refactor: download presets and modules from dataset repo at runtime

.gitignore

@@ -1,2 +1,4 @@
 .vscode/*
-*.pyc
+*.pyc
+presets/*
+modules/*

app.py

@@ -13,6 +13,16 @@ from functools import partial, reduce
 from itertools import accumulate
 from torchcomp import coef2ms, ms2coef
 from copy import deepcopy
+from huggingface_hub import snapshot_download
+
+preset_path = snapshot_download(
+    "yoyolicoris/diffvox",
+    repo_type="dataset",
+    local_dir="./",
+    local_files_only=False,
+    allow_patterns=["presets/*", "modules/*"],
+)
+
 
 from modules.utils import vec2statedict, get_chunks
 from modules.fx import clip_delay_eq_Q

modules/functional.py

@@ -1,229 +0,0 @@
-import torch
-import torch.nn.functional as F
-from torchcomp import compexp_gain, db2amp
-from torchlpc import sample_wise_lpc
-from typing import List, Tuple, Union, Any, Optional
-import math
-
-
-def inv_22(a, b, c, d):
-    return torch.stack([d, -b, -c, a]).view(2, 2) / (a * d - b * c)
-
-
-def eig_22(a, b, c, d):
-    # https://croninprojects.org/Vince/Geodesy/FindingEigenvectors.pdf
-    T = a + d
-    D = a * d - b * c
-    half_T = T * 0.5
-    root = torch.sqrt(half_T * half_T - D)  # + 0j)
-    L = torch.stack([half_T + root, half_T - root])
-
-    y = (L - a) / b
-    # y = c / L
-    V = torch.stack([torch.ones_like(y), y])
-    return L, V / V.abs().square().sum(0).sqrt()
-
-
-def fir(x, b):
-    padded = F.pad(x.reshape(-1, 1, x.size(-1)), (b.size(0) - 1, 0))
-    return F.conv1d(padded, b.flip(0).view(1, 1, -1)).view(*x.shape)
-
-
-def allpole(x: torch.Tensor, a: torch.Tensor):
-    h = x.reshape(-1, x.shape[-1])
-    return sample_wise_lpc(
-        h,
-        a.broadcast_to(h.shape + a.shape),
-    ).reshape(*x.shape)
-
-
-def biquad(x: torch.Tensor, b0, b1, b2, a0, a1, a2):
-    b0 = b0 / a0
-    b1 = b1 / a0
-    b2 = b2 / a0
-    a1 = a1 / a0
-    a2 = a2 / a0
-
-    beta1 = b1 - b0 * a1
-    beta2 = b2 - b0 * a2
-
-    tmp = a1.square() - 4 * a2
-    if tmp < 0:
-        pole = 0.5 * (-a1 + 1j * torch.sqrt(-tmp))
-        u = -1j * x[..., :-1]
-        h = sample_wise_lpc(
-            u.reshape(-1, u.shape[-1]),
-            -pole.broadcast_to(u.shape).reshape(-1, u.shape[-1], 1),
-        ).reshape(*u.shape)
-        h = (
-            h.real * (beta1 * pole.real / pole.imag + beta2 / pole.imag)
-            - beta1 * h.imag
-        )
-    else:
-        L, V = eig_22(-a1, -a2, torch.ones_like(a1), torch.zeros_like(a1))
-        inv_V = inv_22(*V.view(-1))
-
-        C = torch.stack([beta1, beta2]) @ V
-
-        # project input to eigen space
-        h = x[..., :-1].unsqueeze(-2) * inv_V[:, :1]
-        L = L.unsqueeze(-1).broadcast_to(h.shape)
-
-        h = (
-            sample_wise_lpc(h.reshape(-1, h.shape[-1]), -L.reshape(-1, L.shape[-1], 1))
-            .reshape(*h.shape)
-            .transpose(-2, -1)
-        ) @ C
-    tmp = b0 * x
-    y = torch.cat([tmp[..., :1], h + tmp[..., 1:]], -1)
-    return y
-
-
-def highpass_biquad_coef(
-    sample_rate: int,
-    cutoff_freq: torch.Tensor,
-    Q: torch.Tensor,
-):
-    w0 = 2 * torch.pi * cutoff_freq / sample_rate
-    alpha = torch.sin(w0) / 2.0 / Q
-
-    b0 = (1 + torch.cos(w0)) / 2
-    b1 = -1 - torch.cos(w0)
-    b2 = b0
-    a0 = 1 + alpha
-    a1 = -2 * torch.cos(w0)
-    a2 = 1 - alpha
-    return b0, b1, b2, a0, a1, a2
-
-
-def apply_biquad(bq):
-    return lambda waveform, *args, **kwargs: biquad(waveform, *bq(*args, **kwargs))
-
-
-highpass_biquad = apply_biquad(highpass_biquad_coef)
-
-
-def lowpass_biquad_coef(
-    sample_rate: int,
-    cutoff_freq: torch.Tensor,
-    Q: torch.Tensor,
-):
-    w0 = 2 * torch.pi * cutoff_freq / sample_rate
-    alpha = torch.sin(w0) / 2 / Q
-
-    b0 = (1 - torch.cos(w0)) / 2
-    b1 = 1 - torch.cos(w0)
-    b2 = b0
-    a0 = 1 + alpha
-    a1 = -2 * torch.cos(w0)
-    a2 = 1 - alpha
-    return b0, b1, b2, a0, a1, a2
-
-
-def equalizer_biquad_coef(
-    sample_rate: int,
-    center_freq: torch.Tensor,
-    gain: torch.Tensor,
-    Q: torch.Tensor,
-):
-
-    w0 = 2 * torch.pi * center_freq / sample_rate
-    A = torch.exp(gain / 40.0 * math.log(10))
-    alpha = torch.sin(w0) / 2 / Q
-
-    b0 = 1 + alpha * A
-    b1 = -2 * torch.cos(w0)
-    b2 = 1 - alpha * A
-
-    a0 = 1 + alpha / A
-    a1 = -2 * torch.cos(w0)
-    a2 = 1 - alpha / A
-    return b0, b1, b2, a0, a1, a2
-
-
-def lowshelf_biquad_coef(
-    sample_rate: int,
-    cutoff_freq: torch.Tensor,
-    gain: torch.Tensor,
-    Q: torch.Tensor,
-):
-
-    w0 = 2 * torch.pi * cutoff_freq / sample_rate
-    A = torch.exp(gain / 40.0 * math.log(10))
-    alpha = torch.sin(w0) / 2 / Q
-    cosw0 = torch.cos(w0)
-    sqrtA = torch.sqrt(A)
-
-    b0 = A * (A + 1 - (A - 1) * cosw0 + 2 * alpha * sqrtA)
-    b1 = 2 * A * (A - 1 - (A + 1) * cosw0)
-    b2 = A * (A + 1 - (A - 1) * cosw0 - 2 * alpha * sqrtA)
-
-    a0 = A + 1 + (A - 1) * cosw0 + 2 * alpha * sqrtA
-    a1 = -2 * (A - 1 + (A + 1) * cosw0)
-    a2 = A + 1 + (A - 1) * cosw0 - 2 * alpha * sqrtA
-
-    return b0, b1, b2, a0, a1, a2
-
-
-def highshelf_biquad_coef(
-    sample_rate: int,
-    cutoff_freq: torch.Tensor,
-    gain: torch.Tensor,
-    Q: torch.Tensor,
-):
-
-    w0 = 2 * torch.pi * cutoff_freq / sample_rate
-    A = torch.exp(gain / 40.0 * math.log(10))
-    alpha = torch.sin(w0) / 2 / Q
-    cosw0 = torch.cos(w0)
-    sqrtA = torch.sqrt(A)
-
-    b0 = A * (A + 1 + (A - 1) * cosw0 + 2 * alpha * sqrtA)
-    b1 = -2 * A * (A - 1 + (A + 1) * cosw0)
-    b2 = A * (A + 1 + (A - 1) * cosw0 - 2 * alpha * sqrtA)
-
-    a0 = A + 1 - (A - 1) * cosw0 + 2 * alpha * sqrtA
-    a1 = 2 * (A - 1 - (A + 1) * cosw0)
-    a2 = A + 1 - (A - 1) * cosw0 - 2 * alpha * sqrtA
-
-    return b0, b1, b2, a0, a1, a2
-
-
-highpass_biquad = apply_biquad(highpass_biquad_coef)
-lowpass_biquad = apply_biquad(lowpass_biquad_coef)
-highshelf_biquad = apply_biquad(highshelf_biquad_coef)
-lowshelf_biquad = apply_biquad(lowshelf_biquad_coef)
-equalizer_biquad = apply_biquad(equalizer_biquad_coef)
-
-
-def avg(rms: torch.Tensor, avg_coef: torch.Tensor):
-    assert torch.all(avg_coef > 0) and torch.all(avg_coef <= 1)
-
-    h = rms * avg_coef
-
-    return sample_wise_lpc(
-        h,
-        (avg_coef - 1).broadcast_to(h.shape).unsqueeze(-1),
-    )
-
-
-def avg_rms(audio: torch.Tensor, avg_coef) -> torch.Tensor:
-    return avg(audio.square().clamp_min(1e-8), avg_coef).sqrt()
-
-
-def compressor_expander(
-    x: torch.Tensor,
-    avg_coef: Union[torch.Tensor, float],
-    cmp_th: Union[torch.Tensor, float],
-    cmp_ratio: Union[torch.Tensor, float],
-    exp_th: Union[torch.Tensor, float],
-    exp_ratio: Union[torch.Tensor, float],
-    at: Union[torch.Tensor, float],
-    rt: Union[torch.Tensor, float],
-    make_up: torch.Tensor,
-    lookahead_func=lambda x: x,
-):
-    rms = avg_rms(x, avg_coef=avg_coef)
-    gain = compexp_gain(rms, cmp_th, cmp_ratio, exp_th, exp_ratio, at, rt)
-    gain = lookahead_func(gain)
-    return x * gain * db2amp(make_up).broadcast_to(x.shape[0], 1)

