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import logging
import math
import torch
import torch.nn as nn
from torch.nn import functional as F
from modules.commons.espnet_positional_embedding import RelPositionalEncoding
from modules.commons.common_layers import SinusoidalPositionalEmbedding, Linear, EncSALayer, DecSALayer, BatchNorm1dTBC
from utils.hparams import hparams
DEFAULT_MAX_SOURCE_POSITIONS = 2000
DEFAULT_MAX_TARGET_POSITIONS = 2000
class TransformerEncoderLayer(nn.Module):
def __init__(self, hidden_size, dropout, kernel_size=None, num_heads=2, norm='ln'):
super().__init__()
self.hidden_size = hidden_size
self.dropout = dropout
self.num_heads = num_heads
self.op = EncSALayer(
hidden_size, num_heads, dropout=dropout,
attention_dropout=0.0, relu_dropout=dropout,
kernel_size=kernel_size
if kernel_size is not None else hparams['enc_ffn_kernel_size'],
padding=hparams['ffn_padding'],
norm=norm, act=hparams['ffn_act'])
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, idim, n_layers=2, n_chans=384, kernel_size=3, dropout_rate=0.1, offset=1.0, padding='SAME'):
"""Initilize duration predictor module.
Args:
idim (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
self.padding = padding
for idx in range(n_layers):
in_chans = idim if idx == 0 else n_chans
self.conv += [torch.nn.Sequential(
torch.nn.ConstantPad1d(((kernel_size - 1) // 2, (kernel_size - 1) // 2)
if padding == 'SAME'
else (kernel_size - 1, 0), 0),
torch.nn.Conv1d(in_chans, n_chans, kernel_size, stride=1, padding=0),
torch.nn.ReLU(),
LayerNorm(n_chans, dim=1),
torch.nn.Dropout(dropout_rate)
)]
if hparams['dur_loss'] in ['mse', 'huber']:
odims = 1
elif hparams['dur_loss'] == 'mog':
odims = 15
elif hparams['dur_loss'] == 'crf':
odims = 32
from torchcrf import CRF
self.crf = CRF(odims, batch_first=True)
self.linear = torch.nn.Linear(n_chans, odims)
def _forward(self, xs, x_masks=None, is_inference=False):
xs = xs.transpose(1, -1) # (B, idim, Tmax)
for f in self.conv:
xs = f(xs) # (B, C, Tmax)
if x_masks is not None:
xs = xs * (1 - x_masks.float())[:, None, :]
xs = self.linear(xs.transpose(1, -1)) # [B, T, C]
xs = xs * (1 - x_masks.float())[:, :, None] # (B, T, C)
if is_inference:
return self.out2dur(xs), xs
else:
if hparams['dur_loss'] in ['mse']:
xs = xs.squeeze(-1) # (B, Tmax)
return xs
def out2dur(self, xs):
if hparams['dur_loss'] in ['mse']:
# NOTE: calculate in log domain
xs = xs.squeeze(-1) # (B, Tmax)
dur = torch.clamp(torch.round(xs.exp() - self.offset), min=0).long() # avoid negative value
elif hparams['dur_loss'] == 'mog':
return NotImplementedError
elif hparams['dur_loss'] == 'crf':
dur = torch.LongTensor(self.crf.decode(xs)).cuda()
return dur
def forward(self, xs, x_masks=None):
"""Calculate forward propagation.
Args:
xs (Tensor): Batch of input sequences (B, Tmax, idim).
x_masks (ByteTensor, optional): Batch of masks indicating padded part (B, Tmax).
Returns:
Tensor: Batch of predicted durations in log domain (B, Tmax).
"""
return self._forward(xs, x_masks, False)
def inference(self, xs, x_masks=None):
"""Inference duration.
Args:
xs (Tensor): Batch of input sequences (B, Tmax, idim).
x_masks (ByteTensor, optional): Batch of masks indicating padded part (B, Tmax).
Returns:
LongTensor: Batch of predicted durations in linear domain (B, Tmax).
"""
return self._forward(xs, x_masks, True)
class LengthRegulator(torch.nn.Module):
def __init__(self, pad_value=0.0):
super(LengthRegulator, self).__init__()
self.pad_value = pad_value
def forward(self, dur, dur_padding=None, alpha=1.0):
"""
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 > 0
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 PitchPredictor(torch.nn.Module):
def __init__(self, idim, n_layers=5, n_chans=384, odim=2, kernel_size=5,
dropout_rate=0.1, padding='SAME'):
"""Initilize pitch predictor module.
