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# coding:utf-8
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.utils import spectral_norm
from torch.nn.utils.parametrizations import weight_norm
# from Utils.ASR.models import ASRCNN
# from Utils.JDC.model import JDCNet
from Modules.hifigan import _tile, AdainResBlk1d
import math
class MelSpec(torch.nn.Module):
def __init__(self,
sample_rate=17402, # https://github.com/fakerybakery/styletts2-cli/blob/main/msinference.py = Default 16000. However 17400 vocalises better also "en_US/vctk_p274"
n_fft=2048,
win_length=1200,
hop_length=300,
n_mels=80
):
'''avoids dependency on torchaudio'''
super().__init__()
self.n_fft = n_fft
self.win_length = win_length if win_length is not None else n_fft
self.hop_length = hop_length if hop_length is not None else self.win_length // 2
# --
f_min = 0.0
f_max = float(sample_rate // 2)
all_freqs = torch.linspace(0, sample_rate // 2, n_fft//2+1)
m_min = 2595.0 * math.log10(1.0 + (f_min / 700.0))
m_max = 2595.0 * math.log10(1.0 + (f_max / 700.0))
m_pts = torch.linspace(m_min, m_max, n_mels + 2)
f_pts = 700.0 * (10 ** (m_pts / 2595.0) - 1.0)
f_diff = f_pts[1:] - f_pts[:-1] # (n_mels + 1)
slopes = f_pts.unsqueeze(0) - all_freqs.unsqueeze(1)
zero = torch.zeros(1)
down_slopes = (-1.0 * slopes[:, :-2]) / f_diff[:-1] # (n_freqs, n_mels)
up_slopes = slopes[:, 2:] / f_diff[1:] # (n_freqs, n_mels)
fb = torch.max(zero, torch.min(down_slopes, up_slopes))
# --
self.register_buffer('fb', fb)
window = torch.hann_window(self.win_length)
self.register_buffer('window', window)
def forward(self, x):
spec_f = torch.stft(x,
self.n_fft,
self.hop_length,
self.win_length,
self.window,
center=True,
pad_mode="reflect",
normalized=False,
onesided=True,
return_complex=True) # [bs, 1025, 56]
mel_specgram = torch.matmul(spec_f.abs().pow(2).transpose(1, 2), self.fb).transpose(1, 2)
return mel_specgram[:, None, :, :] # [bs, 1, 80, time]
class LearnedDownSample(nn.Module):
def __init__(self, dim_in):
super().__init__()
self.conv = spectral_norm(nn.Conv2d(dim_in, dim_in, kernel_size=(
3, 3), stride=(2, 2), groups=dim_in, padding=1))
def forward(self, x):
return self.conv(x)
class ResBlk(nn.Module):
def __init__(self,
dim_in, dim_out):
super().__init__()
self.actv = nn.LeakyReLU(0.2) # .07 also nice
self.downsample_res = LearnedDownSample(dim_in)
self.learned_sc = dim_in != dim_out
self.conv1 = spectral_norm(nn.Conv2d(dim_in, dim_in, 3, 1, 1))
self.conv2 = spectral_norm(nn.Conv2d(dim_in, dim_out, 3, 1, 1))
if self.learned_sc:
self.conv1x1 = spectral_norm(
nn.Conv2d(dim_in, dim_out, 1, 1, 0, bias=False))
def _shortcut(self, x):
if self.learned_sc:
x = self.conv1x1(x)
if x.shape[3] % 2 != 0: # [bs, 128, Freq, Time]
x = torch.cat([x, x[:, :, :, -1:]], dim=3)
return F.interpolate(x, scale_factor=.5, mode='nearest-exact') # F.avg_pool2d(x, 2)
def _residual(self, x):
x = self.actv(x)
x = self.conv1(x)
x = self.downsample_res(x)
x = self.actv(x)
x = self.conv2(x)
return x
def forward(self, x):
x = self._shortcut(x) + self._residual(x)
return x / math.sqrt(2) # unit variance
class StyleEncoder(nn.Module):
# for both acoustic & prosodic ref_s/p
def __init__(self,
dim_in=64,
style_dim=128,
max_conv_dim=512):
super().__init__()
blocks = [spectral_norm(nn.Conv2d(1, dim_in, 3, stride=1, padding=1))]
for _ in range(4):
dim_out = min(dim_in * 2,
max_conv_dim)
blocks += [ResBlk(dim_in, dim_out)]
dim_in = dim_out
blocks += [nn.LeakyReLU(0.24), # w/o this activation - produces no speech
spectral_norm(nn.Conv2d(dim_out, dim_out, 5, stride=1, padding=0)),
nn.LeakyReLU(0.2) # 0.3 sounds nice
]
self.shared = nn.Sequential(*blocks)
self.unshared = nn.Linear(dim_out, style_dim)
def forward(self, x):
x = self.shared(x)
x = x.mean(3, keepdims=True) # comment this line for time varying style vector
x = x.transpose(1, 3)
s = self.unshared(x)
return s
class LinearNorm(torch.nn.Module):
def __init__(self, in_dim, out_dim, bias=True):
super().__init__()
self.linear_layer = torch.nn.Linear(in_dim, out_dim, bias=bias)
def forward(self, x):
return self.linear_layer(x)