modules/fx.py

@@ -1,1061 +0,0 @@
-import torch
-from torch import nn
-import torch.nn.functional as F
-from torch.nn.utils.parametrize import register_parametrization
-from torchcomp import ms2coef, coef2ms, db2amp, amp2db
-from torchaudio.transforms import Spectrogram, InverseSpectrogram
-
-from typing import List, Tuple, Union, Any, Optional, Callable
-import math
-from torch_fftconv import fft_conv1d
-from functools import reduce
-
-from .functional import (
-    compressor_expander,
-    lowpass_biquad,
-    highpass_biquad,
-    equalizer_biquad,
-    lowshelf_biquad,
-    highshelf_biquad,
-    lowpass_biquad_coef,
-    highpass_biquad_coef,
-    highshelf_biquad_coef,
-    lowshelf_biquad_coef,
-    equalizer_biquad_coef,
-)
-from .utils import chain_functions
-
-
-class Clip(nn.Module):
-    def __init__(self, max: Optional[float] = None, min: Optional[float] = None):
-        super().__init__()
-        self.min = min
-        self.max = max
-
-    def forward(self, x):
-        if self.min is not None:
-            x = torch.clip(x, min=self.min)
-        if self.max is not None:
-            x = torch.clip(x, max=self.max)
-        return x
-
-
-def clip_delay_eq_Q(m: nn.Module, Q: float):
-    if isinstance(m, Delay) and isinstance(m.eq, LowPass):
-        register_parametrization(m.eq.params, "Q", Clip(max=Q))
-    return m
-
-
-float2param = lambda x: nn.Parameter(
-    torch.tensor(x, dtype=torch.float32) if not isinstance(x, torch.Tensor) else x
-)
-
-STEREO_NORM = math.sqrt(2)
-
-
-def broadcast2stereo(m, args):
-    x, *_ = args
-    return x.expand(-1, 2, -1) if x.shape[1] == 1 else x
-
-
-hadamard = lambda x: torch.stack([x.sum(1), x[:, 0] - x[:, 1]], 1) / STEREO_NORM
-
-
-class Hadamard(nn.Module):
-    def forward(self, x):
-        return hadamard(x)
-
-
-class FX(nn.Module):
-    def __init__(self, **kwargs) -> None:
-        super().__init__()
-
-        self.params = nn.ParameterDict({k: float2param(v) for k, v in kwargs.items()})
-
-    def toJSON(self) -> dict[str, Any]:
-        return {k: v.item() for k, v in self.params.items() if v.numel() == 1}
-
-
-class SmoothingCoef(nn.Module):
-    def forward(self, x):
-        return x.sigmoid()
-
-    def right_inverse(self, y):
-        return (y / (1 - y)).log()
-
-
-class CompRatio(nn.Module):
-    def forward(self, x):
-        return x.exp() + 1
-
-    def right_inverse(self, y):
-        return torch.log(y - 1)
-
-
-class MinMax(nn.Module):
-    def __init__(self, min=0.0, max: Union[float, torch.Tensor] = 1.0):
-        super().__init__()
-        if isinstance(min, torch.Tensor):
-            self.register_buffer("min", min, persistent=False)
-        else:
-            self.min = min
-
-        if isinstance(max, torch.Tensor):
-            self.register_buffer("max", max, persistent=False)
-        else:
-            self.max = max
-
-        self._m = SmoothingCoef()
-
-    def forward(self, x):
-        return self._m(x) * (self.max - self.min) + self.min
-
-    def right_inverse(self, y):
-        return self._m.right_inverse((y - self.min) / (self.max - self.min))
-
-
-class WrappedPositive(nn.Module):
-    def __init__(self, period):
-        super().__init__()
-        self.period = period
-
-    def forward(self, x):
-        return x.abs() % self.period
-
-    def right_inverse(self, y):
-        return y
-
-
-class CompressorExpander(FX):
-    cmp_ratio_min: float = 1
-    cmp_ratio_max: float = 20
-
-    def __init__(
-        self,
-        sr: int,
-        cmp_ratio: float = 2.0,
-        exp_ratio: float = 0.5,
-        at_ms: float = 50.0,
-        rt_ms: float = 50.0,
-        avg_coef: float = 0.3,
-        cmp_th: float = -18.0,
-        exp_th: float = -54.0,
-        make_up: float = 0.0,
-        delay: int = 0,
-        lookahead: bool = False,
-        max_lookahead: float = 15.0,
-    ):
-        super().__init__(
-            cmp_th=cmp_th,
-            exp_th=exp_th,
-            make_up=make_up,
-            avg_coef=avg_coef,
-            cmp_ratio=cmp_ratio,
-            exp_ratio=exp_ratio,
-        )
-        # deprecated, please use lookahead instead
-        self.delay = delay
-        self.sr = sr
-
-        self.params["at"] = nn.Parameter(ms2coef(torch.tensor(at_ms), sr))
-        self.params["rt"] = nn.Parameter(ms2coef(torch.tensor(rt_ms), sr))
-
-        if lookahead:
-            self.params["lookahead"] = nn.Parameter(torch.ones(1) / sr * 1000)
-            register_parametrization(
-                self.params, "lookahead", WrappedPositive(max_lookahead)
-            )
-            sinc_length = int(sr * (max_lookahead + 1) * 0.001) + 1
-            left_pad_size = int(sr * 0.001)
-            self._pad_size = (left_pad_size, sinc_length - left_pad_size - 1)
-            self.register_buffer(
-                "_arange",
-                torch.arange(sinc_length) - left_pad_size,
-                persistent=False,
-            )
-        self.lookahead = lookahead
-
-        register_parametrization(self.params, "at", SmoothingCoef())
-        register_parametrization(self.params, "rt", SmoothingCoef())
-        register_parametrization(self.params, "avg_coef", SmoothingCoef())
-        register_parametrization(
-            self.params, "cmp_ratio", MinMax(self.cmp_ratio_min, self.cmp_ratio_max)
-        )
-        register_parametrization(self.params, "exp_ratio", SmoothingCoef())
-
-    def extra_repr(self) -> str:
-        with torch.no_grad():
-            s = (
-                f"attack: {coef2ms(self.params.at, self.sr).item()} (ms)\n"
-                f"release: {coef2ms(self.params.rt, self.sr).item()} (ms)\n"
-                f"avg_coef: {self.params.avg_coef.item()}\n"
-                f"compressor_ratio: {self.params.cmp_ratio.item()}\n"
-                f"expander_ratio: {self.params.exp_ratio.item()}\n"
-                f"compressor_threshold: {self.params.cmp_th.item()} (dB)\n"
-                f"expander_threshold: {self.params.exp_th.item()} (dB)\n"
-                f"make_up: {self.params.make_up.item()} (dB)"
-            )
-            if self.lookahead:
-                s += f"\nlookahead: {self.params.lookahead.item()} (ms)"
-        return s
-
-    def toJSON(self) -> dict[str, Any]:
-        return {
-            "Attack (ms)": coef2ms(self.params.at, self.sr).item(),
-            "Release (ms)": coef2ms(self.params.rt, self.sr).item(),
-            "Average Coefficient": self.params.avg_coef.item(),
-            "Compressor Ratio": self.params.cmp_ratio.item(),
-            "Expander Ratio": self.params.exp_ratio.item(),
-            "Compressor Threshold (dB)": self.params.cmp_th.item(),
-            "Expander Threshold (dB)": self.params.exp_th.item(),
-            "Make Up (dB)": self.params.make_up.item(),
-        } | ({"Lookahead (ms)": self.params.lookahead.item()} if self.lookahead else {})
-
-    def forward(self, x):
-        if self.lookahead:
-            lookahead_in_samples = self.params.lookahead * 0.001 * self.sr
-            sinc_filter = torch.sinc(self._arange - lookahead_in_samples)
-            lookahead_func = lambda gain: F.conv1d(
-                F.pad(
-                    gain.view(-1, 1, gain.size(-1)), self._pad_size, mode="replicate"
-                ),
-                sinc_filter[None, None, :],
-            ).view(*gain.shape)
-        else:
-            lookahead_func = lambda x: x
-
-        return compressor_expander(
-            x.reshape(-1, x.shape[-1]),
-            lookahead_func=lookahead_func,
-            **{k: v for k, v in self.params.items() if k != "lookahead"},
-        ).view(*x.shape)
-
-
-class Panning(FX):
-    def __init__(self, pan: float = 0.0):
-        assert pan <= 100 and pan >= -100
-        super().__init__(pan=(pan + 100) / 200)
-
-        register_parametrization(self.params, "pan", SmoothingCoef())
-
-        self.register_forward_pre_hook(broadcast2stereo)
-
-    def extra_repr(self) -> str:
-        with torch.no_grad():
-            s = f"pan: {self.params.pan.item() * 200 - 100}"
-        return s
-
-    def toJSON(self) -> dict[str, Any]:
-        return {
-            "Pan": self.params.pan.item() * 200 - 100,
-        }
-
-    def forward(self, x: torch.Tensor):
-        angle = self.params.pan.view(1) * torch.pi * 0.5
-        amp = torch.concat([angle.cos(), angle.sin()]).view(2, 1) * STEREO_NORM
-        return x * amp
-
-
-class StereoWidth(Panning):
-    def forward(self, x: torch.Tensor):
-        return chain_functions(hadamard, super().forward, hadamard)(x)
-
-
-class ImpulseResponse(nn.Module):
-    def forward(self, h):
-        return torch.cat([torch.ones_like(h[..., :1]), h], dim=-1)
-
-
-class FIR(FX):
-    def __init__(
-        self,
-        length: int,
-        channels: int = 2,
-        conv_method: str = "direct",
-    ):
