技能创建器
skill-creator
创建、改进和评估自定义技能,把专业流程转化为可重复使用的 AI 能力。
audio-analysis【数据与研究】Use PaddleSpeech audio classification, speaker vector/verification, keyword spotting, SSL vector, audio augmentation, and audio-search planning workflows.
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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.
| User goal | Use |
|---|---|
| Classify environmental/audio events | paddlespeech cls; read references/cli-and-api.md |
| Extract speaker embedding | paddlespeech vector --task spk |
| Score whether two clips match | paddlespeech vector --task score; use scripts/build_vector_job.py for job files |
| Detect a keyword | paddlespeech kws --threshold ... |
| Extract SSL representation | route to ../speech-to-text/SKILL.md for paddlespeech ssl --task vector |
| Build Milvus/MySQL audio retrieval app | read references/audio-workflows.md, then route deployment/service parts to ../deployment-serving/SKILL.md |
Validate WAV format/sample rate with ../speech-to-text/scripts/validate_audio_inputs.py when possible.
Use parser/help checks before model execution:
paddlespeech cls --help
paddlespeech vector --help
paddlespeech kws --help
Confirm model downloads before running default inference.
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/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.