations, particularly OpenAI's PyTorch version:

*   **Unmatched Speed:** Achieve over 70x faster inference compared to OpenAI's PyTorch code, making it the fastest Whisper implementation available.
*   **JAX/TPU Optimization:** Leverages JAX for highly efficient execution on CPUs, GPUs, and especially TPUs.
*   **Scalability:** Designed for high-throughput, rapid processing of long audio files, ideal for production environments.

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 sanchit-gandhi/whisper-jax
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
openai/whisper
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. openai/whisper · recommended 2×
  2. NVIDIA/NeMo · recommended 2×
  3. mozilla/DeepSpeech · recommended 2×
  4. Google Chirp · recommended 1×
  5. facebookresearch/seamless_communication · recommended 1×
  • CATEGORY QUERY
    What are the best high-performance deep learning models for accurate audio transcription?
    you: not recommended
    AI recommended (in order):
    1. OpenAI Whisper (openai/whisper)
    2. Google Chirp
    3. NVIDIA NeMo (NVIDIA/NeMo)
    4. Meta AI SeamlessM4T (facebookresearch/seamless_communication)
    5. AssemblyAI
    6. DeepMind's Perceiver IO
    7. Mozilla DeepSpeech (mozilla/DeepSpeech)

    AI recommended 7 alternatives but never named sanchit-gandhi/whisper-jax. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking an optimized speech recognition library for rapid processing of long audio files.
    you: not recommended
    AI recommended (in order):
    1. NVIDIA NeMo (NVIDIA/NeMo)
    2. Google Cloud Speech-to-Text API
    3. AssemblyAI API
    4. DeepSpeech (mozilla/DeepSpeech)
    5. Whisper (openai/whisper)
    6. AWS Transcribe

    AI recommended 6 alternatives but never named sanchit-gandhi/whisper-jax. 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 sanchit-gandhi/whisper-jax?
    pass
    AI named sanchit-gandhi/whisper-jax explicitly

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

  • If a team adopts sanchit-gandhi/whisper-jax in production, what risks or prerequisites should they evaluate first?
    pass
    AI named sanchit-gandhi/whisper-jax 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 sanchit-gandhi/whisper-jax solve, and who is the primary audience?
    pass
    AI did not name sanchit-gandhi/whisper-jax — 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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MARKDOWN (README)
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sanchit-gandhi/whisper-jax — 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