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from typing import Optional
import numpy as np
import torch
from torch import Tensor, tensor
from torchsynth.config import SynthConfig
from torchsynth.module import (
ADSR,
VCA,
AudioMixer,
ControlRateUpsample,
MonophonicKeyboard,
SineVCO,
SquareSawVCO,
VCO, LFO, ModulationMixer,
)
from torchsynth.signal import Signal
from torchsynth.synth import AbstractSynth
# from configurations.read_configuration import get_conf_sample_rate
from melody_synth.non_random_LFOs import SinLFO, SawLFO, TriLFO, SquareLFO, RSawLFO
class TriangleVCO(VCO):
"""This is an expanded module that inherits VCO producing Triangle waves."""
def oscillator(self, argument: Signal, midi_f0: Tensor) -> Signal:
return torch.arcsin(torch.sin(argument * 2)) * 2.0 / torch.pi
class AmpModTorchSynth(AbstractSynth):
"""This is an abstract class using the modules provided by 1B1Synth to assemble synthesizers that generate the
training set. (The implementation of this class references code in TorchSynth) """
def __init__(
self,
synthconfig: Optional[SynthConfig] = None,
nebula: Optional[str] = "nebula",
*args,
**kwargs,
):
AbstractSynth.__init__(self, synthconfig=synthconfig, *args, **kwargs)
self.share_modules = [
("keyboard", MonophonicKeyboard),
("adsr_1", ADSR),
("adsr_2", ADSR),
("upsample", ControlRateUpsample),
("vca", VCA),
("lfo_amp_sin", SinLFO),
("lfo_pitch_sin_1", SinLFO),
("lfo_pitch_sin_2", SinLFO),
(
"mixer",
AudioMixer,
{
"n_input": 2,
"curves": [1.0, 1.0],
"names": ["vco_1", "vco_2"],
},
)
]
def output(self) -> Tensor:
"""Synthesizes the signal as Tensor"""
midi_f0, note_on_duration = self.keyboard()
adsr1 = self.adsr_1(note_on_duration)
adsr1 = self.upsample(adsr1)
adsr2 = self.adsr_2(note_on_duration)
adsr2 = self.upsample(adsr2)
amp_modulation = self.lfo_amp_sin()
amp_modulation = self.upsample(amp_modulation)
pitch_modulation_1 = self.lfo_pitch_sin_1()
pitch_modulation_1 = self.upsample(pitch_modulation_1)
pitch_modulation_2 = self.lfo_pitch_sin_2()
pitch_modulation_2 = self.upsample(pitch_modulation_2)
vco_amp1 = adsr1 * (amp_modulation * 0.5 + 0.5)
vco_amp2 = adsr2 * (amp_modulation * 0.5 + 0.5)
vco_1_out = self.vco_1(midi_f0, pitch_modulation_1)
vco_1_out = self.vca(vco_1_out, vco_amp1)
vco_2_out = self.vco_2(midi_f0, pitch_modulation_2)
vco_2_out = self.vca(vco_2_out, vco_amp2)
return self.mixer(vco_1_out, vco_2_out)
def get_signal(self, amp_mod_depth, amp_waveform, duration_l, amp1, amp2):
"""Synthesizes the signal as Tensor"""
midi_f0, note_on_duration = self.keyboard()
adsr1 = self.adsr_1(note_on_duration)
adsr1 = self.upsample(adsr1)
adsr2 = self.adsr_2(note_on_duration)
adsr2 = self.upsample(adsr2)
amp_modulation = self.lfo_amp_sin()
amp_modulation = self.upsample(amp_modulation)
pitch_modulation_1 = self.lfo_pitch_sin_1()
pitch_modulation_1 = self.upsample(pitch_modulation_1)
pitch_modulation_2 = self.lfo_pitch_sin_2()
pitch_modulation_2 = self.upsample(pitch_modulation_2)
vco_amp1 = adsr1 * (amp_modulation * 0.5 + 0.5)
vco_amp2 = adsr2 * (amp_modulation * 0.5 + 0.5)
vco_1_out = self.vco_1(midi_f0, pitch_modulation_1)
vco_1_out = self.vca(vco_1_out, vco_amp1)
