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# *****************************************************************************
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
# * Neither the name of the NVIDIA CORPORATION nor the
# names of its contributors may be used to endorse or promote products
# derived from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#
# *****************************************************************************
import warnings
from typing import List, Optional, Tuple, Union
import torch
from torch import nn, Tensor
from torch.nn import functional as F
__all__ = [
"Tacotron2",
]
def _get_linear_layer(in_dim: int, out_dim: int, bias: bool = True, w_init_gain: str = "linear") -> torch.nn.Linear:
r"""Linear layer with xavier uniform initialization.
Args:
in_dim (int): Size of each input sample.
out_dim (int): Size of each output sample.
bias (bool, optional): If set to ``False``, the layer will not learn an additive bias. (Default: ``True``)
w_init_gain (str, optional): Parameter passed to ``torch.nn.init.calculate_gain``
for setting the gain parameter of ``xavier_uniform_``. (Default: ``linear``)
Returns:
(torch.nn.Linear): The corresponding linear layer.
"""
linear = torch.nn.Linear(in_dim, out_dim, bias=bias)
torch.nn.init.xavier_uniform_(linear.weight, gain=torch.nn.init.calculate_gain(w_init_gain))
return linear
def _get_conv1d_layer(
in_channels: int,
out_channels: int,
kernel_size: int = 1,
stride: int = 1,
padding: Optional[Union[str, int, Tuple[int]]] = None,
dilation: int = 1,
bias: bool = True,
w_init_gain: str = "linear",
) -> torch.nn.Conv1d:
r"""1D convolution with xavier uniform initialization.
Args:
in_channels (int): Number of channels in the input image.
out_channels (int): Number of channels produced by the convolution.
kernel_size (int, optional): Number of channels in the input image. (Default: ``1``)
stride (int, optional): Number of channels in the input image. (Default: ``1``)
padding (str, int or tuple, optional): Padding added to both sides of the input.
(Default: dilation * (kernel_size - 1) / 2)
dilation (int, optional): Number of channels in the input image. (Default: ``1``)
w_init_gain (str, optional): Parameter passed to ``torch.nn.init.calculate_gain``
for setting the gain parameter of ``xavier_uniform_``. (Default: ``linear``)
Returns:
(torch.nn.Conv1d): The corresponding Conv1D layer.
"""
if padding is None:
if kernel_size % 2 != 1:
raise ValueError("kernel_size must be odd")
padding = int(dilation * (kernel_size - 1) / 2)
conv1d = torch.nn.Conv1d(
in_channels,
out_channels,
kernel_size=kernel_size,
stride=stride,
padding=padding,
dilation=dilation,
bias=bias,
)
torch.nn.init.xavier_uniform_(conv1d.weight, gain=torch.nn.init.calculate_gain(w_init_gain))
return conv1d
def _get_mask_from_lengths(lengths: Tensor) -> Tensor:
r"""Returns a binary mask based on ``lengths``. The ``i``-th row and ``j``-th column of the mask
is ``1`` if ``j`` is smaller than ``i``-th element of ``lengths.
Args:
lengths (Tensor): The length of each element in the batch, with shape (n_batch, ).
Returns:
mask (Tensor): The binary mask, with shape (n_batch, max of ``lengths``).
"""
max_len = torch.max(lengths).item()
ids = torch.arange(0, max_len, device=lengths.device, dtype=lengths.dtype)
mask = (ids < lengths.unsqueeze(1)).byte()
mask = torch.le(mask, 0)
return mask
class _LocationLayer(nn.Module):
r"""Location layer used in the Attention model.
Args:
attention_n_filter (int): Number of filters for attention model.
attention_kernel_size (int): Kernel size for attention model.
attention_hidden_dim (int): Dimension of attention hidden representation.
"""
def __init__(
self,
attention_n_filter: int,
attention_kernel_size: int,
attention_hidden_dim: int,
):
super().__init__()
padding = int((attention_kernel_size - 1) / 2)
self.location_conv = _get_conv1d_layer(
2,
attention_n_filter,
kernel_size=attention_kernel_size,
padding=padding,
bias=False,
stride=1,
dilation=1,
)
self.location_dense = _get_linear_layer(
attention_n_filter, attention_hidden_dim, bias=False, w_init_gain="tanh"
)
def forward(self, attention_weights_cat: Tensor) -> Tensor:
r"""Location layer used in the Attention model.
Args:
attention_weights_cat (Tensor): Cumulative and previous attention weights
with shape (n_batch, 2, max of ``text_lengths``).
Returns:
processed_attention (Tensor): Cumulative and previous attention weights
with shape (n_batch, ``attention_hidden_dim``).
"""
# (n_batch, attention_n_filter, text_lengths.max())
processed_attention = self.location_conv(attention_weights_cat)
processed_attention = processed_attention.transpose(1, 2)
# (n_batch, text_lengths.max(), attention_hidden_dim)
processed_attention = self.location_dense(processed_attention)
return processed_attention
class _Attention(nn.Module):
r"""Locally sensitive attention model.
