forked from mrq/DL-Art-School
533 lines
20 KiB
Python
533 lines
20 KiB
Python
from math import sqrt
|
|
import torch
|
|
from munch import munchify
|
|
from torch.autograd import Variable
|
|
from torch import nn
|
|
from torch.nn import functional as F
|
|
from models.tacotron2.layers import ConvNorm, LinearNorm
|
|
from models.tacotron2.hparams import create_hparams
|
|
from trainer.networks import register_model
|
|
from models.tacotron2.taco_utils import get_mask_from_lengths
|
|
from utils.util import opt_get, checkpoint
|
|
|
|
|
|
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 cumulative 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 Prenet(nn.Module):
|
|
def __init__(self, in_dim, sizes):
|
|
super(Prenet, self).__init__()
|
|
in_sizes = [in_dim] + sizes[:-1]
|
|
self.layers = nn.ModuleList(
|
|
[LinearNorm(in_size, out_size, bias=False)
|
|
for (in_size, out_size) in zip(in_sizes, sizes)])
|
|
|
|
def forward(self, x):
|
|
for linear in self.layers:
|
|
x = F.dropout(F.relu(linear(x)), p=0.5, training=True)
|
|
return x
|
|
|
|
|
|
class Postnet(nn.Module):
|
|
"""Postnet
|
|
- Five 1-d convolution with 512 channels and kernel size 5
|
|
"""
|
|
|
|
def __init__(self, hparams):
|
|
super(Postnet, self).__init__()
|
|
self.convolutions = nn.ModuleList()
|
|
|
|
self.convolutions.append(
|
|
nn.Sequential(
|
|
ConvNorm(hparams.n_mel_channels, hparams.postnet_embedding_dim,
|
|
kernel_size=hparams.postnet_kernel_size, stride=1,
|
|
padding=int((hparams.postnet_kernel_size - 1) / 2),
|
|
dilation=1, w_init_gain='tanh'),
|
|
nn.BatchNorm1d(hparams.postnet_embedding_dim))
|
|
)
|
|
|
|
for i in range(1, hparams.postnet_n_convolutions - 1):
|
|
self.convolutions.append(
|
|
nn.Sequential(
|
|
ConvNorm(hparams.postnet_embedding_dim,
|
|
hparams.postnet_embedding_dim,
|
|
kernel_size=hparams.postnet_kernel_size, stride=1,
|
|
padding=int((hparams.postnet_kernel_size - 1) / 2),
|
|
dilation=1, w_init_gain='tanh'),
|
|
nn.BatchNorm1d(hparams.postnet_embedding_dim))
|
|
)
|
|
|
|
self.convolutions.append(
|
|
nn.Sequential(
|
|
ConvNorm(hparams.postnet_embedding_dim, hparams.n_mel_channels,
|
|
kernel_size=hparams.postnet_kernel_size, stride=1,
|
|
padding=int((hparams.postnet_kernel_size - 1) / 2),
|
|
dilation=1, w_init_gain='linear'),
|
|
nn.BatchNorm1d(hparams.n_mel_channels))
|
|
)
|
|
|
|
def forward(self, x):
|
|
for i in range(len(self.convolutions) - 1):
|
|
x = F.dropout(torch.tanh(self.convolutions[i](x)), 0.5, self.training)
|
|
x = F.dropout(self.convolutions[-1](x), 0.5, self.training)
|
|
|
|
return x
|
|
|
|
|
|
class Encoder(nn.Module):
|
|
"""Encoder module:
|
|
- Three 1-d convolution banks
|
|
- Bidirectional LSTM
|
|
"""
|
|
def __init__(self, hparams):
|
|
super(Encoder, self).__init__()
|
|
|
|
convolutions = []
|
|
for _ in range(hparams.encoder_n_convolutions):
|
|
conv_layer = nn.Sequential(
|
|
ConvNorm(hparams.encoder_embedding_dim,
|
|
hparams.encoder_embedding_dim,
|
|
kernel_size=hparams.encoder_kernel_size, stride=1,
|
|
padding=int((hparams.encoder_kernel_size - 1) / 2),
|
|
dilation=1, w_init_gain='relu'),
|
|
nn.BatchNorm1d(hparams.encoder_embedding_dim))
