RRepoGEO

REPOGEO REPORT · LITE

spring-media/TransformerTTS

Default branch main · commit 36380554 · scanned 5/23/2026, 7:47:06 PM

GitHub: 1,162 stars · 221 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
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 spring-media/TransformerTTS, 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 key differentiators to the README's opening

    Why:

    CURRENT
    <h2 align="center">
<p>A Text-to-Speech Transformer in TensorFlow 2</p>
</h2>

Implementation of a non-autoregressive Transformer based neural network for Text-to-Speech (TTS). <br>
This repo is based, among others, on the following papers:
- Neural Speech Synthesis with Transformer Network
- FastSpeech: Fast, Robust and Controllable Text to Speech
- FastSpeech 2: Fast and High-Quality End-to-End Text to Speech
- FastPitch: Parallel Text-to-speech with Pitch Prediction
    COPY-PASTE FIX
    <h2 align="center">
<p>A Fast, Robust, and Controllable Non-Autoregressive Text-to-Speech Transformer in TensorFlow 2</p>
</h2>

This repository provides a TensorFlow 2 implementation of a non-autoregressive Transformer-based neural network for Text-to-Speech (TTS), inspired by models like FastSpeech, FastSpeech 2, and FastPitch. Being non-autoregressive, this model offers robustness, speed, and controllable pitch, making it ideal for high-quality speech synthesis.
  • mediumtopics#2
    Add specific keywords to repository topics

    Why:

    CURRENT
    axelspringerai, deep-learning, python, tensorflow, text-to-speech, tts
    COPY-PASTE FIX
    axelspringerai, deep-learning, python, tensorflow, text-to-speech, tts, non-autoregressive-tts, fastspeech, fastpitch, speech-synthesis, controllable-pitch
  • lowlicense#3
    Clarify the project's license in the README

    Why:

    COPY-PASTE FIX
    ## License
This project's licensing terms are detailed in the LICENSE file. Please refer to it for specific conditions regarding use, distribution, and modification.

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 spring-media/TransformerTTS
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
espnet/espnet
Recommended in 3 of 2 queries
COMPETITOR LEADERBOARD
  1. espnet/espnet · recommended 3×
  2. NVIDIA/NeMo · recommended 3×
  3. Tacotron 2 · recommended 2×
  4. VITS · recommended 2×
  5. FastSpeech 2 · recommended 2×
  • CATEGORY QUERY
    How to implement a fast and robust text-to-speech system using deep learning?
    you: not recommended
    AI recommended (in order):
    1. Tacotron 2
    2. WaveGlow
    3. Hifi-GAN
    4. VITS
    5. FastSpeech 2
    6. FastSpeech 2s
    7. Glow-TTS
    8. YourTTS

    AI recommended 8 alternatives but never named spring-media/TransformerTTS. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a non-autoregressive text-to-speech model with controllable pitch for Python.
    you: not recommended
    AI recommended (in order):
    1. FastSpeech 2
    2. espnet (espnet/espnet)
    3. NVIDIA NeMo (NVIDIA/NeMo)
    4. Glow-TTS
    5. NVIDIA NeMo (NVIDIA/NeMo)
    6. VITS
    7. espnet (espnet/espnet)
    8. Coqui TTS (coqui-ai/TTS)
    9. Grad-TTS (huawei-noah/Speech-Backbones)
    10. espnet (espnet/espnet)
    11. Tacotron 2
    12. WaveGlow (NVIDIA/waveglow)
    13. HiFi-GAN (jik876/hifi-gan)
    14. NVIDIA NeMo (NVIDIA/NeMo)

    AI recommended 14 alternatives but never named spring-media/TransformerTTS. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • 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 spring-media/TransformerTTS?
    pass
    AI named spring-media/TransformerTTS explicitly

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

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

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

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spring-media/TransformerTTS — 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