Args:
attention_rnn_dim (int): Number of hidden units for RNN.
encoder_embedding_dim (int): Number of embedding dimensions in the Encoder.
attention_hidden_dim (int): Dimension of attention hidden representation.
attention_location_n_filter (int): Number of filters for Attention model.
attention_location_kernel_size (int): Kernel size for Attention model.
"""
def __init__(
self,
attention_rnn_dim: int,
encoder_embedding_dim: int,
attention_hidden_dim: int,
attention_location_n_filter: int,
attention_location_kernel_size: int,
) -> None:
super().__init__()
self.query_layer = _get_linear_layer(attention_rnn_dim, attention_hidden_dim, bias=False, w_init_gain="tanh")
self.memory_layer = _get_linear_layer(
encoder_embedding_dim, attention_hidden_dim, bias=False, w_init_gain="tanh"
)
self.v = _get_linear_layer(attention_hidden_dim, 1, bias=False)
self.location_layer = _LocationLayer(
attention_location_n_filter,
attention_location_kernel_size,
attention_hidden_dim,
)
self.score_mask_value = -float("inf")
def _get_alignment_energies(self, query: Tensor, processed_memory: Tensor, attention_weights_cat: Tensor) -> Tensor:
r"""Get the alignment vector.
Args:
query (Tensor): Decoder output with shape (n_batch, n_mels * n_frames_per_step).
processed_memory (Tensor): Processed Encoder outputs
with shape (n_batch, max of ``text_lengths``, attention_hidden_dim).
attention_weights_cat (Tensor): Cumulative and previous attention weights
with shape (n_batch, 2, max of ``text_lengths``).
Returns:
alignment (Tensor): attention weights, it is a tensor with shape (batch, max of ``text_lengths``).
"""
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))
alignment = energies.squeeze(2)
return alignment
def forward(
self,
attention_hidden_state: Tensor,
memory: Tensor,
processed_memory: Tensor,
attention_weights_cat: Tensor,
mask: Tensor,
) -> Tuple[Tensor, Tensor]:
r"""Pass the input through the Attention model.
Args:
attention_hidden_state (Tensor): Attention rnn last output with shape (n_batch, ``attention_rnn_dim``).
memory (Tensor): Encoder outputs with shape (n_batch, max of ``text_lengths``, ``encoder_embedding_dim``).
processed_memory (Tensor): Processed Encoder outputs
with shape (n_batch, max of ``text_lengths``, ``attention_hidden_dim``).
attention_weights_cat (Tensor): Previous and cumulative attention weights
with shape (n_batch, current_num_frames * 2, max of ``text_lengths``).
mask (Tensor): Binary mask for padded data with shape (n_batch, current_num_frames).
Returns:
attention_context (Tensor): Context vector with shape (n_batch, ``encoder_embedding_dim``).
attention_weights (Tensor): Attention weights with shape (n_batch, max of ``text_lengths``).
"""
alignment = self._get_alignment_energies(attention_hidden_state, processed_memory, attention_weights_cat)
alignment = alignment.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):
r"""Prenet Module. It is consists of ``len(output_size)`` linear layers.
Args:
in_dim (int): The size of each input sample.
output_sizes (list): The output dimension of each linear layers.
"""
def __init__(self, in_dim: int, out_sizes: List[int]) -> None:
super().__init__()
in_sizes = [in_dim] + out_sizes[:-1]
self.layers = nn.ModuleList(
[_get_linear_layer(in_size, out_size, bias=False) for (in_size, out_size) in zip(in_sizes, out_sizes)]
)
def forward(self, x: Tensor) -> Tensor:
r"""Pass the input through Prenet.
Args:
x (Tensor): The input sequence to Prenet with shape (n_batch, in_dim).
Return:
x (Tensor): Tensor with shape (n_batch, sizes[-1])
"""
for linear in self.layers:
x = F.dropout(F.relu(linear(x)), p=0.5, training=True)
return x
class _Postnet(nn.Module):
r"""Postnet Module.
Args:
n_mels (int): Number of mel bins.
postnet_embedding_dim (int): Postnet embedding dimension.
postnet_kernel_size (int): Postnet kernel size.
postnet_n_convolution (int): Number of postnet convolutions.
"""
def __init__(
self,
n_mels: int,
postnet_embedding_dim: int,
postnet_kernel_size: int,
postnet_n_convolution: int,
):
super().__init__()
self.convolutions = nn.ModuleList()
for i in range(postnet_n_convolution):
in_channels = n_mels if i == 0 else postnet_embedding_dim
out_channels = n_mels if i == (postnet_n_convolution - 1) else postnet_embedding_dim
init_gain = "linear" if i == (postnet_n_convolution - 1) else "tanh"
num_features = n_mels if i == (postnet_n_convolution - 1) else postnet_embedding_dim
self.convolutions.append(
nn.Sequential(
_get_conv1d_layer(
in_channels,
out_channels,
kernel_size=postnet_kernel_size,
stride=1,
padding=int((postnet_kernel_size - 1) / 2),
dilation=1,
w_init_gain=init_gain,
),
nn.BatchNorm1d(num_features),
)
)
self.n_convs = len(self.convolutions)
def forward(self, x: Tensor) -> Tensor:
r"""Pass the input through Postnet.
