RRepoGEO

REPOGEO REPORT · LITE

buriburisuri/speech-to-text-wavenet

Default branch master · commit 7b0d2e92 · scanned 6/29/2026, 7:23:56 PM

GitHub: 4,005 stars · 789 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 buriburisuri/speech-to-text-wavenet, 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 the README's opening paragraph to clarify project type and audience

    Why:

    CURRENT
    A tensorflow implementation of speech recognition based on DeepMind's WaveNet: A Generative Model for Raw Audio. (Hereafter the Paper)
    COPY-PASTE FIX
    This repository presents an open-source, end-to-end TensorFlow implementation of English speech recognition, specifically adapting DeepMind's WaveNet (a generative model for raw audio) for sentence-level speech-to-text tasks. It serves as a practical example and research base for developers and researchers exploring WaveNet's application beyond text-to-speech.
  • hightopics#2
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    speech-to-text, wavenet, tensorflow, speech-recognition, deep-learning, audio-processing, machine-learning, generative-models, research-project
  • mediumreadme#3
    Highlight the project's unique contribution and problem solved in the README

    Why:

    CURRENT
    Although ibab and tomlepaine have already implemented WaveNet with tensorflow, they did not implement speech recognition. That's why we decided to implement it ourselves.
    COPY-PASTE FIX
    Unlike other WaveNet implementations in TensorFlow that focus on audio generation, this project specifically tackles end-to-end speech recognition, filling a gap for researchers interested in applying WaveNet to speech-to-text.

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 buriburisuri/speech-to-text-wavenet
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
tensorflow/tensorflow
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. tensorflow/tensorflow · recommended 2×
  2. TensorSpeech/TensorFlowASR · recommended 1×
  3. NVIDIA/NeMo · recommended 1×
  4. keras-team/keras · recommended 1×
  5. tensorflow/hub · recommended 1×
  • CATEGORY QUERY
    How to implement end-to-end English speech recognition using a TensorFlow model?
    you: not recommended
    AI recommended (in order):
    1. TensorFlow ASR (TensorSpeech/TensorFlowASR)
    2. NeMo (NVIDIA/NeMo)
    3. TensorFlow Lite (tensorflow/tensorflow)
    4. Keras (keras-team/keras)
    5. `tf.data` API (tensorflow/tensorflow)
    6. TensorFlow Hub (tensorflow/hub)
    7. TensorFlow Addons (tensorflow/addons)

    AI recommended 7 alternatives but never named buriburisuri/speech-to-text-wavenet. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a robust speech-to-text solution based on generative audio models like WaveNet.
    you: not recommended
    AI recommended (in order):
    1. Google Cloud Speech-to-Text
    2. AWS Transcribe
    3. AssemblyAI
    4. Deepgram
    5. Microsoft Azure Speech-to-Text
    6. OpenAI Whisper

    AI recommended 6 alternatives but never named buriburisuri/speech-to-text-wavenet. 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 buriburisuri/speech-to-text-wavenet?
    pass
    AI named buriburisuri/speech-to-text-wavenet explicitly

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

  • If a team adopts buriburisuri/speech-to-text-wavenet in production, what risks or prerequisites should they evaluate first?
    pass
    AI named buriburisuri/speech-to-text-wavenet 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 buriburisuri/speech-to-text-wavenet solve, and who is the primary audience?
    pass
    AI did not name buriburisuri/speech-to-text-wavenet — 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?

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MARKDOWN (README)
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buriburisuri/speech-to-text-wavenet — 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