feat: add Qwen3-ForcedAligner-0.6B CoreML conversion and inference#21
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Alex-Wengg wants to merge 3 commits intomainfrom
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feat: add Qwen3-ForcedAligner-0.6B CoreML conversion and inference#21Alex-Wengg wants to merge 3 commits intomainfrom
Alex-Wengg wants to merge 3 commits intomainfrom
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Add CoreML conversion pipeline for Qwen3-ForcedAligner-0.6B, a non-autoregressive forced alignment model that produces per-word timestamps from audio + text. The pipeline splits the model into 5 CoreML components: - Audio conv frontend (per-chunk mel → conv features) - Audio transformer (cross-chunk bidirectional attention + projection) - Token embedding (vocab → hidden states) - Decoder prefill (28-layer Qwen3 decoder, single NAR pass) - LM head (hidden states → 5000 timestamp bins) Key design decisions: - Audio encoder split into conv + transformer to preserve cross-chunk attention (monolithic per-chunk approach had 20.7ms AAS vs 4.4ms split) - MRoPE cos/sin computed outside the model for flexibility - Last mel chunk trimmed after conv to remove padding artifacts - Decoder and LM head use FLOAT32 precision to avoid FP16 overflow Parity vs PyTorch (3 LibriSpeech test-clean samples, 54 boundaries): - AAS: 4.4ms, within 20ms: 95.4%, within 80ms: 99.1% Co-Authored-By: Claude <noreply@anthropic.com>
The inference script supports two audio encoder paths with auto-detection. Split encoder (audio_conv + audio_transformer) preserves cross-chunk attention for 4.4ms AAS. Monolithic encoder (audio_encoder) is faster but lacks cross-chunk attention (20.7ms AAS). Added comparison table and updated architecture, I/O shapes, inference pipeline, conversion, and parity sections.
Document 5 bugs encountered during FluidAudio Swift integration: MLMultiArray stride issues, encoder 3D shape, Slaney vs HTK mel, STFT center padding, and MRoPE position clamping.
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Summary
Files
convert-coreml.pyindividual_components.pyrun_coreml_inference.pycompare-models.pypyproject.toml/uv.lockREADME.mdproblems_encountered.mdCoreML Components (5 models)
[1, 128, 100]mel[1, 13, 1024]conv features[1, 256, 1024]features[1, 256, 1024]embeddings[1, seq, int32][1, seq, 1024][1, 1024, 1024]+ RoPE[1, 1024, 1024][1, seq, 1024][1, seq, 5000]timestampsParity vs PyTorch (3 LibriSpeech test-clean samples, 54 word boundaries)
Test plan
cd models/stt/qwen3-forced-aligner-0.6b/coreml && uv syncuv run python convert-coreml.py— convert all 5 componentsuv run python compare-models.py --num-files 3— generate PyTorch referenceuv run python run_coreml_inference.py compare— verify CoreML vs PyTorch parity🤖 Generated with Claude Code