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import math
import torch
from torch import nn
from typing import Optional, Any
from torch import Tensor
import torch.nn.functional as F
import torchaudio
import torchaudio.functional as audio_F
import random
random.seed(0)
def _get_activation_fn(activ):
if activ == "relu":
return nn.ReLU()
elif activ == "lrelu":
return nn.LeakyReLU(0.2)
elif activ == "swish":
return lambda x: x * torch.sigmoid(x)
else:
raise RuntimeError(
"Unexpected activ type %s, expected [relu, lrelu, swish]" % activ
)
class LinearNorm(torch.nn.Module):
def __init__(self, in_dim, out_dim, bias=True, w_init_gain="linear"):
super(LinearNorm, self).__init__()
self.linear_layer = torch.nn.Linear(in_dim, out_dim, bias=bias)
torch.nn.init.xavier_uniform_(
self.linear_layer.weight, gain=torch.nn.init.calculate_gain(w_init_gain)
)
def forward(self, x):
return self.linear_layer(x)
class ConvNorm(torch.nn.Module):
def __init__(
self,
in_channels,
out_channels,
kernel_size=1,
stride=1,
padding=None,
dilation=1,
bias=True,
w_init_gain="linear",
param=None,
):
super(ConvNorm, self).__init__()
if padding is None:
assert kernel_size % 2 == 1
padding = int(dilation * (kernel_size - 1) / 2)
self.conv = torch.nn.Conv1d(
in_channels,
out_channels,
kernel_size=kernel_size,
stride=stride,
padding=padding,
dilation=dilation,
bias=bias,
)
torch.nn.init.xavier_uniform_(
self.conv.weight,
gain=torch.nn.init.calculate_gain(w_init_gain, param=param),
)
def forward(self, signal):
conv_signal = self.conv(signal)
return conv_signal
class CausualConv(nn.Module):
def __init__(
self,
in_channels,
out_channels,
kernel_size=1,
stride=1,
padding=1,
dilation=1,
bias=True,
w_init_gain="linear",
param=None,
):
super(CausualConv, self).__init__()
if padding is None:
assert kernel_size % 2 == 1
padding = int(dilation * (kernel_size - 1) / 2) * 2
else:
self.padding = padding * 2
self.conv = nn.Conv1d(
in_channels,
out_channels,
kernel_size=kernel_size,
stride=stride,
padding=self.padding,
dilation=dilation,
bias=bias,
)
torch.nn.init.xavier_uniform_(
self.conv.weight,
gain=torch.nn.init.calculate_gain(w_init_gain, param=param),
)
def forward(self, x):
x = self.conv(x)
x = x[:, :, : -self.padding]
return x
class CausualBlock(nn.Module):
def __init__(self, hidden_dim, n_conv=3, dropout_p=0.2, activ="lrelu"):
super(CausualBlock, self).__init__()
self.blocks = nn.ModuleList(
[
self._get_conv(
hidden_dim, dilation=3**i, activ=activ, dropout_p=dropout_p
)
for i in range(n_conv)
]
)
def forward(self, x):
for block in self.blocks:
res = x
x = block(x)
x += res
return x
def _get_conv(self, hidden_dim, dilation, activ="lrelu", dropout_p=0.2):
layers = [
CausualConv(
hidden_dim,
hidden_dim,
kernel_size=3,
padding=dilation,
dilation=dilation,
),
_get_activation_fn(activ),
nn.BatchNorm1d(hidden_dim),
nn.Dropout(p=dropout_p),
CausualConv(hidden_dim, hidden_dim, kernel_size=3, padding=1, dilation=1),
_get_activation_fn(activ),
nn.Dropout(p=dropout_p),
]
return nn.Sequential(*layers)
class ConvBlock(nn.Module):
def __init__(self, hidden_dim, n_conv=3, dropout_p=0.2, activ="relu"):
super().__init__()
self._n_groups = 8
self.blocks = nn.ModuleList(
[
self._get_conv(
hidden_dim, dilation=3**i, activ=activ, dropout_p=dropout_p
)
for i in range(n_conv)
]
)
def forward(self, x):
for block in self.blocks:
res = x
x = block(x)
x += res
return x
def _get_conv(self, hidden_dim, dilation, activ="relu", dropout_p=0.2):
layers = [
ConvNorm(
hidden_dim,
hidden_dim,
kernel_size=3,
padding=dilation,
dilation=dilation,
),
_get_activation_fn(activ),
