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import copy
import torch
import torch.nn as nn
from s3prl import Output
from s3prl.nn.vq_apc import VqApcLayer
class MaskConvBlock(nn.Module):
"""
Masked Convolution Blocks as described in NPC paper
"""
def __init__(self, input_size, hidden_size, kernel_size, mask_size):
super(MaskConvBlock, self).__init__()
assert kernel_size - mask_size > 0, "Mask > kernel somewhere in the model"
# CNN for computing feature (ToDo: other activation?)
self.act = nn.Tanh()
self.pad_size = (kernel_size - 1) // 2
self.conv = nn.Conv1d(
in_channels=input_size,
out_channels=hidden_size,
kernel_size=kernel_size,
padding=self.pad_size,
)
# Fixed mask for NPC
mask_head = (kernel_size - mask_size) // 2
mask_tail = mask_head + mask_size
conv_mask = torch.ones_like(self.conv.weight)
conv_mask[:, :, mask_head:mask_tail] = 0
self.register_buffer("conv_mask", conv_mask)
def forward(self, feat):
feat = nn.functional.conv1d(
feat,
self.conv_mask * self.conv.weight,
bias=self.conv.bias,
padding=self.pad_size,
)
feat = feat.permute(0, 2, 1) # BxCxT -> BxTxC
feat = self.act(feat)
return feat
class ConvBlock(nn.Module):
"""
Convolution Blocks as described in NPC paper
"""
def __init__(
self, input_size, hidden_size, residual, dropout, batch_norm, activate
):
super(ConvBlock, self).__init__()
self.residual = residual
if activate == "relu":
self.act = nn.ReLU()
elif activate == "tanh":
self.act = nn.Tanh()
else:
raise NotImplementedError
self.conv = nn.Conv1d(
input_size, hidden_size, kernel_size=3, stride=1, padding=1
)
self.linear = nn.Conv1d(
hidden_size, hidden_size, kernel_size=1, stride=1, padding=0
)
self.batch_norm = batch_norm
if batch_norm:
self.bn1 = nn.BatchNorm1d(hidden_size)
self.bn2 = nn.BatchNorm1d(hidden_size)
self.dropout = nn.Dropout(dropout)
def forward(self, feat):
res = feat
out = self.conv(feat)
if self.batch_norm:
out = self.bn1(out)
out = self.act(out)
out = self.linear(out)
if self.batch_norm:
out = self.bn2(out)
out = self.dropout(out)
if self.residual:
out = out + res
return self.act(out)
class CnnNpc(nn.Module):
"""
The NPC model with stacked ConvBlocks & Masked ConvBlocks
"""
def __init__(
self,
input_size,
hidden_size,
n_blocks,
dropout,
residual,
kernel_size,
mask_size,
vq=None,
batch_norm=True,
activate="relu",
disable_cross_layer=False,
dim_bottleneck=None,
):
super(CnnNpc, self).__init__()
# Setup
assert kernel_size % 2 == 1, "Kernel size can only be odd numbers"
assert mask_size % 2 == 1, "Mask size can only be odd numbers"
assert n_blocks >= 1, "At least 1 block needed"
self.code_dim = hidden_size
self.n_blocks = n_blocks
self.input_mask_size = mask_size
self.kernel_size = kernel_size
self.disable_cross_layer = disable_cross_layer
self.apply_vq = vq is not None
self.apply_ae = dim_bottleneck is not None
if self.apply_ae:
assert not self.apply_vq
self.dim_bottleneck = dim_bottleneck
# Build blocks
self.blocks, self.masked_convs = [], []
cur_mask_size = mask_size
for i in range(n_blocks):
h_dim = input_size if i == 0 else hidden_size
res = False if i == 0 else residual
# ConvBlock
self.blocks.append(
ConvBlock(h_dim, hidden_size, res, dropout, batch_norm, activate)
)