class LayerNorm(nn.Module):
def __init__(self, channels, eps=1e-5):
super().__init__()
self.channels = channels
self.eps = eps
self.gamma = nn.Parameter(torch.ones(channels))
self.beta = nn.Parameter(torch.zeros(channels))
def forward(self, x):
x = x.transpose(1, -1)
x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
return x.transpose(1, -1)
class TextEncoder(nn.Module):
def __init__(self, channels, kernel_size, depth, n_symbols):
super().__init__()
self.embedding = nn.Embedding(n_symbols, channels)
padding = (kernel_size - 1) // 2
self.cnn = nn.ModuleList()
for _ in range(depth):
self.cnn.append(nn.Sequential(
weight_norm(nn.Conv1d(channels, channels, kernel_size=kernel_size, padding=padding)),
LayerNorm(channels),
nn.LeakyReLU(0.24))
)
self.lstm = nn.LSTM(channels, channels//2, 1,
batch_first=True, bidirectional=True)
def forward(self, x):
x = self.embedding(x) # [B, T, emb]
x = x.transpose(1, 2)
for c in self.cnn:
x = c(x)
x = x.transpose(1, 2)
x, _ = self.lstm(x)
return x
class AdaLayerNorm(nn.Module):
def __init__(self, style_dim, channels=None, eps=1e-5):
super().__init__()
self.eps = eps
self.fc = nn.Linear(style_dim, 1024)
def forward(self, x, s):
h = self.fc(s)
gamma = h[:, :, :512]
beta = h[:, :, 512:1024]
x = F.layer_norm(x, (512, ), eps=self.eps)
x = (1 + gamma) * x + beta
return x # [1, 75, 512]
class ProsodyPredictor(nn.Module):
def __init__(self, style_dim, d_hid, nlayers, max_dur=50):
super().__init__()
self.text_encoder = DurationEncoder(sty_dim=style_dim,
d_model=d_hid,
nlayers=nlayers) # called outside forward
self.lstm = nn.LSTM(d_hid + style_dim, d_hid // 2,
1, batch_first=True, bidirectional=True)
self.duration_proj = LinearNorm(d_hid, max_dur)
self.shared = nn.LSTM(d_hid + style_dim, d_hid //
2, 1, batch_first=True, bidirectional=True)
self.F0 = nn.ModuleList([
AdainResBlk1d(d_hid, d_hid, style_dim),
AdainResBlk1d(d_hid, d_hid // 2, style_dim, upsample=True),
AdainResBlk1d(d_hid // 2, d_hid // 2, style_dim),
])
self.N = nn.ModuleList([
AdainResBlk1d(d_hid, d_hid, style_dim),
AdainResBlk1d(d_hid, d_hid // 2, style_dim, upsample=True),
AdainResBlk1d(d_hid // 2, d_hid // 2, style_dim)
])
self.F0_proj = nn.Conv1d(d_hid // 2, 1, 1, 1, 0)
self.N_proj = nn.Conv1d(d_hid // 2, 1, 1, 1, 0)
def F0Ntrain(self, x, s):
x, _ = self.shared(x) # [bs, time, ch] LSTM
x = x.transpose(1, 2) # [bs, ch, time]
F0 = x
for block in self.F0:
# print(f'LOOP {F0.shape=} {s.shape=}\n')
# )N F0.shape=torch.Size([1, 512, 147]) s.shape=torch.Size([1, 128])
# This is an AdainResBlk1d expects conv1d dimensions
F0 = block(F0, s)
F0 = self.F0_proj(F0)
N = x
for block in self.N:
N = block(N, s)
N = self.N_proj(N)
return F0, N
def forward(self, d_en=None, s=None):
blend = self.text_encoder(d_en, s)
x, _ = self.lstm(blend)
dur = self.duration_proj(x) # [bs, 150, 50]
_, input_length, classifier_50 = dur.shape
dur = dur[0, :, :]
dur = torch.sigmoid(dur).sum(1)
dur = dur.round().clamp(min=1).to(torch.int64)
aln_trg = torch.zeros(1,
dur.sum(),
input_length,
device=s.device)
c_frame = 0
for i in range(input_length):
aln_trg[:, c_frame:c_frame + dur[i], i] = 1
c_frame += dur[i]
en = torch.bmm(aln_trg, blend)
F0_pred, N_pred = self.F0Ntrain(en, s)
return aln_trg, F0_pred, N_pred
class DurationEncoder(nn.Module):
def __init__(self, sty_dim=128, d_model=512, nlayers=3):
super().__init__()
self.lstms = nn.ModuleList()
for _ in range(nlayers):
self.lstms.append(nn.LSTM(d_model + sty_dim,
d_model // 2,
num_layers=1,
batch_first=True,
bidirectional=True
))
self.lstms.append(AdaLayerNorm(sty_dim, d_model))
def forward(self, x, style):
_, _, input_lengths = x.shape # [bs, 512, time]
style = _tile(style, length=x.shape[2]).transpose(1, 2)
x = x.transpose(1, 2)
for block in self.lstms:
if isinstance(block, AdaLayerNorm):
x = block(x, style) # LSTM has transposed x
else:
x = torch.cat([x, style], axis=2)
# LSTM
x,_ = block(x) # expects [bs, time, chan] OUTPUTS [bs, time, 2*chan] 2x FROM BIDIRECTIONAL
return torch.cat([x, style], axis=2) # predictor.lstm()
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