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glow-tts with relative pos encoding

This commit is contained in:
erogol 2020-08-15 00:17:49 +02:00
parent 09ad6a09b0
commit 14356d3250
9 changed files with 319 additions and 498 deletions
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2
TTS/tts/utils/generic_utils.py
View File

@ -121,6 +121,8 @@ def setup_model(num_chars, num_speakers, c, speaker_embedding_dim=None):
num_splits=4,
num_sqz=2,
sigmoid_scale=False,
rel_attn_window_size=4,
input_length=None,
mean_only=True,
hidden_channels_enc=192,
hidden_channels_dec=192,

16
mozilla_voice_tts/bin/train_glow_tts.py
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@ -18,24 +18,28 @@ from mozilla_voice_tts.tts.datasets.TTSDataset import MyDataset
from mozilla_voice_tts.tts.layers.losses import GlowTTSLoss
from mozilla_voice_tts.utils.console_logger import ConsoleLogger
from mozilla_voice_tts.tts.utils.distribute import (DistributedSampler,
init_distributed, reduce_tensor)
init_distributed,
reduce_tensor)
from mozilla_voice_tts.tts.utils.generic_utils import check_config, setup_model
from mozilla_voice_tts.tts.utils.io import save_best_model, save_checkpoint
from mozilla_voice_tts.tts.utils.measures import alignment_diagonal_score
from mozilla_voice_tts.tts.utils.speakers import (get_speakers, load_speaker_mapping,
from mozilla_voice_tts.tts.utils.speakers import (get_speakers,
load_speaker_mapping,
save_speaker_mapping)
from mozilla_voice_tts.tts.utils.synthesis import synthesis
from mozilla_voice_tts.tts.utils.text.symbols import make_symbols, phonemes, symbols
from mozilla_voice_tts.tts.utils.visual import plot_alignment, plot_spectrogram
from mozilla_voice_tts.utils.audio import AudioProcessor
from mozilla_voice_tts.utils.generic_utils import (KeepAverage, count_parameters,
create_experiment_folder, get_git_branch,
from mozilla_voice_tts.utils.generic_utils import (
KeepAverage, count_parameters, create_experiment_folder, get_git_branch,
remove_experiment_folder, set_init_dict)
from mozilla_voice_tts.utils.io import copy_config_file, load_config
from mozilla_voice_tts.utils.radam import RAdam
from mozilla_voice_tts.utils.tensorboard_logger import TensorboardLogger
from mozilla_voice_tts.utils.training import (NoamLR, adam_weight_decay, check_update,
gradual_training_scheduler, set_weight_decay,
from mozilla_voice_tts.utils.training import (NoamLR, adam_weight_decay,
check_update,
gradual_training_scheduler,
set_weight_decay,
setup_torch_training_env)
use_cuda, num_gpus = setup_torch_training_env(True, False)

