use crate::config::FftLearnConfig;
use crate::learned_compile::{CompiledLearnedMel, compile_learned_mel, default_hard_threshold};
use crate::learned_model::FastLearnedFftModel;
use crate::peak::{
WelchPeakParams, WelchPeaksScratch, peaks_from_block_segment_spectrum_streaming,
peaks_from_segment_spectrum_streaming,
};
use crate::rlx_fft::{rlx_fft_forward, rlx_fft_forward_block};
use crate::welch::{compile_welch_rlx_fft, hann_window, welch_windowed_segments};
use anyhow::{Result, ensure};
use rlx_ir::infer::GraphExt;
use rlx_ir::{DType, Graph, Shape};
use rlx_runtime::{CompiledGraph, Device, Session};
pub fn build_welch_peaks_fused_graph(batch: usize, peak_params: WelchPeakParams) -> Graph {
let n = peak_params.welch.n_fft;
let seg_batch = batch * peak_params.welch.n_segments;
let mut g = Graph::new("welch_peaks_fused");
let segs = g.input("segs", Shape::new(&[seg_batch, n], DType::F32));
let zeros = g.sub(segs, segs);
let block_in = g.concat_(vec![segs, zeros], 1);
let spec = g.fft(block_in, false);
let peaks = g.welch_peaks(spec, peak_params.k, peak_params.welch.n_segments);
g.set_outputs(vec![peaks]);
g
}
pub fn compile_welch_peaks_fused(
batch: usize,
peak_params: WelchPeakParams,
device: Device,
) -> Result<CompiledGraph> {
Ok(Session::new(device).compile(build_welch_peaks_fused_graph(batch, peak_params)))
}
pub struct CompiledRlxWelchPeaksFused {
exec: CompiledGraph,
pub peak_params: WelchPeakParams,
batch: usize,
window: Vec<f32>,
pub run_device: Device,
}
impl CompiledRlxWelchPeaksFused {
pub fn compile(batch: usize, peak_params: WelchPeakParams, device: Device) -> Result<Self> {
Ok(Self {
exec: compile_welch_peaks_fused(batch, peak_params, device)?,
peak_params,
batch,
window: hann_window(peak_params.welch.n_fft),
run_device: device,
})
}
pub fn welch_peaks_batch(&mut self, signal: &[f32]) -> Result<Vec<f32>> {
let frame = self.peak_params.frame_len();
ensure!(signal.len() == self.batch * frame);
let segs =
welch_windowed_segments(signal, self.batch, self.peak_params.welch, &self.window)?;
Ok(self.exec.run(&[("segs", &segs)]).remove(0))
}
}
pub struct CompiledRlxWelchPeaks {
exec: CompiledGraph,
pub peak_params: WelchPeakParams,
batch: usize,
window: Vec<f32>,
pub run_device: Device,
}
pub fn compile_rlx_welch_peaks(
batch: usize,
peak_params: WelchPeakParams,
device: Device,
) -> Result<CompiledRlxWelchPeaks> {
let exec = compile_welch_rlx_fft(batch, peak_params.welch, device)?;
Ok(CompiledRlxWelchPeaks {
exec,
peak_params,
batch,
window: hann_window(peak_params.welch.n_fft),
run_device: device,
})
}
impl CompiledRlxWelchPeaks {
pub fn welch_peaks_batch(
&mut self,
signal: &[f32],
scratch: &mut WelchPeaksScratch,
) -> Result<Vec<f32>> {
let frame = self.peak_params.frame_len();
ensure!(signal.len() == self.batch * frame);
let segs =
welch_windowed_segments(signal, self.batch, self.peak_params.welch, &self.window)?;
let n_seg = self.peak_params.welch.n_segments;
let spec = rlx_fft_forward(
&mut self.exec,
&segs,
self.batch * n_seg,
self.peak_params.welch.n_fft,
);
let psd = scratch.ensure(self.batch, self.peak_params.n_bins());
Ok(peaks_from_segment_spectrum_streaming(
&spec,
self.batch,
self.peak_params,
psd,
))
}
pub fn welch_peaks_batch_block(
&mut self,
signal: &[f32],
scratch: &mut WelchPeaksScratch,
) -> Result<Vec<f32>> {
let frame = self.peak_params.frame_len();
ensure!(signal.len() == self.batch * frame);
let segs =
welch_windowed_segments(signal, self.batch, self.peak_params.welch, &self.window)?;
let n_seg = self.peak_params.welch.n_segments;
let spec = rlx_fft_forward_block(
&mut self.exec,
&segs,
self.batch * n_seg,
self.peak_params.welch.n_fft,
);
let psd = scratch.ensure(self.batch, self.peak_params.n_bins());
Ok(peaks_from_block_segment_spectrum_streaming(
&spec,
self.batch,
self.peak_params,
psd,
))
}
}
pub struct CompiledLearnedWelchPeaks {
spectrum: CompiledLearnedMel,
pub peak_params: WelchPeakParams,
welch_batch: usize,
window: Vec<f32>,
}
pub fn compile_learned_welch_peaks(
model: &FastLearnedFftModel,
welch_batch: usize,
peak_params: WelchPeakParams,
device: Device,
hard_gate_threshold: f32,
) -> Result<CompiledLearnedWelchPeaks> {
ensure!(peak_params.welch.n_fft == model.n_fft);
let seg_batch = welch_batch * peak_params.welch.n_segments;
let cfg = FftLearnConfig::new(model.n_fft, seg_batch)?;
let spectrum = compile_learned_mel(model, &cfg, device, hard_gate_threshold)?;
Ok(CompiledLearnedWelchPeaks {
spectrum,
peak_params,
welch_batch,
window: hann_window(peak_params.welch.n_fft),
})
}
impl CompiledLearnedWelchPeaks {
pub fn run_device(&self) -> Device {
self.spectrum.run_device
}
pub fn welch_peaks_batch(
&mut self,
signal: &[f32],
scratch: &mut WelchPeaksScratch,
) -> Result<Vec<f32>> {
let frame = self.peak_params.frame_len();
ensure!(signal.len() == self.welch_batch * frame);
let segs = welch_windowed_segments(
signal,
self.welch_batch,
self.peak_params.welch,
&self.window,
)?;
let spec = self.spectrum.spectrum_batch(&segs)?;
let psd = scratch.ensure(self.welch_batch, self.peak_params.n_bins());
Ok(peaks_from_segment_spectrum_streaming(
&spec,
self.welch_batch,
self.peak_params,
psd,
))
}
}
pub fn default_welch_peaks_hard_threshold() -> f32 {
default_hard_threshold()
}