mirror of https://github.com/coqui-ai/TTS.git
489 lines
17 KiB
Python
489 lines
17 KiB
Python
import sys
|
|
import time
|
|
|
|
import numpy as np
|
|
import torch
|
|
import torch.nn as nn
|
|
import torch.nn.functional as F
|
|
|
|
# fix this
|
|
from TTS.utils.audio import AudioProcessor as ap
|
|
from TTS.vocoder.utils.distribution import sample_from_discretized_mix_logistic, sample_from_gaussian
|
|
|
|
|
|
def stream(string, variables):
|
|
sys.stdout.write(f"\r{string}" % variables)
|
|
|
|
|
|
# pylint: disable=abstract-method
|
|
# relates https://github.com/pytorch/pytorch/issues/42305
|
|
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)
|
|
return x + residual
|
|
|
|
|
|
class MelResNet(nn.Module):
|
|
def __init__(self, num_res_blocks, in_dims, compute_dims, res_out_dims, pad):
|
|
super().__init__()
|
|
k_size = pad * 2 + 1
|
|
self.conv_in = nn.Conv1d(in_dims, compute_dims, kernel_size=k_size, bias=False)
|
|
self.batch_norm = nn.BatchNorm1d(compute_dims)
|
|
self.layers = nn.ModuleList()
|
|
for _ in range(num_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,
|
|
num_res_blocks,
|
|
res_out_dims,
|
|
pad,
|
|
use_aux_net,
|
|
):
|
|
super().__init__()
|
|
self.total_scale = np.cumproduct(upsample_scales)[-1]
|
|
self.indent = pad * self.total_scale
|
|
self.use_aux_net = use_aux_net
|
|
if use_aux_net:
|
|
self.resnet = MelResNet(num_res_blocks, feat_dims, compute_dims, res_out_dims, pad)
|
|
self.resnet_stretch = Stretch2d(self.total_scale, 1)
|
|
self.up_layers = nn.ModuleList()
|
|
for scale in upsample_scales:
|
|
k_size = (1, scale * 2 + 1)
|
|
padding = (0, scale)
|
|
stretch = Stretch2d(scale, 1)
|
|
conv = nn.Conv2d(1, 1, kernel_size=k_size, padding=padding, bias=False)
|
|
conv.weight.data.fill_(1.0 / k_size[1])
|
|
self.up_layers.append(stretch)
|
|
self.up_layers.append(conv)
|
|
|
|
def forward(self, m):
|
|
if self.use_aux_net:
|
|
aux = self.resnet(m).unsqueeze(1)
|
|
aux = self.resnet_stretch(aux)
|
|
aux = aux.squeeze(1)
|
|
aux = aux.transpose(1, 2)
|
|
else:
|
|
aux = None
|
|
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
|
|
|
|
|
|
class Upsample(nn.Module):
|
|
def __init__(self, scale, pad, num_res_blocks, feat_dims, compute_dims, res_out_dims, use_aux_net):
|
|
super().__init__()
|
|
self.scale = scale
|
|
self.pad = pad
|
|
self.indent = pad * scale
|
|
self.use_aux_net = use_aux_net
|
|
self.resnet = MelResNet(num_res_blocks, feat_dims, compute_dims, res_out_dims, pad)
|
|
|
|
def forward(self, m):
|
|
if self.use_aux_net:
|
|
aux = self.resnet(m)
|
|
aux = torch.nn.functional.interpolate(aux, scale_factor=self.scale, mode="linear", align_corners=True)
|
|
aux = aux.transpose(1, 2)
|
|
else:
|
|
aux = None
|
|
m = torch.nn.functional.interpolate(m, scale_factor=self.scale, mode="linear", align_corners=True)
|
|
m = m[:, :, self.indent : -self.indent]
|
|
m = m * 0.045 # empirically found
|
|
|
|
return m.transpose(1, 2), aux
|
|
|
|
|
|
class WaveRNN(nn.Module):
|
|
def __init__(
|
|
self,
|
|
rnn_dims,
|
|
fc_dims,
|
|
mode,
|
|
mulaw,
|
|
pad,
|
|
use_aux_net,
|
|
use_upsample_net,
|
|
upsample_factors,
|
|
feat_dims,
|
|
compute_dims,
|
|
res_out_dims,
|
|