202
mozilla_voice_tts/tts/layers/glow_tts/attention.py
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@ -1,202 +0,0 @@
import copy
import math
import numpy as np
import torch
from torch import nn
from torch.nn import functional as F
def convert_pad_shape(pad_shape):
l = pad_shape[::-1]
pad_shape = [item for sublist in l for item in sublist]
return pad_shape
class MultiHeadAttention(nn.Module):
def __init__(self,
channels,
out_channels,
n_heads,
window_size=None,
heads_share=True,
dropout_p=0.,
input_length=None,
proximal_bias=False,
proximal_init=False):
super().__init__()
assert channels % n_heads == 0
self.channels = channels
self.out_channels = out_channels
self.n_heads = n_heads
self.window_size = window_size
self.heads_share = heads_share
self.input_length = input_length
self.proximal_bias = proximal_bias
self.dropout_p = dropout_p
self.attn = None
self.k_channels = channels // n_heads
self.conv_q = nn.Conv1d(channels, channels, 1)
self.conv_k = nn.Conv1d(channels, channels, 1)
self.conv_v = nn.Conv1d(channels, channels, 1)
if window_size is not None:
n_heads_rel = 1 if heads_share else n_heads
rel_stddev = self.k_channels**-0.5
self.emb_rel_k = nn.Parameter(
torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
* rel_stddev)
self.emb_rel_v = nn.Parameter(
torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
* rel_stddev)
self.conv_o = nn.Conv1d(channels, out_channels, 1)
self.drop = nn.Dropout(dropout_p)
nn.init.xavier_uniform_(self.conv_q.weight)
nn.init.xavier_uniform_(self.conv_k.weight)
if proximal_init:
self.conv_k.weight.data.copy_(self.conv_q.weight.data)
self.conv_k.bias.data.copy_(self.conv_q.bias.data)
nn.init.xavier_uniform_(self.conv_v.weight)
def forward(self, x, c, attn_mask=None):
q = self.conv_q(x)
k = self.conv_k(c)
v = self.conv_v(c)
x, self.attn = self.attention(q, k, v, mask=attn_mask)
x = self.conv_o(x)
return x
def attention(self, query, key, value, mask=None):
# reshape [b, d, t] -> [b, n_h, t, d_k]
b, d, t_s, t_t = (*key.size(), query.size(2))
query = query.view(b, self.n_heads, self.k_channels,
t_t).transpose(2, 3)
key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
value = value.view(b, self.n_heads, self.k_channels,
t_s).transpose(2, 3)
scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(
self.k_channels)
if self.window_size is not None:
assert t_s == t_t, "Relative attention is only available for self-attention."
key_relative_embeddings = self._get_relative_embeddings(
self.emb_rel_k, t_s)
rel_logits = self._matmul_with_relative_keys(
query, key_relative_embeddings)
rel_logits = self._relative_position_to_absolute_position(
rel_logits)
scores_local = rel_logits / math.sqrt(self.k_channels)
scores = scores + scores_local
if self.proximal_bias:
assert t_s == t_t, "Proximal bias is only available for self-attention."
scores = scores + self._attention_bias_proximal(t_s).to(
device=scores.device, dtype=scores.dtype)
if mask is not None:
scores = scores.masked_fill(mask == 0, -1e4)
if self.input_length is not None:
block_mask = torch.ones_like(scores).triu(
-self.input_length).tril(self.input_length)
scores = scores * block_mask + -1e4 * (1 - block_mask)
p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
p_attn = self.drop(p_attn)
output = torch.matmul(p_attn, value)
if self.window_size is not None:
relative_weights = self._absolute_position_to_relative_position(
p_attn)
value_relative_embeddings = self._get_relative_embeddings(
self.emb_rel_v, t_s)
output = output + self._matmul_with_relative_values(
relative_weights, value_relative_embeddings)
output = output.transpose(2, 3).contiguous().view(
b, d, t_t) # [b, n_h, t_t, d_k] -> [b, d, t_t]
return output, p_attn
def _matmul_with_relative_values(self, x, y):
"""
x: [b, h, l, m]
y: [h or 1, m, d]
ret: [b, h, l, d]
"""
ret = torch.matmul(x, y.unsqueeze(0))
return ret
def _matmul_with_relative_keys(self, x, y):
"""
x: [b, h, l, d]
y: [h or 1, m, d]
ret: [b, h, l, m]
"""
ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
return ret
def _get_relative_embeddings(self, relative_embeddings, length):
max_relative_position = 2 * self.window_size + 1
# Pad first before slice to avoid using cond ops.
pad_length = max(length - (self.window_size + 1), 0)
slice_start_position = max((self.window_size + 1) - length, 0)
slice_end_position = slice_start_position + 2 * length - 1
if pad_length > 0:
padded_relative_embeddings = F.pad(
relative_embeddings,
convert_pad_shape([[0, 0], [pad_length, pad_length],
[0, 0]]))
else:
padded_relative_embeddings = relative_embeddings
used_relative_embeddings = padded_relative_embeddings[:,
slice_start_position:
slice_end_position]
return used_relative_embeddings
def _relative_position_to_absolute_position(self, x):
"""
x: [b, h, l, 2*l-1]
ret: [b, h, l, l]
"""
batch, heads, length, _ = x.size()
# Concat columns of pad to shift from relative to absolute indexing.
x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [0, 0], [0,
1]]))
# Concat extra elements so to add up to shape (len+1, 2*len-1).
x_flat = x.view([batch, heads, length * 2 * length])
x_flat = F.pad(
x_flat, convert_pad_shape([[0, 0], [0, 0], [0,
length - 1]]))
# Reshape and slice out the padded elements.
x_final = x_flat.view([batch, heads, length + 1,
2 * length - 1])[:, :, :length, length - 1:]
return x_final
def _absolute_position_to_relative_position(self, x):
"""
x: [b, h, l, l]
ret: [b, h, l, 2*l-1]
"""
batch, heads, length, _ = x.size()
# padd along column
x = F.pad(
x,
convert_pad_shape([[0, 0], [0, 0], [0, 0], [0,
length - 1]]))
x_flat = x.view([batch, heads, length**2 + length * (length - 1)])
# add 0's in the beginning that will skew the elements after reshape
x_flat = F.pad(
x_flat, convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:]
return x_final
def _attention_bias_proximal(self, length):
"""Bias for self-attention to encourage attention to close positions.
Args:
length: an integer scalar.
Returns:
a Tensor with shape [1, 1, length, length]
"""
r = torch.arange(length, dtype=torch.float32)
diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
return torch.unsqueeze(
torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)