-        super().__init__(kernel=torch.zeros(channels, length - 1))
-        self._padding = length - 1
-        self.channels = channels
-
-        match conv_method:
-            case "direct":
-                self.conv_func = F.conv1d
-            case "fft":
-                self.conv_func = fft_conv1d
-            case _:
-                raise ValueError(f"Unknown conv_method: {conv_method}")
-
-        if channels == 2:
-            self.register_forward_pre_hook(broadcast2stereo)
-
-    def forward(self, x: torch.Tensor):
-        zero_padded = F.pad(x[..., :-1], (self._padding, 0), "constant", 0)
-        return x + self.conv_func(
-            zero_padded, self.params.kernel.flip(1).unsqueeze(1), groups=self.channels
-        )
-
-
-class QFactor(nn.Module):
-    def forward(self, x):
-        return x.exp()
-
-    def right_inverse(self, y):
-        return y.log()
-
-
-class LowPass(FX):
-    def __init__(
-        self,
-        sr: int,
-        freq: float = 17500.0,
-        Q: float = 0.707,
-        min_freq: float = 200.0,
-        max_freq: float = 18000,
-        min_Q: float = 0.5,
-        max_Q: float = 10.0,
-    ):
-        super().__init__(freq=freq, Q=Q)
-
-        self.sr = sr
-        register_parametrization(self.params, "freq", MinMax(min_freq, max_freq))
-        register_parametrization(self.params, "Q", MinMax(min_Q, max_Q))
-
-    def forward(self, x):
-        return lowpass_biquad(
-            x, sample_rate=self.sr, cutoff_freq=self.params.freq, Q=self.params.Q
-        )
-
-    def extra_repr(self) -> str:
-        with torch.no_grad():
-            s = f"freq: {self.params.freq.item():.4f}, Q: {self.params.Q.item():.4f}"
-        return s
-
-    def toJSON(self) -> dict[str, Any]:
-        return {
-            "Frequency (Hz)": self.params.freq.item(),
-            "Q": self.params.Q.item(),
-        }
-
-
-class HighPass(LowPass):
-    def __init__(
-        self,
-        *args,
-        freq: float = 200.0,
-        min_freq: float = 16.0,
-        max_freq: float = 5300.0,
-        **kwargs,
-    ):
-        super().__init__(
-            *args, freq=freq, min_freq=min_freq, max_freq=max_freq, **kwargs
-        )
-
-    def forward(self, x):
-        return highpass_biquad(
-            x, sample_rate=self.sr, cutoff_freq=self.params.freq, Q=self.params.Q
-        )
-
-
-class Peak(FX):
-    def __init__(
-        self,
-        sr: int,
-        gain: float = 0.0,
-        freq: float = 2000.0,
-        Q: float = 0.707,
-        min_freq: float = 33.0,
-        max_freq: float = 17500.0,
-        min_Q: float = 0.2,
-        max_Q: float = 20,
-    ):
-        super().__init__(freq=freq, Q=Q, gain=gain)
-
-        self.sr = sr
-
-        register_parametrization(self.params, "freq", MinMax(min_freq, max_freq))
-        register_parametrization(self.params, "Q", MinMax(min_Q, max_Q))
-
-    def forward(self, x):
-        return equalizer_biquad(
-            x,
-            sample_rate=self.sr,
-            center_freq=self.params.freq,
-            Q=self.params.Q,
-            gain=self.params.gain,
-        )
-
-    def extra_repr(self) -> str:
-        with torch.no_grad():
-            s = f"freq: {self.params.freq.item():.4f}, gain: {self.params.gain.item():.4f}, Q: {self.params.Q.item():.4f}"
-        return s
-
-    def toJSON(self) -> dict[str, Any]:
-        return {
-            "Frequency (Hz)": self.params.freq.item(),
-            "Gain (dB)": self.params.gain.item(),
-            "Q": self.params.Q.item(),
-        }
-
-
-class LowShelf(FX):
-    def __init__(
-        self,
-        sr: int,
-        gain: float = 0.0,
-        freq: float = 115.0,
-        min_freq: float = 30,
-        max_freq: float = 200,
-    ):
-        super().__init__(freq=freq, gain=gain)
-
-        self.sr = sr
-        register_parametrization(self.params, "freq", MinMax(min_freq, max_freq))
-
-        self.register_buffer("Q", torch.tensor(0.707), persistent=False)
-
-    def forward(self, x):
-        return lowshelf_biquad(
-            x,
-            sample_rate=self.sr,
-            cutoff_freq=self.params.freq,
-            gain=self.params.gain,
-            Q=self.Q,
-        )
-
-    def extra_repr(self) -> str:
-        with torch.no_grad():
-            s = f"freq: {self.params.freq.item():.4f}, gain: {self.params.gain.item():.4f}"
-        return s
-
-    def toJSON(self) -> dict[str, Any]:
-        return {
-            "Frequency (Hz)": self.params.freq.item(),
-            "Gain (dB)": self.params.gain.item(),
-        }
-
-
-class HighShelf(LowShelf):
-    def __init__(
-        self,
-        *args,
-        freq: float = 4525,
-        min_freq: float = 750,
-        max_freq: float = 8300,
-        **kwargs,
-    ):
-        super().__init__(
-            *args, freq=freq, min_freq=min_freq, max_freq=max_freq, **kwargs
-        )
-
-    def forward(self, x):
-        return highshelf_biquad(
-            x,
-            sample_rate=self.sr,
-            cutoff_freq=self.params.freq,
-            gain=self.params.gain,
-            Q=self.Q,
-        )
-
-
-def module2coeffs(
-    m: Union[LowPass, HighPass, Peak, LowShelf, HighShelf],
-) -> Tuple[
-    torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor
-]:
-    match m:
-        case LowPass():
-            return lowpass_biquad_coef(m.sr, m.params.freq, m.params.Q)
-        case HighPass():
-            return highpass_biquad_coef(m.sr, m.params.freq, m.params.Q)
-        case Peak():
-            return equalizer_biquad_coef(m.sr, m.params.freq, m.params.Q, m.params.gain)
-        case LowShelf():
-            return lowshelf_biquad_coef(m.sr, m.params.freq, m.params.gain, m.Q)
-        case HighShelf():
-            return highshelf_biquad_coef(m.sr, m.params.freq, m.params.gain, m.Q)
-        case _:
-            raise ValueError(f"Unknown module: {m}")
-
-
-class AlwaysNegative(nn.Module):
-    def forward(self, x):
-        return -F.softplus(x)
-
-    def right_inverse(self, y):
-        return torch.log(y.neg().exp() - 1)
-
-
-class Reverb(FX):
-    def __init__(self, ir_len=60000, n_fft=384, hop_length=192, downsample_factor=1):
-        super().__init__(
-            log_mag=torch.full((2, n_fft // downsample_factor // 2 + 1), -1.0),
-            log_mag_delta=torch.full((2, n_fft // downsample_factor // 2 + 1), -5.0),
-        )
-
-        self.steps = (ir_len - n_fft + hop_length - 1) // hop_length
-        self.n_fft = n_fft
-        self.hop_length = hop_length
-        self.downsample_factor = downsample_factor
-
-        self._noise_angle = nn.Parameter(
-            torch.rand(2, n_fft // 2 + 1, self.steps) * 2 * torch.pi
-        )
-
-        self.register_buffer(
-            "_arange", torch.arange(self.steps, dtype=torch.float32), persistent=False
-        )
-        self.spec_forward = Spectrogram(n_fft, hop_length=hop_length, power=None)
-        self.spec_inverse = InverseSpectrogram(
-            n_fft,
-            hop_length=hop_length,
-        )
-
-        register_parametrization(self.params, "log_mag", AlwaysNegative())
-        register_parametrization(self.params, "log_mag_delta", AlwaysNegative())
-
-        self.register_forward_pre_hook(broadcast2stereo)
-
-    def forward(self, x):
-        h = x
-        H = self.spec_forward(h)
-
-        log_mag = self.params.log_mag
-        log_mag_delta = self.params.log_mag_delta
-
-        if self.downsample_factor > 1:
-            log_mag = F.interpolate(
-                log_mag.unsqueeze(0),
-                size=self._noise_angle.size(1),
-                align_corners=True,
-                mode="linear",
-            ).squeeze(0)
-            log_mag_delta = F.interpolate(
-                log_mag_delta.unsqueeze(0),
-                size=self._noise_angle.size(1),
-                align_corners=True,
-                mode="linear",
-            ).squeeze(0)
-
-        ir_2d = torch.exp(
-            log_mag.unsqueeze(-1)
-            + log_mag_delta.unsqueeze(-1) * self._arange
-            + self._noise_angle * 1j
-        )
-
-        padded_H = F.pad(H.flatten(1, 2), (ir_2d.shape[-1] - 1, 0))
-
-        H = F.conv1d(
-            padded_H,
-            hadamard(ir_2d.unsqueeze(0)).flatten(1, 2).flip(-1).transpose(0, 1),
-            groups=H.shape[2] * 2,
-        ).view(*H.shape)
-
-        h = self.spec_inverse(H)
-        return h
-
-
-class Delay(FX):
-    min_delay: float = 100
-    max_delay: float = 1000
-
-    def __init__(
-        self,
-        sr: int,
-        delay=200.0,
-        feedback=0.1,
-        gain=0.1,
-        ir_duration: float = 2,
-        eq: Optional[nn.Module] = None,
-        recursive_eq=False,
-    ):
-        super().__init__(
-            delay=delay,
-            feedback=feedback,
-            gain=gain,
-        )
-        self.sr = sr
