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# Copyright 2022 Christian J. Steinmetz
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# TCN implementation adapted from:
# https://github.com/csteinmetz1/micro-tcn/blob/main/microtcn/tcn.py
import torch
from argparse import ArgumentParser
from deepafx_st.utils import center_crop, causal_crop
class FiLM(torch.nn.Module):
def __init__(self, num_features, cond_dim):
super().__init__()
self.num_features = num_features
self.bn = torch.nn.BatchNorm1d(num_features, affine=False)
self.adaptor = torch.nn.Linear(cond_dim, num_features * 2)
def forward(self, x, cond):
# project conditioning to 2 x num. conv channels
cond = self.adaptor(cond)
# split the projection into gain and bias
g, b = torch.chunk(cond, 2, dim=-1)
# add virtual channel dim if needed
if g.ndim == 2:
g = g.unsqueeze(1)
b = b.unsqueeze(1)
# reshape for application
g = g.permute(0, 2, 1)
b = b.permute(0, 2, 1)
x = self.bn(x) # apply BatchNorm without affine
x = (x * g) + b # then apply conditional affine
return x
class ConditionalTCNBlock(torch.nn.Module):
def __init__(
self, in_ch, out_ch, cond_dim, kernel_size=3, dilation=1, causal=False, **kwargs
):
super().__init__()
self.in_ch = in_ch
self.out_ch = out_ch
self.kernel_size = kernel_size
self.dilation = dilation
self.causal = causal
self.conv1 = torch.nn.Conv1d(
in_ch,
out_ch,
kernel_size=kernel_size,
padding=0,
dilation=dilation,
bias=True,
)
self.film = FiLM(out_ch, cond_dim)
self.relu = torch.nn.PReLU(out_ch)
self.res = torch.nn.Conv1d(
in_ch, out_ch, kernel_size=1, groups=in_ch, bias=False
)
def forward(self, x, p):
x_in = x
x = self.conv1(x)
x = self.film(x, p) # apply FiLM conditioning
x = self.relu(x)
x_res = self.res(x_in)
if self.causal:
x = x + causal_crop(x_res, x.shape[-1])
else:
x = x + center_crop(x_res, x.shape[-1])
return x
class ConditionalTCN(torch.nn.Module):
"""Temporal convolutional network with conditioning module.
Args:
sample_rate (float): Audio sample rate.
num_control_params (int, optional): Dimensionality of the conditioning signal. Default: 24
ninputs (int, optional): Number of input channels (mono = 1, stereo 2). Default: 1
noutputs (int, optional): Number of output channels (mono = 1, stereo 2). Default: 1
nblocks (int, optional): Number of total TCN blocks. Default: 10
kernel_size (int, optional: Width of the convolutional kernels. Default: 3
dialation_growth (int, optional): Compute the dilation factor at each block as dilation_growth ** (n % stack_size). Default: 1
channel_growth (int, optional): Compute the output channels at each black as in_ch * channel_growth. Default: 2
channel_width (int, optional): When channel_growth = 1 all blocks use convolutions with this many channels. Default: 64
stack_size (int, optional): Number of blocks that constitute a single stack of blocks. Default: 10
causal (bool, optional): Causal TCN configuration does not consider future input values. Default: False
"""
def __init__(
self,
sample_rate,
num_control_params=24,
ninputs=1,
noutputs=1,
nblocks=10,
kernel_size=15,
dilation_growth=2,
channel_growth=1,
channel_width=64,
stack_size=10,
causal=False,
skip_connections=False,
**kwargs,
):
super().__init__()
self.num_control_params = num_control_params
self.ninputs = ninputs
self.noutputs = noutputs
self.nblocks = nblocks
self.kernel_size = kernel_size
self.dilation_growth = dilation_growth
self.channel_growth = channel_growth
self.channel_width = channel_width
self.stack_size = stack_size
self.causal = causal
self.skip_connections = skip_connections
self.sample_rate = sample_rate
self.blocks = torch.nn.ModuleList()
for n in range(nblocks):
in_ch = out_ch if n > 0 else ninputs
if self.channel_growth > 1:
out_ch = in_ch * self.channel_growth
else:
out_ch = self.channel_width
dilation = self.dilation_growth ** (n % self.stack_size)
self.blocks.append(
ConditionalTCNBlock(
in_ch,
out_ch,
self.num_control_params,
kernel_size=self.kernel_size,
dilation=dilation,
padding="same" if self.causal else "valid",
causal=self.causal,
)
)
self.output = torch.nn.Conv1d(out_ch, noutputs, kernel_size=1)
self.receptive_field = self.compute_receptive_field()
# print(
# f"TCN receptive field: {self.receptive_field} samples",
# f" or {(self.receptive_field/self.sample_rate)*1e3:0.3f} ms",
# )
def forward(self, x, p, **kwargs):
# causally pad input signal
x = torch.nn.functional.pad(x, (self.receptive_field - 1, 0))
# iterate over blocks passing conditioning
for idx, block in enumerate(self.blocks):
x = block(x, p)
if self.skip_connections:
if idx == 0:
skips = x
else:
skips = center_crop(skips, x[-1]) + x
else:
skips = 0
# final 1x1 convolution to collapse channels
out = self.output(x + skips)
return out
def compute_receptive_field(self):
"""Compute the receptive field in samples."""
rf = self.kernel_size
for n in range(1, self.nblocks):
dilation = self.dilation_growth ** (n % self.stack_size)
rf = rf + ((self.kernel_size - 1) * dilation)
return rf
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