63
mozilla_voice_tts/tts/layers/glow_tts/commons.py
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@ -1,63 +0,0 @@
import math
import numpy as np
import torch
from torch import nn
from torch.nn import functional as F
def convert_pad_shape(pad_shape):
l = pad_shape[::-1]
pad_shape = [item for sublist in l for item in sublist]
return pad_shape
def shift_1d(x):
x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
return x
def sequence_mask(length, max_length=None):
if max_length is None:
max_length = length.max()
x = torch.arange(max_length, dtype=length.dtype, device=length.device)
return x.unsqueeze(0) < length.unsqueeze(1)
def maximum_path(value, mask, max_neg_val=-np.inf):
""" Numpy-friendly version. It's about 4 times faster than torch version.
value: [b, t_x, t_y]
mask: [b, t_x, t_y]
"""
value = value * mask
device = value.device
dtype = value.dtype
value = value.cpu().detach().numpy()
mask = mask.cpu().detach().numpy().astype(np.bool)
b, t_x, t_y = value.shape
direction = np.zeros(value.shape, dtype=np.int64)
v = np.zeros((b, t_x), dtype=np.float32)
x_range = np.arange(t_x, dtype=np.float32).reshape(1, -1)
for j in range(t_y):
v0 = np.pad(v, [[0, 0], [1, 0]],
mode="constant",
constant_values=max_neg_val)[:, :-1]
v1 = v
max_mask = (v1 >= v0)
v_max = np.where(max_mask, v1, v0)
direction[:, :, j] = max_mask
index_mask = (x_range <= j)
v = np.where(index_mask, v_max + value[:, :, j], max_neg_val)
direction = np.where(mask, direction, 1)
path = np.zeros(value.shape, dtype=np.float32)
index = mask[:, :, 0].sum(1).astype(np.int64) - 1
index_range = np.arange(b)
for j in reversed(range(t_y)):
path[index_range, index, j] = 1
index = index + direction[index_range, index, j] - 1
path = path * mask.astype(np.float32)
path = torch.from_numpy(path).to(device=device, dtype=dtype)
return path

10
mozilla_voice_tts/tts/layers/glow_tts/duration_predictor.py
View File

@ -1,21 +1,16 @@
import copy
import math
import numpy as np
import scipy
import torch
from torch import nn
from torch.nn import functional as F
class DurationPredictor(nn.Module):
def __init__(self, in_channels, filter_channels, kernel_size, dropout_p):
super().__init__()
# class arguments
self.in_channels = in_channels
self.filter_channels = filter_channels
self.kernel_size = kernel_size
self.dropout_p = dropout_p
# layers
self.drop = nn.Dropout(dropout_p)
self.conv_1 = nn.Conv1d(in_channels,
filter_channels,
@ -27,6 +22,7 @@ class DurationPredictor(nn.Module):
kernel_size,
padding=kernel_size // 2)
self.norm_2 = nn.GroupNorm(1, filter_channels)
# output layer
self.proj = nn.Conv1d(filter_channels, 1, 1)
def forward(self, x, x_mask):