-        self.ir_length = int(sr * max(ir_duration, self.max_delay * 0.002))
-
-        register_parametrization(
-            self.params, "delay", MinMax(self.min_delay, self.max_delay)
-        )
-        register_parametrization(self.params, "feedback", SmoothingCoef())
-        register_parametrization(self.params, "gain", SmoothingCoef())
-
-        self.eq = eq
-        self.recursive_eq = recursive_eq
-
-        self.register_buffer(
-            "_arange", torch.arange(self.ir_length, dtype=torch.float32)
-        )
-
-        self.odd_pan = Panning(0)
-        self.even_pan = Panning(0)
-
-    def forward(self, x):
-        assert x.size(1) == 1, x.size()
-        delay_in_samples = self.sr * self.params.delay * 0.001
-        num_delays = self.ir_length // int(delay_in_samples.item() + 1)
-        series = torch.arange(1, num_delays + 1, device=x.device)
-        decays = self.params.feedback ** (series - 1)
-
-        if self.recursive_eq and self.eq is not None:
-            sinc_index = self._arange - delay_in_samples
-            single_sinc_filter = torch.sinc(sinc_index)
-            eq_sinc_filter = self.eq(single_sinc_filter)
-            H = torch.fft.rfft(eq_sinc_filter)
-            H_powered = torch.polar(
-                H.abs() ** series.unsqueeze(-1), H.angle() * series.unsqueeze(-1)
-            )
-            sinc_filters = torch.fft.irfft(H_powered, n=self.ir_length)
-        else:
-            delays_in_samples = delay_in_samples * series
-            sinc_indexes = self._arange - delays_in_samples.unsqueeze(-1)
-            sinc_filters = torch.sinc(sinc_indexes)
-
-        decayed_sinc_filters = sinc_filters * decays.unsqueeze(-1)
-        return self._filter(x, decayed_sinc_filters)
-
-    def _filter(self, x: torch.Tensor, decayed_sinc_filters: torch.Tensor):
-        odd_delay_filters = torch.sum(decayed_sinc_filters[::2], 0)
-        even_delay_filters = torch.sum(decayed_sinc_filters[1::2], 0)
-        stacked_filters = torch.stack([odd_delay_filters, even_delay_filters])
-
-        if self.eq is not None and not self.recursive_eq:
-            stacked_filters = self.eq(stacked_filters)
-
-        gained_odd_even_filters = stacked_filters * self.params.gain
-        padded_x = F.pad(x, (gained_odd_even_filters.size(-1) - 1, 0))
-        conv1d = F.conv1d if x.size(-1) > 44100 * 20 else fft_conv1d
-        return sum(
-            [
-                panner(s)
-                for panner, s in zip(
-                    [self.odd_pan, self.even_pan],
-                    # fft_conv1d(
-                    conv1d(
-                        padded_x,
-                        gained_odd_even_filters.flip(-1).unsqueeze(1),
-                    ).chunk(2, 1),
-                )
-            ]
-        )
-
-    def extra_repr(self) -> str:
-        with torch.no_grad():
-            s = (
-                f"delay: {self.sr * self.params.delay.item() * 0.001} (samples)\n"
-                f"feedback: {self.params.feedback.item()}\n"
-                f"gain: {self.params.gain.item()}"
-            )
-        return s
-
-    def toJSON(self) -> dict[str, Any]:
-        return {
-            "Delay (ms)": self.params.delay.item(),
-            "Feedback (dB)": self.params.feedback.log10().mul(20).item(),
-            "Gain (dB)": self.params.gain.log10().mul(20).item(),
-            "Odd delays": self.odd_pan.toJSON(),
-            "Even delays": self.even_pan.toJSON(),
-        }
-
-
-class SurrogateDelay(Delay):
-    def __init__(self, *args, dropout=0.5, straight_through=False, **kwargs):
-        super().__init__(*args, **kwargs)
-
-        self.dropout = dropout
-        self.straight_through = straight_through
-        self.log_damp = nn.Parameter(torch.ones(1) * -0.01)
-        register_parametrization(self, "log_damp", AlwaysNegative())
-
-    def forward(self, x):
-        assert x.size(1) == 1, x.size()
-        if not self.training:
-            return super().forward(x)
-
-        log_damp = self.log_damp
-        delay_in_samples = self.sr * self.params.delay * 0.001
-        num_delays = self.ir_length // int(delay_in_samples.item() + 1)
-        series = torch.arange(1, num_delays + 1, device=x.device)
-        decays = self.params.feedback ** (series - 1)
-
-        if self.recursive_eq and self.eq is not None:
-            exp_factor = self._arange[: self.ir_length // 2 + 1]
-            damped_exp = torch.exp(
-                log_damp * exp_factor
-                - 1j * delay_in_samples / self.ir_length * 2 * torch.pi * exp_factor
-            )
-            sinc_filter = torch.fft.irfft(damped_exp, n=self.ir_length)
-            if self.straight_through:
-                sinc_index = self._arange - delay_in_samples
-                hard_sinc_filter = torch.sinc(sinc_index)
-                sinc_filter = sinc_filter + (hard_sinc_filter - sinc_filter).detach()
-
-            eq_sinc_filter = self.eq(sinc_filter)
-            H = torch.fft.rfft(eq_sinc_filter)
-
-            # use polar form to avoid NaN
-            H_powered = torch.polar(
-                H.abs() ** series.unsqueeze(-1), H.angle() * series.unsqueeze(-1)
-            )
-            sinc_filters = torch.fft.irfft(H_powered, n=self.ir_length)
-        else:
-            exp_factors = series.unsqueeze(-1) * self._arange[: self.ir_length // 2 + 1]
-            damped_exps = torch.exp(
-                log_damp * exp_factors
-                - 1j * delay_in_samples / self.ir_length * 2 * torch.pi * exp_factors
-            )
-            sinc_filters = torch.fft.irfft(damped_exps, n=self.ir_length)
-            if self.straight_through:
-                delays_in_samples = delay_in_samples * series
-                sinc_indexes = self._arange - delays_in_samples.unsqueeze(-1)
-                hard_sinc_filters = torch.sinc(sinc_indexes)
-                sinc_filters = (
-                    sinc_filters + (hard_sinc_filters - sinc_filters).detach()
-                )
-
-        decayed_sinc_filters = sinc_filters * decays.unsqueeze(-1)
-
-        dropout_mask = torch.rand(x.size(0), device=x.device) < self.dropout
-        if not torch.any(dropout_mask):
-            return self._filter(x, decayed_sinc_filters)
-        elif torch.all(dropout_mask):
-            return super().forward(x)
-
-        out = torch.zeros((x.size(0), 2, x.size(2)), device=x.device)
-        out[~dropout_mask] = self._filter(x[~dropout_mask], decayed_sinc_filters)
-        out[dropout_mask] = super().forward(x[dropout_mask])
-        return out
-
-    def extra_repr(self) -> str:
-        with torch.no_grad():
-            return super().extra_repr() + f"\ndamp: {self.log_damp.exp().item()}"
-
-
-class FSDelay(FX):
-    def __init__(
-        self,
-        sr: int,
-        delay=200.0,
-        feedback=0.1,
-        gain=0.1,
-        ir_duration: float = 6,
-        eq: Optional[LowPass] = None,
-        recursive_eq=False,
-    ):
-        super().__init__(
-            delay=delay,
-            feedback=feedback,
-            gain=gain,
-        )
-        self.sr = sr
-        self.ir_length = int(sr * max(ir_duration, Delay.max_delay * 0.002))
-
-        register_parametrization(
-            self.params, "delay", MinMax(Delay.min_delay, Delay.max_delay)
-        )
-        register_parametrization(self.params, "gain", SmoothingCoef())
-
-        T_60 = ir_duration * 0.75
-        max_delay_in_samples = sr * Delay.max_delay * 0.001
-        maximum_decay = db2amp(torch.tensor(-60 / sr / T_60 * max_delay_in_samples))
-        register_parametrization(self.params, "feedback", MinMax(0, maximum_decay))
-
-        self.eq = eq
-        self.recursive_eq = recursive_eq
-
-        self.odd_pan = Panning(0)
-        self.even_pan = Panning(0)
-
-        self.register_buffer(
-            "_arange", torch.arange(self.ir_length, dtype=torch.float32)
-        )
-
-    def _get_h(self):
-        freqs = self._arange[: self.ir_length // 2 + 1] / self.ir_length * 2 * torch.pi
-        delay_in_samples = self.sr * self.params.delay * 0.001
-
-        # construct it like a fdn
-        Dinv = torch.exp(1j * freqs * delay_in_samples)
-        Dinv2 = torch.exp(2j * freqs * delay_in_samples)
-        if self.recursive_eq and self.eq is not None:
-            b0, b1, b2, a0, a1, a2 = module2coeffs(self.eq)
-            z_inv = torch.exp(-1j * freqs)
-            z_inv2 = torch.exp(-2j * freqs)
-            eq_H = (b0 + b1 * z_inv + b2 * z_inv2) / (a0 + a1 * z_inv + a2 * z_inv2)
-            damp = eq_H * self.params.feedback
-            det = Dinv2 - damp * damp
-        else:
-            damp = torch.full_like(Dinv, self.params.feedback) + 0j
-            det = Dinv2 - self.params.feedback.square()
-        inv_Dinv_m_A = torch.stack([Dinv, damp], 0) / det
-        h = torch.fft.irfft(inv_Dinv_m_A, n=self.ir_length) * self.params.gain
-
-        if self.eq is not None and not self.recursive_eq:
-            h = self.eq(h)
-        return h
-
-    def forward(self, x):
-        assert x.size(1) == 1, x.size()
-        h = self._get_h()
-
-        padded_x = F.pad(x, (h.size(-1) - 1, 0))
-        conv1d = F.conv1d if x.size(-1) > 44100 * 20 else fft_conv1d
-        return sum(
-            [
-                panner(s)
-                for panner, s in zip(
-                    [self.odd_pan, self.even_pan],
-                    conv1d(
-                        padded_x,
-                        h.flip(-1).unsqueeze(1),
-                    ).chunk(2, 1),
-                )
-            ]
-        )
-
-    def extra_repr(self) -> str:
-        with torch.no_grad():
-            s = (
-                f"delay: {self.sr * self.params.delay.item() * 0.001} (samples)\n"
-                f"feedback: {self.params.feedback.item()}\n"
-                f"gain: {self.params.gain.item()}"
-            )
-        return s
-
-
-class FSSurrogateDelay(FSDelay):
-    def __init__(self, *args, straight_through=False, **kwargs):
-        super().__init__(*args, **kwargs)
-
-        self.straight_through = straight_through
-        self.log_damp = nn.Parameter(torch.ones(1) * -0.0001)
-        register_parametrization(self, "log_damp", AlwaysNegative())
-
-    def _get_h(self):
-        if not self.training:
-            return super()._get_h()
-
-        log_damp = self.log_damp
-        delay_in_samples = self.sr * self.params.delay * 0.001
-
-        exp_factor = self._arange[: self.ir_length // 2 + 1]
-        freqs = exp_factor / self.ir_length * 2 * torch.pi
-        D = torch.exp(log_damp * exp_factor - 1j * delay_in_samples * freqs)
-        D2 = torch.exp(log_damp * exp_factor * 2 - 2j * delay_in_samples * freqs)
-
-        if self.straight_through:
-            D_orig = torch.exp(-1j * delay_in_samples * freqs)
-            D2_orig = torch.exp(-2j * delay_in_samples * freqs)
-            D = torch.stack([D, D_orig], 0)
-            D2 = torch.stack([D2, D2_orig], 0)
-
-        if self.recursive_eq and self.eq is not None:
-            b0, b1, b2, a0, a1, a2 = module2coeffs(self.eq)
-            z_inv = torch.exp(-1j * freqs)
-            z_inv2 = torch.exp(-2j * freqs)
-            eq_H = (b0 + b1 * z_inv + b2 * z_inv2) / (a0 + a1 * z_inv + a2 * z_inv2)
-            damp = eq_H * self.params.feedback
-            odd_H = D / (1 - damp * damp * D2)
-            even_H = odd_H * D * damp
-        else:
-            damp = torch.full_like(D, self.params.feedback) + 0j
-            odd_H = D / (1 - self.params.feedback.square() * D2)
-            even_H = odd_H * D * self.params.feedback
-
-        inv_Dinv_m_A = torch.stack([odd_H, even_H], 0)
-        h = torch.fft.irfft(inv_Dinv_m_A, n=self.ir_length)
-
-        if self.straight_through:
-            damped_h, orig_h = h.unbind(1)
-            h = damped_h + (orig_h - damped_h).detach()
-
-        if self.eq is not None and not self.recursive_eq:
-            h = self.eq(h)
-        return h * self.params.gain
-
-    def extra_repr(self) -> str:
-        with torch.no_grad():
-            return super().extra_repr() + f"\ndamp: {self.log_damp.exp().item()}"
-
-
-class SendFXsAndSum(FX):
-    def __init__(self, *args, cross_send=True, pan_direct=False):
-        super().__init__(
-            **(
-                {
-                    f"sends_{i}": torch.full([len(args) - i - 1], 0.01)
-                    for i in range(len(args) - 1)
-                }
-                if cross_send
-                else {}
-            )
-        )
-        self.effects = nn.ModuleList(args)
-        if pan_direct:
-            self.pan = Panning()
-
-        if cross_send:
-            for i in range(len(args) - 1):
-                register_parametrization(self.params, f"sends_{i}", SmoothingCoef())
-
-    def forward(self, x):
-        if hasattr(self, "pan"):
-            di = self.pan(x)
-        else:
-            di = x
-
-        if len(self.params) == 0:
-            return di, reduce(
-                lambda x, y: x[..., : y.shape[-1]] + y[..., : x.shape[-1]],
-                map(lambda f: f(x), self.effects),
-            )
-
-        def f(states, ps):
-            x, cum_sends = states
-            m, send_gains = ps
-            h = m(cum_sends[0])
-            return (
-                x[..., : h.shape[-1]] + h[..., : x.shape[-1]],
-                (
-                    None
-                    if cum_sends.size(0) == 1
-                    else cum_sends[1:, ..., : h.shape[-1]]
-                    + send_gains[:, None, None, None] * h[..., : cum_sends.shape[-1]]
-                ),
-            )
-
-        return (
-            di,
-            reduce(
-                f,
-                zip(
-                    self.effects,
-                    [self.params[f"sends_{i}"] for i in range(len(self.effects) - 1)]
-                    + [None],
-                ),
-                (
-                    torch.zeros_like(x),
-                    x.unsqueeze(0).expand(len(self.effects), -1, -1, -1),
-                ),
-            )[0],
-        )
-
-
-class UniLossLess(nn.Module):
-    def forward(self, x):
-        tri = x.triu(1)
-        return torch.linalg.matrix_exp(tri - tri.T)
-
-
-class FDN(FX):
-    max_delay = 100
-
-    def __init__(
-        self,
-        sr: int,
-        ir_duration: float = 1.0,
-        delays=(997, 1153, 1327, 1559, 1801, 2099),
-        trainable_delay=False,
-        num_decay_freq=1,
-        delay_independent_decay=False,
-        eq: Optional[nn.Module] = None,
-    ):
-        # beta = torch.distributions.Beta(1.1, 6)
-        num_delays = len(delays)
-        super().__init__(
-            b=torch.ones(num_delays, 2) / num_delays,
-            c=torch.zeros(2, num_delays),
-            U=torch.randn(num_delays, num_delays) / num_delays**0.5,
-            gamma=torch.rand(
-                num_decay_freq, num_delays if not delay_independent_decay else 1
-            )
-            * 0.2
-            + 0.4,
-            # delays=beta.sample((num_delays,)) * 64,
-        )
-
-        self.sr = sr
-        self.ir_length = int(sr * ir_duration)
-
-        # ir_duration = T_60
-        T_60 = ir_duration * 0.75
-        delays = torch.tensor(delays)
-        if delay_independent_decay:
-            gamma_max = db2amp(-60 / sr / T_60 * delays.min())
-        else:
-            gamma_max = db2amp(-60 / sr / T_60 * delays)
-
-        register_parametrization(self.params, "gamma", MinMax(0, gamma_max))
-        register_parametrization(self.params, "U", UniLossLess())
-
-        if not trainable_delay:
-            self.register_buffer(
-                "delays",
-                delays,
-            )
-        else:
-            self.params["delays"] = nn.Parameter(delays / sr * 1000)
-            register_parametrization(self.params, "delays", MinMax(0, self.max_delay))
-
-        self.register_forward_pre_hook(broadcast2stereo)
-
-        self.eq = eq
-
-    def forward(self, x):
-        conv1d = F.conv1d if x.size(-1) > 44100 * 20 else fft_conv1d
-
-        c = self.params.c + 0j
-        b = self.params.b + 0j
-
-        gamma = self.params.gamma
-        delays = self.delays if hasattr(self, "delays") else self.params.delays
-
-        if gamma.size(0) > 1:
-            gamma = F.interpolate(
-                gamma.T.unsqueeze(1),
-                size=self.ir_length // 2 + 1,
-                align_corners=True,
-                mode="linear",
-            ).transpose(0, 2)
-
-        if gamma.size(2) == 1:
-            gamma = gamma ** (delays / delays.min())
-
-        A = self.params.U * gamma
-
-        freqs = (
-            torch.arange(self.ir_length // 2 + 1, device=x.device)
-            / self.ir_length
-            * 2
-            * torch.pi
-        )
-        invD = torch.exp(1j * freqs[:, None] * delays)
-        # H = c @ torch.linalg.inv(torch.diag_embed(invD) - A) @ b
-        H = c @ torch.linalg.solve(torch.diag_embed(invD) - A, b)
-
-        h = torch.fft.irfft(H.permute(1, 2, 0), n=self.ir_length)
-
-        if self.eq is not None:
-            h = self.eq(h)
-
-        # return fft_conv1d(
-        return conv1d(
-            F.pad(x, (self.ir_length - 1, 0)),
-            h.flip(-1),
-        )
-
-    def toJSON(self) -> dict[str, Any]:
-        return {
-            "T60 (s)": {
-                f"{f:.2f} Hz": g.item()
-                for f, g in zip(
-                    torch.linspace(0, 22050, self.params.gamma.numel()),
-                    -60 * self.delays.min() / amp2db(self.params.gamma) / 44100,
-                )
-            },
-            "Gain (dB, approx)": amp2db(
-                torch.linalg.norm(self.params.b) * torch.linalg.norm(self.params.c)
-            ).item(),
-        }