|
|
convolutions.append(conv_layer)
|
|
self.convolutions = nn.ModuleList(convolutions)
|
|
|
|
self.lstm = nn.LSTM(hparams.encoder_embedding_dim,
|
|
int(hparams.encoder_embedding_dim / 2), 1,
|
|
batch_first=True, bidirectional=True)
|
|
|
|
def forward(self, x, input_lengths):
|
|
for conv in self.convolutions:
|
|
x = F.dropout(F.relu(conv(x)), 0.5, self.training)
|
|
|
|
x = x.transpose(1, 2)
|
|
|
|
# pytorch tensor are not reversible, hence the conversion
|
|
input_lengths = input_lengths.cpu().numpy()
|
|
x = nn.utils.rnn.pack_padded_sequence(
|
|
x, input_lengths, batch_first=True)
|
|
|
|
self.lstm.flatten_parameters()
|
|
outputs, _ = self.lstm(x)
|
|
|
|
outputs, _ = nn.utils.rnn.pad_packed_sequence(
|
|
outputs, batch_first=True)
|
|
|
|
return outputs
|
|
|
|
def inference(self, x):
|
|
for conv in self.convolutions:
|
|
x = F.dropout(F.relu(conv(x)), 0.5, self.training)
|
|
|
|
x = x.transpose(1, 2)
|
|
|
|
self.lstm.flatten_parameters()
|
|
outputs, _ = self.lstm(x)
|
|
|
|
return outputs
|
|
|
|
|
|
class Decoder(nn.Module):
|
|
def __init__(self, hparams):
|
|
super(Decoder, self).__init__()
|
|
self.n_mel_channels = hparams.n_mel_channels
|
|
self.n_frames_per_step = hparams.n_frames_per_step
|
|
self.encoder_embedding_dim = hparams.encoder_embedding_dim
|
|
self.attention_rnn_dim = hparams.attention_rnn_dim
|
|
self.decoder_rnn_dim = hparams.decoder_rnn_dim
|
|
self.prenet_dim = hparams.prenet_dim
|
|
self.max_decoder_steps = hparams.max_decoder_steps
|
|
self.gate_threshold = hparams.gate_threshold
|
|
self.p_attention_dropout = hparams.p_attention_dropout
|
|
self.p_decoder_dropout = hparams.p_decoder_dropout
|
|
|
|
self.prenet = Prenet(
|
|
hparams.n_mel_channels * hparams.n_frames_per_step,
|
|
[hparams.prenet_dim, hparams.prenet_dim])
|
|
|
|
self.attention_rnn = nn.LSTMCell(
|
|
hparams.prenet_dim + hparams.encoder_embedding_dim,
|
|
hparams.attention_rnn_dim)
|
|
|
|
self.attention_layer = Attention(
|
|
hparams.attention_rnn_dim, hparams.encoder_embedding_dim,
|
|
hparams.attention_dim, hparams.attention_location_n_filters,
|
|
hparams.attention_location_kernel_size)
|
|
|
|
self.decoder_rnn = nn.LSTMCell(
|
|
hparams.attention_rnn_dim + hparams.encoder_embedding_dim,
|
|
hparams.decoder_rnn_dim, 1)
|
|
|
|
self.linear_projection = LinearNorm(
|
|
hparams.decoder_rnn_dim + hparams.encoder_embedding_dim,
|
|
hparams.n_mel_channels * hparams.n_frames_per_step)
|
|
|
|
self.gate_layer = LinearNorm(
|
|
hparams.decoder_rnn_dim + hparams.encoder_embedding_dim, 1,
|
|
bias=True, w_init_gain='sigmoid')
|
|
|
|
def get_go_frame(self, memory):
|
|
""" Gets all zeros frames to use as first decoder input
|
|
PARAMS
|
|
------
|
|
memory: decoder outputs
|
|
|
|
RETURNS
|
|
-------
|
|
decoder_input: all zeros frames
|
|
"""
|
|
B = memory.size(0)
|
|
decoder_input = Variable(memory.data.new(
|
|
B, self.n_mel_channels * self.n_frames_per_step).zero_())
|
|
return decoder_input
|
|
|
|
def initialize_decoder_states(self, memory, mask):
|
|
""" Initializes attention rnn states, decoder rnn states, attention
|
|
weights, attention cumulative weights, attention context, stores memory