63
mozilla_voice_tts/tts/layers/glow_tts/encoder.py
View File

@ -1,33 +1,13 @@
import math
import torch
from torch import nn
from torch.nn import functional as F
from mozilla_voice_tts.tts.layers.glow_tts.transformer import Transformer
from mozilla_voice_tts.tts.utils.generic_utils import sequence_mask
from mozilla_voice_tts.tts.layers.glow_tts.glow import ConvLayerNorm
from mozilla_voice_tts.tts.layers.glow_tts.duration_predictor import DurationPredictor
class PositionalEncoding(nn.Module):
def __init__(self, d_model, max_len=5000):
super(PositionalEncoding, self).__init__()
self.register_buffer('pe', self._get_pe_matrix(d_model, max_len))
def forward(self, x):
return x + self.pe[:x.size(0)].unsqueeze(1)
def _get_pe_matrix(self, d_model, max_len):
pe = torch.zeros(max_len, d_model)
position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
div_term = torch.pow(10000,
torch.arange(0, d_model, 2).float() / d_model)
pe[:, 0::2] = torch.sin(position / div_term)
pe[:, 1::2] = torch.cos(position / div_term)
return pe
class Encoder(nn.Module):
"""Glow-TTS encoder module. We use Pytorch TransformerEncoder instead
of the one with relative position embedding. We use positional encoding
@ -60,12 +40,13 @@ class Encoder(nn.Module):
num_layers,
kernel_size,
dropout_p,
rel_attn_window_size=None,
input_length=None,
mean_only=False,
use_prenet=False,
c_in_channels=0):
super().__init__()
# class arguments
self.num_chars = num_chars
self.out_channels = out_channels
self.hidden_channels = hidden_channels
@ -78,11 +59,10 @@ class Encoder(nn.Module):
self.mean_only = mean_only
self.use_prenet = use_prenet
self.c_in_channels = c_in_channels
# embedding layer
self.emb = nn.Embedding(num_chars, hidden_channels)
nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5)
self.register_buffer('pe', PositionalEncoding(hidden_channels).pe)
# optional convolutional prenet
if use_prenet:
self.pre = ConvLayerNorm(hidden_channels,
hidden_channels,
@ -90,49 +70,50 @@ class Encoder(nn.Module):
kernel_size=5,
num_layers=3,
dropout_p=0.5)
encoder = nn.TransformerEncoderLayer(hidden_channels,
# text encoder
self.encoder = Transformer(hidden_channels,
filter_channels,
num_heads,
dim_feedforward=filter_channels,
dropout=dropout_p)
self.encoder = nn.TransformerEncoder(encoder, num_layers)
num_layers,
kernel_size=kernel_size,
dropout_p=dropout_p,
rel_attn_window_size=rel_attn_window_size,
input_length=input_length)
# final projection layers
self.proj_m = nn.Conv1d(hidden_channels, out_channels, 1)
if not mean_only:
self.proj_s = nn.Conv1d(hidden_channels, out_channels, 1)
# duration predictor
self.duration_predictor = DurationPredictor(
hidden_channels + c_in_channels, filter_channels_dp, kernel_size,
dropout_p)
def forward(self, x, x_lengths, g=None):
# pass embedding layer
# embedding layer
# [B ,T, D]
x = self.emb(x) * math.sqrt(self.hidden_channels)
x = x + self.pe[:x.shape[1]].unsqueeze(0)
# [B, D, T]
x = torch.transpose(x, 1, -1)
# compute input sequence mask
x_mask = torch.unsqueeze(sequence_mask(x_lengths, x.size(2)),
1).to(x.dtype)
# pass pre-conv layers
# pre-conv layers
if self.use_prenet:
x = self.pre(x, x_mask)
# pass encoder
x = self.encoder(x.permute(2, 0, 1),
src_key_padding_mask=~(x_mask.squeeze(1).to(bool)))
x = x.permute(1, 2, 0)
# encoder
x = self.encoder(x, x_mask)
# set duration predictor input
if g is not None:
g_exp = g.expand(-1, -1, x.size(-1))
x_dp = torch.cat([torch.detach(x), g_exp], 1)
else:
x_dp = torch.detach(x)
# pass final projection layer
# final projection layer
x_m = self.proj_m(x) * x_mask
if not self.mean_only:
x_logs = self.proj_s(x) * x_mask
else:
x_logs = torch.zeros_like(x_m)
# pass duration predictor
# duration predictor
logw = self.duration_predictor(x_dp, x_mask)
return x_m, x_logs, logw, x_mask

