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Voice Scale — Audio QA Pipeline

Automated quality assurance pipeline for the Voice Scale crowdsourced vegetable-name dataset (English, Tamil, Sinhala). Runs 14 checks across four layers before any clip is accepted into the final dataset.


Pipeline overview

Raw WAV clips
     │
     ▼
Layer 1 — Signal Checks        T-01 duration & silence
  (runs on every submission)   T-02 clipping & distortion
                               T-03 SNR
                               T-04 sample rate & format
     │
     ▼
Layer 2 — Transcription        T-05 Whisper (English)
  (catches wrong words)        T-06 Whisper (Tamil)
                               T-07 language ID
                               T-08 duplicate detection
     │
     ▼
Layer 3 — Embedding Analysis   T-09 wav2vec2 extractor  ← must run first
  (catches semantic outliers)  T-10 per-class outlier detector
                               T-11 cross-language consistency
                               T-12 UMAP visualisation
     │
     ▼
Layer 4 — Reporting            T-13 aggregator & HTML dashboard
                               T-14 speaker-level fairness audit

Every task in layers 1–3 writes a CSV with columns clip_id, verdict, reason, score. T-13 merges them all.


Task assignments

Task Title Owner
T-01 Duration & silence validator Praveen
T-02 Clipping & distortion detector Ashini
T-03 SNR estimator Chanupa
T-04 Sample rate & format validator Didula
T-05 Whisper transcription runner (English) Sheron
T-06 Whisper transcription runner (Tamil) Thila
T-07 Language ID verifier Kusal
T-08 Duplicate & near-duplicate detector Lasana
T-09 wav2vec2 embedding extractor Sahe
T-10 Per-class outlier detector Teshan
T-11 Cross-language consistency checker Yohan
T-12 UMAP visualisation generator Ifaz
T-13 QA results aggregator & dashboard Thisen
T-14 Speaker-level fairness auditor Thisen

Key dependencies

  • T-09 must complete before T-10, T-11, T-12 (they all read its .npy output)
  • T-13 and T-14 must run after all layer 1–3 tasks are complete

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