fix(tts/kokoro-ane): apply misaki's ɾ → T flap step to English phonemes

[Alex-Wengg]

The English frontend reads the misaki lexicon raw, so it keeps the flap ɾ. misaki's own G2P.__call__ rewrites it to T before Kokoro v1.0 sees it. Baseline Kokoro read "metal" as "mattle".

• Apply ɾ → T in the English phonemizer path (also used by englishPhonemes(for:))
• Keep the glottal stop ʔ: misaki also rewrites it to t, but that made Kokoro read "button" as "butt" and "mitten" as "mit"

Results (tts-benchmark --backend kokoro-ane, M5 Pro, macOS 27):

	12 flap/glottal-dense sentences	minimax-english (100)
main	WER 3.67% / CER 0.56%	0.68% / 0.17%
ɾ→T + ʔ→t	1.67% / 1.04%	0.68% / 0.17%
this PR (ɾ→T)	0.00% / 0.00%	0.68% / 0.17%

Reviewer notes: the gain is small (one word in a targeted set; MiniMax unchanged). The Mandarin variant's English runs (englishPhonemes(for:)) also get the rewrite; that path is untested. Unit test added, not run locally (no XCTest here).

Found while adding Paradee (#998), which needs both rewrites.

🤖 Generated with Claude Code

[github-actions]

Sortformer High-Latency Benchmark Results

ES2004a Performance (30.4s latency config)

Metric	Value	Target	Status
DER	30.3%	<35%	✅
Miss Rate	28.2%	-	-
False Alarm	0.9%	-	-
Speaker Error	1.2%	-	-
RTFx	18.2x	>1.0x	✅
Speakers	4/4	-	-

_{Sortformer High-Latency • ES2004a • Runtime: 3m 35s • 2026-10-08T02:35:20.057Z}

[github-actions]

VAD Benchmark Results

Performance Comparison

Dataset	Accuracy	Precision	Recall	F1-Score	RTFx	Files
MUSAN	94.0%	89.3%	100.0%	94.3%	414.1x faster	50
VOiCES	94.0%	89.3%	100.0%	94.3%	411.9x 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%

[github-actions]

Supertonic3 Smoke Test ✅

Check	Result
Build	✅
Model download (incl. VectorEstimatorVariants/ int4 buckets)	✅
Model load	✅
Synthesis pipeline (--ve-variant int4)	✅
Output WAV	✅ (364.7 KB)

_{Runtime: 0m36s}

_{Note: CI VMs lack a physical Neural Engine; the ANE-bucketed VectorEstimator falls back to CPU here. This validates download + variant resolution + synthesis, not ANE residency/perf.}

[github-actions]

PocketTTS Smoke Test ✅

Check	Result
Build	✅
Model download	✅
Model load	✅
Synthesis pipeline	✅
Output WAV	✅ (157.5 KB)

_{Runtime: 0m22s}

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

[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	10.4%	<20%	✅	Diarization Error Rate (lower is better)
RTFx	11.22x	>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	18.009	19.3	Fetching diarization models
Model Compile	7.718	8.3	CoreML compilation
Audio Load	0.059	0.1	Loading audio file
Segmentation	23.155	24.8	VAD + speech detection
Embedding	93.173	99.7	Speaker embedding extraction
Clustering (VBx)	0.119	0.1	Hungarian algorithm + VBx clustering
Total	93.484	100	Full VBx pipeline

Speaker Diarization Research Comparison

Offline VBx achieves competitive accuracy with batch processing

Method	DER	Mode	Description
FluidAudio (Offline)	10.4%	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 • 116.4s processing • Test runtime: 2m 5s • 10/07/2026, 11:02 PM EST}

[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%	4.81x	✅
test-other	1.19%	0.00%	3.10x	✅

Parakeet v2 (English-optimized)

Dataset	WER Avg	WER Med	RTFx	Status
test-clean	0.80%	0.00%	4.41x	✅
test-other	1.00%	0.00%	2.36x	✅

Streaming (v3)

Metric	Value	Description
WER	0.00%	Word Error Rate in streaming mode
RTFx	0.44x	Streaming real-time factor
Avg Chunk Time	1.989s	Average time to process each chunk
Max Chunk Time	2.810s	Maximum chunk processing time
First Token	2.583s	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	0.44x	Streaming real-time factor
Avg Chunk Time	2.055s	Average time to process each chunk
Max Chunk Time	2.503s	Maximum chunk processing time
First Token	2.099s	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: 9m7s • 10/07/2026, 10:53 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]

Parakeet EOU Benchmark Results ✅

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

Performance Metrics

Metric	Value	Description
WER (Avg)	7.03%	Average Word Error Rate
WER (Med)	4.17%	Median Word Error Rate
RTFx	7.52x	Real-time factor (higher = faster)
Total Audio	470.6s	Total audio duration processed
Total Time	63.6s	Total processing time

Streaming Metrics

Metric	Value	Description
Avg Chunk Time	0.064s	Average chunk processing time
Max Chunk Time	0.127s	Maximum chunk processing time
EOU Detections	0	Total End-of-Utterance detections

_{Test runtime: 1m12s • 10/07/2026, 10:11 PM EST}

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

[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	17.42x	>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	12.767	21.2	Fetching diarization models
Model Compile	5.471	9.1	CoreML compilation
Audio Load	0.085	0.1	Loading audio file
Segmentation	18.059	30.0	Detecting speech regions
Embedding	30.098	50.0	Extracting speaker voices
Clustering	12.039	20.0	Grouping same speakers
Total	60.223	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 • 60.2s diarization time • Test runtime: 3m 12s • 10/07/2026, 10:21 PM EST}