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import math
import torch
import torch.nn as nn
from torch.nn import functional as F
from modules.commons.rotary_embedding_torch import RotaryEmbedding
from modules.commons.common_layers import SinusoidalPositionalEmbedding, EncSALayer
from modules.commons.espnet_positional_embedding import RelPositionalEncoding
DEFAULT_MAX_SOURCE_POSITIONS = 2000
DEFAULT_MAX_TARGET_POSITIONS = 2000
class TransformerEncoderLayer(nn.Module):
def __init__(self, hidden_size, dropout, kernel_size=None, act='gelu', num_heads=2, rotary_embed=None):
super().__init__()
self.op = EncSALayer(
hidden_size, num_heads, dropout=dropout,
attention_dropout=0.0, relu_dropout=dropout,
kernel_size=kernel_size,
act=act, rotary_embed=rotary_embed
)
def forward(self, x, **kwargs):
return self.op(x, **kwargs)
######################
# fastspeech modules
######################
class LayerNorm(torch.nn.LayerNorm):
"""Layer normalization module.
:param int nout: output dim size
:param int dim: dimension to be normalized
"""
def __init__(self, nout, dim=-1):
"""Construct an LayerNorm object."""
super(LayerNorm, self).__init__(nout, eps=1e-12)
self.dim = dim
def forward(self, x):
"""Apply layer normalization.
:param torch.Tensor x: input tensor
:return: layer normalized tensor
:rtype torch.Tensor
"""
if self.dim == -1:
return super(LayerNorm, self).forward(x)
return super(LayerNorm, self).forward(x.transpose(1, -1)).transpose(1, -1)
class DurationPredictor(torch.nn.Module):
"""Duration predictor module.
This is a module of duration predictor described in `FastSpeech: Fast, Robust and Controllable Text to Speech`_.
The duration predictor predicts a duration of each frame in log domain from the hidden embeddings of encoder.
.. _`FastSpeech: Fast, Robust and Controllable Text to Speech`:
https://arxiv.org/pdf/1905.09263.pdf
Note:
The calculation domain of outputs is different between in `forward` and in `inference`. In `forward`,
the outputs are calculated in log domain but in `inference`, those are calculated in linear domain.
"""
def __init__(self, in_dims, n_layers=2, n_chans=384, kernel_size=3,
dropout_rate=0.1, offset=1.0, dur_loss_type='mse'):
"""Initialize duration predictor module.
Args:
in_dims (int): Input dimension.
n_layers (int, optional): Number of convolutional layers.
n_chans (int, optional): Number of channels of convolutional layers.
kernel_size (int, optional): Kernel size of convolutional layers.
dropout_rate (float, optional): Dropout rate.
offset (float, optional): Offset value to avoid nan in log domain.
"""
super(DurationPredictor, self).__init__()
self.offset = offset
self.conv = torch.nn.ModuleList()
self.kernel_size = kernel_size
for idx in range(n_layers):
in_chans = in_dims if idx == 0 else n_chans
self.conv.append(torch.nn.Sequential(
torch.nn.Identity(), # this is a placeholder for ConstantPad1d which is now merged into Conv1d
torch.nn.Conv1d(in_chans, n_chans, kernel_size, stride=1, padding=kernel_size // 2),
torch.nn.ReLU(),
LayerNorm(n_chans, dim=1),
torch.nn.Dropout(dropout_rate)
))
self.loss_type = dur_loss_type
if self.loss_type in ['mse', 'huber']:
self.out_dims = 1
# elif hparams['dur_loss_type'] == 'mog':
# out_dims = 15
# elif hparams['dur_loss_type'] == 'crf':
# out_dims = 32
# from torchcrf import CRF
# self.crf = CRF(out_dims, batch_first=True)
else:
raise NotImplementedError()
self.linear = torch.nn.Linear(n_chans, self.out_dims)
def out2dur(self, xs):
if self.loss_type in ['mse', 'huber']:
# NOTE: calculate loss in log domain
dur = xs.squeeze(-1).exp() - self.offset # (B, Tmax)
# elif hparams['dur_loss_type'] == 'crf':
# dur = torch.LongTensor(self.crf.decode(xs)).cuda()
else:
raise NotImplementedError()
return dur
def forward(self, xs, x_masks=None, infer=True):
"""Calculate forward propagation.
Args:
xs (Tensor): Batch of input sequences (B, Tmax, idim).
x_masks (BoolTensor, optional): Batch of masks indicating padded part (B, Tmax).
infer (bool): Whether inference
Returns:
(train) FloatTensor, (infer) LongTensor: Batch of predicted durations in linear domain (B, Tmax).