# Masked ConvBlock on each or last layer
cur_mask_size = cur_mask_size + 2
if self.disable_cross_layer and (i != (n_blocks - 1)):
self.masked_convs.append(None)
else:
self.masked_convs.append(
MaskConvBlock(hidden_size, hidden_size, kernel_size, cur_mask_size)
)
self.blocks = nn.ModuleList(self.blocks)
self.masked_convs = nn.ModuleList(self.masked_convs)
# Creates N-group VQ
if self.apply_vq:
self.vq_layers = []
vq_config = copy.deepcopy(vq)
codebook_size = vq_config.pop("codebook_size")
self.vq_code_dims = vq_config.pop("code_dim")
assert len(self.vq_code_dims) == len(codebook_size)
assert sum(self.vq_code_dims) == hidden_size
for cs, cd in zip(codebook_size, self.vq_code_dims):
self.vq_layers.append(
VqApcLayer(
input_size=cd, code_dim=cd, codebook_size=cs, **vq_config
)
)
self.vq_layers = nn.ModuleList(self.vq_layers)
# Back to spectrogram
if self.apply_ae:
self.ae_bottleneck = nn.Linear(hidden_size, self.dim_bottleneck, bias=False)
self.postnet = nn.Linear(self.dim_bottleneck, input_size)
else:
self.postnet = nn.Linear(hidden_size, input_size)
def create_msg(self):
msg_list = []
msg_list.append(
"Model spec.| Method = NPC\t| # of Blocks = {}\t".format(self.n_blocks)
)
msg_list.append(
" | Desired input mask size = {}".format(self.input_mask_size)
)
msg_list.append(
" | Receptive field size = {}".format(
self.kernel_size + 2 * self.n_blocks
)
)
return msg_list
def report_ppx(self):
"""
Returns perplexity of VQ distribution
"""
if self.apply_vq:
# ToDo: support more than 2 groups
rt = [vq_layer.report_ppx() for vq_layer in self.vq_layers] + [None]
return rt[0], rt[1]
else:
return None, None
def report_usg(self):
"""
Returns usage of VQ codebook
"""
if self.apply_vq:
# ToDo: support more than 2 groups
rt = [vq_layer.report_usg() for vq_layer in self.vq_layers] + [None]
return rt[0], rt[1]
else:
return None, None
def get_unmasked_feat(self, sp_seq, n_layer):
"""
Returns unmasked features from n-th layer ConvBlock
"""
unmasked_feat = sp_seq.permute(0, 2, 1) # BxTxC -> BxCxT
for i in range(self.n_blocks):
unmasked_feat = self.blocks[i](unmasked_feat)
if i == n_layer:
unmasked_feat = unmasked_feat.permute(0, 2, 1)
break
return unmasked_feat
def forward(self, sp_seq, testing=False):
# BxTxC -> BxCxT (reversed in Masked ConvBlock)
unmasked_feat = sp_seq.permute(0, 2, 1)
# Forward through each layer
for i in range(self.n_blocks):
unmasked_feat = self.blocks[i](unmasked_feat)
if self.disable_cross_layer:
# Last layer masked feature only
if i == (self.n_blocks - 1):
feat = self.masked_convs[i](unmasked_feat)
else:
# Masked feature aggregation
masked_feat = self.masked_convs[i](unmasked_feat)
if i == 0:
feat = masked_feat
else:
feat = feat + masked_feat
# Apply bottleneck and predict spectrogram
if self.apply_vq:
q_feat = []
offet = 0
for vq_layer, cd in zip(self.vq_layers, self.vq_code_dims):
q_f = vq_layer(feat[:, :, offet : offet + cd], testing).output
q_feat.append(q_f)
offet += cd
q_feat = torch.cat(q_feat, dim=-1)
pred = self.postnet(q_feat)
elif self.apply_ae:
feat = self.ae_bottleneck(feat)
pred = self.postnet(feat)
else:
pred = self.postnet(feat)
return Output(hidden_states=feat, prediction=pred)
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