Args:
idim (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.conv = torch.nn.ModuleList()
self.kernel_size = kernel_size
self.padding = padding
for idx in range(n_layers):
in_chans = idim if idx == 0 else n_chans
self.conv += [torch.nn.Sequential(
torch.nn.ConstantPad1d(((kernel_size - 1) // 2, (kernel_size - 1) // 2)
if padding == 'SAME'
else (kernel_size - 1, 0), 0),
torch.nn.Conv1d(in_chans, n_chans, kernel_size, stride=1, padding=0),
torch.nn.ReLU(),
LayerNorm(n_chans, dim=1),
torch.nn.Dropout(dropout_rate)
)]
self.linear = torch.nn.Linear(n_chans, odim)
self.embed_positions = SinusoidalPositionalEmbedding(idim, 0, init_size=4096)
self.pos_embed_alpha = nn.Parameter(torch.Tensor([1]))
def forward(self, xs):
"""
:param xs: [B, T, H]
:return: [B, T, H]
"""
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)
# NOTE: calculate in log domain
xs = self.linear(xs.transpose(1, -1)) # (B, Tmax, H)
return xs
class EnergyPredictor(PitchPredictor):
pass
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 FFTBlocks(nn.Module):
def __init__(self, hidden_size, num_layers, ffn_kernel_size=9, dropout=None, num_heads=2,
use_pos_embed=True, use_last_norm=True, norm='ln', use_pos_embed_alpha=True):
super().__init__()
self.num_layers = num_layers
embed_dim = self.hidden_size = hidden_size
self.dropout = dropout if dropout is not None else hparams['dropout']
self.use_pos_embed = use_pos_embed
self.use_last_norm = use_last_norm
if use_pos_embed:
self.max_source_positions = DEFAULT_MAX_TARGET_POSITIONS
self.padding_idx = 0
self.pos_embed_alpha = nn.Parameter(torch.Tensor([1])) if use_pos_embed_alpha else 1
self.embed_positions = SinusoidalPositionalEmbedding(
embed_dim, self.padding_idx, init_size=DEFAULT_MAX_TARGET_POSITIONS,
)
self.layers = nn.ModuleList([])
self.layers.extend([
TransformerEncoderLayer(self.hidden_size, self.dropout,
kernel_size=ffn_kernel_size, num_heads=num_heads)
for _ in range(self.num_layers)
])
if self.use_last_norm:
if norm == 'ln':
self.layer_norm = nn.LayerNorm(embed_dim)
elif norm == 'bn':
self.layer_norm = BatchNorm1dTBC(embed_dim)
else:
self.layer_norm = None
def forward(self, x, padding_mask=None, attn_mask=None, return_hiddens=False):
"""
:param x: [B, T, C]
:param padding_mask: [B, T]
:return: [B, T, C] or [L, B, T, C]
"""
# padding_mask = x.abs().sum(-1).eq(0).data if padding_mask is None else padding_mask
padding_mask = x.abs().sum(-1).eq(0).detach() if padding_mask is None else padding_mask
nonpadding_mask_TB = 1 - padding_mask.transpose(0, 1).float()[:, :, None] # [T, B, 1]
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)
# B x T x C -> T x B x C
x = x.transpose(0, 1) * nonpadding_mask_TB
hiddens = []
for layer in self.layers:
x = layer(x, encoder_padding_mask=padding_mask, attn_mask=attn_mask) * nonpadding_mask_TB
hiddens.append(x)
if self.use_last_norm:
x = self.layer_norm(x) * nonpadding_mask_TB
if return_hiddens:
x = torch.stack(hiddens, 0) # [L, T, B, C]
x = x.transpose(1, 2) # [L, B, T, C]
else:
x = x.transpose(0, 1) # [B, T, C]
return x
class FastspeechEncoder(FFTBlocks):
'''
compared to FFTBlocks:
- input is [B, T, H], not [B, T, C]
- supports "relative" positional encoding
'''
def __init__(self, hidden_size=None, num_layers=None, kernel_size=None, num_heads=2):
hidden_size = hparams['hidden_size'] if hidden_size is None else hidden_size
kernel_size = hparams['enc_ffn_kernel_size'] if kernel_size is None else kernel_size
num_layers = hparams['dec_layers'] if num_layers is None else num_layers
super().__init__(hidden_size, num_layers, kernel_size, num_heads=num_heads,
use_pos_embed=False) # use_pos_embed_alpha for compatibility
#self.embed_tokens = embed_tokens
self.embed_scale = math.sqrt(hidden_size)
self.padding_idx = 0
if hparams.get('rel_pos') is not None and hparams['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(self, hubert):
"""
:param hubert: [B, T, H ]
:return: {
'encoder_out': [T x B x C]
}
"""
# encoder_padding_mask = txt_tokens.eq(self.padding_idx).data
encoder_padding_mask = (hubert==0).all(-1)
x = self.forward_embedding(hubert) # [B, T, H]
x = super(FastspeechEncoder, self).forward(x, encoder_padding_mask)
return x
def forward_embedding(self, hubert):
# embed tokens and positions
x = self.embed_scale * hubert
if hparams['use_pos_embed']:
positions = self.embed_positions(hubert)
x = x + positions
x = F.dropout(x, p=self.dropout, training=self.training)
return x
class FastspeechDecoder(FFTBlocks):
def __init__(self, hidden_size=None, num_layers=None, kernel_size=None, num_heads=None):
num_heads = hparams['num_heads'] if num_heads is None else num_heads
hidden_size = hparams['hidden_size'] if hidden_size is None else hidden_size
kernel_size = hparams['dec_ffn_kernel_size'] if kernel_size is None else kernel_size
num_layers = hparams['dec_layers'] if num_layers is None else num_layers
super().__init__(hidden_size, num_layers, kernel_size, num_heads=num_heads)
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