125
mozilla_voice_tts/tts/layers/glow_tts/glow_v2.py
View File

@ -1,125 +0,0 @@
import torch
from torch import nn
from torch.nn import functional as F
from mozilla_voice_tts.tts.utils.generic_utils import sequence_mask
from mozilla_voice_tts.tts.layers.glow_tts.glow import InvConvNear, CouplingBlock, ActNorm, BatchNorm
def squeeze(x, x_mask=None, num_sqz=2):
b, c, t = x.size()
t = (t // num_sqz) * num_sqz
x = x[:, :, :t]
x_sqz = x.view(b, c, t // num_sqz, num_sqz)
x_sqz = x_sqz.permute(0, 3, 1,
2).contiguous().view(b, c * num_sqz, t // num_sqz)
if x_mask is not None:
x_mask = x_mask[:, :, num_sqz - 1::num_sqz]
else:
x_mask = torch.ones(b, 1, t // num_sqz).to(device=x.device,
dtype=x.dtype)
return x_sqz * x_mask, x_mask
def unsqueeze(x, x_mask=None, num_sqz=2):
b, c, t = x.size()
x_unsqz = x.view(b, num_sqz, c // num_sqz, t)
x_unsqz = x_unsqz.permute(0, 2, 3,
1).contiguous().view(b, c // num_sqz,
t * num_sqz)
if x_mask is not None:
x_mask = x_mask.unsqueeze(-1).repeat(1, 1, 1,
num_sqz).view(b, 1, t * num_sqz)
else:
x_mask = torch.ones(b, 1, t * num_sqz).to(device=x.device,
dtype=x.dtype)
return x_unsqz * x_mask, x_mask
class Decoder(nn.Module):
"""Stack of Glow Modules"""
def __init__(self,
in_channels,
hidden_channels,
kernel_size,
dilation_rate,
num_blocks,
num_coupling_layers,
dropout_p=0.,
num_splits=4,
num_sqz=2,
sigmoid_scale=False,
c_in_channels=0):
super().__init__()
self.in_channels = in_channels
self.hidden_channels = hidden_channels
self.kernel_size = kernel_size
self.dilation_rate = dilation_rate
self.num_blocks = num_blocks
self.num_coupling_layers = num_coupling_layers
self.dropout_p = dropout_p
self.num_splits = num_splits
self.num_sqz = num_sqz
self.sigmoid_scale = sigmoid_scale
self.c_in_channels = c_in_channels
self.norm_layers = nn.ModuleList()
self.invconv_layers = nn.ModuleList()
self.coupling_layers = nn.ModuleList()
for _ in range(num_blocks):
self.norm_layers.append(ActNorm(in_channels=in_channels * num_sqz))
self.invconv_layers.append(
InvConvNear(channels=in_channels * num_sqz,
num_splits=num_splits))
self.coupling_layers.append(
CouplingBlock(in_channels * num_sqz,
hidden_channels,
kernel_size=kernel_size,
dilation_rate=dilation_rate,
num_layers=num_coupling_layers,
c_in_channels=c_in_channels,
dropout_p=dropout_p,
sigmoid_scale=sigmoid_scale))
def forward(self, x, x_mask, g=None, reverse=False):
if not reverse:
# norm_layers = self.norm_layers
invconv_layers = self.invconv_layers
coupling_layers = self.coupling_layers
logdet_tot = 0
else:
# norm_layers = reversed(self.norm_layers)
invconv_layers = self.invconv_layers[::-1]
coupling_layers = self.coupling_layers[::-1]
logdet_tot = None
if self.num_sqz > 1:
x, x_mask = squeeze(x, x_mask, self.num_sqz)
for idx in range(len(self.invconv_layers)):
if not reverse:
# x, log_det = norm_layers[idx](x, reverse)
# logdet_tot += log_det
x, log_det = invconv_layers[idx](x, x_mask, reverse)
logdet_tot += log_det
x, log_det = coupling_layers[idx](x, x_mask, g=g, reverse=reverse)
logdet_tot += log_det
else:
x, log_det = coupling_layers[idx](x, x_mask, g=g, reverse=reverse)
x, log_det = invconv_layers[idx](x, x_mask, reverse)
# x, logdet = norm_layers[idx](x, reverse)
if self.num_sqz > 1:
x, x_mask = unsqueeze(x, x_mask, self.num_sqz)
return x, logdet_tot
def store_inverse(self):
for f in self.flows:
f.store_inverse()