Args:
x (Tensor): The input sequence with shape (n_batch, ``n_mels``, max of ``mel_specgram_lengths``).
Return:
x (Tensor): Tensor with shape (n_batch, ``n_mels``, max of ``mel_specgram_lengths``).
"""
for i, conv in enumerate(self.convolutions):
if i < self.n_convs - 1:
x = F.dropout(torch.tanh(conv(x)), 0.5, training=self.training)
else:
x = F.dropout(conv(x), 0.5, training=self.training)
return x
class _Encoder(nn.Module):
r"""Encoder Module.
Args:
encoder_embedding_dim (int): Number of embedding dimensions in the encoder.
encoder_n_convolution (int): Number of convolution layers in the encoder.
encoder_kernel_size (int): The kernel size in the encoder.
Examples
>>> encoder = _Encoder(3, 512, 5)
>>> input = torch.rand(10, 20, 30)
>>> output = encoder(input) # shape: (10, 30, 512)
"""
def __init__(
self,
encoder_embedding_dim: int,
encoder_n_convolution: int,
encoder_kernel_size: int,
) -> None:
super().__init__()
self.convolutions = nn.ModuleList()
for _ in range(encoder_n_convolution):
conv_layer = nn.Sequential(
_get_conv1d_layer(
encoder_embedding_dim,
encoder_embedding_dim,
kernel_size=encoder_kernel_size,
stride=1,
padding=int((encoder_kernel_size - 1) / 2),
dilation=1,
w_init_gain="relu",
),
nn.BatchNorm1d(encoder_embedding_dim),
)
self.convolutions.append(conv_layer)
self.lstm = nn.LSTM(
encoder_embedding_dim,
int(encoder_embedding_dim / 2),
1,
batch_first=True,
bidirectional=True,
)
self.lstm.flatten_parameters()
def forward(self, x: Tensor, input_lengths: Tensor) -> Tensor:
r"""Pass the input through the Encoder.
Args:
x (Tensor): The input sequences with shape (n_batch, encoder_embedding_dim, n_seq).
input_lengths (Tensor): The length of each input sequence with shape (n_batch, ).
Return:
x (Tensor): A tensor with shape (n_batch, n_seq, encoder_embedding_dim).
"""
for conv in self.convolutions:
x = F.dropout(F.relu(conv(x)), 0.5, self.training)
x = x.transpose(1, 2)
input_lengths = input_lengths.cpu()
x = nn.utils.rnn.pack_padded_sequence(x, input_lengths, batch_first=True)
outputs, _ = self.lstm(x)
outputs, _ = nn.utils.rnn.pad_packed_sequence(outputs, batch_first=True)
return outputs
class _Decoder(nn.Module):
r"""Decoder with Attention model.
Args:
n_mels (int): number of mel bins
n_frames_per_step (int): number of frames processed per step, only 1 is supported
encoder_embedding_dim (int): the number of embedding dimensions in the encoder.
decoder_rnn_dim (int): number of units in decoder LSTM
decoder_max_step (int): maximum number of output mel spectrograms
decoder_dropout (float): dropout probability for decoder LSTM
decoder_early_stopping (bool): stop decoding when all samples are finished
attention_rnn_dim (int): number of units in attention LSTM
attention_hidden_dim (int): dimension of attention hidden representation
attention_location_n_filter (int): number of filters for attention model
attention_location_kernel_size (int): kernel size for attention model
attention_dropout (float): dropout probability for attention LSTM
prenet_dim (int): number of ReLU units in prenet layers
gate_threshold (float): probability threshold for stop token
"""
def __init__(
self,
n_mels: int,
n_frames_per_step: int,
encoder_embedding_dim: int,
decoder_rnn_dim: int,
decoder_max_step: int,
decoder_dropout: float,
decoder_early_stopping: bool,
attention_rnn_dim: int,
attention_hidden_dim: int,
attention_location_n_filter: int,
attention_location_kernel_size: int,
attention_dropout: float,
prenet_dim: int,
gate_threshold: float,
) -> None:
super().__init__()
self.n_mels = n_mels
self.n_frames_per_step = n_frames_per_step
self.encoder_embedding_dim = encoder_embedding_dim
self.attention_rnn_dim = attention_rnn_dim
self.decoder_rnn_dim = decoder_rnn_dim
self.prenet_dim = prenet_dim
self.decoder_max_step = decoder_max_step
self.gate_threshold = gate_threshold
self.attention_dropout = attention_dropout
self.decoder_dropout = decoder_dropout
self.decoder_early_stopping = decoder_early_stopping
self.prenet = _Prenet(n_mels * n_frames_per_step, [prenet_dim, prenet_dim])
self.attention_rnn = nn.LSTMCell(prenet_dim + encoder_embedding_dim, attention_rnn_dim)
self.attention_layer = _Attention(
attention_rnn_dim,
encoder_embedding_dim,
attention_hidden_dim,
attention_location_n_filter,
attention_location_kernel_size,
)
self.decoder_rnn = nn.LSTMCell(attention_rnn_dim + encoder_embedding_dim, decoder_rnn_dim, True)
self.linear_projection = _get_linear_layer(decoder_rnn_dim + encoder_embedding_dim, n_mels * n_frames_per_step)
self.gate_layer = _get_linear_layer(
decoder_rnn_dim + encoder_embedding_dim, 1, bias=True, w_init_gain="sigmoid"
)
def _get_initial_frame(self, memory: Tensor) -> Tensor:
r"""Gets all zeros frames to use as the first decoder input.
