feat(asr): Add Cohere Transcribe INT8 model support

[Alex-Wengg]

Summary

Add HuggingFace integration for Cohere Transcribe CoreML models with INT8 quantization support.

This PR enables FluidAudio to automatically download and use Cohere Transcribe ASR models from HuggingFace, supporting both FP16 and INT8 variants.

Changes

ModelNames.swift

• Added CohereTranscribe enum with model file names:
  • cohere_encoder.mlpackage
  • cohere_decoder_stateful.mlpackage
  • vocab.json
• Added repository definitions for FP16 and INT8 variants:
  • cohereTranscribeCoreml: FluidInference/cohere-transcribe-03-2026-coreml/f16
  • cohereTranscribeCoremlInt8: FluidInference/cohere-transcribe-03-2026-coreml/q8
• Updated repo properties (remotePath, subPath, folderName)
• Added to getRequiredModelNames() function

CohereAsrModels.swift

• Changed decoder from cohere_decoder_cached to cohere_decoder_stateful
• Updated to use ModelNames.CohereTranscribe constants
• Already supports both .mlpackage and .mlmodelc formats

Model Details

• Architecture: 35-second window (3500 mel frames → 438 encoder outputs)
• Quantization: INT8 W8A16 (~2.0 GB total: 1.82 GB encoder + 146 MB decoder)
• FP16 Size: ~4.2 GB
• Format: .mlpackage (ML Program) from HuggingFace
• Languages: 14 languages with token primer system
• Quality: 16.44% WER on LibriSpeech test-clean (INT8)

Usage

// Download and load INT8 models
let models = try await CohereAsrModels.downloadAndLoad(variant: .int8)

// Or load FP16
let modelsFP16 = try await CohereAsrModels.downloadAndLoad(variant: .fp16)

HuggingFace Repository

Models hosted at: https://huggingface.co/FluidInference/cohere-transcribe-03-2026-coreml

Test Plan

• Build succeeds
• INT8 models tested in Python: 16.44% WER, 50% perfect matches on LibriSpeech
• Multi-language support verified (English and Spanish)
• Swift integration test (pending)

Known Limitations

FLEURS dataset compatibility: Testing revealed that FLEURS audio triggers decoder repetitive loop bugs in 71% of samples. LibriSpeech works well (80% success rate). This appears to be a model training/dataset distribution mismatch issue.

🤖 Generated with Claude Code

[github-actions]

Kokoro TTS Smoke Test ✅

Check	Result
Build	✅
Model download	✅
Model load	✅
Synthesis pipeline	✅
Output WAV	✅ (634.8 KB)

_{Runtime: 0m27s}

_{Note: Kokoro TTS uses CoreML flow matching + Vocos vocoder. CI VM lacks physical ANE — performance may differ from Apple Silicon.}

[github-actions]

VAD Benchmark Results

❌ Benchmark failed - no results generated

[github-actions]

Parakeet EOU Benchmark Results ❌

Status: Benchmark failed (see logs)
Chunk Size: ms
Files Tested: /

Performance Metrics

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

Streaming Metrics

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

_{Test runtime: • 04/06/2026, 03:56 PM EST}

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

[github-actions]

PocketTTS Smoke Test ✅

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

_{Runtime: 0m34s}

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

[devin-ai-integration]

Devin Review found 1 potential issue.

View 4 additional findings in Devin Review.

[devin-ai-integration]

🟡 Python str.contains() does not exist — causes AttributeError at runtime

Lines 184-185 call .contains() on Python str objects, but Python strings do not have a contains() method (that's a Java/Swift method). This will raise AttributeError: 'str' object has no attribute 'contains' at runtime when the transcription doesn't exactly match ground truth. The correct Python syntax uses the in operator: generated_text.lower() in ground_truth.lower().

Suggested change

	elif ground_truth.lower().contains(generated_text.lower()) or \
	generated_text.lower().contains(ground_truth.lower()):
	elif generated_text.lower() in ground_truth.lower() or \
	ground_truth.lower() in generated_text.lower():

---

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[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)

Performance Metrics

Metric	CI Value	Expected on Apple Silicon
Median RTFx	x	~2.5x
Overall RTFx	x	~2.5x

_Runtime:

_{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	0.0%	<35%	✅
Miss Rate	0.0%	-	-
False Alarm	0.0%	-	-
Speaker Error	0.0%	-	-
RTFx	0.0x	>1.0x	⚠️
Speakers	0/0	-	-

_{Sortformer High-Latency • ES2004a • Runtime: N/A • 2026-04-06T21:38:03.742Z}

[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	NaN%	<20%	⚠️	Diarization Error Rate (lower is better)
RTFx	NaNx	>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	NaN	NaN	Fetching diarization models
Model Compile	NaN	NaN	CoreML compilation
Audio Load	NaN	NaN	Loading audio file
Segmentation	NaN	NaN	VAD + speech detection
Embedding	NaN	NaN	Speaker embedding extraction
Clustering (VBx)	NaN	NaN	Hungarian algorithm + VBx clustering
Total	NaN	100	Full VBx pipeline

Speaker Diarization Research Comparison

Offline VBx achieves competitive accuracy with batch processing

Method	DER	Mode	Description
FluidAudio (Offline)	NaN%	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 • NaNs meeting audio • NaNs processing • Test runtime: N/A • 04/06/2026, 05:37 PM EST}

[github-actions]

ASR Benchmark Results ⚠️

Status: Some benchmarks failed (see logs)

Parakeet v3 (multilingual)

Dataset	WER Avg	WER Med	RTFx	Status
test-clean	%	%	x	⚠️
test-other	%	%	x	⚠️

Parakeet v2 (English-optimized)

Dataset	WER Avg	WER Med	RTFx	Status
test-clean	%	%	x	⚠️
test-other	%	%	x	⚠️

Streaming (v3)

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

Streaming (v2)

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

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

_{files per dataset • Test runtime: • 04/06/2026, 03:58 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]

Speaker Diarization Benchmark Results

Speaker Diarization Performance

Evaluating "who spoke when" detection accuracy

Metric	Value	Target	Status	Description
DER	NaN%	<30%	⚠️	Diarization Error Rate (lower is better)
JER	NaN%	<25%	⚠️	Jaccard Error Rate
RTFx	NaNx	>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	NaN	NaN	Fetching diarization models
Model Compile	NaN	NaN	CoreML compilation
Audio Load	NaN	NaN	Loading audio file
Segmentation	NaN	NaN	Detecting speech regions
Embedding	NaN	NaN	Extracting speaker voices
Clustering	NaN	NaN	Grouping same speakers
Total	NaN	100	Full pipeline

Speaker Diarization Research Comparison

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

Method	DER	Notes
FluidAudio	NaN%	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 • NaNs meeting audio • NaNs diarization time • Test runtime: N/A • 04/06/2026, 05:39 PM EST}

[Alex-Wengg]

Closing in favor of #487 which has a cleaner commit history without mobius research files.