"""
xs = xs.transpose(1, -1) # (B, idim, Tmax)
masks = 1 - x_masks.float()
masks_ = masks[:, None, :]
for f in self.conv:
xs = f(xs) # (B, C, Tmax)
if x_masks is not None:
xs = xs * masks_
xs = self.linear(xs.transpose(1, -1)) # [B, T, C]
xs = xs * masks[:, :, None] # (B, T, C)
dur_pred = self.out2dur(xs)
if infer:
dur_pred = dur_pred.clamp(min=0.) # avoid negative value
return dur_pred
class VariancePredictor(torch.nn.Module):
def __init__(self, vmin, vmax, in_dims,
n_layers=5, n_chans=512, kernel_size=5,
dropout_rate=0.1):
"""Initialize variance predictor module.
Args:
in_dims (int): Input dimension.
n_layers (int, optional): Number of convolutional layers.
n_chans (int, optional): Number of channels of convolutional layers.
kernel_size (int, optional): Kernel size of convolutional layers.
dropout_rate (float, optional): Dropout rate.
"""
super(VariancePredictor, self).__init__()
self.vmin = vmin
self.vmax = vmax
self.conv = torch.nn.ModuleList()
self.kernel_size = kernel_size
for idx in range(n_layers):
in_chans = in_dims if idx == 0 else n_chans
self.conv.append(torch.nn.Sequential(
torch.nn.Conv1d(in_chans, n_chans, kernel_size, stride=1, padding=kernel_size // 2),
torch.nn.ReLU(),
LayerNorm(n_chans, dim=1),
torch.nn.Dropout(dropout_rate)
))
self.linear = torch.nn.Linear(n_chans, 1)
self.embed_positions = SinusoidalPositionalEmbedding(in_dims, 0, init_size=4096)
self.pos_embed_alpha = nn.Parameter(torch.Tensor([1]))
def out2value(self, xs):
return (xs + 1) / 2 * (self.vmax - self.vmin) + self.vmin
def forward(self, xs, infer=True):
"""
:param xs: [B, T, H]
:param infer: whether inference
:return: [B, T]
"""
positions = self.pos_embed_alpha * self.embed_positions(xs[..., 0])
xs = xs + positions
xs = xs.transpose(1, -1) # (B, idim, Tmax)
for f in self.conv:
xs = f(xs) # (B, C, Tmax)
xs = self.linear(xs.transpose(1, -1)).squeeze(-1) # (B, Tmax)
if infer:
xs = self.out2value(xs)
return xs
class PitchPredictor(torch.nn.Module):
def __init__(self, vmin, vmax, num_bins, deviation,
in_dims, n_layers=5, n_chans=384, kernel_size=5,
dropout_rate=0.1):
"""Initialize pitch predictor module.
Args:
in_dims (int): Input dimension.
n_layers (int, optional): Number of convolutional layers.
n_chans (int, optional): Number of channels of convolutional layers.
kernel_size (int, optional): Kernel size of convolutional layers.
dropout_rate (float, optional): Dropout rate.
