RRepoGEO

REPOGEO REPORT · LITE

facebookresearch/omnilingual-asr

Default branch main · commit 81f51e22 · scanned 5/23/2026, 6:06:55 AM

GitHub: 2,812 stars · 251 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
22 /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
1 / 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 facebookresearch/omnilingual-asr, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • mediumreadme#1
    Add a 'License' section to the README

    Why:

    COPY-PASTE FIX
    ## License

This project's licensing details are provided in the LICENSE file. Please review the LICENSE file for the specific terms and conditions that apply to this software.
  • mediumreadme#2
    Add a 'Comparison to Alternatives' section in the README

    Why:

    COPY-PASTE FIX
    ## Comparison to Alternatives

Unlike many existing ASR systems that require extensive data or separate models for each language, Omnilingual ASR offers a single, unified model capable of handling over 1,600 diverse languages, excelling particularly in low-resource settings by enabling new language support with minimal paired examples. This approach significantly differentiates it from systems like Whisper, Wav2Vec2, or DeepSpeech by focusing on extreme multilingualism and data efficiency for new language integration.

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 facebookresearch/omnilingual-asr
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Mozilla Common Voice
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Mozilla Common Voice · recommended 2×
  2. mozilla/DeepSpeech · recommended 2×
  3. Wav2Vec2 · recommended 2×
  4. huggingface/transformers · recommended 2×
  5. openai/whisper · recommended 2×
  • CATEGORY QUERY
    What open-source speech recognition system supports many low-resource languages globally?
    you: not recommended
    AI recommended (in order):
    1. Mozilla Common Voice
    2. DeepSpeech (mozilla/DeepSpeech)
    3. Wav2Vec2
    4. Hugging Face Transformers (huggingface/transformers)
    5. Whisper (openai/whisper)
    6. Kaldi (kaldi-asr/kaldi)
    7. ESPnet (espnet/espnet)

    AI recommended 7 alternatives but never named facebookresearch/omnilingual-asr. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I implement ASR for a new language with minimal data and effort?
    you: not recommended
    AI recommended (in order):
    1. Mozilla Common Voice
    2. DeepSpeech (mozilla/DeepSpeech)
    3. Coqui STT (coqui-ai/STT)
    4. Google Cloud Speech-to-Text
    5. Hugging Face Transformers (huggingface/transformers)
    6. Wav2Vec2
    7. Whisper (openai/whisper)
    8. Kaldi (kaldi-asr/kaldi)
    9. SpeechBrain (speechbrain/speechbrain)

    AI recommended 9 alternatives but never named facebookresearch/omnilingual-asr. 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 facebookresearch/omnilingual-asr?
    pass
    AI did not name facebookresearch/omnilingual-asr — 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 facebookresearch/omnilingual-asr in production, what risks or prerequisites should they evaluate first?
    pass
    AI named facebookresearch/omnilingual-asr 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 facebookresearch/omnilingual-asr solve, and who is the primary audience?
    pass
    AI did not name facebookresearch/omnilingual-asr — 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?

Embed your GEO score

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

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MARKDOWN (README)
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HTML
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facebookresearch/omnilingual-asr — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite