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synth.py
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import torch
from torch import nn
import torch.nn.functional as F
import librosa
import numpy as np
class Synth:
def __init__(self, device):
self.device = device
def fit(
self,
target,
sr,
minimize_num_freqs=False,
carrier_stereo_detune=0.0,
mod_stereo_detune=0.0,
time_stretch=1,
learning_rate=0.01,
n_iter=1000,
out_sr=44100,
):
if len(target.shape) == 2:
target = target[:, 0]
target = torch.tensor(target).to(self.device).to(torch.float32)
target -= torch.mean(target)
target /= torch.max(torch.abs(target))
dur = len(target) / sr
if dur > 5:
raise ValueError("Duration must be less than 5 seconds")
t = torch.linspace(0, dur, len(target)).to(self.device)
net = FMNet().to(self.device)
loss_fn = nn.MSELoss()
optimizer = torch.optim.Adam(net.parameters(), lr=learning_rate)
for i in range(n_iter):
optimizer.zero_grad()
out = net(t)
carrier_spiky = torch.sum(net.carrier_weights_ ** 2)
mod_spiky = torch.sum(net.mod_weights_ ** 2)
sig_loss = loss_fn(out, target)
if minimize_num_freqs:
loss = sig_loss * 100 - carrier_spiky - mod_spiky
else:
loss = sig_loss
loss.backward()
if i % 100 == 0:
print(
f"i: {i}, sig_loss: {sig_loss.item():.3f}, carrier_spiky: {carrier_spiky.item():.3f}, mod_spiky: {mod_spiky.item():.3f}"
)
optimizer.step()
out = self.make_output(
net, dur * time_stretch, out_sr, carrier_stereo_detune, mod_stereo_detune
)
del net
return out
def make_output(self, net, dur, sr, carrier_stereo_detune, mod_stereo_detune):
t2 = torch.linspace(0, dur, int(dur * sr)).to(self.device)
carrier_fq = net.carrier_fq.data
mod_fq = net.mod_fq.data
net.carrier_fq.data = carrier_fq * (1 + carrier_stereo_detune)
net.mod_fq.data = mod_fq * (1 + mod_stereo_detune)
left = net(t2)
net.carrier_fq.data = carrier_fq / (1 + carrier_stereo_detune)
net.mod_fq.data = mod_fq / (1 + mod_stereo_detune)
right = net(t2)
out = torch.vstack([left, right]).T.cpu().detach().numpy()
net.carrier_fq.data = carrier_fq
net.mod_fq.data = mod_fq
# remove clicks
n_fade = 100
out[:n_fade] *= np.repeat(np.linspace(0, 1, n_fade).reshape([-1, 1]), 2, axis=1)
out[-n_fade:] *= np.repeat(
np.linspace(1, 0, n_fade).reshape([-1, 1]), 2, axis=1
)
# normalize
out /= np.max(out)
return out
class Envelope(nn.Module):
def __init__(self, n_freqs, min_slope=-2.0, max_slope=8.0):
super().__init__()
self.slope = nn.Parameter(torch.rand(n_freqs) - 0.5)
self.offset = nn.Parameter(torch.rand(n_freqs) - 0.5)
self.min_slope = min_slope
self.max_slope = max_slope
def forward(self, t):
slope = 2.0 ** (
torch.sigmoid(self.slope) * (self.max_slope - self.min_slope)
+ self.min_slope
)
offset = torch.tanh(self.offset) / 2
t = t / t.max() - 0.5
bell = 1 / torch.sqrt(
(
1
+ (
((t.reshape([-1, 1]) @ slope.reshape([1, -1])) + (slope * offset))
** 2
)
)
)
return bell
class FMNet(nn.Module):
def __init__(self, fq_grad=True):
super().__init__()
carrier_hz = librosa.midi_to_hz(np.arange(1, 128))
self.carrier_fq = nn.Parameter(
torch.tensor(carrier_hz).to(torch.float32), requires_grad=fq_grad
)
self.carrier_weight = nn.Parameter(torch.rand(self.carrier_fq.shape) - 0.5)
self.carrier_env = Envelope(n_freqs=self.carrier_fq.shape)
mod_hz = librosa.midi_to_hz(np.arange(1, 128))
self.mod_fq = nn.Parameter(
torch.tensor(mod_hz).to(torch.float32), requires_grad=fq_grad
)
self.mod_weight = nn.Parameter(torch.rand(self.mod_fq.shape) - 0.5)
self.mod_env = Envelope(n_freqs=self.mod_fq.shape)
self.phase_offset = nn.Parameter(torch.tensor([0.0]))
def forward(self, t):
# self.phase_offset_ = 2 * np.pi * torch.sigmoid(self.phase_offset)
self.phase_offset_ = 2 * np.pi * torch.sigmoid(self.phase_offset)
self.mod_phases_ = (
2 * np.pi * self.mod_fq.reshape([-1, 1]) @ t.reshape([1, -1])
+ self.phase_offset_
)
self.mod_waves_ = torch.cos(self.mod_phases_)
self.mod_weights_ = F.softmax(self.mod_weight)
self.mod_amps_ = self.mod_env(t) * self.mod_weights_
# self.mod_amps_ = self.mod_weights_
self.mods_ = (self.mod_waves_.T * self.mod_amps_).T
self.mod_ = torch.sum(self.mods_, axis=0)
self.carrier_phases_ = (
2 * np.pi * self.carrier_fq.reshape([-1, 1]) @ t.reshape([1, -1])
+ self.mod_
+ self.phase_offset_
)
self.carrier_waves_ = torch.sin(self.carrier_phases_)
self.carrier_weights_ = F.softmax(self.carrier_weight)
self.carrier_amps_ = self.carrier_env(t) * self.carrier_weights_
# self.carrier_amps_ = self.carrier_weights_
self.carriers_ = (self.carrier_waves_.T * self.carrier_amps_).T
self.carrier_ = torch.sum(self.carriers_, axis=0)
return self.carrier_