vco_2_out = self.vco_2(midi_f0, pitch_modulation_1)
vco_2_out = self.vca(vco_2_out, vco_amp2)
return self.mixer(vco_1_out, vco_2_out)
class DoubleSawSynth(AmpModTorchSynth):
"""In addition to the shared modules, this synthesizer uses two "SquareSawVCO" modules to generate square and
sawtooth waves"""
def __init__(
self,
synthconfig: Optional[SynthConfig] = None,
nebula: Optional[str] = "saw_square_voice",
*args,
**kwargs,
):
AmpModTorchSynth.__init__(self, synthconfig=synthconfig, *args, **kwargs)
# Register all modules as children
module_list = self.share_modules
module_list.append(("vco_1", SquareSawVCO))
module_list.append(("vco_2", SquareSawVCO))
self.add_synth_modules(module_list)
class SinSawSynth(AmpModTorchSynth):
"""In addition to the shared modules, this synthesizer uses a "SinVco" and a "SquareSawVCO" to generate
sine and sawtooth/square waves """
def __init__(
self,
synthconfig: Optional[SynthConfig] = None,
nebula: Optional[str] = "sin_saw_voice",
*args,
**kwargs,
):
AmpModTorchSynth.__init__(self, synthconfig=synthconfig, *args, **kwargs)
# Register all modules as children
module_list = self.share_modules
module_list.append(("vco_1", SineVCO))
module_list.append(("vco_2", SquareSawVCO))
self.add_synth_modules(module_list)
class SinTriangleSynth(AmpModTorchSynth):
"""In addition to the shared modules, this synthesizer uses a "SinVco" and a "TriangleVCO" to generate
sine and triangle waves """
def __init__(
self,
synthconfig: Optional[SynthConfig] = None,
nebula: Optional[str] = "sin_tri_voice",
*args,
**kwargs,
):
AmpModTorchSynth.__init__(self, synthconfig=synthconfig, *args, **kwargs)
# Register all modules as children
module_list = self.share_modules
module_list.append(("vco_1", SineVCO))
module_list.append(("vco_2", TriangleVCO))
self.add_synth_modules(module_list)
class TriangleSawSynth(AmpModTorchSynth):
"""In addition to the shared modules, this synthesizer uses a "TriangleVCO" and a "SquareSawVCO" to generate
triangle and sawtooth/square waves """
def __init__(
self,
synthconfig: Optional[SynthConfig] = None,
nebula: Optional[str] = "triangle_saw_voice",
*args,
**kwargs,
):
AmpModTorchSynth.__init__(self, synthconfig=synthconfig, *args, **kwargs)
# Register all modules as children
module_list = self.share_modules
module_list.append(("vco_1", TriangleVCO))
module_list.append(("vco_2", SquareSawVCO))
self.add_synth_modules(module_list)
def amp_mod_with_duration(env, duration_l):
env_np = env.detach().numpy()[0] + 1e-30
env_np_shift = np.hstack([[0], env_np[:-1]])
env_np_sign = (env_np - env_np_shift)[:duration_l] + 1e-30
env_np_sign_nor = np.around(env_np_sign / np.abs(env_np_sign))
env_np_sign_nor_shift = np.hstack([[0], env_np_sign_nor[:-1]])
extreme_points = (env_np_sign_nor - env_np_sign_nor_shift)
(max_loc,) = np.where(extreme_points == -2)
n_max = len(max_loc)
if n_max == 0:
return env
else:
last_max_loc = max_loc[n_max - 1] - 1
# new_env = np.hstack([env_np[:last_max_loc], np.ones(len(env_np) - last_max_loc) * env_np[last_max_loc]])
new_env = np.hstack([env_np[:last_max_loc], (env_np[last_max_loc:] * 0.8 + 0.2)])
return tensor([new_env])
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