RRepoGEO

REPOGEO REPORT · LITE

k2-fsa/icefall

Default branch master · commit 88c30de1 · scanned 5/19/2026, 2:51:57 AM

GitHub: 1,412 stars · 411 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
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 k2-fsa/icefall, 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
    Reproducible recipes and examples for developing and training state-of-the-art Automatic Speech Recognition (ASR) models using k2-fsa and lhotse, primarily for ASR researchers and practitioners.
  • mediumreadme#2
    Clarify the README's opening sentence to immediately state its core purpose

    Why:

    CURRENT
    The icefall project contains speech-related recipes for various datasets using k2-fsa and lhotse.
    COPY-PASTE FIX
    The icefall project provides reproducible recipes and examples for developing and training state-of-the-art Automatic Speech Recognition (ASR) models for various datasets using k2-fsa and lhotse.

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 k2-fsa/icefall
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Kaldi
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Kaldi · recommended 2×
  2. Whisper · recommended 1×
  3. ESPnet · recommended 1×
  4. NeMo · recommended 1×
  5. SpeechBrain · recommended 1×
  • CATEGORY QUERY
    Looking for open-source automatic speech recognition models and training recipes for various datasets.
    you: not recommended
    AI recommended (in order):
    1. Whisper
    2. ESPnet
    3. NeMo
    4. SpeechBrain
    5. Kaldi
    6. fairseq

    AI recommended 6 alternatives but never named k2-fsa/icefall. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are good Python frameworks for developing and deploying speech recognition systems?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. SpeechRecognition
    3. PyTorch
    4. TensorFlow
    5. torchaudio
    6. tf.audio
    7. Kaldi
    8. pykaldi
    9. DeepSpeech

    AI recommended 9 alternatives but never named k2-fsa/icefall. 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 k2-fsa/icefall?
    pass
    AI named k2-fsa/icefall explicitly

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

  • If a team adopts k2-fsa/icefall in production, what risks or prerequisites should they evaluate first?
    pass
    AI named k2-fsa/icefall 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 k2-fsa/icefall solve, and who is the primary audience?
    pass
    AI named k2-fsa/icefall 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 k2-fsa/icefall. 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/k2-fsa/icefall.svg)](https://repogeo.com/en/r/k2-fsa/icefall)
HTML
<a href="https://repogeo.com/en/r/k2-fsa/icefall"><img src="https://repogeo.com/badge/k2-fsa/icefall.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

k2-fsa/icefall — 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