Make speakers publically mutable in DiarizerTimeline

[SGD2718]

Summary

• expose a public setter for DiarizerTimeline.speakers
• keep the existing queue-synchronized access pattern for reads and writes

Testing

• not run

[claude]

Claude finished @SGD2718's task —— View job

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PR Review: Make speakers publicly mutable in DiarizerTimeline

Review Progress:

• Create pending PR review
• Get PR diff to understand changes
• Read modified file for context
• Add inline review comments
• Submit comprehensive review

✅ Review Complete

I've submitted a comprehensive code review focusing on:

Thread Safety: ✅ The implementation correctly maintains thread safety using queue.sync for both getter and setter.

API Design Concerns: ⚠️ The public setter for the entire speakers dictionary raises questions about:

• Data consistency with existing timeline state
• Unclear use cases and missing documentation
• Potential for breaking internal invariants

Recommendations:

• Add documentation explaining safe usage patterns
• Consider validation in the setter
• Add unit tests for concurrent access and edge cases

The change is technically sound but would benefit from clearer documentation and use case definition to prevent misuse that could break timeline consistency.

[claude]

Code Review Summary

✅ Thread Safety - Good

The implementation correctly maintains thread safety by using queue.sync for both getter and setter, consistent with the existing pattern in the class.

⚠️ API Design Concerns

Lines 663-664: The public setter for the entire speakers dictionary raises some design questions:

1. Data Consistency Risk: Directly setting _speakers could break internal consistency if the new speakers don't align with existing _finalizedPredictions, _tentativePredictions, or states arrays.

2. Use Case Unclear: What's the intended use case? Common scenarios might be:

   • Restoring speakers from saved state
   • Bulk speaker updates after enrollment
   • Merging timelines from different sources

3. Missing Documentation: The setter lacks documentation about when/how it should be used safely.

🔍 Suggestions

Consider these alternatives:

• Add validation in the setter to ensure consistency with existing timeline state
• Provide more granular methods like addSpeaker(), removeSpeaker(), or updateSpeaker()
• Add comprehensive documentation explaining safe usage patterns
• Consider if a replaceSpeakers(keepingSegments:) method would be more explicit

Testing Note: The PR mentions "not run" for testing. Consider adding unit tests that verify:

• Thread safety under concurrent access
• Behavior when setting speakers on a timeline with existing segments
• Performance impact of the setter

📋 Minor Items

• The change is minimal and maintains existing patterns
• No breaking changes to the public API
• Follows Swift property syntax conventions

Overall: The implementation is technically sound but would benefit from clearer documentation and use case definition.

[github-actions]

Parakeet EOU Benchmark Results ✅

Status: Benchmark passed
Chunk Size: 320ms
Files Tested: 100/100

Performance Metrics

Metric	Value	Description
WER (Avg)	0.00%	Average Word Error Rate
WER (Med)	0.00%	Median Word Error Rate
RTFx	0.00x	Real-time factor (higher = faster)
Total Audio	0.0s	Total audio duration processed
Total Time	0.0s	Total processing time

Streaming Metrics

Metric	Value	Description
Avg Chunk Time	0.000s	Average chunk processing time
Max Chunk Time	0.000s	Maximum chunk processing time
EOU Detections	0	Total End-of-Utterance detections

_{Test runtime: 0m25s • 03/19/2026, 08:24 PM EST}

_{RTFx = Real-Time Factor (higher is better) • Processing includes: Model inference, audio preprocessing, state management, and file I/O}

[github-actions]

Qwen3-ASR int8 Smoke Test ✅

Check	Result
Build	✅
Model download	✅
Model load	✅
Transcription pipeline	✅
Decoder size	571 MB (vs 1.1 GB f32)

_{Runtime: 3m5s}

_{Note: CI VM lacks physical GPU — CoreML MLState (macOS 15) KV cache produces degraded results on virtualized runners. On Apple Silicon: ~1.3% WER / 2.5x RTFx.}

[github-actions]

Sortformer High-Latency Benchmark Results

ES2004a Performance (30.4s latency config)

Metric	Value	Target	Status
DER	33.4%	<35%	✅
Miss Rate	24.4%	-	-
False Alarm	0.2%	-	-
Speaker Error	8.8%	-	-
RTFx	10.8x	>1.0x	✅
Speakers	4/4	-	-

_{Sortformer High-Latency • ES2004a • Runtime: 4m 29s • 2026-03-20T00:37:57.201Z}

[github-actions]

ASR Benchmark Results ✅

Status: All benchmarks passed

Parakeet v3 (multilingual)

Dataset	WER Avg	WER Med	RTFx	Status
test-clean	0.57%	0.00%	4.61x	✅
test-other	1.19%	0.00%	3.11x	✅

Parakeet v2 (English-optimized)

Dataset	WER Avg	WER Med	RTFx	Status
test-clean	0.80%	0.00%	4.45x	✅
test-other	1.78%	0.00%	3.39x	✅