modules/rt.py

@@ -1,150 +0,0 @@
-import numpy as np
-from numba import njit, prange
-from scipy.signal import firwin2
-import torch
-
-from .fx import Delay, FDN, module2coeffs
-
-
-@njit
-def rt_fdn(
-    x: np.ndarray,
-    delay_steps: np.ndarray,
-    firs: np.ndarray,
-    U: np.ndarray,
-):
-    _, T = x.shape
-    M = delay_steps.shape[0]
-    order = firs.shape[1]
-    y = np.zeros_like(x)
-    buf_size = delay_steps.max() + order
-    delay_buf = np.zeros((M, buf_size), dtype=x.dtype)
-    read_pointer = 0
-
-    for t in range(T):
-        # out = delay_buf[(range(M), read_pointers)]
-        # for i in prange(M):
-        #     out[i] = delay_buf[i, read_pointers[i]]
-        out = delay_buf[:, read_pointer]
-        y[:, t] = out
-
-        s = out * firs[:, 0]
-        # indexes = (read_pointers[:, None] - np.arange(1, order)) % buf_sizes[:, None]
-        # reg = np.take_along_axis(delay_buf, indexes, axis=1)
-        # s += firs[:, 1:] @ reg.T
-        # for j in prange(M):
-        #     s[j] += firs[j, 1:] @ delay_buf[j, indexes[j]]
-        for i in prange(M):
-            for j in prange(1, order):
-                s[i] += firs[i, j] * delay_buf[i, (read_pointer - j) % buf_size]
-        # for i in prange(1, order):
-        #     s += firs[:, i] * delay_buf[:, (read_pointer - i) % buf_size]
-
-        feedback = U @ s + x[:, t]
-        w_pointers = (read_pointer + delay_steps) % buf_size
-        # delay_buf[(range(M), w_pointers)] = s + B @ x[:, t]
-        for i in prange(M):
-            delay_buf[i, w_pointers[i]] = feedback[i]
-        read_pointer = (read_pointer + 1) % buf_size
-
-    return y
-
-
-@njit
-def rt_delay(
-    x: np.ndarray,
-    delay_step: int,
-    b0: float,
-    b1: float,
-    b2: float,
-    a1: float,
-    a2: float,
-):
-    T = x.shape[0]
-    y = np.zeros((2, T), dtype=x.dtype)
-    buf_size = delay_step + 1
-    read_pointer = 0
-    delay_buf = np.zeros((2, buf_size), dtype=x.dtype)
-    bq_buf = np.zeros((2, 2), dtype=x.dtype)
-
-    for t in range(T):
-        out = delay_buf[:, read_pointer]
-        y[:, t] = out
-
-        s = bq_buf[:, 0] + b0 * out
-        bq_buf[:, 0] = bq_buf[:, 1] + b1 * out - a1 * s
-        bq_buf[:, 1] = b2 * out - a2 * s
-
-        w_pointer = (read_pointer + delay_step) % buf_size
-        # cross feeding because of ping-pong delay
-        delay_buf[0, w_pointer] = s[1] + x[t]
-        delay_buf[1, w_pointer] = s[0]
-
-        read_pointer = (read_pointer + 1) % buf_size
-
-    return y
-
-
-class RealTimeDelay(Delay):
-    def forward(self, x):
-        assert x.size(1) == 1, x.size()
-        assert x.size(0) == 1, x.size()
-        with torch.no_grad():
-            delay_in_samples = round(self.sr * self.params.delay.item() * 0.001)
-            feedback = self.params.feedback.item()
-
-            if self.recursive_eq and self.eq is not None:
-                b0, b1, b2, a0, a1, a2 = [p.item() for p in module2coeffs(self.eq)]
-                b0, b1, b2, a1, a2 = b0 / a0, b1 / a0, b2 / a0, a1 / a0, a2 / a0
-            else:
-                b0, b1, b2, a1, a2 = 1.0, 0.0, 0.0, 0.0, 0.0
-
-            b0 = b0 * feedback
-            b1 = b1 * feedback
-            b2 = b2 * feedback
-            x_numpy = x.squeeze().cpu().numpy()
-            y_numpy = rt_delay(x_numpy, delay_in_samples, b0, b1, b2, a1, a2)
-        y = torch.from_numpy(y_numpy).unsqueeze(0).to(x.device) * self.params.gain
-        return self.odd_pan(y[:, :1]) + self.even_pan(y[:, 1:])
-
-
-class RealTimeFDN(FDN):
-    def forward(self, x):
-        assert x.size(1) == 2, x.size()
-        assert x.size(0) == 1, x.size()
-        with torch.no_grad():
-            delays = self.delays if hasattr(self, "delays") else self.params.delays
-
-            c = self.params.c
-            b = self.params.b
-            gamma = self.params.gamma.clone()
-
-            if gamma.size(1) == 1:
-                gamma = gamma ** (delays / delays.min())
-
-            freqs = np.linspace(0, 1, gamma.size(0))
-            firs = np.apply_along_axis(
-                lambda x: firwin2(gamma.size(0) * 2 - 1, freqs, x, fs=2),
-                1,
-                gamma.cpu().numpy().T,
-            ).astype(np.float32)
-            shifted_delays = delays - firs.shape[1] // 2
-
-            U = self.params.U
-
-            x = b @ x.squeeze()
-
-            y_numpy = rt_fdn(
-                x.cpu().numpy(),
-                # delays.cpu().numpy(),
-                shifted_delays.cpu().numpy(),
-                # firs.cpu().numpy(),
-                firs,
-                U.cpu().numpy(),
-            )
-            y = c @ torch.from_numpy(y_numpy).to(x.device)
-            y = y.unsqueeze(0)
-
-        if self.eq is not None:
-            y = self.eq(y)
-        return y

