RRepoGEO

REPOGEO REPORT · LITE

QwenLM/Qwen3-ASR-Toolkit

Default branch main · commit c1976f5f · scanned 6/2/2026, 9:31:42 PM

GitHub: 964 stars · 93 forks

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 QwenLM/Qwen3-ASR-Toolkit, 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
  • highreadme#1
    Reposition the README's opening to emphasize API toolkit functionality

    Why:

    CURRENT
    Qwen3-ASR is now **open-sourced** 🎉🎉🎉. Welcome to visit the **GitHub** and **blog** for more information. The open-source model offers functionality comparable to the API and supports free, fast local deployment. Qwen3-ASR open-source model includes two powerful **all-in-one speech recognition models (0.6B/1.7B)** that support language identification and ASR for **52 languages and dialects**, as well as a novel non-autoregressive speech forced-alignment model that can align text–speech pairs in 11 languages. Its powerful performance is sufficient to deliver highly compelling speech-to-text transcription capabilities. Welcome to use it!
    COPY-PASTE FIX
    This is the official Python toolkit for the Qwen3-ASR API, designed to overcome common API limitations for robust, high-throughput speech recognition. It enables parallel processing of long audio/video files, breaking the typical 3-minute duration constraint. While the Qwen3-ASR model itself is now open-sourced, this toolkit specifically enhances the experience of using the Qwen3-ASR API.
  • mediumhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://github.com/QwenLM/Qwen3-ASR

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 QwenLM/Qwen3-ASR-Toolkit
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
AWS Transcribe
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. AWS Transcribe · recommended 2×
  2. NVIDIA/NeMo · recommended 1×
  3. openai/whisper · recommended 1×
  4. guillaumekln/faster-whisper · recommended 1×
  5. sanchit-gandhi/whisper-jax · recommended 1×
  • CATEGORY QUERY
    Python tool for high-throughput speech recognition of extended audio content?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA NeMo (NVIDIA/NeMo)
    2. OpenAI Whisper (openai/whisper)
    3. faster-whisper (guillaumekln/faster-whisper)
    4. whisper-jax (sanchit-gandhi/whisper-jax)
    5. Google Cloud Speech-to-Text API
    6. AssemblyAI API
    7. AWS Transcribe
    8. Mozilla DeepSpeech (mozilla/DeepSpeech)

    AI recommended 8 alternatives but never named QwenLM/Qwen3-ASR-Toolkit. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a versatile ASR solution supporting many languages and dialects.
    you: not recommended
    AI recommended (in order):
    1. Google Cloud Speech-to-Text
    2. Azure Cognitive Services Speech
    3. AWS Transcribe
    4. OpenAI Whisper
    5. Deepgram
    6. AssemblyAI

    AI recommended 6 alternatives but never named QwenLM/Qwen3-ASR-Toolkit. 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 QwenLM/Qwen3-ASR-Toolkit?
    pass
    AI named QwenLM/Qwen3-ASR-Toolkit explicitly

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

  • If a team adopts QwenLM/Qwen3-ASR-Toolkit in production, what risks or prerequisites should they evaluate first?
    pass
    AI named QwenLM/Qwen3-ASR-Toolkit 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 QwenLM/Qwen3-ASR-Toolkit solve, and who is the primary audience?
    pass
    AI named QwenLM/Qwen3-ASR-Toolkit explicitly

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

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  • Brand-free category queries5 vs 2 in Lite
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