README.md

metadata

license: apache-2.0
tags:
  - automatic-speech-recognition
  - librispeech
  - error-analysis

Worst-100 test-clean clips — audio, transcripts, and the vocabulary finding

The 100 LibriSpeech test-clean clips where the block-4 production model (4.60% WER) made the most word errors — with audio embedded so the failures can be listened to, plus the model's transcript next to the reference for each clip.

The finding this dataset produced

47% of the word errors in these clips are on words that never appeared in the 30-hour training vocabulary at all (20,066 distinct words seen). The model was not mishearing these words — it had never met them. Repeat offenders are book-specific names (rodolfo, montrose, boolooroo). Full breakdown in worst100_oov_analysis.json.

This measurement is the primary justification for the 100-hour training phase: a coverage problem calls for coverage. See Diffusion-ASR/pipeline-code-and-docs for the full recipe and evidence record.

How to read this dataset (verified 2026-08-11)

• Ranking basis: clips are ranked by absolute word-error count on Whisper-normalized text — not by WER percentage. A long clip with 20 errors at 23% WER outranks a short clip at 60%.
• Tie band: the rank-100 cutoff is 4 errors, and 66 clips tie at exactly 4 — which of them appear here is arbitrary among equals.
• substitution_pairs are raw alignment output, not claimed confusions: across a garbled stretch, positional pairing produces items like have→all that carry no meaning individually. Use error_classes for interpretation.
• Some references contain genuine dialect spellings (e.g. allers) — that is the LibriSpeech ground truth, not a transcription error.
• Integrity verification: all 100 references byte-match the official test-clean transcripts; all 100 hypotheses byte-match the model's recorded decode outputs; stored error counts reproduce exactly under the stated normalization.