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import math
import paddle
from paddle import nn
from typing import Optional, Any
from paddle import Tensor
import paddle.nn.functional as F
import paddleaudio
import paddleaudio.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 nn.Swish()
else:
raise RuntimeError('Unexpected activ type %s, expected [relu, lrelu, swish]' % activ)
class LinearNorm(paddle.nn.Layer):
def __init__(self, in_dim, out_dim, bias=True, w_init_gain='linear'):
super(LinearNorm, self).__init__()
self.linear_layer = paddle.nn.Linear(in_dim, out_dim, bias_attr=bias)
if float('.'.join(paddle.__version__.split('.')[:2])) >= 2.3:
gain = paddle.nn.initializer.calculate_gain(w_init_gain)
paddle.nn.initializer.XavierUniform()(self.linear_layer.weight)
self.linear_layer.weight.set_value(gain * self.linear_layer.weight)
def forward(self, x):
return self.linear_layer(x)
class ConvNorm(paddle.nn.Layer):
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 = paddle.nn.Conv1D(in_channels, out_channels,
kernel_size=kernel_size, stride=stride,
padding=padding, dilation=dilation,
bias_attr=bias)
if float('.'.join(paddle.__version__.split('.')[:2])) >= 2.3:
gain = paddle.nn.initializer.calculate_gain(w_init_gain, param=param)
paddle.nn.initializer.XavierUniform()(self.conv.weight)
self.conv.weight.set_value(gain * self.conv.weight)
def forward(self, signal):
conv_signal = self.conv(signal)
return conv_signal
class CausualConv(nn.Layer):
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_attr=bias)
if float('.'.join(paddle.__version__.split('.')[:2])) >= 2.3:
gain = paddle.nn.initializer.calculate_gain(w_init_gain, param=param)
paddle.nn.initializer.XavierUniform()(self.conv.weight)
self.conv.weight.set_value(gain * self.conv.weight)
def forward(self, x):
x = self.conv(x)
x = x[:, :, :-self.padding]
return x
class CausualBlock(nn.Layer):
def __init__(self, hidden_dim, n_conv=3, dropout_p=0.2, activ='lrelu'):
super(CausualBlock, self).__init__()
self.blocks = nn.LayerList([
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.Layer):
def __init__(self, hidden_dim, n_conv=3, dropout_p=0.2, activ='relu'):
super().__init__()
self._n_groups = 8
self.blocks = nn.LayerList([
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.Layer):
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([0, 2, 1])
processed_attention = self.location_dense(processed_attention)
return processed_attention
class Attention(nn.Layer):
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(paddle.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, axis=1)
attention_context = paddle.bmm(attention_weights.unsqueeze(1), memory)
attention_context = attention_context.squeeze(1)
return attention_context, attention_weights
class ForwardAttentionV2(nn.Layer):
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(paddle.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[:] = paddle.where(mask, paddle.full(log_energy.shape, self.score_mask_value, log_energy.dtype), log_energy)
#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.shape[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 = paddle.logsumexp(paddle.conat(log_alpha_shift_padded,2), 2)
log_alpha_new = biased + log_energy
attention_weights = F.softmax(log_alpha_new, axis=1)
attention_context = paddle.bmm(attention_weights.unsqueeze(1), memory)
attention_context = attention_context.squeeze(1)
return attention_context, attention_weights, log_alpha_new
class PhaseShuffle2D(nn.Layer):
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 = paddle.concat([right, left], axis=3)
return shuffled
class PhaseShuffle1D(nn.Layer):
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 = paddle.concat([right, left], axis=2)
return shuffled
class MFCC(nn.Layer):
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 = paddle.matmul(mel_specgram.transpose([0, 2, 1]), self.dct_mat).transpose([0, 2, 1])
# unpack batch
if unsqueezed:
mfcc = mfcc.squeeze(0)
return mfcc
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