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# Copyright (c) 2023 Amphion.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
class ResBlock(nn.Module):
def __init__(self, dims):
super().__init__()
self.conv1 = nn.Conv1d(dims, dims, kernel_size=1, bias=False)
self.conv2 = nn.Conv1d(dims, dims, kernel_size=1, bias=False)
self.batch_norm1 = nn.BatchNorm1d(dims)
self.batch_norm2 = nn.BatchNorm1d(dims)
def forward(self, x):
residual = x
x = self.conv1(x)
x = self.batch_norm1(x)
x = F.relu(x)
x = self.conv2(x)
x = self.batch_norm2(x)
x = x + residual
return x
class MelResNet(nn.Module):
def __init__(self, res_blocks, in_dims, compute_dims, res_out_dims, pad):
super().__init__()
kernel_size = pad * 2 + 1
self.conv_in = nn.Conv1d(
in_dims, compute_dims, kernel_size=kernel_size, bias=False
)
self.batch_norm = nn.BatchNorm1d(compute_dims)
self.layers = nn.ModuleList()
for i in range(res_blocks):
self.layers.append(ResBlock(compute_dims))
self.conv_out = nn.Conv1d(compute_dims, res_out_dims, kernel_size=1)
def forward(self, x):
x = self.conv_in(x)
x = self.batch_norm(x)
x = F.relu(x)
for f in self.layers:
x = f(x)
x = self.conv_out(x)
return x
class Stretch2d(nn.Module):
def __init__(self, x_scale, y_scale):
super().__init__()
self.x_scale = x_scale
self.y_scale = y_scale
def forward(self, x):
b, c, h, w = x.size()
x = x.unsqueeze(-1).unsqueeze(3)
x = x.repeat(1, 1, 1, self.y_scale, 1, self.x_scale)
return x.view(b, c, h * self.y_scale, w * self.x_scale)
class UpsampleNetwork(nn.Module):
def __init__(
self, feat_dims, upsample_scales, compute_dims, res_blocks, res_out_dims, pad
):
super().__init__()
total_scale = np.cumproduct(upsample_scales)[-1]
self.indent = pad * total_scale
self.resnet = MelResNet(res_blocks, feat_dims, compute_dims, res_out_dims, pad)
self.resnet_stretch = Stretch2d(total_scale, 1)
self.up_layers = nn.ModuleList()
for scale in upsample_scales:
kernel_size = (1, scale * 2 + 1)
padding = (0, scale)
stretch = Stretch2d(scale, 1)
conv = nn.Conv2d(1, 1, kernel_size=kernel_size, padding=padding, bias=False)
conv.weight.data.fill_(1.0 / kernel_size[1])
self.up_layers.append(stretch)
self.up_layers.append(conv)
def forward(self, m):
aux = self.resnet(m).unsqueeze(1)
aux = self.resnet_stretch(aux)
aux = aux.squeeze(1)
m = m.unsqueeze(1)
for f in self.up_layers:
m = f(m)
m = m.squeeze(1)[:, :, self.indent : -self.indent]
return m.transpose(1, 2), aux.transpose(1, 2)
class WaveRNN(nn.Module):
def __init__(self, cfg):
super().__init__()
self.cfg = cfg
self.pad = self.cfg.VOCODER.MEL_FRAME_PAD
if self.cfg.VOCODER.MODE == "mu_law_quantize":
self.n_classes = 2**self.cfg.VOCODER.BITS
elif self.cfg.VOCODER.MODE == "mu_law" or self.cfg.VOCODER:
self.n_classes = 30
self._to_flatten = []
self.rnn_dims = self.cfg.VOCODER.RNN_DIMS
self.aux_dims = self.cfg.VOCODER.RES_OUT_DIMS // 4
self.hop_length = self.cfg.VOCODER.HOP_LENGTH
self.fc_dims = self.cfg.VOCODER.FC_DIMS
self.upsample_factors = self.cfg.VOCODER.UPSAMPLE_FACTORS
self.feat_dims = self.cfg.VOCODER.INPUT_DIM
self.compute_dims = self.cfg.VOCODER.COMPUTE_DIMS
self.res_out_dims = self.cfg.VOCODER.RES_OUT_DIMS
self.res_blocks = self.cfg.VOCODER.RES_BLOCKS
self.upsample = UpsampleNetwork(
self.feat_dims,
self.upsample_factors,
self.compute_dims,
self.res_blocks,
self.res_out_dims,
self.pad,
)
self.I = nn.Linear(self.feat_dims + self.aux_dims + 1, self.rnn_dims)
self.rnn1 = nn.GRU(self.rnn_dims, self.rnn_dims, batch_first=True)
self.rnn2 = nn.GRU(
self.rnn_dims + self.aux_dims, self.rnn_dims, batch_first=True
)
self._to_flatten += [self.rnn1, self.rnn2]
self.fc1 = nn.Linear(self.rnn_dims + self.aux_dims, self.fc_dims)
self.fc2 = nn.Linear(self.fc_dims + self.aux_dims, self.fc_dims)
self.fc3 = nn.Linear(self.fc_dims, self.n_classes)
self.num_params()
self._flatten_parameters()
def forward(self, x, mels):
device = next(self.parameters()).device
self._flatten_parameters()
batch_size = x.size(0)
h1 = torch.zeros(1, batch_size, self.rnn_dims, device=device)
h2 = torch.zeros(1, batch_size, self.rnn_dims, device=device)
mels, aux = self.upsample(mels)
aux_idx = [self.aux_dims * i for i in range(5)]
a1 = aux[:, :, aux_idx[0] : aux_idx[1]]
a2 = aux[:, :, aux_idx[1] : aux_idx[2]]
a3 = aux[:, :, aux_idx[2] : aux_idx[3]]
a4 = aux[:, :, aux_idx[3] : aux_idx[4]]
x = torch.cat([x.unsqueeze(-1), mels, a1], dim=2)
x = self.I(x)
res = x
x, _ = self.rnn1(x, h1)
x = x + res
res = x
x = torch.cat([x, a2], dim=2)
x, _ = self.rnn2(x, h2)
x = x + res
x = torch.cat([x, a3], dim=2)
x = F.relu(self.fc1(x))
x = torch.cat([x, a4], dim=2)
x = F.relu(self.fc2(x))
return self.fc3(x)
def num_params(self, print_out=True):
parameters = filter(lambda p: p.requires_grad, self.parameters())
parameters = sum([np.prod(p.size()) for p in parameters]) / 1_000_000
if print_out:
print("Trainable Parameters: %.3fM" % parameters)
return parameters
def _flatten_parameters(self):
[m.flatten_parameters() for m in self._to_flatten]
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