nn.GroupNorm(num_groups=self._n_groups, num_channels=hidden_dim),
nn.Dropout(p=dropout_p),
ConvNorm(hidden_dim, hidden_dim, kernel_size=3, padding=1, dilation=1),
_get_activation_fn(activ),
nn.Dropout(p=dropout_p),
]
return nn.Sequential(*layers)
class LocationLayer(nn.Module):
def __init__(self, attention_n_filters, attention_kernel_size, attention_dim):
super(LocationLayer, self).__init__()
padding = int((attention_kernel_size - 1) / 2)
self.location_conv = ConvNorm(
2,
attention_n_filters,
kernel_size=attention_kernel_size,
padding=padding,
bias=False,
stride=1,
dilation=1,
)
self.location_dense = LinearNorm(
attention_n_filters, attention_dim, bias=False, w_init_gain="tanh"
)
def forward(self, attention_weights_cat):
processed_attention = self.location_conv(attention_weights_cat)
processed_attention = processed_attention.transpose(1, 2)
processed_attention = self.location_dense(processed_attention)
return processed_attention
class Attention(nn.Module):
def __init__(
self,
attention_rnn_dim,
embedding_dim,
attention_dim,
attention_location_n_filters,
attention_location_kernel_size,
):
super(Attention, self).__init__()
self.query_layer = LinearNorm(
attention_rnn_dim, attention_dim, bias=False, w_init_gain="tanh"
)
self.memory_layer = LinearNorm(
embedding_dim, attention_dim, bias=False, w_init_gain="tanh"
)
self.v = LinearNorm(attention_dim, 1, bias=False)
self.location_layer = LocationLayer(
attention_location_n_filters, attention_location_kernel_size, attention_dim
)
self.score_mask_value = -float("inf")
def get_alignment_energies(self, query, processed_memory, attention_weights_cat):
"""
PARAMS
------
query: decoder output (batch, n_mel_channels * n_frames_per_step)
processed_memory: processed encoder outputs (B, T_in, attention_dim)
attention_weights_cat: cumulative and prev. att weights (B, 2, max_time)
RETURNS
-------
alignment (batch, max_time)
"""
processed_query = self.query_layer(query.unsqueeze(1))
processed_attention_weights = self.location_layer(attention_weights_cat)
energies = self.v(
torch.tanh(processed_query + processed_attention_weights + processed_memory)
)
energies = energies.squeeze(-1)
return energies
def forward(
self,
attention_hidden_state,
memory,
processed_memory,
attention_weights_cat,
mask,
):
"""
PARAMS
------
attention_hidden_state: attention rnn last output
memory: encoder outputs
processed_memory: processed encoder outputs
attention_weights_cat: previous and cummulative attention weights
mask: binary mask for padded data
"""
alignment = self.get_alignment_energies(
attention_hidden_state, processed_memory, attention_weights_cat
)
if mask is not None:
alignment.data.masked_fill_(mask, self.score_mask_value)
attention_weights = F.softmax(alignment, dim=1)
attention_context = torch.bmm(attention_weights.unsqueeze(1), memory)
attention_context = attention_context.squeeze(1)
return attention_context, attention_weights
class ForwardAttentionV2(nn.Module):
def __init__(
self,
attention_rnn_dim,
embedding_dim,
attention_dim,
attention_location_n_filters,
attention_location_kernel_size,
):
super(ForwardAttentionV2, self).__init__()
self.query_layer = LinearNorm(
attention_rnn_dim, attention_dim, bias=False, w_init_gain="tanh"
)
self.memory_layer = LinearNorm(
embedding_dim, attention_dim, bias=False, w_init_gain="tanh"
)
self.v = LinearNorm(attention_dim, 1, bias=False)
self.location_layer = LocationLayer(
attention_location_n_filters, attention_location_kernel_size, attention_dim
)
self.score_mask_value = -float(1e20)
