RRepoGEO

REPOGEO REPORT · LITE

lucasnewman/f5-tts-mlx

Default branch main · commit 85d015f6 · scanned 6/11/2026, 6:07:17 AM

GitHub: 634 stars · 64 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 lucasnewman/f5-tts-mlx, 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 opening to highlight MLX/Apple Silicon value

    Why:

    CURRENT
    # F5 TTS — MLX

Implementation of F5-TTS, with the MLX framework.

F5 TTS is a non-autoregressive, zero-shot text-to-speech system using a flow-matching mel spectrogram generator with a diffusion transformer (DiT).
    COPY-PASTE FIX
    # F5 TTS — MLX: Zero-Shot Text-to-Speech for Apple Silicon

This repository provides an efficient, non-autoregressive, zero-shot text-to-speech (TTS) system, F5-TTS, implemented with the MLX framework for optimal performance on Apple Silicon. It enables rapid, high-quality speech generation directly on your M-series Mac.
  • mediumtopics#2
    Add more specific topics for Apple Silicon and zero-shot capability

    Why:

    CURRENT
    diffusion-transformer, flow-matching, mlx, text-to-speech, tts
    COPY-PASTE FIX
    diffusion-transformer, flow-matching, mlx, text-to-speech, tts, apple-silicon, m-series, zero-shot
  • mediumhomepage#3
    Add repository URL as homepage

    Why:

    COPY-PASTE FIX
    https://github.com/lucasnewman/f5-tts-mlx

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 lucasnewman/f5-tts-mlx
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenAI TTS
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenAI TTS · recommended 1×
  2. ElevenLabs · recommended 1×
  3. Apple's Built-in Speech Synthesis · recommended 1×
  4. Coqui TTS · recommended 1×
  5. Bark · recommended 1×
  • CATEGORY QUERY
    How to generate high-quality, zero-shot text-to-speech quickly on Apple Silicon?
    you: not recommended
    AI recommended (in order):
    1. OpenAI TTS
    2. ElevenLabs
    3. Apple's Built-in Speech Synthesis
    4. Coqui TTS
    5. Bark
    6. Meta Voicebox

    AI recommended 6 alternatives but never named lucasnewman/f5-tts-mlx. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a non-autoregressive text-to-speech library using diffusion transformers with MLX.
    you: not recommended
    AI recommended (in order):
    1. MLX (apple/mlx)
    2. Hugging Face Transformers (huggingface/transformers)
    3. Diffusers (huggingface/diffusers)
    4. PyTorch (pytorch/pytorch)
    5. TensorFlow (tensorflow/tensorflow)
    6. Coqui TTS (coqui-ai/TTS)
    7. NVIDIA NeMo (NVIDIA/NeMo)
    8. TensorFlow TTS (TensorFlow/TTS)

    AI recommended 8 alternatives but never named lucasnewman/f5-tts-mlx. 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 lucasnewman/f5-tts-mlx?
    pass
    AI did not name lucasnewman/f5-tts-mlx — 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 lucasnewman/f5-tts-mlx in production, what risks or prerequisites should they evaluate first?
    pass
    AI named lucasnewman/f5-tts-mlx 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 lucasnewman/f5-tts-mlx solve, and who is the primary audience?
    pass
    AI named lucasnewman/f5-tts-mlx explicitly

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

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lucasnewman/f5-tts-mlx — 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