RRepoGEO

REPOGEO REPORT · LITE

NVIDIA/audio-flamingo

Default branch main · commit f4579633 · scanned 5/22/2026, 1:38:20 PM

GitHub: 1,126 stars · 95 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
35 /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
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 NVIDIA/audio-flamingo, 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 to clarify the repo's purpose

    Why:

    CURRENT
    In this repo, we present the **Audio Flamingo** series of advanced audio understanding Language models:
    COPY-PASTE FIX
    This repository provides the official PyTorch implementation for the **Audio Flamingo** series of advanced audio understanding language models, enabling researchers and developers to build and experiment with state-of-the-art audio-language capabilities.
  • highlicense#2
    Add a LICENSE file to the repository root

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root with the Apache-2.0 License text, or the appropriate open-source license for this project.
  • mediumabout#3
    Refine the repository's 'About' description

    Why:

    CURRENT
    PyTorch implementation of Audio Flamingo: Series of Advanced Audio Understanding Language Models
    COPY-PASTE FIX
    Official PyTorch implementation for the Audio Flamingo series, enabling advanced audio understanding, captioning, and question answering with large language models.

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 NVIDIA/audio-flamingo
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Transformers
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers · recommended 1×
  2. PyTorch · recommended 1×
  3. torchaudio · recommended 1×
  4. PyTorch Lightning · recommended 1×
  5. TensorFlow · recommended 1×
  • CATEGORY QUERY
    How to implement an audio-language model for complex sound event understanding?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. PyTorch
    3. torchaudio
    4. PyTorch Lightning
    5. TensorFlow
    6. TensorFlow Audio
    7. Keras
    8. OpenAI CLIP
    9. SpeechBrain
    10. fairseq

    AI recommended 10 alternatives but never named NVIDIA/audio-flamingo. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a PyTorch library for generating descriptive audio captions and answering questions.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. Audio Captioning Toolkit (ACT) (audio-captioning/toolkit)
    3. fairseq (facebookresearch/fairseq)
    4. SpeechBrain (speechbrain/speechbrain)
    5. OpenAI Whisper (openai/whisper)

    AI recommended 5 alternatives but never named NVIDIA/audio-flamingo. 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 NVIDIA/audio-flamingo?
    pass
    AI named NVIDIA/audio-flamingo explicitly

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

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

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

Embed your GEO score

Drop this badge into the README of NVIDIA/audio-flamingo. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/NVIDIA/audio-flamingo.svg)](https://repogeo.com/en/r/NVIDIA/audio-flamingo)
HTML
<a href="https://repogeo.com/en/r/NVIDIA/audio-flamingo"><img src="https://repogeo.com/badge/NVIDIA/audio-flamingo.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

NVIDIA/audio-flamingo — 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