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model.py
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model.py
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from math import sqrt
import numpy as np
from numpy import finfo
import torch
from torch.autograd import Variable
from torch import nn
from torch.nn import functional as F
from layers import ConvNorm, LinearNorm
from utils import to_gpu, get_mask_from_lengths
from modules import GST
drop_rate = 0.5
def load_model(hparams):
model = Tacotron2(hparams).cuda()
if hparams.fp16_run:
model.decoder.attention_layer.score_mask_value = finfo('float16').min
return model
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, attention_weights=None):
"""
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
"""
if attention_weights is None:
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=drop_rate, 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)), drop_rate, self.training)
x = F.dropout(self.convolutions[-1](x), drop_rate, 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):
if x.size()[0] > 1:
print("here")
x_embedded = []
for b_ind in range(x.size()[0]): # TODO: Speed up
curr_x = x[b_ind:b_ind+1, :, :input_lengths[b_ind]].clone()
for conv in self.convolutions:
curr_x = F.dropout(F.relu(conv(curr_x)), drop_rate, self.training)
x_embedded.append(curr_x[0].transpose(0, 1))
x = torch.nn.utils.rnn.pad_sequence(x_embedded, batch_first=True)
else:
for conv in self.convolutions:
x = F.dropout(F.relu(conv(x)), drop_rate, 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)), drop_rate, 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 + hparams.token_embedding_size + hparams.speaker_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.p_teacher_forcing = hparams.p_teacher_forcing
self.prenet_f0 = ConvNorm(
1, hparams.prenet_f0_dim,
kernel_size=hparams.prenet_f0_kernel_size,
padding=max(0, int(hparams.prenet_f0_kernel_size/2)),
bias=False, stride=1, dilation=1)
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.prenet_f0_dim + self.encoder_embedding_dim,
hparams.attention_rnn_dim)
self.attention_layer = Attention(
hparams.attention_rnn_dim, self.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 + self.encoder_embedding_dim,
hparams.decoder_rnn_dim, 1)
self.linear_projection = LinearNorm(
hparams.decoder_rnn_dim + self.encoder_embedding_dim,
hparams.n_mel_channels * hparams.n_frames_per_step)
self.gate_layer = LinearNorm(
hparams.decoder_rnn_dim + self.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 get_end_f0(self, f0s):
B = f0s.size(0)
dummy = Variable(f0s.data.new(B, 1, f0s.size(1)).zero_())
return dummy
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)
if len(gate_outputs.size()) > 1:
gate_outputs = gate_outputs.transpose(0, 1)
else:
gate_outputs = gate_outputs[None]
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, attention_weights=None):
""" 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)
self.attention_cell = F.dropout(
self.attention_cell, 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, attention_weights)
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)
self.decoder_cell = F.dropout(
self.decoder_cell, 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, f0s):
""" 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)
# audio features
f0_dummy = self.get_end_f0(f0s)
f0s = torch.cat((f0s, f0_dummy), dim=2)
f0s = F.relu(self.prenet_f0(f0s))
f0s = f0s.permute(2, 0, 1)
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:
if len(mel_outputs) == 0 or np.random.uniform(0.0, 1.0) <= self.p_teacher_forcing:
decoder_input = torch.cat((decoder_inputs[len(mel_outputs)],
f0s[len(mel_outputs)]), dim=1)
else:
decoder_input = torch.cat((self.prenet(mel_outputs[-1]),
f0s[len(mel_outputs)]), dim=1)
mel_output, gate_output, attention_weights = self.decode(
decoder_input)
mel_outputs += [mel_output.squeeze(1)]
gate_outputs += [gate_output.squeeze()]
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, f0s):
""" 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)
f0_dummy = self.get_end_f0(f0s)
f0s = torch.cat((f0s, f0_dummy), dim=2)
f0s = F.relu(self.prenet_f0(f0s))
f0s = f0s.permute(2, 0, 1)
mel_outputs, gate_outputs, alignments = [], [], []
while True:
if len(mel_outputs) < len(f0s):
f0 = f0s[len(mel_outputs)]
else:
f0 = f0s[-1] * 0
decoder_input = torch.cat((self.prenet(decoder_input), f0), dim=1)
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