Args:
memory (Tensor): Encoder outputs with shape (n_batch, max of ``text_lengths``, ``encoder_embedding_dim``).
Returns:
decoder_input (Tensor): all zeros frames with shape
(n_batch, max of ``text_lengths``, ``n_mels * n_frames_per_step``).
"""
n_batch = memory.size(0)
dtype = memory.dtype
device = memory.device
decoder_input = torch.zeros(n_batch, self.n_mels * self.n_frames_per_step, dtype=dtype, device=device)
return decoder_input
def _initialize_decoder_states(
self, memory: Tensor
) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor]:
r"""Initializes attention rnn states, decoder rnn states, attention
weights, attention cumulative weights, attention context, stores memory
and stores processed memory.
Args:
memory (Tensor): Encoder outputs with shape (n_batch, max of ``text_lengths``, ``encoder_embedding_dim``).
Returns:
attention_hidden (Tensor): Hidden state of the attention LSTM with shape (n_batch, ``attention_rnn_dim``).
attention_cell (Tensor): Hidden state of the attention LSTM with shape (n_batch, ``attention_rnn_dim``).
decoder_hidden (Tensor): Hidden state of the decoder LSTM with shape (n_batch, ``decoder_rnn_dim``).
decoder_cell (Tensor): Hidden state of the decoder LSTM with shape (n_batch, ``decoder_rnn_dim``).
attention_weights (Tensor): Attention weights with shape (n_batch, max of ``text_lengths``).
attention_weights_cum (Tensor): Cumulated attention weights with shape (n_batch, max of ``text_lengths``).
attention_context (Tensor): Context vector with shape (n_batch, ``encoder_embedding_dim``).
processed_memory (Tensor): Processed encoder outputs
with shape (n_batch, max of ``text_lengths``, ``attention_hidden_dim``).
"""
n_batch = memory.size(0)
max_time = memory.size(1)
dtype = memory.dtype
device = memory.device
attention_hidden = torch.zeros(n_batch, self.attention_rnn_dim, dtype=dtype, device=device)
attention_cell = torch.zeros(n_batch, self.attention_rnn_dim, dtype=dtype, device=device)
decoder_hidden = torch.zeros(n_batch, self.decoder_rnn_dim, dtype=dtype, device=device)
decoder_cell = torch.zeros(n_batch, self.decoder_rnn_dim, dtype=dtype, device=device)
attention_weights = torch.zeros(n_batch, max_time, dtype=dtype, device=device)
attention_weights_cum = torch.zeros(n_batch, max_time, dtype=dtype, device=device)
attention_context = torch.zeros(n_batch, self.encoder_embedding_dim, dtype=dtype, device=device)
processed_memory = self.attention_layer.memory_layer(memory)
return (
attention_hidden,
attention_cell,
decoder_hidden,
decoder_cell,
attention_weights,
attention_weights_cum,
attention_context,
processed_memory,
)
def _parse_decoder_inputs(self, decoder_inputs: Tensor) -> Tensor:
r"""Prepares decoder inputs.
Args:
decoder_inputs (Tensor): Inputs used for teacher-forced training, i.e. mel-specs,
with shape (n_batch, ``n_mels``, max of ``mel_specgram_lengths``)
Returns:
inputs (Tensor): Processed decoder inputs with shape (max of ``mel_specgram_lengths``, n_batch, ``n_mels``).
