[Alex-Wengg]

Closes #899.

#900 fixed #899's bug. The issue stayed open for one question: should #702's similarity floors (0.30 / 0.50) become the sliding-window streaming default? Measured on main b3eda84, streaming SlidingWindowAsrManager with the CLI's default --streaming config:

	Floors off (default)	Floors on
Earnings-22 keyword clips (772): TP/FP/FN	1220/4/54	1082/2/192
Earnings-22: WER	26.10%	19.68%
FLEURS en_us (200) with an unrelated term list: false term insertions	332	40

Floors on lose 138 real brand and personal names (recall 95.8% → 84.9%) and gain none, but they stop the rescue pass firing on unrelated speech. Batch shows the same trade-off. The default stays off. This PR documents the floors in CustomVocabulary.md as an opt-in, with these numbers and the VocabularyRescorer.Config to enable them. Since #1100, floors-off false insertions on the FLEURS set are 220.

Docs only.

🤖 Generated with Claude Code

[github-actions]

Speaker Diarization Benchmark Results

Speaker Diarization Performance

Evaluating "who spoke when" detection accuracy

Metric	Value	Target	Status	Description
DER	15.1%	<30%	✅	Diarization Error Rate (lower is better)
JER	24.9%	<25%	✅	Jaccard Error Rate
RTFx	17.16x	>1.0x	✅	Real-Time Factor (higher is faster)

Diarization Pipeline Timing Breakdown

Time spent in each stage of speaker diarization

Stage	Time (s)	%	Description
Model Download	12.590	20.6	Fetching diarization models
Model Compile	5.396	8.8	CoreML compilation
Audio Load	0.060	0.1	Loading audio file
Segmentation	18.331	30.0	Detecting speech regions
Embedding	30.552	50.0	Extracting speaker voices
Clustering	12.221	20.0	Grouping same speakers
Total	61.139	100	Full pipeline

Speaker Diarization Research Comparison

Research baselines typically achieve 18-30% DER on standard datasets

Method	DER	Notes
FluidAudio	15.1%	On-device CoreML
Research baseline	18-30%	Standard dataset performance

Note: RTFx shown above is from GitHub Actions runner. On Apple Silicon with ANE:

• M2 MacBook Air (2022): Runs at 150 RTFx real-time
• Performance scales with Apple Neural Engine capabilities

_{🎯 Speaker Diarization Test • AMI Corpus ES2004a • 1049.0s meeting audio • 61.1s diarization time • Test runtime: 4m 22s • 11/01/2026, 05:14 PM EST}