데이터

audiogear

Annotates speech and TTS datasets with audio quality metrics, transcriptions, speaker labels, and filtering outputs, then generates self-contained HTML reports for dataset QA.

★ 14 GitHub Stars
94 품질 점수
1 파일

이 Skill 소개

Annotate speech/TTS datasets with per-clip quality metrics (MOS, SNR, STOI, pitch, bandwidth, speaking rate, WER/CER, gender/emotion), multi-ASR consensus transcription and speaker labeling using the audiogear pipeline; then filter clips by quality and build listenable HTML QA reports. Use when asked to compute audio dataset metrics, build extended_metadata.csv, transcribe unlabeled audio, select clean TTS training data, or QA a speech dataset.

사용 사례

  • Compute per-clip speech quality metrics
  • Transcribe datasets without labels
  • Select clean TTS training clips
  • Assign speaker labels and consensus transcripts
  • Generate audio dataset QA reports

기능

  • GPU- and CPU-based metric pipelines configured with Hydra
  • Multi-ASR consensus transcription and speaker labeling
  • CSV shard processing with resume and merge support
  • Quality filtering with drop reasons
  • Self-contained HTML reports with playable audio

요구 사항

  • Python 3.10–3.12
  • uv
  • espeak-ng for SpeakingRateMetric
  • HF_TOKEN for gated pyannote models

지원 Agent

Claude Code

Skill 파일

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