Streaming (v3)

Metric	Value	Description
WER	0.00%	Word Error Rate in streaming mode
RTFx	0.57x	Streaming real-time factor
Avg Chunk Time	1.683s	Average time to process each chunk
Max Chunk Time	2.074s	Maximum chunk processing time
First Token	1.979s	Latency to first transcription token
Total Chunks	31	Number of chunks processed

Streaming (v2)

Metric	Value	Description
WER	0.00%	Word Error Rate in streaming mode
RTFx	0.60x	Streaming real-time factor
Avg Chunk Time	1.493s	Average time to process each chunk
Max Chunk Time	1.758s	Maximum chunk processing time
First Token	1.544s	Latency to first transcription token
Total Chunks	31	Number of chunks processed

_{Streaming tests use 5 files with 0.5s chunks to simulate real-time audio streaming}

_{25 files per dataset • Test runtime: 8m4s • 03/19/2026, 08:32 PM EST}

_{RTFx = Real-Time Factor (higher is better) • Calculated as: Total audio duration ÷ Total processing time
Processing time includes: Model inference on Apple Neural Engine, audio preprocessing, state resets between files, token-to-text conversion, and file I/O
Example: RTFx of 2.0x means 10 seconds of audio processed in 5 seconds (2x faster than real-time)}

Expected RTFx Performance on Physical M1 Hardware:

• M1 Mac: ~28x (clean), ~25x (other)
• CI shows ~0.5-3x due to virtualization limitations

_{Testing methodology follows HuggingFace Open ASR Leaderboard}

[github-actions]

Offline VBx Pipeline Results

Speaker Diarization Performance (VBx Batch Mode)

Optimal clustering with Hungarian algorithm for maximum accuracy

Metric	Value	Target	Status	Description
DER	14.5%	<20%	✅	Diarization Error Rate (lower is better)
RTFx	5.20x	>1.0x	✅	Real-Time Factor (higher is faster)

Offline VBx Pipeline Timing Breakdown

Time spent in each stage of batch diarization

Stage	Time (s)	%	Description
Model Download	9.793	4.9	Fetching diarization models
Model Compile	4.197	2.1	CoreML compilation
Audio Load	0.043	0.0	Loading audio file
Segmentation	21.361	10.6	VAD + speech detection
Embedding	200.963	99.6	Speaker embedding extraction
Clustering (VBx)	0.750	0.4	Hungarian algorithm + VBx clustering
Total	201.854	100	Full VBx pipeline

Speaker Diarization Research Comparison

Offline VBx achieves competitive accuracy with batch processing

Method	DER	Mode	Description
FluidAudio (Offline)	14.5%	VBx Batch	On-device CoreML with optimal clustering
FluidAudio (Streaming)	17.7%	Chunk-based	First-occurrence speaker mapping
Research baseline	18-30%	Various	Standard dataset performance

Pipeline Details:

• Mode: Offline VBx with Hungarian algorithm for optimal speaker-to-cluster assignment
• Segmentation: VAD-based voice activity detection
• Embeddings: WeSpeaker-compatible speaker embeddings
• Clustering: PowerSet with VBx refinement
• Accuracy: Higher than streaming due to optimal post-hoc mapping

_{🎯 Offline VBx Test • AMI Corpus ES2004a • 1049.0s meeting audio • 223.1s processing • Test runtime: 3m 53s • 03/19/2026, 08:42 PM EST}

[github-actions]

PocketTTS Smoke Test ✅

Check	Result
Build	✅
Model download	✅
Model load	✅
Synthesis pipeline	✅
Output WAV	✅ (228.8 KB)

_{Runtime: 0m42s}

_{Note: PocketTTS uses CoreML MLState (macOS 15) KV cache + Mimi streaming state. CI VM lacks physical GPU — audio quality may differ from Apple Silicon.}

[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	21.51x	>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	10.516	21.6	Fetching diarization models
Model Compile	4.507	9.2	CoreML compilation
Audio Load	0.151	0.3	Loading audio file
Segmentation	14.619	30.0	Detecting speech regions
Embedding	24.366	49.9	Extracting speaker voices
Clustering	9.746	20.0	Grouping same speakers
Total	48.788	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 • 48.7s diarization time • Test runtime: 3m 54s • 03/19/2026, 08:34 PM EST}

[github-actions]

VAD Benchmark Results

Performance Comparison

Dataset	Accuracy	Precision	Recall	F1-Score	RTFx	Files
MUSAN	92.0%	86.2%	100.0%	92.6%	641.7x faster	50
VOiCES	92.0%	86.2%	100.0%	92.6%	396.5x faster	50

Dataset Details

• MUSAN: Music, Speech, and Noise dataset - standard VAD evaluation
• VOiCES: Voices Obscured in Complex Environmental Settings - tests robustness in real-world conditions

✅: Average F1-Score above 70%