modules/utils.py

@@ -1,64 +0,0 @@
-import torch
-import numpy as np
-from functools import reduce, partial
-from operator import mul
-from torch.nn.utils.parametrize import is_parametrized, remove_parametrizations
-
-
-def chain_functions(*functions):
-    return lambda initial: reduce(lambda x, f: f(x), functions, initial)
-
-
-def remove_fx_parametrisation(fx):
-    def remover(m):
-        if not is_parametrized(m):
-            return
-        for k in list(m.parametrizations.keys()):
-            remove_parametrizations(m, k)
-
-    fx.apply(remover)
-    return fx
-
-
-def get_chunks(keys, original_shapes):
-    (position, _), *_ = filter(lambda i_k: "U.original" in i_k[1], enumerate(keys))
-    original_chunks = list(map(partial(reduce, mul), original_shapes))
-    U_matrix_shape = original_shapes[position]
-
-    dimensions_not_need = np.ravel_multi_index(
-        np.tril_indices(**dict(zip(("n", "m"), U_matrix_shape))), U_matrix_shape
-    ) + sum(original_chunks[:position])
-
-    selected_chunks = (
-        original_chunks[:position]
-        + [original_chunks[position] - dimensions_not_need.size]
-        + original_chunks[position + 1 :]
-    )
-    return selected_chunks, position, U_matrix_shape, dimensions_not_need
-
-
-def vec2statedict(
-    x: torch.Tensor,
-    keys,
-    original_shapes,
-    selected_chunks,
-    position,
-    U_matrix_shape,
-):
-    chunks = list(torch.split(x, selected_chunks))
-    U = x.new_zeros(reduce(mul, U_matrix_shape))
-    U[
-        np.ravel_multi_index(
-            np.triu_indices(n=U_matrix_shape[0], k=1, m=U_matrix_shape[1]),
-            U_matrix_shape,
-        )
-    ] = chunks[position]
-    chunks[position] = U
-
-    state_dict = dict(
-        zip(
-            keys,
-            map(lambda x, shape: x.reshape(*shape), chunks, original_shapes),
-        )
-    )
-    return state_dict

presets/fx_config.yaml

@@ -1,188 +0,0 @@
-epochs: 2000
-data_dir: null
-log_dir: null
-lufs: -18
-sr: 44100
-chunk_duration: 12
-chunk_overlap: 5
-device: cuda
-batch_size: 35
-dataset: medley_vocal
-regularise_delay: true
-model:
-  _target_: torch.nn.Sequential
-  _args_:
-  - _target_: modules.fx.Peak
-    sr: 44100
-    freq: 800
-    min_freq: 33
-    max_freq: 5400
-  - _target_: modules.fx.Peak
-    sr: 44100
-    freq: 4000
-    min_freq: 200
-    max_freq: 17500
-  - _target_: modules.fx.LowShelf
-    sr: 44100
-    freq: 115
-    min_freq: 30
-    max_freq: 200
-  - _target_: modules.fx.HighShelf
-    sr: 44100
-    freq: 6000
-    min_freq: 750
-    max_freq: 8300
-  - _target_: modules.fx.LowPass
-    sr: 44100
-    freq: 17500
-    min_freq: 200
-    max_freq: 18000
-  - _target_: modules.fx.HighPass
-    sr: 44100
-    freq: 200
-    min_freq: 16
-    max_freq: 5300
-  - _target_: modules.fx.CompressorExpander
-    sr: 44100
-    cmp_ratio: 2.0
-    exp_ratio: 0.5
-    at_ms: 50.0
-    rt_ms: 50.0
-    avg_coef: 0.3
-    cmp_th: -18.0
-    exp_th: -48.0
-    make_up: 0.0
-    lookahead: true
-    max_lookahead: 15
-  - _target_: modules.fx.SendFXsAndSum
-    _args_:
-    - _target_: modules.fx.SurrogateDelay
-      sr: 44100
-      delay: 400
-      dropout: 0
-      straight_through: true
-      recursive_eq: true
-      ir_duration: 4
-      eq:
-        _target_: modules.fx.LowPass
-        sr: 44100
-        freq: 8000
-        min_freq: 200
-        max_freq: 16000
-        min_Q: 0.5
-        max_Q: 2
-    - _target_: modules.fx.FDN
-      sr: 44100
-      delays:
-      - 997
-      - 1153
-      - 1327
-      - 1559
-      - 1801
-      - 2099
-      num_decay_freq: 49
-      delay_independent_decay: true
-      ir_duration: 12
-      eq:
-        _target_: torch.nn.Sequential
-        _args_:
-        - _target_: modules.fx.Peak
-          sr: 44100
-          freq: 800
-          min_freq: 200
-          max_freq: 2500
-          min_Q: 0.1
-          max_Q: 3
-        - _target_: modules.fx.Peak
-          sr: 44100
-          freq: 4000
-          min_freq: 600
-          max_freq: 7000
-          min_Q: 0.1
-          max_Q: 3
-        - _target_: modules.fx.LowShelf
-          sr: 44100
-          freq: 115
-          min_freq: 30
-          max_freq: 450
-        - _target_: modules.fx.HighShelf
-          sr: 44100
-          freq: 8000
-          min_freq: 1500
-          max_freq: 16000
-    cross_send: true
-    pan_direct: true
-optimiser:
-  _target_: torch.optim.Adam
-  lr: 0.01
-mss:
-  fft_sizes:
-  - 128
-  - 512
-  - 2048
-  hop_sizes:
-  - 32
-  - 128
-  - 512
-mldr:
-  s_taus:
-  - 50
-  - 100
-  l_taus:
-  - 1000
-  - 2000
-loss_fn:
-  _target_: loss.SumLosses
-  weights:
-  - 1.0
-  - 0.5
-  - 0.5
-  - 0.25
-  loss_fns:
-  - _target_: auraloss.freq.MultiResolutionSTFTLoss
-    fft_sizes:
-    - 128
-    - 512
-    - 2048
-    hop_sizes:
-    - 32
-    - 128
-    - 512
-    win_lengths:
-    - 128
-    - 512
-    - 2048
-    sample_rate: 44100
-    perceptual_weighting: true
-  - _target_: auraloss.freq.SumAndDifferenceSTFTLoss
-    fft_sizes:
-    - 128
-    - 512
-    - 2048
-    hop_sizes:
-    - 32
-    - 128
-    - 512
-    win_lengths:
-    - 128
-    - 512
-    - 2048
-    sample_rate: 44100
-    perceptual_weighting: true
-  - _target_: loss.ldr.MLDRLoss
-    sr: 44100
-    s_taus:
-    - 50
-    - 100
-    l_taus:
-    - 1000
-    - 2000
-  - _target_: loss.ldr.MLDRLoss
-    sr: 44100
-    mid_side: true
-    s_taus:
-    - 50
-    - 100
-    l_taus:
-    - 1000
-    - 2000

presets/internal/feature_mask.npy

@@ -1,3 +0,0 @@
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-oid sha256:47a6ca4536dd9215ec45cc9d3aecc575e2b75d6a8375082b42d8b1a1ec055820
-size 279

presets/internal/gaussian.npz

@@ -1,3 +0,0 @@
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-oid sha256:c01b52c46bb4ae3202814b813ed1bfe4c82f6b36d646fe08bc13371e1119db75
-size 68620