"""
# (n_batch, n_mels, mel_specgram_lengths.max()) -> (n_batch, mel_specgram_lengths.max(), n_mels)
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,
)
# (n_batch, mel_specgram_lengths.max(), n_mels) -> (mel_specgram_lengths.max(), n_batch, n_mels)
decoder_inputs = decoder_inputs.transpose(0, 1)
return decoder_inputs
def _parse_decoder_outputs(
self, mel_specgram: Tensor, gate_outputs: Tensor, alignments: Tensor
) -> Tuple[Tensor, Tensor, Tensor]:
r"""Prepares decoder outputs for output
Args:
mel_specgram (Tensor): mel spectrogram with shape (max of ``mel_specgram_lengths``, n_batch, ``n_mels``)
gate_outputs (Tensor): predicted stop token with shape (max of ``mel_specgram_lengths``, n_batch)
alignments (Tensor): sequence of attention weights from the decoder
with shape (max of ``mel_specgram_lengths``, n_batch, max of ``text_lengths``)
Returns:
mel_specgram (Tensor): mel spectrogram with shape (n_batch, ``n_mels``, max of ``mel_specgram_lengths``)
gate_outputs (Tensor): predicted stop token with shape (n_batch, max of ``mel_specgram_lengths``)
alignments (Tensor): sequence of attention weights from the decoder
with shape (n_batch, max of ``mel_specgram_lengths``, max of ``text_lengths``)
"""
# (mel_specgram_lengths.max(), n_batch, text_lengths.max())
# -> (n_batch, mel_specgram_lengths.max(), text_lengths.max())
alignments = alignments.transpose(0, 1).contiguous()
# (mel_specgram_lengths.max(), n_batch) -> (n_batch, mel_specgram_lengths.max())
gate_outputs = gate_outputs.transpose(0, 1).contiguous()
# (mel_specgram_lengths.max(), n_batch, n_mels) -> (n_batch, mel_specgram_lengths.max(), n_mels)
mel_specgram = mel_specgram.transpose(0, 1).contiguous()
# decouple frames per step
shape = (mel_specgram.shape[0], -1, self.n_mels)
mel_specgram = mel_specgram.view(*shape)
# (n_batch, mel_specgram_lengths.max(), n_mels) -> (n_batch, n_mels, T_out)
mel_specgram = mel_specgram.transpose(1, 2)
return mel_specgram, gate_outputs, alignments
def decode(
self,
decoder_input: Tensor,
attention_hidden: Tensor,
attention_cell: Tensor,
decoder_hidden: Tensor,
decoder_cell: Tensor,
attention_weights: Tensor,
attention_weights_cum: Tensor,
attention_context: Tensor,
memory: Tensor,
processed_memory: Tensor,
mask: Tensor,
) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor]:
r"""Decoder step using stored states, attention and memory
Args:
decoder_input (Tensor): Output of the Prenet with shape (n_batch, ``prenet_dim``).
attention_hidden (Tensor): Hidden state of the attention LSTM with shape (n_batch, ``attention_rnn_dim``).
attention_cell (Tensor): Hidden state of the attention LSTM with shape (n_batch, ``attention_rnn_dim``).
decoder_hidden (Tensor): Hidden state of the decoder LSTM with shape (n_batch, ``decoder_rnn_dim``).
decoder_cell (Tensor): Hidden state of the decoder LSTM with shape (n_batch, ``decoder_rnn_dim``).
attention_weights (Tensor): Attention weights with shape (n_batch, max of ``text_lengths``).
attention_weights_cum (Tensor): Cumulated attention weights with shape (n_batch, max of ``text_lengths``).
attention_context (Tensor): Context vector with shape (n_batch, ``encoder_embedding_dim``).
memory (Tensor): Encoder output with shape (n_batch, max of ``text_lengths``, ``encoder_embedding_dim``).
processed_memory (Tensor): Processed Encoder outputs
with shape (n_batch, max of ``text_lengths``, ``attention_hidden_dim``).
mask (Tensor): Binary mask for padded data with shape (n_batch, current_num_frames).
Returns:
decoder_output: Predicted mel spectrogram for the current frame with shape (n_batch, ``n_mels``).
gate_prediction (Tensor): Prediction of the stop token with shape (n_batch, ``1``).
attention_hidden (Tensor): Hidden state of the attention LSTM with shape (n_batch, ``attention_rnn_dim``).
attention_cell (Tensor): Hidden state of the attention LSTM with shape (n_batch, ``attention_rnn_dim``).
decoder_hidden (Tensor): Hidden state of the decoder LSTM with shape (n_batch, ``decoder_rnn_dim``).
decoder_cell (Tensor): Hidden state of the decoder LSTM with shape (n_batch, ``decoder_rnn_dim``).
attention_weights (Tensor): Attention weights with shape (n_batch, max of ``text_lengths``).
attention_weights_cum (Tensor): Cumulated attention weights with shape (n_batch, max of ``text_lengths``).
attention_context (Tensor): Context vector with shape (n_batch, ``encoder_embedding_dim``).
"""
cell_input = torch.cat((decoder_input, attention_context), -1)
attention_hidden, attention_cell = self.attention_rnn(cell_input, (attention_hidden, attention_cell))
attention_hidden = F.dropout(attention_hidden, self.attention_dropout, self.training)
attention_weights_cat = torch.cat((attention_weights.unsqueeze(1), attention_weights_cum.unsqueeze(1)), dim=1)
attention_context, attention_weights = self.attention_layer(
attention_hidden, memory, processed_memory, attention_weights_cat, mask
)
attention_weights_cum += attention_weights
decoder_input = torch.cat((attention_hidden, attention_context), -1)
decoder_hidden, decoder_cell = self.decoder_rnn(decoder_input, (decoder_hidden, decoder_cell))
decoder_hidden = F.dropout(decoder_hidden, self.decoder_dropout, self.training)
decoder_hidden_attention_context = torch.cat((decoder_hidden, 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,
attention_hidden,
attention_cell,
decoder_hidden,
decoder_cell,
attention_weights,
attention_weights_cum,
attention_context,
)
def forward(
self, memory: Tensor, mel_specgram_truth: Tensor, memory_lengths: Tensor
) -> Tuple[Tensor, Tensor, Tensor]:
r"""Decoder forward pass for training.