318
mozilla_voice_tts/tts/layers/glow_tts/transformer.py
View File

@ -1,67 +1,232 @@
import copy
import math
import numpy as np
import torch
from torch import nn
from torch.nn import functional as F
from mozilla_voice_tts.tts.layers.glow_tts.attention import MultiHeadAttention
from mozilla_voice_tts.tts.layers.glow_tts.glow import LayerNorm
class Transformer(nn.Module):
class RelativePositionMultiHeadAttention(nn.Module):
"""Implementation of https://arxiv.org/pdf/1803.02155.pdf
Visualization of the algorithm
https://raw.githubusercontent.com/Separius/CudaRelativeAttention/master/algorithm.png
"""
def __init__(self,
hidden_channels,
filter_channels,
channels,
out_channels,
num_heads,
num_layers,
kernel_size=1,
dropout_p=0.,
rel_attn_window_size=None,
heads_share=True,
dropout_p=0.,
input_length=None,
**kwargs):
proximal_bias=False,
proximal_init=False):
super().__init__()
self.hidden_channels = hidden_channels
self.filter_channels = filter_channels
assert channels % num_heads == 0, " [!] channels should be divisible by num_heads."
# class attributes
self.channels = channels
self.out_channels = out_channels
self.num_heads = num_heads
self.num_layers = num_layers
self.kernel_size = kernel_size
self.dropout_p = dropout_p
self.rel_attn_window_size = rel_attn_window_size
self.heads_share = heads_share
self.input_length = input_length
self.proximal_bias = proximal_bias
self.dropout_p = dropout_p
self.attn = None
# query, key, value layers
self.k_channels = channels // num_heads
self.conv_q = nn.Conv1d(channels, channels, 1)
self.conv_k = nn.Conv1d(channels, channels, 1)
self.conv_v = nn.Conv1d(channels, channels, 1)
# output layers
self.conv_o = nn.Conv1d(channels, out_channels, 1)
self.dropout = nn.Dropout(dropout_p)
# relative positional encoding layers
if rel_attn_window_size is not None:
n_heads_rel = 1 if heads_share else num_heads
rel_stddev = self.k_channels**-0.5
emb_rel_k = nn.Parameter(
torch.randn(n_heads_rel, rel_attn_window_size * 2 + 1,
self.k_channels) * rel_stddev)
emb_rel_v = nn.Parameter(
torch.randn(n_heads_rel, rel_attn_window_size * 2 + 1,
self.k_channels) * rel_stddev)
self.register_parameter('emb_rel_k', emb_rel_k)
self.register_parameter('emb_rel_v', emb_rel_v)
self.drop = nn.Dropout(dropout_p)
self.attn_layers = nn.ModuleList()
self.norm_layers_1 = nn.ModuleList()
self.ffn_layers = nn.ModuleList()
self.norm_layers_2 = nn.ModuleList()
for _ in range(self.num_layers):
self.attn_layers.append(
MultiHeadAttention(hidden_channels,
hidden_channels,
num_heads,
window_size=rel_attn_window_size,
dropout_p=dropout_p,
input_length=input_length))
self.norm_layers_1.append(nn.GroupNorm(1, hidden_channels))
self.ffn_layers.append(
FFN(hidden_channels,
hidden_channels,
filter_channels,
kernel_size,
dropout_p=dropout_p))
self.norm_layers_2.append(nn.GroupNorm(1, hidden_channels))
# init layers
nn.init.xavier_uniform_(self.conv_q.weight)
nn.init.xavier_uniform_(self.conv_k.weight)
# proximal bias
if proximal_init:
self.conv_k.weight.data.copy_(self.conv_q.weight.data)
self.conv_k.bias.data.copy_(self.conv_q.bias.data)
nn.init.xavier_uniform_(self.conv_v.weight)
def forward(self, x, x_mask):
attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
for i in range(self.num_layers):
x = x * x_mask
y = self.attn_layers[i](x, x, attn_mask)
y = self.drop(y)
x = self.norm_layers_1[i](x + y)
y = self.ffn_layers[i](x, x_mask)
y = self.drop(y)
x = self.norm_layers_2[i](x + y)
x = x * x_mask
def forward(self, x, c, attn_mask=None):
q = self.conv_q(x)
k = self.conv_k(c)
v = self.conv_v(c)
x, self.attn = self.attention(q, k, v, mask=attn_mask)
x = self.conv_o(x)
return x
def attention(self, query, key, value, mask=None):
# reshape [b, d, t] -> [b, n_h, t, d_k]
b, d, t_s, t_t = (*key.size(), query.size(2))
query = query.view(b, self.num_heads, self.k_channels,
