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 layers import ConvNorm, LinearNorm from models.tacotron2.hparams import create_hparams from trainer.networks import register_model from taco_utils import to_gpu, get_mask_from_lengths from utils.util import opt_get 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 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, 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_batch(self, batch): text_padded, input_lengths, mel_padded, gate_padded, \ output_lengths = 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() return ( (text_padded, input_lengths, mel_padded, max_len, output_lengths), (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): text_inputs, text_lengths, mels, max_len, output_lengths = inputs 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): kw = opt_get(opt_net, ['kwargs'], {}) hparams = create_hparams() hparams.update(kw) 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)