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audio-analysis

audio-analysis

Use PaddleSpeech audio classification, speaker vector/verification, keyword spotting, SSL vector, audio augmentation, and audio-search planning workflows.

Independent GitHub snapshot · Automated classification · Original documentation governs

Documentation de la Skill

GitHub

name: audio-analysis description: "Use PaddleSpeech audio classification, speaker vector/verification, keyword spotting, SSL vector, audio augmentation, and audio-search planning workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Audio Analysis

Use this sub-skill for PaddleSpeech audio classification (cls), speaker embeddings and scoring (vector), keyword spotting (kws), SSL vector extraction, audio augmentation utility checks, ESC-50/VoxCeleb/HeySnips recipe planning, and audio search orientation.

Choose the Workflow

User goalUse
Classify environmental/audio eventspaddlespeech cls; read references/cli-and-api.md
Extract speaker embeddingpaddlespeech vector --task spk
Score whether two clips matchpaddlespeech vector --task score; use scripts/build_vector_job.py for job files
Detect a keywordpaddlespeech kws --threshold ...
Extract SSL representationroute to ../speech-to-text/SKILL.md for paddlespeech ssl --task vector
Build Milvus/MySQL audio retrieval appread references/audio-workflows.md, then route deployment/service parts to ../deployment-serving/SKILL.md

Safe Workflow

  1. Validate WAV format/sample rate with ../speech-to-text/scripts/validate_audio_inputs.py when possible.

  2. Use parser/help checks before model execution:

    paddlespeech cls --help
    paddlespeech vector --help
    paddlespeech kws --help
    
  3. Confirm model downloads before running default inference.

  4. For vector score, prepare pairs explicitly:

    python scripts/build_vector_job.py --output pairs.job --pair demo:enroll.wav:test.wav
    paddlespeech vector --task score --input pairs.job
    

References and Helper

  • references/cli-and-api.md covers CLS/vector/KWS commands and executor behavior.
  • references/audio-workflows.md covers ESC-50, VoxCeleb, HeySnips, augmentation, and audio-search boundaries.
  • references/troubleshooting.md covers sample-rate, top-k, vector pair, KWS threshold, cache, and service-app issues.
  • scripts/build_vector_job.py builds vector embedding or score job files.

Do Not Do by Default

  • Do not run ESC-50, VoxCeleb, HeySnips, or audio-search recipes without approval; they require datasets, services, downloads, or long training.
  • Do not start Milvus/MySQL/Docker Compose for audio search unless the user requested that service workflow.
  • Do not treat a vector augmentation unit test as proof that pretrained speaker recognition model inference has run.
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