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import torch
import multiprocessing
from abc import ABC, abstractmethod
import deepafx_st.utils as utils
import numpy as np
class Processor(torch.nn.Module, ABC):
"""Processor base class."""
def __init__(
self,
):
super().__init__()
def denormalize_params(self, p):
"""This method takes a tensor of parameters scaled from 0-1 and
restores them back to the original parameter range."""
# check if the number of parameters is correct
params = p # torch.split(p, 1, -1)
if len(params) != self.num_control_params:
raise RuntimeError(
f"Invalid number of parameters. ",
f"Expected {self.num_control_params} but found {len(params)} {params.shape}.",
)
# iterate over the parameters and expand from 0-1 to full range
denorm_params = []
for param, port in zip(params, self.ports):
# check if parameter exceeds range
if param > 1.0 or param < 0.0:
raise RuntimeError(
f"""Parameter '{port["name"]}' exceeds range: {param}"""
)
# denormalize and store result
denorm_params.append(utils.denormalize(param, port["max"], port["min"]))
return denorm_params
def normalize_params(self, *params):
"""This method creates a vector of parameters normalized from 0-1."""
# check if the number of parameters is correct
if len(params) != self.num_control_params:
raise RuntimeError(
f"Invalid number of parameters. ",
f"Expected {self.num_control_params} but found {len(params)}.",
)
norm_params = []
for param, port in zip(params, self.ports):
norm_params.append(utils.normalize(param, port["max"], port["min"]))
p = torch.tensor(norm_params).view(1, -1)
return p
# def run_series(self, inputs, params):
# """Run the process function in a loop given a list of inputs and parameters"""
# p_b_denorm = [p for p in self.denormalize_params(params)]
# y = self.process_fn(inputs, self.sample_rate, *p_b_denorm)
# return y
def run_series(self, inputs, params, sample_rate=24000):
"""Run the process function in a loop given a list of inputs and parameters"""
if params.ndim == 1:
params = np.reshape(params, (1, -1))
inputs = np.reshape(inputs, (1, -1))
bs = inputs.shape[0]
ys = []
params = np.clip(params, 0, 1)
for bidx in range(bs):
p_b_denorm = [p for p in self.denormalize_params(params[bidx, :])]
y = self.process_fn(
inputs[bidx, ...].reshape(-1),
sample_rate,
*p_b_denorm,
)
ys.append(y)
y = np.stack(ys, axis=0)
return y
@abstractmethod
def forward(self, x, p):
pass
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