REPOGEO REPORT · LITE

hirofumi0810/neural_sp

Default branch master · commit b91877c6 · scanned 6/15/2026, 2:18:09 AM

GitHub: 594 stars · 134 forks

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 hirofumi0810/neural_sp, 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

Add a concise, differentiating introductory statement to README

Why:

CURRENT

# NeuralSP: Neural network based Speech Processing

COPY-PASTE FIX

# NeuralSP: End-to-end Automatic Speech Recognition and Language Modeling Toolkit

NeuralSP is a PyTorch-based research toolkit focused on advanced end-to-end Automatic Speech Recognition (ASR) and Language Modeling (LM). It provides a flexible framework for experimenting with state-of-the-art architectures like Transformers, Conformer, and RNN-Transducers, with strong integration for Kaldi-based feature extraction and data preparation, specifically designed for researchers and developers in speech technology.

mediumhomepage#2
Add a homepage URL to the repository's About section
Why:
COPY-PASTE FIX
```
A valid URL pointing to the project's official website, documentation, or a relevant resource.
```

mediumtopics#3

Add specific topics for Kaldi integration and end-to-end ASR

Why:

CURRENT

asr, attention, attention-mechanism, automatic-speech-recognition, ctc, language-model, language-modeling, pytorch, rnn-transducer, seq2seq, sequence-to-sequence, speech, speech-recognition, streaming, transformer, transformer-xl

COPY-PASTE FIX

asr, attention, attention-mechanism, automatic-speech-recognition, ctc, language-model, language-modeling, pytorch, rnn-transducer, seq2seq, sequence-to-sequence, speech, speech-recognition, streaming, transformer, transformer-xl, kaldi-integration, end-to-end-asr

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 hirofumi0810/neural_sp

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Hugging Face Transformers

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Hugging Face Transformers · recommended 1×
PyTorch-Kaldi · recommended 1×
NeMo · recommended 1×
SpeechBrain · recommended 1×
torchaudio · recommended 1×

CATEGORY QUERY
How to build an end-to-end automatic speech recognition system using PyTorch?
you: not recommended
AI recommended (in order):
1. Hugging Face Transformers
2. PyTorch-Kaldi
3. NeMo
4. SpeechBrain
5. torchaudio
AI recommended 5 alternatives but never named hirofumi0810/neural_sp. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Seeking a robust PyTorch library for streaming automatic speech recognition with transformer models.
you: not recommended
AI recommended (in order):
1. NVIDIA NeMo (NVIDIA/NeMo)
2. SpeechBrain (SpeechBrain/SpeechBrain)
3. ESPnet (espnet/espnet)
4. transformers (huggingface/transformers)
5. accelerate (huggingface/accelerate)
6. torchaudio (pytorch/audio)
7. OpenAI Whisper (openai/whisper)
AI recommended 7 alternatives but never named hirofumi0810/neural_sp. 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 hirofumi0810/neural_sp?
pass
AI did not name hirofumi0810/neural_sp — 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 hirofumi0810/neural_sp in production, what risks or prerequisites should they evaluate first?
pass
AI named hirofumi0810/neural_sp 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 hirofumi0810/neural_sp solve, and who is the primary audience?
pass
AI named hirofumi0810/neural_sp explicitly
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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hirofumi0810/neural_sp — 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