|
|
and stores processed memory
|
|
PARAMS
|
|
------
|
|
memory: Encoder outputs
|
|
mask: Mask for padded data if training, expects None for inference
|
|
"""
|
|
B = memory.size(0)
|
|
MAX_TIME = memory.size(1)
|
|
|
|
self.attention_hidden = Variable(memory.data.new(
|
|
B, self.attention_rnn_dim).zero_())
|
|
self.attention_cell = Variable(memory.data.new(
|
|
B, self.attention_rnn_dim).zero_())
|
|
|
|
self.decoder_hidden = Variable(memory.data.new(
|
|
B, self.decoder_rnn_dim).zero_())
|
|
self.decoder_cell = Variable(memory.data.new(
|
|
B, self.decoder_rnn_dim).zero_())
|
|
|
|
self.attention_weights = Variable(memory.data.new(
|
|
B, MAX_TIME).zero_())
|
|
self.attention_weights_cum = Variable(memory.data.new(
|
|
B, MAX_TIME).zero_())
|
|
self.attention_context = Variable(memory.data.new(
|
|
B, self.encoder_embedding_dim).zero_())
|
|
|
|
self.memory = memory
|
|
self.processed_memory = self.attention_layer.memory_layer(memory)
|
|
self.mask = mask
|
|
|
|
def parse_decoder_inputs(self, decoder_inputs):
|
|
""" Prepares decoder inputs, i.e. mel outputs
|
|
PARAMS
|
|
------
|
|
decoder_inputs: inputs used for teacher-forced training, i.e. mel-specs
|
|
|
|
RETURNS
|
|
-------
|
|
inputs: processed decoder inputs
|
|
|
|
"""
|
|
# (B, n_mel_channels, T_out) -> (B, T_out, n_mel_channels)
|
|
decoder_inputs = decoder_inputs.transpose(1, 2)
|
|
decoder_inputs = decoder_inputs.view(
|
|
decoder_inputs.size(0),
|
|
int(decoder_inputs.size(1)/self.n_frames_per_step), -1)
|
|
# (B, T_out, n_mel_channels) -> (T_out, B, n_mel_channels)
|
|
decoder_inputs = decoder_inputs.transpose(0, 1)
|
|
return decoder_inputs
|
|
|
|
def parse_decoder_outputs(self, mel_outputs, gate_outputs, alignments):
|
|
""" Prepares decoder outputs for output
|
|
PARAMS
|
|
------
|
|
mel_outputs:
|
|
gate_outputs: gate output energies
|
|
alignments:
|
|
|
|
RETURNS
|
|
-------
|
|
mel_outputs:
|
|
gate_outpust: gate output energies
|
|
alignments:
|
|
"""
|
|
# (T_out, B) -> (B, T_out)
|
|
alignments = torch.stack(alignments).transpose(0, 1)
|
|
# (T_out, B) -> (B, T_out)
|
|
gate_outputs = torch.stack(gate_outputs).transpose(0, 1)
|
|
gate_outputs = gate_outputs.contiguous()
|
|
# (T_out, B, n_mel_channels) -> (B, T_out, n_mel_channels)
|
|
mel_outputs = torch.stack(mel_outputs).transpose(0, 1).contiguous()
|
|
# decouple frames per step
|
|
mel_outputs = mel_outputs.view(
|
|
mel_outputs.size(0), -1, self.n_mel_channels)
|
|
# (B, T_out, n_mel_channels) -> (B, n_mel_channels, T_out)
|
|
mel_outputs = mel_outputs.transpose(1, 2)
|
|
|
|
return mel_outputs, gate_outputs, alignments
|
|
|
|
def decode(self, decoder_input):
|
|
""" Decoder step using stored states, attention and memory
|
|
PARAMS
|
|
------
|
|
decoder_input: previous mel output
|
|
|
|
RETURNS
|
|
-------
|
|
mel_output:
|
|
gate_output: gate output energies
|
|
attention_weights:
|
|
"""
|
|
cell_input = torch.cat((decoder_input, self.attention_context), -1)
|
|
self.attention_hidden, self.attention_cell = self.attention_rnn(
|
|
cell_input, (self.attention_hidden, self.attention_cell))
|
|
self.attention_hidden = F.dropout(
|
|
