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fix(podspec): sync version to v0.15.6 + CI guard against tag drift - #873

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fix/podspec-version-drift
Aug 19, 2026
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fix(podspec): sync version to v0.15.6 + CI guard against tag drift#873
Alex-Wengg merged 1 commit into
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fix/podspec-version-drift

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Summary

Already-published tags keep the stale string (history is immutable); tag-only Podfile pins are unaffected either way. Starting with the next release the version string will be accurate again.

Test plan

  • pod ipc spec FluidAudio.podspec parses clean with the new version.
  • Ran the workflow's grep/sed extraction locally against the bumped podspec: extracts 0.15.6, matches tag v0.15.6.

🤖 Generated with Claude Code

The podspec self-reported 0.12.2 while tags advanced to v0.15.6, so any
consumer resolving FluidAudio by version constraint gets a lie about what
the tag contains (reported in react-native-fluidaudio#4 — the ~> 0.7 pin
there can never reach the streaming managers, which first shipped in
v0.15.3). Releases bump tags manually and nothing checked the podspec, so
the string drifted silently for three minor versions.

Bump spec.version to match the latest release and add a tag-push workflow
that fails when the podspec at the tag disagrees with the tag name, so the
mismatch surfaces at release time instead of in downstream Podfiles.
@Alex-Wengg
Alex-Wengg enabled auto-merge (squash) August 19, 2026 21:57
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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: 0m25s

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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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 6.30x Real-time factor (higher = faster)
Total Audio 470.6s Total audio duration processed
Total Time 74.2s Total processing time

Streaming Metrics

Metric Value Description
Avg Chunk Time 0.074s Average chunk processing time
Max Chunk Time 0.148s Maximum chunk processing time
EOU Detections 0 Total End-of-Utterance detections

Test runtime: 1m58s • 08/19/2026, 06:05 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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VAD Benchmark Results

Performance Comparison

Dataset Accuracy Precision Recall F1-Score RTFx Files
MUSAN 94.0% 89.3% 100.0% 94.3% 409.9x faster 50
VOiCES 94.0% 89.3% 100.0% 94.3% 443.4x 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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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 7.75x >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 23.550 17.4 Fetching diarization models
Model Compile 10.093 7.4 CoreML compilation
Audio Load 0.062 0.0 Loading audio file
Segmentation 37.427 27.6 VAD + speech detection
Embedding 135.182 99.8 Speaker embedding extraction
Clustering (VBx) 0.130 0.1 Hungarian algorithm + VBx clustering
Total 135.488 100 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 • 172.7s processing • Test runtime: 2m 58s • 08/19/2026, 06:12 PM EST

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PocketTTS Smoke Test ✅

Check Result
Build
Model download
Model load
Synthesis pipeline
Output WAV ✅ (142.5 KB)

Runtime: 0m26s

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% 5.13x
test-other 1.75% 0.00% 3.21x

Parakeet v2 (English-optimized)

Dataset WER Avg WER Med RTFx Status
test-clean 0.80% 0.00% 4.38x
test-other 1.00% 0.00% 3.71x

Streaming (v3)

Metric Value Description
WER 0.00% Word Error Rate in streaming mode
RTFx 0.57x Streaming real-time factor
Avg Chunk Time 1.653s Average time to process each chunk
Max Chunk Time 2.470s Maximum chunk processing time
First Token 1.893s 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 0.67x Streaming real-time factor
Avg Chunk Time 1.421s Average time to process each chunk
Max Chunk Time 1.591s Maximum chunk processing time
First Token 1.420s 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 • 08/19/2026, 06:15 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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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 16.96x >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 15.443 25.0 Fetching diarization models
Model Compile 6.618 10.7 CoreML compilation
Audio Load 0.085 0.1 Loading audio file
Segmentation 18.552 30.0 Detecting speech regions
Embedding 30.920 50.0 Extracting speaker voices
Clustering 12.368 20.0 Grouping same speakers
Total 61.886 100 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 • 61.8s diarization time • Test runtime: 4m 25s • 08/19/2026, 06:15 PM EST

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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 16.8x >1.0x
Speakers 4/4 - -

Sortformer High-Latency • ES2004a • Runtime: 3m 29s • 2026-08-19T22:22:41.525Z

@Alex-Wengg
Alex-Wengg disabled auto-merge August 19, 2026 23:05
@Alex-Wengg
Alex-Wengg merged commit 8a2bf2c into main Aug 19, 2026
11 checks passed
@Alex-Wengg
Alex-Wengg deleted the fix/podspec-version-drift branch August 19, 2026 23:05
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