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fix(tts/kokoro-ane): throw instead of trapping on non-finite PostAlbert durations#816

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fix/kokoro-ane-nan-duration-guard
Jul 23, 2026
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fix(tts/kokoro-ane): throw instead of trapping on non-finite PostAlbert durations#816
Alex-Wengg merged 1 commit into
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fix/kokoro-ane-nan-duration-guard

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Summary

Fixes the hard crash reported in #738 (comment): on iOS 27 public beta, Kokoro synthesis kills the host app with

Fatal error: Float value cannot be converted to Int32 because it is either infinite or NaN

at KokoroAneSynthesizer.swift:74 (d=NaN).

Root cause

iOS 27's new E5 CoreML runtime flags the dynamic-shape Kokoro stages at load time (MIL program has non-constant (dynamic) shapes for external input but FlexibleShapeInformation attribute is missing) and then mis-executes them — PostAlbert returns NaN for its duration output. The pred_dur conversion did Int32(Float(d).rounded()), which is a Swift runtime trap on NaN, so the whole host app crashed instead of receiving an error. Same class of on-device dynamic-shape misbehavior as #739 (there the symptom was zeros; here NaNs).

Change

  • Extract the rounding into KokoroAneSynthesizer.predictedDurations(from:):
    • throws new KokoroAneError.nonFiniteModelOutput(stage:output:) on NaN/±inf, with a message pointing at the iOS 27 runtime and MacOS 27 Beta 2 Release Notes mentions ios 27 locking down ANE #738
    • clamps finite values to [1, maxAcousticFrames] so huge garbage values can't trap Int32() either; absurd totals still surface through the existing acousticFramesExceedCap check
  • Output for well-formed durations is byte-identical (small positive floats, far below the cap).
  • Unit tests for rounding/clamping/NaN/inf paths — pure function, no models needed.

This makes the failure catchable; it does not make Kokoro work on iOS 27 betas. The model-side fix (attaching flexible-shape info so E5 compiles the dynamic stages correctly) is separate follow-up work. Affected users can try KokoroAneComputeUnits.cpuAndGpu as a possible workaround.

Testing

  • swift build clean, swift format lint clean
  • New KokoroAnePredictedDurationTests (run on CI; no model downloads required)

🤖 Generated with Claude Code

…rt durations

On iOS 27 betas the new E5 CoreML runtime mis-executes the dynamic-shape
Kokoro stages ("MIL program has non-constant (dynamic) shapes ... E5
function will be produced with unknown input shapes") and PostAlbert
returns NaN for its 'duration' output. The pred_dur conversion then hit
Int32(Float.nan), a Swift runtime trap that crashed the host app
(issue #738, report from iOS 27 public beta).

Extract the rounding into predictedDurations(from:), which now:
- throws KokoroAneError.nonFiniteModelOutput on NaN/inf instead of
  trapping, so host apps get a catchable error naming the likely cause
- clamps finite values to [1, maxAcousticFrames] so huge garbage values
  cannot trap Int32() either; absurd totals still surface via the
  existing acousticFramesExceedCap check

Behavior for well-formed outputs is unchanged (durations are small
positive floats, far below the cap).

Adds pure-function unit tests for rounding, clamping, and the NaN/inf
throw path (no models required).
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VAD Benchmark Results

Performance Comparison

Dataset Accuracy Precision Recall F1-Score RTFx Files
MUSAN 94.0% 89.3% 100.0% 94.3% 726.8x faster 50
VOiCES 94.0% 89.3% 100.0% 94.3% 791.7x 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%

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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 11.07x Real-time factor (higher = faster)
Total Audio 470.6s Total audio duration processed
Total Time 43.5s Total processing time

Streaming Metrics

Metric Value Description
Avg Chunk Time 0.044s Average chunk processing time
Max Chunk Time 0.087s Maximum chunk processing time
EOU Detections 0 Total End-of-Utterance detections

Test runtime: 1m34s • 07/23/2026, 06:34 PM EST

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

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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 11.15x >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 23.737 25.2 Fetching diarization models
Model Compile 10.173 10.8 CoreML compilation
Audio Load 0.060 0.1 Loading audio file
Segmentation 26.471 28.1 VAD + speech detection
Embedding 93.898 99.8 Speaker embedding extraction
Clustering (VBx) 0.097 0.1 Hungarian algorithm + VBx clustering
Total 94.131 100 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 • 120.5s processing • Test runtime: 2m 18s • 07/23/2026, 06:36 PM EST

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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: 0m42s

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.

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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 20.98x >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 11.937 23.9 Fetching diarization models
Model Compile 5.116 10.2 CoreML compilation
Audio Load 0.036 0.1 Loading audio file
Segmentation 14.998 30.0 Detecting speech regions
Embedding 24.997 50.0 Extracting speaker voices
Clustering 9.999 20.0 Grouping same speakers
Total 50.017 100 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 • 50.0s diarization time • Test runtime: 2m 39s • 07/23/2026, 06:39 PM EST

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PocketTTS Smoke Test ✅

Check Result
Build
Model download
Model load
Synthesis pipeline
Output WAV ✅ (153.8 KB)

Runtime: 1m58s

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.

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ASR Benchmark Results ✅

Status: All benchmarks passed

Parakeet v3 (multilingual)

Dataset WER Avg WER Med RTFx Status
test-clean 0.57% 0.00% 4.93x
test-other 1.19% 0.00% 3.22x

Parakeet v2 (English-optimized)

Dataset WER Avg WER Med RTFx Status
test-clean 0.80% 0.00% 4.72x
test-other 1.00% 0.00% 3.25x

Streaming (v3)

Metric Value Description
WER 0.00% Word Error Rate in streaming mode
RTFx 0.57x Streaming real-time factor
Avg Chunk Time 1.569s Average time to process each chunk
Max Chunk Time 1.881s Maximum chunk processing time
First Token 1.876s 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 0.55x Streaming real-time factor
Avg Chunk Time 1.618s Average time to process each chunk
Max Chunk Time 2.164s Maximum chunk processing time
First Token 1.693s 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: 10m25s • 07/23/2026, 06:43 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

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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 20.5x >1.0x
Speakers 4/4 - -

Sortformer High-Latency • ES2004a • Runtime: 3m 21s • 2026-07-23T22:48:50.156Z

@Alex-Wengg
Alex-Wengg merged commit 2610459 into main Jul 23, 2026
11 checks passed
@Alex-Wengg
Alex-Wengg deleted the fix/kokoro-ane-nan-duration-guard branch July 23, 2026 22:49
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