self.attention_hidden, self.p_attention_dropout, self.training)
|
|
|
|
attention_weights_cat = torch.cat(
|
|
(self.attention_weights.unsqueeze(1),
|
|
self.attention_weights_cum.unsqueeze(1)), dim=1)
|
|
self.attention_context, self.attention_weights = self.attention_layer(
|
|
self.attention_hidden, self.memory, self.processed_memory,
|
|
attention_weights_cat, self.mask)
|
|
|
|
self.attention_weights_cum += self.attention_weights
|
|
decoder_input = torch.cat(
|
|
(self.attention_hidden, self.attention_context), -1)
|
|
self.decoder_hidden, self.decoder_cell = self.decoder_rnn(
|
|
decoder_input, (self.decoder_hidden, self.decoder_cell))
|
|
self.decoder_hidden = F.dropout(
|
|
self.decoder_hidden, self.p_decoder_dropout, self.training)
|
|
|
|
decoder_hidden_attention_context = torch.cat(
|
|
(self.decoder_hidden, self.attention_context), dim=1)
|
|
decoder_output = self.linear_projection(
|
|
decoder_hidden_attention_context)
|
|
|
|
gate_prediction = self.gate_layer(decoder_hidden_attention_context)
|
|
return decoder_output, gate_prediction, self.attention_weights
|
|
|
|
def forward(self, memory, decoder_inputs, memory_lengths):
|
|
""" Decoder forward pass for training
|
|
PARAMS
|
|
------
|
|
memory: Encoder outputs
|
|
decoder_inputs: Decoder inputs for teacher forcing. i.e. mel-specs
|
|
memory_lengths: Encoder output lengths for attention masking.
|
|
|
|
RETURNS
|
|
-------
|
|
mel_outputs: mel outputs from the decoder
|
|
gate_outputs: gate outputs from the decoder
|
|
alignments: sequence of attention weights from the decoder
|
|
"""
|
|
|
|
decoder_input = self.get_go_frame(memory).unsqueeze(0)
|
|
decoder_inputs = self.parse_decoder_inputs(decoder_inputs)
|
|
decoder_inputs = torch.cat((decoder_input, decoder_inputs), dim=0)
|
|
decoder_inputs = self.prenet(decoder_inputs)
|
|
|
|
self.initialize_decoder_states(
|
|
memory, mask=~get_mask_from_lengths(memory_lengths))
|
|
|
|
mel_outputs, gate_outputs, alignments = [], [], []
|
|
while len(mel_outputs) < decoder_inputs.size(0) - 1:
|
|
decoder_input = decoder_inputs[len(mel_outputs)]
|
|
mel_output, gate_output, attention_weights = self.decode(decoder_input)
|
|
mel_outputs += [mel_output.squeeze(1)]
|
|
gate_outputs += [gate_output.squeeze(1)]
|
|
alignments += [attention_weights]
|
|
|
|
mel_outputs, gate_outputs, alignments = self.parse_decoder_outputs(
|
|
mel_outputs, gate_outputs, alignments)
|
|
|
|
return mel_outputs, gate_outputs, alignments
|
|
|
|
def inference(self, memory):
|
|
""" Decoder inference
|
|
PARAMS
|
|
------
|
|
memory: Encoder outputs
|
|
|
|
RETURNS
|
|
-------
|
|
mel_outputs: mel outputs from the decoder
|
|
gate_outputs: gate outputs from the decoder
|
|
alignments: sequence of attention weights from the decoder
|
|
"""
|
|
decoder_input = self.get_go_frame(memory)
|
|
|
|
self.initialize_decoder_states(memory, mask=None)
|
|
|
|
mel_outputs, gate_outputs, alignments = [], [], []
|
|
while True:
|
|
decoder_input = self.prenet(decoder_input)
|
|
mel_output, gate_output, alignment = self.decode(decoder_input)
|
|
|
|
mel_outputs += [mel_output.squeeze(1)]
|
|
gate_outputs += [gate_output]
|
|
alignments += [alignment]