Args:
memory (Tensor): Encoder outputs
with shape (n_batch, max of ``text_lengths``, ``encoder_embedding_dim``).
mel_specgram_truth (Tensor): Decoder ground-truth mel-specs for teacher forcing
with shape (n_batch, ``n_mels``, max of ``mel_specgram_lengths``).
memory_lengths (Tensor): Encoder output lengths for attention masking
(the same as ``text_lengths``) with shape (n_batch, ).
Returns:
mel_specgram (Tensor): Predicted mel spectrogram
with shape (n_batch, ``n_mels``, max of ``mel_specgram_lengths``).
gate_outputs (Tensor): Predicted stop token for each timestep
with shape (n_batch, max of ``mel_specgram_lengths``).
alignments (Tensor): Sequence of attention weights from the decoder
with shape (n_batch, max of ``mel_specgram_lengths``, max of ``text_lengths``).
"""
decoder_input = self._get_initial_frame(memory).unsqueeze(0)
decoder_inputs = self._parse_decoder_inputs(mel_specgram_truth)
decoder_inputs = torch.cat((decoder_input, decoder_inputs), dim=0)
decoder_inputs = self.prenet(decoder_inputs)
mask = _get_mask_from_lengths(memory_lengths)
(
attention_hidden,
attention_cell,
decoder_hidden,
decoder_cell,
attention_weights,
attention_weights_cum,
attention_context,
processed_memory,
) = self._initialize_decoder_states(memory)
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_hidden,
attention_cell,
decoder_hidden,
decoder_cell,
attention_weights,
attention_weights_cum,
attention_context,
) = self.decode(
decoder_input,
attention_hidden,
attention_cell,
decoder_hidden,
decoder_cell,
attention_weights,
attention_weights_cum,
attention_context,
memory,
processed_memory,
mask,
)
mel_outputs += [mel_output.squeeze(1)]
gate_outputs += [gate_output.squeeze(1)]
alignments += [attention_weights]
mel_specgram, gate_outputs, alignments = self._parse_decoder_outputs(
torch.stack(mel_outputs), torch.stack(gate_outputs), torch.stack(alignments)
)
return mel_specgram, gate_outputs, alignments
def _get_go_frame(self, memory: Tensor) -> Tensor:
"""Gets all zeros frames to use as the first decoder input
args:
memory (Tensor): Encoder outputs
with shape (n_batch, max of ``text_lengths``, ``encoder_embedding_dim``).
returns:
decoder_input (Tensor): All zeros frames with shape(n_batch, ``n_mels`` * ``n_frame_per_step``).
"""
n_batch = memory.size(0)
dtype = memory.dtype
device = memory.device
decoder_input = torch.zeros(n_batch, self.n_mels * self.n_frames_per_step, dtype=dtype, device=device)
return decoder_input
@torch.jit.export
def infer(self, memory: Tensor, memory_lengths: Tensor) -> Tuple[Tensor, Tensor, Tensor, Tensor]:
"""Decoder inference
Args:
memory (Tensor): Encoder outputs
with shape (n_batch, max of ``text_lengths``, ``encoder_embedding_dim``).
memory_lengths (Tensor): Encoder output lengths for attention masking
(the same as ``text_lengths``) with shape (n_batch, ).
Returns:
mel_specgram (Tensor): Predicted mel spectrogram
with shape (n_batch, ``n_mels``, max of ``mel_specgram_lengths``).
mel_specgram_lengths (Tensor): the length of the predicted mel spectrogram (n_batch, ))
gate_outputs (Tensor): Predicted stop token for each timestep
with shape (n_batch, max of ``mel_specgram_lengths``).
alignments (Tensor): Sequence of attention weights from the decoder
with shape (n_batch, max of ``mel_specgram_lengths``, max of ``text_lengths``).