num_res_blocks,
|
|
hop_length,
|
|
sample_rate,
|
|
):
|
|
super().__init__()
|
|
self.mode = mode
|
|
self.mulaw = mulaw
|
|
self.pad = pad
|
|
self.use_upsample_net = use_upsample_net
|
|
self.use_aux_net = use_aux_net
|
|
if isinstance(self.mode, int):
|
|
self.n_classes = 2 ** self.mode
|
|
elif self.mode == "mold":
|
|
self.n_classes = 3 * 10
|
|
elif self.mode == "gauss":
|
|
self.n_classes = 2
|
|
else:
|
|
raise RuntimeError("Unknown model mode value - ", self.mode)
|
|
|
|
self.rnn_dims = rnn_dims
|
|
self.aux_dims = res_out_dims // 4
|
|
self.hop_length = hop_length
|
|
self.sample_rate = sample_rate
|
|
|
|
if self.use_upsample_net:
|
|
assert (
|
|
np.cumproduct(upsample_factors)[-1] == self.hop_length
|
|
), " [!] upsample scales needs to be equal to hop_length"
|
|
self.upsample = UpsampleNetwork(
|
|
feat_dims,
|
|
upsample_factors,
|
|
compute_dims,
|
|
num_res_blocks,
|
|
res_out_dims,
|
|
pad,
|
|
use_aux_net,
|
|
)
|
|
else:
|
|
self.upsample = Upsample(
|
|
hop_length,
|
|
pad,
|
|
num_res_blocks,
|
|
feat_dims,
|
|
compute_dims,
|
|
res_out_dims,
|
|
use_aux_net,
|
|
)
|
|
if self.use_aux_net:
|
|
self.I = nn.Linear(feat_dims + self.aux_dims + 1, rnn_dims)
|
|
self.rnn1 = nn.GRU(rnn_dims, rnn_dims, batch_first=True)
|
|
self.rnn2 = nn.GRU(rnn_dims + self.aux_dims, rnn_dims, batch_first=True)
|
|
self.fc1 = nn.Linear(rnn_dims + self.aux_dims, fc_dims)
|
|
self.fc2 = nn.Linear(fc_dims + self.aux_dims, fc_dims)
|
|
self.fc3 = nn.Linear(fc_dims, self.n_classes)
|
|
else:
|
|
self.I = nn.Linear(feat_dims + 1, rnn_dims)
|
|
self.rnn1 = nn.GRU(rnn_dims, rnn_dims, batch_first=True)
|
|
self.rnn2 = nn.GRU(rnn_dims, rnn_dims, batch_first=True)
|
|
self.fc1 = nn.Linear(rnn_dims, fc_dims)
|
|
self.fc2 = nn.Linear(fc_dims, fc_dims)
|
|
self.fc3 = nn.Linear(fc_dims, self.n_classes)
|
|
|
|
def forward(self, x, mels):
|
|
bsize = x.size(0)
|
|
h1 = torch.zeros(1, bsize, self.rnn_dims).to(x.device)
|
|
h2 = torch.zeros(1, bsize, self.rnn_dims).to(x.device)
|
|
mels, aux = self.upsample(mels)
|
|
|
|
if self.use_aux_net:
|
|
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)
|
|
if self.use_aux_net
|
|
else torch.cat([x.unsqueeze(-1), mels], dim=2)
|
|
)
|
|
x = self.I(x)
|
|
res = x
|
|
self.rnn1.flatten_parameters()
|
|
x, _ = self.rnn1(x, h1)
|
|
|
|
x = x + res
|
|
res = x
|
|
x = torch.cat([x, a2], dim=2) if self.use_aux_net else x
|
|
self.rnn2.flatten_parameters()
|
|
x, _ = self.rnn2(x, h2)
|
|
|
|
x = x + res
|
|
x = torch.cat([x, a3], dim=2) if self.use_aux_net else x
|
|
x = F.relu(self.fc1(x))
|
|
|
|
x = torch.cat([x, a4], dim=2) if self.use_aux_net else x
|
|
x = F.relu(self.fc2(x))
|
|
return self.fc3(x)
|
|
|
|
def inference(self, mels, batched=None, target=None, overlap=None):
|
|
|
|
self.eval()
|
|
output = []
|
|
start = time.time()
|
|
rnn1 = self.get_gru_cell(self.rnn1)
|
|
rnn2 = self.get_gru_cell(self.rnn2)
|
|
|
|
with torch.no_grad():
|
|
if isinstance(mels, np.ndarray):
|
|
mels = torch.FloatTensor(mels).type_as(mels)
|
|
|
|
if mels.ndim == 2:
|
|
mels = mels.unsqueeze(0)
|
|
wave_len = (mels.size(-1) - 1) * self.hop_length
|
|
|
|
mels = self.pad_tensor(mels.transpose(1, 2), pad=self.pad, side="both")
