feat: add Qwen3-ASR-0.6B CoreML speech recognition

[Alex-Wengg]

Beta: Qwen3-ASR is experimental and under active development.

Encoder-decoder ASR pipeline using Qwen3-ASR-0.6B converted to CoreML.

Performance

Dataset	WER	CER	RTFx
LibriSpeech test-clean (2620 files)	4.4%	-	3.8x
AISHELL-1 Chinese (7176 files)	10.3%	6.6%	3.8x

Supported Languages

30 languages with automatic detection: Chinese, English, Cantonese, Japanese, Korean, Vietnamese, Thai, Indonesian, Malay, Hindi, Arabic, Turkish, Russian, German, French, Spanish, Portuguese, Italian, Dutch, Polish, Swedish, Danish, Finnish, Czech, Filipino, Persian, Greek, Hungarian, Macedonian, Romanian.

Components

• Qwen3AsrManager: Autoregressive decoder with batched prefill
• WhisperMelSpectrogram: Whisper-compatible mel spectrogram (pure Swift/vDSP)
• Qwen3RoPE: Multi-resolution rotary position embeddings (M-RoPE)
• Qwen3AsrModels: Model loading with auto-download from HuggingFace
• CLI: qwen3-benchmark and qwen3-transcribe commands

Models

CoreML Model: FluidInference/qwen3-asr-0.6b-coreml

Only f32 variant recommended (int8 is slower due to autoregressive decoding overhead).

Swift 6 Compatibility

• @preconcurrency import CoreML for actor isolation
• Sendable conformance for cross-isolation boundary support

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🤖 Generated with Claude Code

[github-actions]

VAD Benchmark Results

Performance Comparison

Dataset	Accuracy	Precision	Recall	F1-Score	RTFx	Files
MUSAN	92.0%	86.2%	100.0%	92.6%	655.2x faster	50
VOiCES	92.0%	86.2%	100.0%	92.6%	676.1x 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]

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	3.96x	>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	11.601	4.4	Fetching diarization models
Model Compile	4.972	1.9	CoreML compilation
Audio Load	0.085	0.0	Loading audio file
Segmentation	36.850	13.9	VAD + speech detection
Embedding	261.385	98.6	Speaker embedding extraction
Clustering (VBx)	3.091	1.2	Hungarian algorithm + VBx clustering
Total	265.012	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 • 301.3s processing • Test runtime: 6m 51s • 02/11/2026, 04:41 PM EST}

[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	0.1%	-	-
Speaker Error	8.9%	-	-
RTFx	19.1x	>1.0x	✅
Speakers	4/4	-	-

_{Sortformer High-Latency • ES2004a • Runtime: 1m 46s • 2026-02-11T21:27:50.015Z}

[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	12.85x	>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	10.765	13.2	Fetching diarization models
Model Compile	4.613	5.6	CoreML compilation
Audio Load	0.096	0.1	Loading audio file
Segmentation	24.488	30.0	Detecting speech regions
Embedding	40.813	50.0	Extracting speaker voices
Clustering	16.325	20.0	Grouping same speakers
Total	81.691	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 • 81.6s diarization time • Test runtime: 3m 49s • 02/11/2026, 04:30 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%	5.27x	✅
test-other	1.80%	0.00%	3.14x	✅

Parakeet v2 (English-optimized)

Dataset	WER Avg	WER Med	RTFx	Status
test-clean	0.80%	0.00%	4.97x	✅
test-other	1.22%	0.00%	3.02x	✅

Streaming (v3)

Metric	Value	Description
WER	0.00%	Word Error Rate in streaming mode
RTFx	0.59x	Streaming real-time factor
Avg Chunk Time	1.533s	Average time to process each chunk
Max Chunk Time	2.261s	Maximum chunk processing time
First Token	2.001s	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	0.56x	Streaming real-time factor
Avg Chunk Time	1.615s	Average time to process each chunk
Max Chunk Time	2.175s	Maximum chunk processing time
First Token	1.716s	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: 7m50s • 02/11/2026, 04:30 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	7.55x	Real-time factor (higher = faster)
Total Audio	470.6s	Total audio duration processed
Total Time	68.1s	Total processing time

Streaming Metrics

Metric	Value	Description
Avg Chunk Time	0.068s	Average chunk processing time
Max Chunk Time	0.136s	Maximum chunk processing time
EOU Detections	0	Total End-of-Utterance detections

_{Test runtime: 1m26s • 02/11/2026, 04:23 PM EST}

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

[reneleonhardt]

Thank you very much for adding this great model!

Beta: Qwen3-ASR is experimental and under active development.

What is this referring to?
I couldn't find "beta" or "experimental" in the model card, GitHub, the blog entry or the paper.
https://github.com/QwenLM/Qwen3-ASR
https://huggingface.co/Qwen/Qwen3-ASR-0.6B
https://qwen.ai/blog?id=qwen3asr
https://arxiv.org/abs/2601.21337

[Alex-Wengg]

@reneleonhardt its beta for our coreml conversion not the original model