"""
batch_size, device = memory.size(0), memory.device
decoder_input = self._get_go_frame(memory)
mask = _get_mask_from_lengths(memory_lengths)
(
attention_hidden,
attention_cell,
decoder_hidden,
decoder_cell,
attention_weights,
attention_weights_cum,
attention_context,
processed_memory,
) = self._initialize_decoder_states(memory)
mel_specgram_lengths = torch.zeros([batch_size], dtype=torch.int32, device=device)
finished = torch.zeros([batch_size], dtype=torch.bool, device=device)
mel_specgrams: List[Tensor] = []
gate_outputs: List[Tensor] = []
alignments: List[Tensor] = []
for _ in range(self.decoder_max_step):
decoder_input = self.prenet(decoder_input)
(
mel_specgram,
gate_output,
attention_hidden,
attention_cell,
decoder_hidden,
decoder_cell,
attention_weights,
attention_weights_cum,
attention_context,
) = self.decode(
decoder_input,
attention_hidden,
attention_cell,
decoder_hidden,
decoder_cell,
attention_weights,
attention_weights_cum,
attention_context,
memory,
processed_memory,
mask,
)
mel_specgrams.append(mel_specgram.unsqueeze(0))
gate_outputs.append(gate_output.transpose(0, 1))
alignments.append(attention_weights)
mel_specgram_lengths[~finished] += 1
finished |= torch.sigmoid(gate_output.squeeze(1)) > self.gate_threshold
if self.decoder_early_stopping and torch.all(finished):
break
decoder_input = mel_specgram
if len(mel_specgrams) == self.decoder_max_step:
warnings.warn(
"Reached max decoder steps. The generated spectrogram might not cover " "the whole transcript."
)
mel_specgrams = torch.cat(mel_specgrams, dim=0)
gate_outputs = torch.cat(gate_outputs, dim=0)
alignments = torch.cat(alignments, dim=0)
mel_specgrams, gate_outputs, alignments = self._parse_decoder_outputs(mel_specgrams, gate_outputs, alignments)
return mel_specgrams, mel_specgram_lengths, gate_outputs, alignments
class Tacotron2(nn.Module):
r"""Tacotron2 model from *Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions*
:cite:`shen2018natural` based on the implementation from
`Nvidia Deep Learning Examples <https://github.com/NVIDIA/DeepLearningExamples/>`_.
See Also:
* :class:`torchaudio.pipelines.Tacotron2TTSBundle`: TTS pipeline with pretrained model.
Args:
mask_padding (bool, optional): Use mask padding (Default: ``False``).
n_mels (int, optional): Number of mel bins (Default: ``80``).
n_symbol (int, optional): Number of symbols for the input text (Default: ``148``).
n_frames_per_step (int, optional): Number of frames processed per step, only 1 is supported (Default: ``1``).
symbol_embedding_dim (int, optional): Input embedding dimension (Default: ``512``).
encoder_n_convolution (int, optional): Number of encoder convolutions (Default: ``3``).
encoder_kernel_size (int, optional): Encoder kernel size (Default: ``5``).
encoder_embedding_dim (int, optional): Encoder embedding dimension (Default: ``512``).
decoder_rnn_dim (int, optional): Number of units in decoder LSTM (Default: ``1024``).
decoder_max_step (int, optional): Maximum number of output mel spectrograms (Default: ``2000``).
decoder_dropout (float, optional): Dropout probability for decoder LSTM (Default: ``0.1``).
decoder_early_stopping (bool, optional): Continue decoding after all samples are finished (Default: ``True``).
attention_rnn_dim (int, optional): Number of units in attention LSTM (Default: ``1024``).
attention_hidden_dim (int, optional): Dimension of attention hidden representation (Default: ``128``).
attention_location_n_filter (int, optional): Number of filters for attention model (Default: ``32``).
attention_location_kernel_size (int, optional): Kernel size for attention model (Default: ``31``).
attention_dropout (float, optional): Dropout probability for attention LSTM (Default: ``0.1``).
prenet_dim (int, optional): Number of ReLU units in prenet layers (Default: ``256``).
postnet_n_convolution (int, optional): Number of postnet convolutions (Default: ``5``).
postnet_kernel_size (int, optional): Postnet kernel size (Default: ``5``).
postnet_embedding_dim (int, optional): Postnet embedding dimension (Default: ``512``).
gate_threshold (float, optional): Probability threshold for stop token (Default: ``0.5``).
"""
def __init__(
self,
mask_padding: bool = False,
n_mels: int = 80,
n_symbol: int = 148,
n_frames_per_step: int = 1,
symbol_embedding_dim: int = 512,
encoder_embedding_dim: int = 512,
encoder_n_convolution: int = 3,
encoder_kernel_size: int = 5,
decoder_rnn_dim: int = 1024,
decoder_max_step: int = 2000,
decoder_dropout: float = 0.1,
decoder_early_stopping: bool = True,
attention_rnn_dim: int = 1024,
attention_hidden_dim: int = 128,
attention_location_n_filter: int = 32,
attention_location_kernel_size: int = 31,
attention_dropout: float = 0.1,
prenet_dim: int = 256,
postnet_n_convolution: int = 5,
postnet_kernel_size: int = 5,
postnet_embedding_dim: int = 512,
gate_threshold: float = 0.5,
) -> None:
super().__init__()
self.mask_padding = mask_padding
self.n_mels = n_mels
self.n_frames_per_step = n_frames_per_step
self.embedding = nn.Embedding(n_symbol, symbol_embedding_dim)
torch.nn.init.xavier_uniform_(self.embedding.weight)
self.encoder = _Encoder(encoder_embedding_dim, encoder_n_convolution, encoder_kernel_size)
self.decoder = _Decoder(
n_mels,
n_frames_per_step,
encoder_embedding_dim,
decoder_rnn_dim,
decoder_max_step,
decoder_dropout,
decoder_early_stopping,
attention_rnn_dim,
attention_hidden_dim,
attention_location_n_filter,
attention_location_kernel_size,
attention_dropout,
prenet_dim,
gate_threshold,
)
self.postnet = _Postnet(n_mels, postnet_embedding_dim, postnet_kernel_size, postnet_n_convolution)
def forward(
self,
tokens: Tensor,
token_lengths: Tensor,
mel_specgram: Tensor,
mel_specgram_lengths: Tensor,
) -> Tuple[Tensor, Tensor, Tensor, Tensor]:
r"""Pass the input through the Tacotron2 model. This is in teacher
forcing mode, which is generally used for training.
