RRepoGEO

REPOGEO REPORT · LITE

mravanelli/pytorch-kaldi

Default branch master · commit 9773257d · scanned 5/22/2026, 2:32:41 AM

GitHub: 2,398 stars · 444 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
53 /100
Needs work
Category recall
1 / 2
Avg rank #4.0 when recommended
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 mravanelli/pytorch-kaldi, 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 and opening paragraph to highlight integration

    Why:

    CURRENT
    # The PyTorch-Kaldi Speech Recognition Toolkit

PyTorch-Kaldi is an open-source repository for developing state-of-the-art DNN/HMM speech recognition systems. The DNN part is managed by PyTorch, while feature extraction, label computation, and decoding are performed with the Kaldi toolkit.
    COPY-PASTE FIX
    # PyTorch-Kaldi: A Toolkit for Seamless PyTorch DNN Integration with Kaldi ASR

PyTorch-Kaldi is an open-source toolkit designed to bridge the power of PyTorch deep learning models with Kaldi's robust speech processing pipeline. It enables researchers and developers to build state-of-the-art DNN/HMM speech recognition systems by managing the DNN part with PyTorch, while leveraging Kaldi for efficient feature extraction, label computation, and decoding.
  • highlicense#2
    Add LICENSE file and update About section license

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the root of the repository containing the full text of the Creative Commons Attribution 4.0 International license. Also, update the repository's 'About' section to explicitly state 'Creative Commons Attribution 4.0 International'.
  • mediumcomparison#3
    Add a comparison section to the README

    Why:

    COPY-PASTE FIX
    Add a new section to the README titled 'Why PyTorch-Kaldi? (Comparison with Alternatives)' or 'Key Differentiators' that explains how PyTorch-Kaldi uniquely integrates PyTorch and Kaldi, contrasting it with other ASR toolkits or integration approaches (e.g., PyKaldi, native Kaldi DNNs, ESPnet, SpeechBrain).

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
1 / 2
50% of queries surface mravanelli/pytorch-kaldi
Avg rank
#4.0
Lower is better. #1 = top recommendation.
Share of voice
8%
Of all named tools, what % are you?
Top rival
ESPnet
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. ESPnet · recommended 1×
  2. Kaldi · recommended 1×
  3. NeMo · recommended 1×
  4. TensorFlow ASR · recommended 1×
  5. SpeechBrain · recommended 1×
  • CATEGORY QUERY
    How can I develop state-of-the-art hybrid speech recognition systems with deep neural networks?
    you: #4
    AI recommended (in order):
    1. ESPnet
    2. Kaldi
    3. NeMo
    4. PyTorch-Kaldi ← you
    5. TensorFlow ASR
    6. SpeechBrain
    Show full AI answer
  • CATEGORY QUERY
    What tools integrate PyTorch deep learning models with Kaldi for advanced speech processing?
    you: not recommended
    AI recommended (in order):
    1. PyKaldi (pykaldi/pykaldi)
    2. Kaldi (nnet3/chain models) (kaldi-asr/kaldi)
    3. ESPnet (espnet/espnet)
    4. SpeechBrain (speechbrain/speechbrain)
    5. kaldiio (pykaldi/kaldiio)
    6. K2 (k2-fsa/k2)

    AI recommended 6 alternatives but never named mravanelli/pytorch-kaldi. 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 mravanelli/pytorch-kaldi?
    pass
    AI did not name mravanelli/pytorch-kaldi — 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?

  • If a team adopts mravanelli/pytorch-kaldi in production, what risks or prerequisites should they evaluate first?
    pass
    AI named mravanelli/pytorch-kaldi 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 mravanelli/pytorch-kaldi solve, and who is the primary audience?
    pass
    AI named mravanelli/pytorch-kaldi explicitly

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

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mravanelli/pytorch-kaldi — 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