|
|
mels, aux = self.upsample(mels.transpose(1, 2))
|
|
|
|
if batched:
|
|
mels = self.fold_with_overlap(mels, target, overlap)
|
|
if aux is not None:
|
|
aux = self.fold_with_overlap(aux, target, overlap)
|
|
|
|
b_size, seq_len, _ = mels.size()
|
|
|
|
h1 = torch.zeros(b_size, self.rnn_dims).type_as(mels)
|
|
h2 = torch.zeros(b_size, self.rnn_dims).type_as(mels)
|
|
x = torch.zeros(b_size, 1).type_as(mels)
|
|
|
|
if self.use_aux_net:
|
|
d = self.aux_dims
|
|
aux_split = [aux[:, :, d * i : d * (i + 1)] for i in range(4)]
|
|
|
|
for i in range(seq_len):
|
|
|
|
m_t = mels[:, i, :]
|
|
|
|
if self.use_aux_net:
|
|
a1_t, a2_t, a3_t, a4_t = (a[:, i, :] for a in aux_split)
|
|
|
|
x = torch.cat([x, m_t, a1_t], dim=1) if self.use_aux_net else torch.cat([x, m_t], dim=1)
|
|
x = self.I(x)
|
|
h1 = rnn1(x, h1)
|
|
|
|
x = x + h1
|
|
inp = torch.cat([x, a2_t], dim=1) if self.use_aux_net else x
|
|
h2 = rnn2(inp, h2)
|
|
|
|
x = x + h2
|
|
x = torch.cat([x, a3_t], dim=1) if self.use_aux_net else x
|
|
x = F.relu(self.fc1(x))
|
|
|
|
x = torch.cat([x, a4_t], dim=1) if self.use_aux_net else x
|
|
x = F.relu(self.fc2(x))
|
|
|
|
logits = self.fc3(x)
|
|
|
|
if self.mode == "mold":
|
|
sample = sample_from_discretized_mix_logistic(logits.unsqueeze(0).transpose(1, 2))
|
|
output.append(sample.view(-1))
|
|
x = sample.transpose(0, 1).type_as(mels)
|
|
elif self.mode == "gauss":
|
|
sample = sample_from_gaussian(logits.unsqueeze(0).transpose(1, 2))
|
|
output.append(sample.view(-1))
|
|
x = sample.transpose(0, 1).type_as(mels)
|
|
elif isinstance(self.mode, int):
|
|
posterior = F.softmax(logits, dim=1)
|
|
distrib = torch.distributions.Categorical(posterior)
|
|
|
|
sample = 2 * distrib.sample().float() / (self.n_classes - 1.0) - 1.0
|
|
output.append(sample)
|
|
x = sample.unsqueeze(-1)
|
|
else:
|
|
raise RuntimeError("Unknown model mode value - ", self.mode)
|
|
|
|
if i % 100 == 0:
|
|
self.gen_display(i, seq_len, b_size, start)
|
|
|
|
output = torch.stack(output).transpose(0, 1)
|
|
output = output.cpu()
|
|
if batched:
|
|
output = output.numpy()
|
|
output = output.astype(np.float64)
|
|
|
|
output = self.xfade_and_unfold(output, target, overlap)
|
|
else:
|
|
output = output[0]
|
|
|
|
if self.mulaw and isinstance(self.mode, int):
|
|
output = ap.mulaw_decode(output, self.mode)
|
|
|
|
# Fade-out at the end to avoid signal cutting out suddenly
|
|
fade_out = np.linspace(1, 0, 20 * self.hop_length)
|
|
output = output[:wave_len]
|
|
|
|
if wave_len > len(fade_out):
|
|
output[-20 * self.hop_length :] *= fade_out
|
|
|
|
self.train()
|
|
return output
|
|
|
|
def gen_display(self, i, seq_len, b_size, start):
|
|
gen_rate = (i + 1) / (time.time() - start) * b_size / 1000
|
|
realtime_ratio = gen_rate * 1000 / self.sample_rate
|
|
stream(
|
|
"%i/%i -- batch_size: %i -- gen_rate: %.1f kHz -- x_realtime: %.1f ",
|
|
(i * b_size, seq_len * b_size, b_size, gen_rate, realtime_ratio),
|
|
)
|
|
|
|
def fold_with_overlap(self, x, target, overlap):
|
|
"""Fold the tensor with overlap for quick batched inference.
|
|
Overlap will be used for crossfading in xfade_and_unfold()
|
|
Args:
|
|
x (tensor) : Upsampled conditioning features.
