Fix fabricated citation; credit actual authors
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README.md
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# LocalVQE β Local Voice Quality Enhancement
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Real-time joint acoustic echo cancellation (AEC), noise suppression (NS),
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dereverberation for 16 kHz speech.
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Quality Enhancement* (Indenbom et al., Interspeech 2023,
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[arXiv:2306.03177](https://arxiv.org/abs/2306.03177)), redesigned for
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quantization-aware CPU inference with DCT-II analysis/synthesis in place of
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STFT.
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## Files
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## Usage (GGML / C++ / Go)
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```bash
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# Build the ggml binary
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cd ggml && cmake -B build -DCMAKE_BUILD_TYPE=Release && cmake --build build -j
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# Run inference on a 16 kHz WAV pair
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./build/bin/localvqe localvqe-baseline.gguf \
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--in-wav mic.wav ref.wav --out-wav enhanced.wav
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```
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## Architecture
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| Component | Value |
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| Sample rate | 16 kHz |
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| Analysis basis | DCT-II (Conv1d filterbank, 512 filters, stride 256, frozen) |
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| Mic encoder | 5 blocks: 2 β 64 β 128 β 128 β 128 β 128 |
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| Far-end encoder | 2 blocks: 2 β 32 β 128 |
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| AlignBlock | Cross-attention soft delay, d_max=32 (320 ms), h=32 |
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| Bottleneck |
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| Decoder | 5 sub-pixel conv + BN blocks: 128 β 128 β 128 β 64 β 64 |
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| CCM | 27-ch β
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| Parameters | ~8.0 M |
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##
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@inproceedings{indenbom2023localvqe,
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title={{LocalVQE}: Real Time Deep Voice Quality Enhancement},
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author={Indenbom, Evgenii and Beresford, Nicolae-C{\u a}t{\u a}lin and Adiban, Mohammad and Klej{\v c}ek, Ji{\v r}{\' i}},
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booktitle={Interspeech},
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year={2023}
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}
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```
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# LocalVQE β Local Voice Quality Enhancement
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Real-time joint acoustic echo cancellation (AEC), noise suppression (NS),
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and dereverberation for 16 kHz speech.
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**Derivative of DeepVQE.** The overall architecture (mic encoder, far-end
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encoder, alignment cross-attention, bottleneck, decoder, complex convolving
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mask) is based on **DeepVQE** (Indenbom et al., Interspeech 2023 β *DeepVQE:
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Real Time Deep Voice Quality Enhancement for Joint Acoustic Echo Cancellation,
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Noise Suppression and Dereverberation*,
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[arXiv:2306.03177](https://arxiv.org/abs/2306.03177)). This fork replaces
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STFT with a DCT-II analysis/synthesis filterbank for quantisation-aware
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local CPU/GPU inference, swaps the GRU bottleneck for S4D, and ships a
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streaming GGML graph so the model can run on an F32 CPU pipeline in under
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2 ms/frame at 16 kHz.
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**There is no LocalVQE paper.** Everything specific to this project
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(DCT-II in-graph, GGML runtime, training pipeline, AEC fine-tune recipe)
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is original to this repo.
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**Authors:**
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- Richard Palethorpe ([richiejp](https://github.com/richiejp))
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- Claude (Anthropic)
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**Project source:** <https://github.com/richiejp/LocalVQE>
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## Files
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## Usage (GGML / C++ / Go)
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```bash
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cd ggml && cmake -B build -DCMAKE_BUILD_TYPE=Release && cmake --build build -j
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./build/bin/localvqe localvqe-baseline.gguf \
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--in-wav mic.wav ref.wav --out-wav enhanced.wav
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```
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## Architecture
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| Component | Value |
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|---|---|
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| Sample rate | 16 kHz |
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| Analysis basis | DCT-II (Conv1d filterbank, 512 filters, stride 256, frozen) |
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| Mic encoder | 5 blocks: 2 β 64 β 128 β 128 β 128 β 128 |
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| Far-end encoder | 2 blocks: 2 β 32 β 128 |
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| AlignBlock | Cross-attention soft delay, d_max=32 (320 ms), h=32 |
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| Bottleneck | S4D diagonal state-space |
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| Decoder | 5 sub-pixel conv + BN blocks: 128 β 128 β 128 β 64 β 64 |
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| CCM | 27-ch β 3x3 complex convolving mask (real-valued arithmetic) |
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| Parameters | ~8.0 M |
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## Licence
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Apache-2.0.
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