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效率与协作 · SKILL.md

speechscore-metrics

speechscore-metrics

【效率与协作】Choose, validate, and run SpeechScore objective metrics for intrusive and non-intrusive speech quality assessment.

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技能说明

GitHub

name: speechscore-metrics description: "Choose, validate, and run SpeechScore objective metrics for intrusive and non-intrusive speech quality assessment." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

SpeechScore Metrics

Use this sub-skill when a task asks for objective speech quality assessment with SpeechScore: selecting metrics, scoring one enhanced/degraded file against a clean reference, scoring matched test/reference directories, scoring reference-free MOS-style metrics, tuning window/score_rate/return_mean, or interpreting nested result dictionaries.

Start here:

Operating rules:

  1. Use this sub-skill only for SpeechScore objective metric selection and scoring.
  2. Route audio enhancement, separation, super-resolution, or target-speaker extraction generation to ../clearvoice-inference/ before scoring.
  3. Route benchmark list creation, dataset manifests, training launchers, or training/evaluation data preparation to ../training-and-data-prep/.
  4. Decide reference_path from the metric catalog, not from each source class's intrusive attribute; that attribute is inconsistent in this snapshot.
  5. For dry runs, validate metric names, whether any selected metric requires a reference, and matching directory basenames before reading audio.
  6. For real source-layout runs from outside the SpeechScore component directory, pass --speechscore-dir <speechscore_component_dir> so the helper can import speechscore.py and resolve metric assets.
  7. Prefer window=None for direct source API calls. Use the bundled helper when windowed scoring or explicit non-fixed metric resampling is required, because the inspected source window branch has a maxlen bug and the direct score_rate argument is not reliably honored for non-fixed metrics.
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