|
|
shape=(1, timesteps, features)
|
|
target (int) : Target timesteps for each index of batch
|
|
overlap (int) : Timesteps for both xfade and rnn warmup
|
|
Return:
|
|
(tensor) : shape=(num_folds, target + 2 * overlap, features)
|
|
Details:
|
|
x = [[h1, h2, ... hn]]
|
|
Where each h is a vector of conditioning features
|
|
Eg: target=2, overlap=1 with x.size(1)=10
|
|
folded = [[h1, h2, h3, h4],
|
|
[h4, h5, h6, h7],
|
|
[h7, h8, h9, h10]]
|
|
"""
|
|
|
|
_, total_len, features = x.size()
|
|
|
|
# Calculate variables needed
|
|
num_folds = (total_len - overlap) // (target + overlap)
|
|
extended_len = num_folds * (overlap + target) + overlap
|
|
remaining = total_len - extended_len
|
|
|
|
# Pad if some time steps poking out
|
|
if remaining != 0:
|
|
num_folds += 1
|
|
padding = target + 2 * overlap - remaining
|
|
x = self.pad_tensor(x, padding, side="after")
|
|
|
|
folded = torch.zeros(num_folds, target + 2 * overlap, features).to(x.device)
|
|
|
|
# Get the values for the folded tensor
|
|
for i in range(num_folds):
|
|
start = i * (target + overlap)
|
|
end = start + target + 2 * overlap
|
|
folded[i] = x[:, start:end, :]
|
|
|
|
return folded
|
|
|
|
@staticmethod
|
|
def get_gru_cell(gru):
|
|
gru_cell = nn.GRUCell(gru.input_size, gru.hidden_size)
|
|
gru_cell.weight_hh.data = gru.weight_hh_l0.data
|
|
gru_cell.weight_ih.data = gru.weight_ih_l0.data
|
|
gru_cell.bias_hh.data = gru.bias_hh_l0.data
|
|
gru_cell.bias_ih.data = gru.bias_ih_l0.data
|
|
return gru_cell
|
|
|
|
@staticmethod
|
|
def pad_tensor(x, pad, side="both"):
|
|
# NB - this is just a quick method i need right now
|
|
# i.e., it won't generalise to other shapes/dims
|
|
b, t, c = x.size()
|
|
total = t + 2 * pad if side == "both" else t + pad
|
|
padded = torch.zeros(b, total, c).to(x.device)
|
|
if side in ("before", "both"):
|
|
padded[:, pad : pad + t, :] = x
|
|
elif side == "after":
|
|
padded[:, :t, :] = x
|
|
return padded
|
|
|
|
@staticmethod
|
|
def xfade_and_unfold(y, target, overlap):
|
|
"""Applies a crossfade and unfolds into a 1d array.
|
|
Args:
|
|
y (ndarry) : Batched sequences of audio samples
|
|
shape=(num_folds, target + 2 * overlap)
|
|
dtype=np.float64
|
|
overlap (int) : Timesteps for both xfade and rnn warmup
|
|
Return:
|
|
(ndarry) : audio samples in a 1d array
|
|
shape=(total_len)
|
|
dtype=np.float64
|
|
Details:
|
|
y = [[seq1],
|
|
[seq2],
|
|
[seq3]]
|
|
Apply a gain envelope at both ends of the sequences
|
|
y = [[seq1_in, seq1_target, seq1_out],
|
|
[seq2_in, seq2_target, seq2_out],
|
|
[seq3_in, seq3_target, seq3_out]]
|
|
Stagger and add up the groups of samples:
|
|
[seq1_in, seq1_target, (seq1_out + seq2_in), seq2_target, ...]
|
|
"""
|
|
|
|
num_folds, length = y.shape
|
|
target = length - 2 * overlap
|
|
total_len = num_folds * (target + overlap) + overlap
|
|
|
|
# Need some silence for the rnn warmup
|
|
silence_len = overlap // 2
|
|
fade_len = overlap - silence_len
|
|
silence = np.zeros((silence_len), dtype=np.float64)
|
|
|
|
# Equal power crossfade
|
|
t = np.linspace(-1, 1, fade_len, dtype=np.float64)
|
|
fade_in = np.sqrt(0.5 * (1 + t))
|
|
fade_out = np.sqrt(0.5 * (1 - t))
|
|
|
|
# Concat the silence to the fades
|
|
fade_in = np.concatenate([silence, fade_in])
|
|
fade_out = np.concatenate([fade_out, silence])
|
|
|
|
# Apply the gain to the overlap samples
|
|
y[:, :overlap] *= fade_in
|
|
y[:, -overlap:] *= fade_out
|
|
|
|
unfolded = np.zeros((total_len), dtype=np.float64)
|
|
|
|
# Loop to add up all the samples
|
|
for i in range(num_folds):
|
|
start = i * (target + overlap)
|
|
end = start + target + 2 * overlap
|
|
unfolded[start:end] += y[i]
|
|
|
|
return unfolded
|
|
|
|
def load_checkpoint(
|
|
self, config, checkpoint_path, eval=False
|
|
): # pylint: disable=unused-argument, redefined-builtin
|
|
state = torch.load(checkpoint_path, map_location=torch.device("cpu"))
|
|
self.load_state_dict(state["model"])
|
|
if eval:
|
|
self.eval()
|
|
assert not self.training
|