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LocalVQE v1.3/v1.2 fp32 streaming Core ML exports (16 ms + 256 ms)

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README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: LocalAI-io/LocalVQE
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+ tags:
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+ - coreml
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+ - audio
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+ - speech-enhancement
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+ - acoustic-echo-cancellation
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+ - noise-suppression
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+ - fluidaudio
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+ library_name: fluidaudio
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+ ---
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+
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+ # LocalVQE Core ML
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+
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+ Streaming Core ML exports of [LocalVQE](https://github.com/localai-org/LocalVQE)
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+ (weights: [LocalAI-io/LocalVQE](https://huggingface.co/LocalAI-io/LocalVQE),
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+ Apache-2.0): neural acoustic echo cancellation + noise suppression +
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+ dereverberation for 16 kHz speech, a CPU-tuned derivative of DeepVQE
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+ (Indenbom et al., Interspeech 2023).
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+
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+ Consumed by [FluidAudio](https://github.com/FluidInference/FluidAudio)
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+ (`LocalVqeManager` / `LocalVqeStream`); conversion code in
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+ [FluidInference/mobius](https://github.com/FluidInference/mobius)
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+ `models/enhancement/localvqe/coreml`.
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+
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+ ## Files
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+
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+ | File | Checkpoint | Params | Samples per call |
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+ |---|---|---:|---:|
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+ | `localvqe-v1.3-4.8M-256ms.mlmodelc` | `localvqe-v1.3-4.8M.pt` | 4.8 M | 4096 (16 hops) |
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+ | `localvqe-v1.3-4.8M-16ms.mlmodelc` | `localvqe-v1.3-4.8M.pt` | 4.8 M | 256 (1 hop) |
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+ | `localvqe-v1.2-1.3M-256ms.mlmodelc` | `localvqe-v1.2-1.3M.pt` | 1.3 M | 4096 (16 hops) |
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+ | `localvqe-v1.2-1.3M-16ms.mlmodelc` | `localvqe-v1.2-1.3M.pt` | 1.3 M | 256 (1 hop) |
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+
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+ All fp32, iOS 17 / macOS 14 minimum deployment target. The two chunk sizes
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+ produce identical audio; they trade per-call overhead against latency.
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+
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+ ## Model I/O
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+
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+ Inputs (Float32): `mic` `[1, N]`, `ref` `[1, N]` (far-end reference — what the
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+ loudspeaker played), and 33 `in_<state>` tensors. Outputs: `enhanced` `[1, N]`
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+ and the matching `out_<state>` tensors. Start with all states zero and feed
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+ each call's `out_*` back as the next call's `in_*`.
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+
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+ The enhanced hop lags the input by 256 samples (16 ms): after consuming input
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+ hop *k* the model emits input hop *k−1*. The first emitted hop covers t < 0
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+ and can be dropped; feed one hop of zeros at the end to drain. Output level
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+ matches the upstream GGML engine (2× the upstream PyTorch reference's
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+ overlap-add convention).
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+
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+ ## Parity / speed
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+
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+ Swift output vs the upstream GGML CLI on the upstream double-talk demo clip:
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+ 2.8e-5 max abs diff, 80 dB SNR. Apple M5 Pro, per-call p50 on CPU: v1.3 256 ms
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+ 7.1 ms (36× RT), v1.3 16 ms 1.2 ms (14× RT), v1.2 256 ms 4.2 ms (60× RT),
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+ v1.2 16 ms 0.7 ms (24× RT).
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+
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+ ## Citation
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+
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+ Cite the upstream repository (`CITATION.cff` in
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+ [localai-org/LocalVQE](https://github.com/localai-org/LocalVQE)) and the
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+ DeepVQE paper it derives from (Indenbom et al., Interspeech 2023,
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+ [arXiv:2306.03177](https://arxiv.org/abs/2306.03177)).
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