|
|
|
|
if torch.sigmoid(gate_output.data) > self.gate_threshold:
|
|
break
|
|
elif len(mel_outputs) == self.max_decoder_steps:
|
|
print("Warning! Reached max decoder steps")
|
|
break
|
|
|
|
decoder_input = mel_output
|
|
|
|
mel_outputs, gate_outputs, alignments = self.parse_decoder_outputs(
|
|
mel_outputs, gate_outputs, alignments)
|
|
|
|
return mel_outputs, gate_outputs, alignments
|
|
|
|
|
|
class Tacotron2(nn.Module):
|
|
def __init__(self, hparams):
|
|
super(Tacotron2, self).__init__()
|
|
self.mask_padding = hparams.mask_padding
|
|
self.fp16_run = hparams.fp16_run
|
|
self.n_mel_channels = hparams.n_mel_channels
|
|
self.n_frames_per_step = hparams.n_frames_per_step
|
|
self.embedding = nn.Embedding(
|
|
hparams.n_symbols, hparams.symbols_embedding_dim)
|
|
std = sqrt(2.0 / (hparams.n_symbols + hparams.symbols_embedding_dim))
|
|
val = sqrt(3.0) * std # uniform bounds for std
|
|
self.embedding.weight.data.uniform_(-val, val)
|
|
self.encoder = Encoder(hparams)
|
|
self.decoder = Decoder(hparams)
|
|
self.postnet = Postnet(hparams)
|
|
|
|
def parse_output(self, outputs, output_lengths=None):
|
|
if self.mask_padding and output_lengths is not None:
|
|
mask_fill = outputs[0].shape[-1]
|
|
mask = ~get_mask_from_lengths(output_lengths, mask_fill)
|
|
mask = mask.expand(self.n_mel_channels, mask.size(0), mask.size(1))
|
|
mask = mask.permute(1, 0, 2)
|
|
|
|
outputs[0].data.masked_fill_(mask, 0.0)
|
|
outputs[1].data.masked_fill_(mask, 0.0)
|
|
outputs[2].data.masked_fill_(mask[:, 0, :], 1e3) # gate energies
|
|
|
|
return outputs
|
|
|
|
def forward(self, text_inputs, text_lengths, mels, output_lengths):
|
|
text_lengths, output_lengths = text_lengths.data, output_lengths.data
|
|
|
|
embedded_inputs = self.embedding(text_inputs).transpose(1, 2)
|
|
|
|
encoder_outputs = self.encoder(embedded_inputs, text_lengths)
|
|
|
|
mel_outputs, gate_outputs, alignments = self.decoder(
|
|
encoder_outputs, mels, memory_lengths=text_lengths)
|
|
|
|
mel_outputs_postnet = self.postnet(mel_outputs)
|
|
mel_outputs_postnet = mel_outputs + mel_outputs_postnet
|
|
|
|
return self.parse_output(
|
|
[mel_outputs, mel_outputs_postnet, gate_outputs, alignments],
|
|
output_lengths)
|
|
|
|
def inference(self, inputs):
|
|
embedded_inputs = self.embedding(inputs).transpose(1, 2)
|
|
encoder_outputs = self.encoder.inference(embedded_inputs)
|
|
mel_outputs, gate_outputs, alignments = self.decoder.inference(
|
|
encoder_outputs)
|
|
|
|
mel_outputs_postnet = self.postnet(mel_outputs)
|
|
mel_outputs_postnet = mel_outputs + mel_outputs_postnet
|
|
|
|
outputs = self.parse_output(
|
|
[mel_outputs, mel_outputs_postnet, gate_outputs, alignments])
|
|
|
|
return outputs
|
|
|
|
|
|
@register_model
|
|
def register_nv_tacotron2(opt_net, opt):
|
|
hparams = create_hparams()
|
|
hparams.update(opt_net)
|
|
hparams = munchify(hparams)
|
|
return Tacotron2(hparams)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
tron = register_nv_tacotron2({}, {})
|
|
inputs = torch.randint(high=24, size=(1,12)), torch.tensor([12]), torch.randn((1,80,749)), 800, torch.tensor([749])
|
|
out = tron(inputs)
|
|
print(out) |