b9819977a5
Includes prior-session patches (carry forward so the app compiles): - crates/gpui/build.rs: cross-compile manifest fix - crates/gpui/src/platform.rs: PlatformWindow::activate_with_token trait method - crates/gpui/src/window.rs: Window::activate_with_token public API - crates/gpui_linux/src/linux/wayland/window.rs: WaylandWindow::activate_with_token + activate() keyboard-serial fix Plus the focus-serial fix: - serial.rs: SerialKind::KeyboardEnter - client.rs: store wl_keyboard.enter serial; latest_serial_of() Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
21 lines
1.1 KiB
Markdown
21 lines
1.1 KiB
Markdown
Real time streaming audio denoising using a [Dual-Signal Transformation LSTM Network for Real-Time Noise Suppression](https://arxiv.org/abs/2005.07551).
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Trivial to build as it uses the native rust Candle crate for inference. Easy to integrate into any Rodio pipeline.
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```rust
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# use rodio::{nz, source::UniformSourceIterator, wav_to_file};
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let file = std::fs::File::open("clips_airconditioning.wav")?;
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let decoder = rodio::Decoder::try_from(file)?;
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let resampled = UniformSourceIterator::new(decoder, nz!(1), nz!(16_000));
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let mut denoised = denoise::Denoiser::try_new(resampled)?;
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wav_to_file(&mut denoised, "denoised.wav")?;
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Result::Ok<(), Box<dyn std::error::Error>>
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```
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## Acknowledgements & License
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The trained models in this repo are optimized versions of the models in the [breizhn/DTLN](https://github.com/breizhn/DTLN?tab=readme-ov-file#model-conversion-and-real-time-processing-with-onnx). These are licensed under MIT.
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The FFT code was adapted from Datadog's [dtln-rs Repo](https://github.com/DataDog/dtln-rs/tree/main) also licensed under MIT.
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