def inference_noattention(self, memory, f0s, attention_map):
""" 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)
f0_dummy = self.get_end_f0(f0s)
f0s = torch.cat((f0s, f0_dummy), dim=2)
f0s = F.relu(self.prenet_f0(f0s))
f0s = f0s.permute(2, 0, 1)
mel_outputs, gate_outputs, alignments = [], [], []
for i in range(len(attention_map)):
f0 = f0s[i]
attention = attention_map[i]
decoder_input = torch.cat((self.prenet(decoder_input), f0), dim=1)
mel_output, gate_output, alignment = self.decode(decoder_input, attention)
mel_outputs += [mel_output.squeeze(1)]
gate_outputs += [gate_output]
alignments += [alignment]
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)
if hparams.with_gst:
self.gst = GST(hparams)
self.speaker_embedding = nn.Embedding(
hparams.n_speakers, hparams.speaker_embedding_dim)
def parse_batch(self, batch):
text_padded, input_lengths, mel_padded, gate_padded, \
output_lengths, speaker_ids, f0_padded = batch
text_padded = to_gpu(text_padded).long()
input_lengths = to_gpu(input_lengths).long()
max_len = torch.max(input_lengths.data).item()
mel_padded = to_gpu(mel_padded).float()
gate_padded = to_gpu(gate_padded).float()
output_lengths = to_gpu(output_lengths).long()
speaker_ids = to_gpu(speaker_ids.data).long()
f0_padded = to_gpu(f0_padded).float()
return ((text_padded, input_lengths, mel_padded, max_len,
output_lengths, speaker_ids, f0_padded),
(mel_padded, gate_padded))
def parse_output(self, outputs, output_lengths=None):
if self.mask_padding and output_lengths is not None:
mask = ~get_mask_from_lengths(output_lengths)
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, inputs):
inputs, input_lengths, targets, max_len, \
output_lengths, speaker_ids, f0s = inputs
input_lengths, output_lengths = input_lengths.data, output_lengths.data
embedded_inputs = self.embedding(inputs).transpose(1, 2)
embedded_text = self.encoder(embedded_inputs, input_lengths)
embedded_speakers = self.speaker_embedding(speaker_ids)[:, None]
embedded_gst = self.gst(targets, output_lengths)
embedded_gst = embedded_gst.repeat(1, embedded_text.size(1), 1)
embedded_speakers = embedded_speakers.repeat(1, embedded_text.size(1), 1)
encoder_outputs = torch.cat(
(embedded_text, embedded_gst, embedded_speakers), dim=2)
mel_outputs, gate_outputs, alignments = self.decoder(
encoder_outputs, targets, memory_lengths=input_lengths, f0s=f0s)
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):
text, style_input, speaker_ids, f0s = inputs
embedded_inputs = self.embedding(text).transpose(1, 2)
embedded_text = self.encoder.inference(embedded_inputs)
embedded_speakers = self.speaker_embedding(speaker_ids)[:, None]
if hasattr(self, 'gst'):
if isinstance(style_input, int):
query = torch.zeros(1, 1, self.gst.encoder.ref_enc_gru_size).cuda()
GST = torch.tanh(self.gst.stl.embed)
key = GST[style_input].unsqueeze(0).expand(1, -1, -1)
embedded_gst = self.gst.stl.attention(query, key)
else:
embedded_gst = self.gst(style_input)
embedded_speakers = embedded_speakers.repeat(1, embedded_text.size(1), 1)
if hasattr(self, 'gst'):
embedded_gst = embedded_gst.repeat(1, embedded_text.size(1), 1)
encoder_outputs = torch.cat(
(embedded_text, embedded_gst, embedded_speakers), dim=2)
else:
encoder_outputs = torch.cat(
(embedded_text, embedded_speakers), dim=2)
mel_outputs, gate_outputs, alignments = self.decoder.inference(
encoder_outputs, f0s)
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])
def inference_noattention(self, inputs):
text, style_input, speaker_ids, f0s, attention_map = inputs
embedded_inputs = self.embedding(text).transpose(1, 2)
embedded_text = self.encoder.inference(embedded_inputs)
embedded_speakers = self.speaker_embedding(speaker_ids)[:, None]
if hasattr(self, 'gst'):
if isinstance(style_input, int):
query = torch.zeros(1, 1, self.gst.encoder.ref_enc_gru_size).cuda()
GST = torch.tanh(self.gst.stl.embed)
key = GST[style_input].unsqueeze(0).expand(1, -1, -1)
embedded_gst = self.gst.stl.attention(query, key)
else:
embedded_gst = self.gst(style_input)
embedded_speakers = embedded_speakers.repeat(1, embedded_text.size(1), 1)
if hasattr(self, 'gst'):
embedded_gst = embedded_gst.repeat(1, embedded_text.size(1), 1)
encoder_outputs = torch.cat(
(embedded_text, embedded_gst, embedded_speakers), dim=2)
else:
encoder_outputs = torch.cat(
(embedded_text, embedded_speakers), dim=2)
mel_outputs, gate_outputs, alignments = self.decoder.inference_noattention(
encoder_outputs, f0s, attention_map)
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])