def get_alignment_energies(self, query, processed_memory, attention_weights_cat):
"""
PARAMS
------
query: decoder output (batch, n_mel_channels * n_frames_per_step)
processed_memory: processed encoder outputs (B, T_in, attention_dim)
attention_weights_cat: prev. and cumulative att weights (B, 2, max_time)
RETURNS
-------
alignment (batch, max_time)
"""
processed_query = self.query_layer(query.unsqueeze(1))
processed_attention_weights = self.location_layer(attention_weights_cat)
energies = self.v(
torch.tanh(processed_query + processed_attention_weights + processed_memory)
)
energies = energies.squeeze(-1)
return energies
def forward(
self,
attention_hidden_state,
memory,
processed_memory,
attention_weights_cat,
mask,
log_alpha,
):
"""
PARAMS
------
attention_hidden_state: attention rnn last output
memory: encoder outputs
processed_memory: processed encoder outputs
attention_weights_cat: previous and cummulative attention weights
mask: binary mask for padded data
"""
log_energy = self.get_alignment_energies(
attention_hidden_state, processed_memory, attention_weights_cat
)
# log_energy =
if mask is not None:
log_energy.data.masked_fill_(mask, self.score_mask_value)
# attention_weights = F.softmax(alignment, dim=1)
# content_score = log_energy.unsqueeze(1) #[B, MAX_TIME] -> [B, 1, MAX_TIME]
# log_alpha = log_alpha.unsqueeze(2) #[B, MAX_TIME] -> [B, MAX_TIME, 1]
# log_total_score = log_alpha + content_score
# previous_attention_weights = attention_weights_cat[:,0,:]
log_alpha_shift_padded = []
max_time = log_energy.size(1)
for sft in range(2):
shifted = log_alpha[:, : max_time - sft]
shift_padded = F.pad(shifted, (sft, 0), "constant", self.score_mask_value)
log_alpha_shift_padded.append(shift_padded.unsqueeze(2))
biased = torch.logsumexp(torch.cat(log_alpha_shift_padded, 2), 2)
log_alpha_new = biased + log_energy
attention_weights = F.softmax(log_alpha_new, dim=1)
attention_context = torch.bmm(attention_weights.unsqueeze(1), memory)
attention_context = attention_context.squeeze(1)
return attention_context, attention_weights, log_alpha_new
class PhaseShuffle2d(nn.Module):
def __init__(self, n=2):
super(PhaseShuffle2d, self).__init__()
self.n = n
self.random = random.Random(1)
def forward(self, x, move=None):
# x.size = (B, C, M, L)
if move is None:
move = self.random.randint(-self.n, self.n)
if move == 0:
return x
else:
left = x[:, :, :, :move]
right = x[:, :, :, move:]
shuffled = torch.cat([right, left], dim=3)
return shuffled
class PhaseShuffle1d(nn.Module):
def __init__(self, n=2):
super(PhaseShuffle1d, self).__init__()
self.n = n
self.random = random.Random(1)
def forward(self, x, move=None):
# x.size = (B, C, M, L)
if move is None:
move = self.random.randint(-self.n, self.n)
if move == 0:
return x
else:
left = x[:, :, :move]
right = x[:, :, move:]
shuffled = torch.cat([right, left], dim=2)
return shuffled
class MFCC(nn.Module):
def __init__(self, n_mfcc=40, n_mels=80):
super(MFCC, self).__init__()
self.n_mfcc = n_mfcc
self.n_mels = n_mels
self.norm = "ortho"
dct_mat = audio_F.create_dct(self.n_mfcc, self.n_mels, self.norm)
self.register_buffer("dct_mat", dct_mat)
def forward(self, mel_specgram):
if len(mel_specgram.shape) == 2:
mel_specgram = mel_specgram.unsqueeze(0)
unsqueezed = True
else:
unsqueezed = False
# (channel, n_mels, time).tranpose(...) dot (n_mels, n_mfcc)
# -> (channel, time, n_mfcc).tranpose(...)
mfcc = torch.matmul(mel_specgram.transpose(1, 2), self.dct_mat).transpose(1, 2)
# unpack batch
if unsqueezed:
mfcc = mfcc.squeeze(0)
return mfcc
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