t_t).transpose(2, 3)
key = key.view(b, self.num_heads, self.k_channels, t_s).transpose(2, 3)
value = value.view(b, self.num_heads, self.k_channels,
t_s).transpose(2, 3)
# compute raw attention scores
scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(
self.k_channels)
# relative positional encoding
if self.rel_attn_window_size is not None:
assert t_s == t_t, "Relative attention is only available for self-attention."
# get relative key embeddings
breakpoint()
key_relative_embeddings = self._get_relative_embeddings(
self.emb_rel_k, t_s)
rel_logits = self._matmul_with_relative_keys(
query, key_relative_embeddings)
rel_logits = self._relative_position_to_absolute_position(
rel_logits)
scores_local = rel_logits / math.sqrt(self.k_channels)
scores = scores + scores_local
# proximan bias
if self.proximal_bias:
assert t_s == t_t, "Proximal bias is only available for self-attention."
scores = scores + self._attn_proximity_bias(t_s).to(
device=scores.device, dtype=scores.dtype)
# attention score masking
if mask is not None:
# add small value to prevent oor error.
scores = scores.masked_fill(mask == 0, -1e4)
if self.input_length is not None:
block_mask = torch.ones_like(scores).triu(
-self.input_length).tril(self.input_length)
scores = scores * block_mask + -1e4 * (1 - block_mask)
# attention score normalization
p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
# apply dropout to attention weights
p_attn = self.dropout(p_attn)
# compute output
output = torch.matmul(p_attn, value)
# relative positional encoding for values
if self.rel_attn_window_size is not None:
relative_weights = self._absolute_position_to_relative_position(
p_attn)
value_relative_embeddings = self._get_relative_embeddings(
self.emb_rel_v, t_s)
output = output + self._matmul_with_relative_values(
relative_weights, value_relative_embeddings)
output = output.transpose(2, 3).contiguous().view(
b, d, t_t) # [b, n_h, t_t, d_k] -> [b, d, t_t]
return output, p_attn
def _matmul_with_relative_values(self, p_attn, re):
"""
Args:
p_attn (Tensor): attention weights.
re (Tensor): relative value embedding vector. (a_(i,j)^V)
Shapes:
p_attn: [B, H, T, V]
re: [H or 1, V, D]
logits: [B, H, T, D]
"""
logits = torch.matmul(p_attn, re.unsqueeze(0))
return logits
@staticmethod
def _matmul_with_relative_keys(query, re):
"""
Args:
query (Tensor): batch of query vectors. (x*W^Q)
re (Tensor): relative key embedding vector. (a_(i,j)^K)
Shapes:
query: [B, H, T, D]
re: [H or 1, V, D]
logits: [B, H, T, V]
"""
# logits = torch.einsum('bhld, kmd -> bhlm', [query, re.to(query.dtype)])
logits = torch.matmul(query, re.unsqueeze(0).transpose(-2, -1))
return logits
def _get_relative_embeddings(self, relative_embeddings, length):
"""Convert embedding vestors to a tensor of embeddings
"""
# Pad first before slice to avoid using cond ops.
pad_length = max(length - (self.rel_attn_window_size + 1), 0)
slice_start_position = max((self.rel_attn_window_size + 1) - length, 0)
slice_end_position = slice_start_position + 2 * length - 1
if pad_length > 0:
padded_relative_embeddings = F.pad(
relative_embeddings, [0, 0, pad_length, pad_length, 0, 0])
else:
padded_relative_embeddings = relative_embeddings
used_relative_embeddings = padded_relative_embeddings[:,
slice_start_position:
slice_end_position]
return used_relative_embeddings
@staticmethod
def _relative_position_to_absolute_position(x):
"""Converts tensor from relative to absolute indexing for local attention.
Args:
x: [B, D, length, 2 * length - 1]
Returns:
A Tensor of shape [B, D, length, length]
"""
batch, heads, length, _ = x.size()
# Pad to shift from relative to absolute indexing.
x = F.pad(x, [0, 1, 0, 0, 0, 0, 0, 0])
# Pad extra elements so to add up to shape (len+1, 2*len-1).
x_flat = x.view([batch, heads, length * 2 * length])