"""
super(PitchPredictor, self).__init__()
self.vmin = vmin
self.vmax = vmax
self.interval = (vmax - vmin) / (num_bins - 1) # align with centers of bins
self.sigma = deviation / self.interval
self.register_buffer('x', torch.arange(num_bins).float().reshape(1, 1, -1)) # [1, 1, N]
self.base_pitch_embed = torch.nn.Linear(1, in_dims)
self.conv = torch.nn.ModuleList()
self.kernel_size = kernel_size
for idx in range(n_layers):
in_chans = in_dims if idx == 0 else n_chans
self.conv.append(torch.nn.Sequential(
torch.nn.Conv1d(in_chans, n_chans, kernel_size, stride=1, padding=kernel_size // 2),
torch.nn.ReLU(),
LayerNorm(n_chans, dim=1),
torch.nn.Dropout(dropout_rate)
))
self.linear = torch.nn.Linear(n_chans, num_bins)
self.embed_positions = SinusoidalPositionalEmbedding(in_dims, 0, init_size=4096)
self.pos_embed_alpha = nn.Parameter(torch.Tensor([1]))
def bins_to_values(self, bins):
return bins * self.interval + self.vmin
def out2pitch(self, probs):
logits = probs.sigmoid() # [B, T, N]
# return logits
# logits_sum = logits.sum(dim=2) # [B, T]
bins = torch.sum(self.x * logits, dim=2) / torch.sum(logits, dim=2) # [B, T]
pitch = self.bins_to_values(bins)
# uv = logits_sum / (self.sigma * math.sqrt(2 * math.pi)) < 0.3
# pitch[uv] = torch.nan
return pitch
def forward(self, xs, base):
"""
:param xs: [B, T, H]
:param base: [B, T]
:return: [B, T, N]
"""
xs = xs + self.base_pitch_embed(base[..., None])
positions = self.pos_embed_alpha * self.embed_positions(xs[..., 0])
xs = xs + positions
xs = xs.transpose(1, -1) # (B, idim, Tmax)
for f in self.conv:
xs = f(xs) # (B, C, Tmax)
xs = self.linear(xs.transpose(1, -1)) # (B, Tmax, H)
return self.out2pitch(xs) + base, xs
class RhythmRegulator(torch.nn.Module):
def __init__(self, eps=1e-5):
super().__init__()
self.eps = eps
def forward(self, ph_dur, ph2word, word_dur):
"""
Example (no batch dim version):
1. ph_dur = [4,2,3,2]
2. word_dur = [3,4,2], ph2word = [1,2,2,3]
3. word_dur_in = [4,5,2]
4. alpha_w = [0.75,0.8,1], alpha_ph = [0.75,0.8,0.8,1]
5. ph_dur_out = [3,1.6,2.4,2]
:param ph_dur: [B, T_ph]
:param ph2word: [B, T_ph]
:param word_dur: [B, T_w]
"""
ph_dur = ph_dur.float() * (ph2word > 0)
word_dur = word_dur.float()
word_dur_in = ph_dur.new_zeros(ph_dur.shape[0], ph2word.max() + 1).scatter_add(
1, ph2word, ph_dur
)[:, 1:] # [B, T_ph] => [B, T_w]
alpha_w = word_dur / word_dur_in.clamp(min=self.eps) # avoid dividing by zero
alpha_ph = torch.gather(F.pad(alpha_w, [1, 0]), 1, ph2word) # [B, T_w] => [B, T_ph]
ph_dur_out = ph_dur * alpha_ph
return ph_dur_out.round().long()
class LengthRegulator(torch.nn.Module):
# noinspection PyMethodMayBeStatic
def forward(self, dur, dur_padding=None, alpha=None):
"""
Example (no batch dim version):
1. dur = [2,2,3]
2. token_idx = [[1],[2],[3]], dur_cumsum = [2,4,7], dur_cumsum_prev = [0,2,4]
3. token_mask = [[1,1,0,0,0,0,0],
[0,0,1,1,0,0,0],
[0,0,0,0,1,1,1]]
4. token_idx * token_mask = [[1,1,0,0,0,0,0],
[0,0,2,2,0,0,0],
[0,0,0,0,3,3,3]]
5. (token_idx * token_mask).sum(0) = [1,1,2,2,3,3,3]
:param dur: Batch of durations of each frame (B, T_txt)
:param dur_padding: Batch of padding of each frame (B, T_txt)
:param alpha: duration rescale coefficient
:return:
mel2ph (B, T_speech)
"""
assert alpha is None or alpha > 0
if alpha is not None:
dur = torch.round(dur.float() * alpha).long()
if dur_padding is not None:
dur = dur * (1 - dur_padding.long())
token_idx = torch.arange(1, dur.shape[1] + 1)[None, :, None].to(dur.device)
dur_cumsum = torch.cumsum(dur, 1)
dur_cumsum_prev = F.pad(dur_cumsum, [1, -1], mode='constant', value=0)
pos_idx = torch.arange(dur.sum(-1).max())[None, None].to(dur.device)
token_mask = (pos_idx >= dur_cumsum_prev[:, :, None]) & (pos_idx < dur_cumsum[:, :, None])
mel2ph = (token_idx * token_mask.long()).sum(1)
return mel2ph
class StretchRegulator(torch.nn.Module):