The input ``tokens`` should be padded with zeros to length max of ``token_lengths``.
The input ``mel_specgram`` should be padded with zeros to length max of ``mel_specgram_lengths``.
Args:
tokens (Tensor): The input tokens to Tacotron2 with shape `(n_batch, max of token_lengths)`.
token_lengths (Tensor): The valid length of each sample in ``tokens`` with shape `(n_batch, )`.
mel_specgram (Tensor): The target mel spectrogram
with shape `(n_batch, n_mels, max of mel_specgram_lengths)`.
mel_specgram_lengths (Tensor): The length of each mel spectrogram with shape `(n_batch, )`.
Returns:
[Tensor, Tensor, Tensor, Tensor]:
Tensor
Mel spectrogram before Postnet with shape `(n_batch, n_mels, max of mel_specgram_lengths)`.
Tensor
Mel spectrogram after Postnet with shape `(n_batch, n_mels, max of mel_specgram_lengths)`.
Tensor
The output for stop token at each time step with shape `(n_batch, max of mel_specgram_lengths)`.
Tensor
Sequence of attention weights from the decoder with
shape `(n_batch, max of mel_specgram_lengths, max of token_lengths)`.
"""
embedded_inputs = self.embedding(tokens).transpose(1, 2)
encoder_outputs = self.encoder(embedded_inputs, token_lengths)
mel_specgram, gate_outputs, alignments = self.decoder(
encoder_outputs, mel_specgram, memory_lengths=token_lengths
)
mel_specgram_postnet = self.postnet(mel_specgram)
mel_specgram_postnet = mel_specgram + mel_specgram_postnet
if self.mask_padding:
mask = _get_mask_from_lengths(mel_specgram_lengths)
mask = mask.expand(self.n_mels, mask.size(0), mask.size(1))
mask = mask.permute(1, 0, 2)
mel_specgram.masked_fill_(mask, 0.0)
mel_specgram_postnet.masked_fill_(mask, 0.0)
gate_outputs.masked_fill_(mask[:, 0, :], 1e3)
return mel_specgram, mel_specgram_postnet, gate_outputs, alignments
@torch.jit.export
def infer(self, tokens: Tensor, lengths: Optional[Tensor] = None) -> Tuple[Tensor, Tensor, Tensor]:
r"""Using Tacotron2 for inference. The input is a batch of encoded
sentences (``tokens``) and its corresponding lengths (``lengths``). The
output is the generated mel spectrograms, its corresponding lengths, and
the attention weights from the decoder.
The input `tokens` should be padded with zeros to length max of ``lengths``.
Args:
tokens (Tensor): The input tokens to Tacotron2 with shape `(n_batch, max of lengths)`.
lengths (Tensor or None, optional):
The valid length of each sample in ``tokens`` with shape `(n_batch, )`.
If ``None``, it is assumed that the all the tokens are valid. Default: ``None``
Returns:
(Tensor, Tensor, Tensor):
Tensor
The predicted mel spectrogram with shape `(n_batch, n_mels, max of mel_specgram_lengths)`.
Tensor
The length of the predicted mel spectrogram with shape `(n_batch, )`.
Tensor
Sequence of attention weights from the decoder with shape
`(n_batch, max of mel_specgram_lengths, max of lengths)`.
"""
n_batch, max_length = tokens.shape
if lengths is None:
lengths = torch.tensor([max_length]).expand(n_batch).to(tokens.device, tokens.dtype)
assert lengths is not None # For TorchScript compiler
embedded_inputs = self.embedding(tokens).transpose(1, 2)
encoder_outputs = self.encoder(embedded_inputs, lengths)
mel_specgram, mel_specgram_lengths, _, alignments = self.decoder.infer(encoder_outputs, lengths)
mel_outputs_postnet = self.postnet(mel_specgram)
mel_outputs_postnet = mel_specgram + mel_outputs_postnet
alignments = alignments.unfold(1, n_batch, n_batch).transpose(0, 2)
return mel_outputs_postnet, mel_specgram_lengths, alignments
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