x_flat = F.pad(x_flat, [0, length - 1, 0, 0, 0, 0])
# Reshape and slice out the padded elements.
x_final = x_flat.view([batch, heads, length + 1,
2 * length - 1])[:, :, :length, length - 1:]
return x_final
@staticmethod
def _absolute_position_to_relative_position(x):
"""
x: [B, H, T, T]
ret: [B, H, T, 2*T-1]
"""
batch, heads, length, _ = x.size()
# padd along column
x = F.pad(x, [0, length - 1, 0, 0, 0, 0, 0, 0])
x_flat = x.view([batch, heads, length**2 + length * (length - 1)])
# add 0's in the beginning that will skew the elements after reshape
x_flat = F.pad(x_flat, [length, 0, 0, 0, 0, 0])
x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:]
return x_final
@staticmethod
def _attn_proximity_bias(length):
"""Produce an attention mask that discourages distant
attention values.
Args:
length (int): an integer scalar.
Returns:
a Tensor with shape [1, 1, length, length]
"""
# L
r = torch.arange(length, dtype=torch.float32)
# L x L
diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
# scale mask values
diff = -torch.log1p(torch.abs(diff))
# 1 x 1 x L x L
return diff.unsqueeze(0).unsqueeze(0)
class FFN(nn.Module):
def __init__(self,
@ -87,7 +252,7 @@ class FFN(nn.Module):
out_channels,
kernel_size,
padding=kernel_size // 2)
self.drop = nn.Dropout(dropout_p)
self.dropout = nn.Dropout(dropout_p)
def forward(self, x, x_mask):
x = self.conv_1(x * x_mask)
@ -95,6 +260,63 @@ class FFN(nn.Module):
x = x * torch.sigmoid(1.702 * x)
else:
x = torch.relu(x)
x = self.drop(x)
x = self.dropout(x)
x = self.conv_2(x * x_mask)
return x * x_mask
class Transformer(nn.Module):
def __init__(self,
hidden_channels,
filter_channels,
num_heads,
num_layers,
kernel_size=1,
dropout_p=0.,
rel_attn_window_size=None,
input_length=None):
super().__init__()
self.hidden_channels = hidden_channels
self.filter_channels = filter_channels
self.num_heads = num_heads
self.num_layers = num_layers
self.kernel_size = kernel_size
self.dropout_p = dropout_p
self.rel_attn_window_size = rel_attn_window_size
self.dropout = nn.Dropout(dropout_p)
self.attn_layers = nn.ModuleList()
self.norm_layers_1 = nn.ModuleList()
self.ffn_layers = nn.ModuleList()
self.norm_layers_2 = nn.ModuleList()
for _ in range(self.num_layers):
self.attn_layers.append(
RelativePositionMultiHeadAttention(
hidden_channels,
hidden_channels,
num_heads,
rel_attn_window_size=rel_attn_window_size,
dropout_p=dropout_p,
input_length=input_length))
self.norm_layers_1.append(LayerNorm(hidden_channels))
self.ffn_layers.append(
FFN(hidden_channels,
hidden_channels,
filter_channels,
kernel_size,
dropout_p=dropout_p))
self.norm_layers_2.append(LayerNorm(hidden_channels))
def forward(self, x, x_mask):
attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
for i in range(self.num_layers):
x = x * x_mask
y = self.attn_layers[i](x, x, attn_mask)
y = self.dropout(y)
x = self.norm_layers_1[i](x + y)
y = self.ffn_layers[i](x, x_mask)
y = self.dropout(y)
x = self.norm_layers_2[i](x + y)
x = x * x_mask
return x

6
mozilla_voice_tts/tts/models/glow_tts.py
View File

@ -31,6 +31,8 @@ class GlowTts(nn.Module):
num_splits=4,
num_sqz=1,
sigmoid_scale=False,
rel_attn_window_size=None,
input_length=None,
mean_only=False,
hidden_channels_enc=None,
hidden_channels_dec=None,
@ -56,6 +58,8 @@ class GlowTts(nn.Module):
self.num_splits = num_splits
self.num_sqz = num_sqz
self.sigmoid_scale = sigmoid_scale
self.rel_attn_window_size = rel_attn_window_size
self.input_length = input_length
self.mean_only = mean_only
self.hidden_channels_enc = hidden_channels_enc
self.hidden_channels_dec = hidden_channels_dec
@ -72,6 +76,8 @@ class GlowTts(nn.Module):
num_layers_enc,
kernel_size,
dropout_p,
rel_attn_window_size=rel_attn_window_size,
input_length=input_length,
mean_only=mean_only,
use_prenet=True,
c_in_channels=c_in_channels)