# noinspection PyMethodMayBeStatic
def forward(self, mel2ph, dur=None):
"""
Example (no batch dim version):
1. dur = [2,4,3]
2. mel2ph = [1,1,2,2,2,2,3,3,3]
3. mel2dur = [2,2,4,4,4,4,3,3,3]
4. bound_mask = [0,1,0,0,0,1,0,0,1]
5. 1 - bound_mask * mel2dur = [1,-1,1,1,1,-3,1,1,-2] => pad => [0,1,-1,1,1,1,-3,1,1]
6. stretch_denorm = [0,1,0,1,2,3,0,1,2]
:param dur: Batch of durations of each frame (B, T_txt)
:param mel2ph: Batch of mel2ph (B, T_speech)
:return:
stretch (B, T_speech)
"""
if dur is None:
dur = mel2ph_to_dur(mel2ph, mel2ph.max())
dur = F.pad(dur, [1, 0], value=1) # Avoid dividing by zero
mel2dur = torch.gather(dur, 1, mel2ph)
bound_mask = torch.gt(mel2ph[:, 1:], mel2ph[:, :-1])
bound_mask = F.pad(bound_mask, [0, 1], mode='constant', value=True)
stretch_delta = 1 - bound_mask * mel2dur
stretch_delta = F.pad(stretch_delta, [1, -1], mode='constant', value=0)
stretch_denorm = torch.cumsum(stretch_delta, dim=1)
stretch = stretch_denorm / mel2dur
return stretch * (mel2ph > 0)
def mel2ph_to_dur(mel2ph, T_txt, max_dur=None):
B, _ = mel2ph.shape
dur = mel2ph.new_zeros(B, T_txt + 1).scatter_add(1, mel2ph, torch.ones_like(mel2ph))
dur = dur[:, 1:]
if max_dur is not None:
dur = dur.clamp(max=max_dur)
return dur
class FastSpeech2Encoder(nn.Module):
def __init__(self, hidden_size, num_layers,
ffn_kernel_size=9, ffn_act='gelu',
dropout=None, num_heads=2, use_pos_embed=True, rel_pos=True, use_rope=False):
super().__init__()
self.num_layers = num_layers
embed_dim = self.hidden_size = hidden_size
self.dropout = dropout
self.use_pos_embed = use_pos_embed
if use_pos_embed and use_rope:
rotary_embed = RotaryEmbedding(dim = embed_dim // num_heads)
else:
rotary_embed = None
self.layers = nn.ModuleList([
TransformerEncoderLayer(
self.hidden_size, self.dropout,
kernel_size=ffn_kernel_size, act=ffn_act,
num_heads=num_heads, rotary_embed=rotary_embed
)
for _ in range(self.num_layers)
])
self.layer_norm = nn.LayerNorm(embed_dim)
self.embed_scale = math.sqrt(hidden_size)
self.padding_idx = 0
self.rel_pos = rel_pos
if use_rope:
self.embed_positions = None
elif self.rel_pos:
self.embed_positions = RelPositionalEncoding(hidden_size, dropout_rate=0.0)
else:
self.embed_positions = SinusoidalPositionalEmbedding(
hidden_size, self.padding_idx, init_size=DEFAULT_MAX_TARGET_POSITIONS,
)
def forward_embedding(self, main_embed, extra_embed=None, padding_mask=None):
# embed tokens and positions
x = self.embed_scale * main_embed
if extra_embed is not None:
x = x + extra_embed
if self.use_pos_embed and self.embed_positions is not None:
if self.rel_pos:
x = self.embed_positions(x)
else:
positions = self.embed_positions(~padding_mask)
x = x + positions
x = F.dropout(x, p=self.dropout, training=self.training)
return x
def forward(self, main_embed, extra_embed, padding_mask, attn_mask=None, return_hiddens=False):
x = self.forward_embedding(main_embed, extra_embed, padding_mask=padding_mask) # [B, T, H]
nonpadding_mask_BT = 1 - padding_mask.float()[:, :, None] # [B, T, 1]
# NOTICE:
# The following codes are commented out because
# `self.use_pos_embed` is always False in the older versions,
# and this argument did not compat with `hparams['use_pos_embed']`,
# which defaults to True. The new version fixed this inconsistency,
# resulting in temporary removal of pos_embed_alpha, which has actually
# never been used before.
# if self.use_pos_embed:
# positions = self.pos_embed_alpha * self.embed_positions(x[..., 0])
# x = x + positions
# x = F.dropout(x, p=self.dropout, training=self.training)
x = x * nonpadding_mask_BT
hiddens = []
for layer in self.layers:
x = layer(x, encoder_padding_mask=padding_mask, attn_mask=attn_mask) * nonpadding_mask_BT
if return_hiddens:
hiddens.append(x)
x = self.layer_norm(x) * nonpadding_mask_BT
if return_hiddens:
x = torch.stack(hiddens, 0) # [L, B, T, C]
return x
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