presets/internal/info.json

@@ -1,1695 +0,0 @@
-{
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-        "~/processed/anon_tokyo/run_1",
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-        "~/processed/anon_tokyo/run_1",
-        "~/processed/anon_tokyo/run_1",
-        "~/processed/anon_tokyo/run_1",
-        "~/processed/anon_tokyo/run_1",
-        "~/processed/anon_tokyo/run_1",
-        "~/processed/anon_tokyo/run_1",
-        "~/processed/anon_tokyo/run_1",
-        "~/processed/anon_tokyo/run_1",
-        "~/processed/anon_tokyo/run_1",
-        "~/processed/anon_tokyo/run_1",
-        "~/processed/anon_tokyo/run_1",
-        "~/processed/anon_tokyo/run_1",
-        "~/processed/anon_tokyo/run_1",
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-{
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-        "~/processed/AlexanderRoss_GoodbyeBolero/AlexanderRoss_GoodbyeBolero_STEM_06/run_0",
-        "~/processed/AlexanderRoss_VelvetCurtain/AlexanderRoss_VelvetCurtain_STEM_06/run_0",
-        "~/processed/Auctioneer_OurFutureFaces/Auctioneer_OurFutureFaces_STEM_08/run_0",
-        "~/processed/AvaLuna_Waterduct/AvaLuna_Waterduct_STEM_08/run_0",
-        "~/processed/BigTroubles_Phantom/BigTroubles_Phantom_STEM_04/run_0",
-        "~/processed/BrandonWebster_DontHearAThing/BrandonWebster_DontHearAThing_STEM_02/run_0",
-        "~/processed/BrandonWebster_DontHearAThing/BrandonWebster_DontHearAThing_STEM_01/run_0",
-        "~/processed/BrandonWebster_YesSirICanFly/BrandonWebster_YesSirICanFly_STEM_02/run_0",
-        "~/processed/CatMartino_IPromise/CatMartino_IPromise_STEM_06/run_0",
-        "~/processed/ClaraBerryAndWooldog_AirTraffic/ClaraBerryAndWooldog_AirTraffic_STEM_07/run_0",
-        "~/processed/ClaraBerryAndWooldog_AirTraffic/ClaraBerryAndWooldog_AirTraffic_STEM_08/run_0",
-        "~/processed/ClaraBerryAndWooldog_Boys/ClaraBerryAndWooldog_Boys_STEM_06/run_0",
-        "~/processed/ClaraBerryAndWooldog_Stella/ClaraBerryAndWooldog_Stella_STEM_07/run_0",
-        "~/processed/ClaraBerryAndWooldog_WaltzForMyVictims/ClaraBerryAndWooldog_WaltzForMyVictims_STEM_05/run_0",
-        "~/processed/DeadMilkmen_PrisonersCinema/DeadMilkmen_PrisonersCinema_STEM_12/run_0",
-        "~/processed/Debussy_LenfantProdigue/Debussy_LenfantProdigue_STEM_01/run_0",
-        "~/processed/DreamersOfTheGhetto_HeavyLove/DreamersOfTheGhetto_HeavyLove_STEM_08/run_0",
-        "~/processed/FacesOnFilm_WaitingForGa/FacesOnFilm_WaitingForGa_STEM_03/run_0",
-        "~/processed/FamilyBand_Again/FamilyBand_Again_STEM_09/run_0",
-        "~/processed/Handel_TornamiAVagheggiar/Handel_TornamiAVagheggiar_STEM_01/run_0",
-        "~/processed/HeladoNegro_MitadDelMundo/HeladoNegro_MitadDelMundo_STEM_08/run_0",
-        "~/processed/HeladoNegro_MitadDelMundo/HeladoNegro_MitadDelMundo_STEM_03/run_0",
-        "~/processed/HopAlong_SisterCities/HopAlong_SisterCities_STEM_07/run_0",
-        "~/processed/LizNelson_Coldwar/LizNelson_Coldwar_STEM_02/run_0",
-        "~/processed/LizNelson_ImComingHome/LizNelson_ImComingHome_STEM_02/run_0",
-        "~/processed/LizNelson_ImComingHome/LizNelson_ImComingHome_STEM_03/run_0",
-        "~/processed/LizNelson_ImComingHome/LizNelson_ImComingHome_STEM_04/run_0",
-        "~/processed/LizNelson_ImComingHome/LizNelson_ImComingHome_STEM_01/run_0",
-        "~/processed/LizNelson_Rainfall/LizNelson_Rainfall_STEM_01/run_0",
-        "~/processed/LizNelson_Rainfall/LizNelson_Rainfall_STEM_03/run_0",
-        "~/processed/LizNelson_Rainfall/LizNelson_Rainfall_STEM_02/run_0",
-        "~/processed/MatthewEntwistle_DontYouEver/MatthewEntwistle_DontYouEver_STEM_07/run_0",
-        "~/processed/MatthewEntwistle_Lontano/MatthewEntwistle_Lontano_STEM_02/run_0",
-        "~/processed/Meaxic_TakeAStep/Meaxic_TakeAStep_STEM_08/run_0",
-        "~/processed/Meaxic_TakeAStep/Meaxic_TakeAStep_STEM_04/run_0",
-        "~/processed/MidnightBlue_HuntingSeason/MidnightBlue_HuntingSeason_STEM_01/run_0",
-        "~/processed/MidnightBlue_HuntingSeason/MidnightBlue_HuntingSeason_STEM_02/run_0",
-        "~/processed/MidnightBlue_StarsAreScreaming/MidnightBlue_StarsAreScreaming_STEM_06/run_0",
-        "~/processed/MidnightBlue_StarsAreScreaming/MidnightBlue_StarsAreScreaming_STEM_07/run_0",
-        "~/processed/Mozart_BesterJungling/Mozart_BesterJungling_STEM_01/run_0",
-        "~/processed/Mozart_DiesBildnis/Mozart_DiesBildnis_STEM_01/run_0",
-        "~/processed/MusicDelta_80sRock/MusicDelta_80sRock_STEM_04/run_0",
-        "~/processed/MusicDelta_Beatles/MusicDelta_Beatles_STEM_08/run_0",
-        "~/processed/MusicDelta_Beatles/MusicDelta_Beatles_STEM_07/run_0",
-        "~/processed/MusicDelta_Britpop/MusicDelta_Britpop_STEM_07/run_0",
-        "~/processed/MusicDelta_Britpop/MusicDelta_Britpop_STEM_08/run_0",
-        "~/processed/MusicDelta_Country1/MusicDelta_Country1_STEM_05/run_1",
-        "~/processed/MusicDelta_Country2/MusicDelta_Country2_STEM_05/run_0",
-        "~/processed/MusicDelta_Disco/MusicDelta_Disco_STEM_04/run_0",
-        "~/processed/MusicDelta_Gospel/MusicDelta_Gospel_STEM_06/run_0",
-        "~/processed/MusicDelta_Grunge/MusicDelta_Grunge_STEM_05/run_0",
-        "~/processed/MusicDelta_Hendrix/MusicDelta_Hendrix_STEM_04/run_0",
-        "~/processed/MusicDelta_Punk/MusicDelta_Punk_STEM_04/run_0",
-        "~/processed/MusicDelta_Reggae/MusicDelta_Reggae_STEM_04/run_0",
-        "~/processed/MusicDelta_Rock/MusicDelta_Rock_STEM_05/run_0",
-        "~/processed/MusicDelta_Rockabilly/MusicDelta_Rockabilly_STEM_05/run_0",
-        "~/processed/MutualBenefit_NotForNothing/MutualBenefit_NotForNothing_STEM_06/run_0",
-        "~/processed/PortStWillow_StayEven/PortStWillow_StayEven_STEM_08/run_0",
-        "~/processed/Schubert_Erstarrung/Schubert_Erstarrung_STEM_01/run_0",
-        "~/processed/Schumann_Mignon/Schumann_Mignon_STEM_02/run_0",
-        "~/processed/Snowmine_Curfews/Snowmine_Curfews_STEM_03/run_0",
-        "~/processed/SongYiJeon_TwoMoons/SongYiJeon_TwoMoons_STEM_01/run_0",
-        "~/processed/StevenClark_Bounty/StevenClark_Bounty_STEM_08/run_0",
-        "~/processed/StrandOfOaks_Spacestation/StrandOfOaks_Spacestation_STEM_04/run_0",
-        "~/processed/TheKitchenettes_Alive/TheKitchenettes_Alive_STEM_01/run_0",
-        "~/processed/TheScarletBrand_LesFleursDuMal/TheScarletBrand_LesFleursDuMal_STEM_08/run_0",
-        "~/processed/TleilaxEnsemble_Late/TleilaxEnsemble_Late_STEM_04/run_0",
-        "~/processed/TleilaxEnsemble_Late/TleilaxEnsemble_Late_STEM_05/run_0",
-        "~/processed/TleilaxEnsemble_MelancholyFlowers/TleilaxEnsemble_MelancholyFlowers_STEM_04/run_0",
-        "~/processed/TleilaxEnsemble_MelancholyFlowers/TleilaxEnsemble_MelancholyFlowers_STEM_05/run_0",
-        "~/processed/Torres_NewSkin/Torres_NewSkin_STEM_02/run_0",
-        "~/processed/Torres_NewSkin/Torres_NewSkin_STEM_05/run_0",
-        "~/processed/Torres_NewSkin/Torres_NewSkin_STEM_10/run_0",
-        "~/processed/Torres_NewSkin/Torres_NewSkin_STEM_03/run_0",
-        "~/processed/Wolf_DieBekherte/Wolf_DieBekherte_STEM_01/run_0"
-    ],
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presets/medleydb/raw_params.npy

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-size 42408

presets/rt_config.yaml

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-chunk_duration: 12
-chunk_overlap: 5
-device: cuda
-batch_size: 35
-dataset: medley_vocal
-regularise_delay: true
-model:
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-    cmp_ratio: 2.0
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-    cmp_th: -18.0
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-        - _target_: modules.fx.HighShelf
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