import torch from torch import nn from torch.distributions.multivariate_normal import MultivariateNormal as MVN from torch.nn import functional as F class CapacitronVAE(nn.Module): """Effective Use of Variational Embedding Capacity for prosody transfer. See https://arxiv.org/abs/1906.03402""" def __init__( self, num_mel, capacitron_VAE_embedding_dim, encoder_output_dim=256, reference_encoder_out_dim=128, speaker_embedding_dim=None, text_summary_embedding_dim=None, ): super().__init__() # Init distributions self.prior_distribution = MVN( torch.zeros(capacitron_VAE_embedding_dim), torch.eye(capacitron_VAE_embedding_dim) ) self.approximate_posterior_distribution = None # define output ReferenceEncoder dim to the capacitron_VAE_embedding_dim self.encoder = ReferenceEncoder(num_mel, out_dim=reference_encoder_out_dim) # Init beta, the lagrange-like term for the KL distribution self.beta = torch.nn.Parameter(torch.log(torch.exp(torch.Tensor([1.0])) - 1), requires_grad=True) mlp_input_dimension = reference_encoder_out_dim if text_summary_embedding_dim is not None: self.text_summary_net = TextSummary(text_summary_embedding_dim, encoder_output_dim=encoder_output_dim) mlp_input_dimension += text_summary_embedding_dim if speaker_embedding_dim is not None: # TODO: Test a multispeaker model! mlp_input_dimension += speaker_embedding_dim self.post_encoder_mlp = PostEncoderMLP(mlp_input_dimension, capacitron_VAE_embedding_dim) def forward(self, reference_mel_info=None, text_info=None, speaker_embedding=None): # Use reference if reference_mel_info is not None: reference_mels = reference_mel_info[0] # [batch_size, num_frames, num_mels] mel_lengths = reference_mel_info[1] # [batch_size] enc_out = self.encoder(reference_mels, mel_lengths) # concat speaker_embedding and/or text summary embedding if text_info is not None: text_inputs = text_info[0] # [batch_size, num_characters, num_embedding] input_lengths = text_info[1] text_summary_out = self.text_summary_net(text_inputs, input_lengths).to(reference_mels.device) enc_out = torch.cat([enc_out, text_summary_out], dim=-1) if speaker_embedding is not None: speaker_embedding = torch.squeeze(speaker_embedding) enc_out = torch.cat([enc_out, speaker_embedding], dim=-1) # Feed the output of the ref encoder and information about text/speaker into # an MLP to produce the parameteres for the approximate poterior distributions mu, sigma = self.post_encoder_mlp(enc_out) # convert to cpu because prior_distribution was created on cpu mu = mu.cpu() sigma = sigma.cpu() # Sample from the posterior: z ~ q(z|x) self.approximate_posterior_distribution = MVN(mu, torch.diag_embed(sigma)) VAE_embedding = self.approximate_posterior_distribution.rsample() # Infer from the model, bypasses encoding else: # Sample from the prior: z ~ p(z) VAE_embedding = self.prior_distribution.sample().unsqueeze(0) # reshape to [batch_size, 1, capacitron_VAE_embedding_dim] return VAE_embedding.unsqueeze(1), self.approximate_posterior_distribution, self.prior_distribution, self.beta class ReferenceEncoder(nn.Module): """NN module creating a fixed size prosody embedding from a spectrogram. inputs: mel spectrograms [batch_size, num_spec_frames, num_mel] outputs: [batch_size, embedding_dim] """ def __init__(self, num_mel, out_dim): super().__init__() self.num_mel = num_mel filters = [1] + [32, 32, 64, 64, 128, 128] num_layers = len(filters) - 1 convs = [ nn.Conv2d( in_channels=filters[i], out_channels=filters[i + 1], kernel_size=(3, 3), stride=(2, 2), padding=(2, 2) ) for i in range(num_layers) ] self.convs = nn.ModuleList(convs) self.training = False self.bns = nn.ModuleList([nn.BatchNorm2d(num_features=filter_size) for filter_size in filters[1:]]) post_conv_height = self.calculate_post_conv_height(num_mel, 3, 2, 2, num_layers) self.recurrence = nn.LSTM( input_size=filters[-1] * post_conv_height, hidden_size=out_dim, batch_first=True, bidirectional=False ) def forward(self, inputs, input_lengths): batch_size = inputs.size(0) x = inputs.view(batch_size, 1, -1, self.num_mel) # [batch_size, num_channels==1, num_frames, num_mel] valid_lengths = input_lengths.float() # [batch_size] for conv, bn in zip(self.convs, self.bns): x = conv(x) x = bn(x) x = F.relu(x) # Create the post conv width mask based on the valid lengths of the output of the convolution. # The valid lengths for the output of a convolution on varying length inputs is # ceil(input_length/stride) + 1 for stride=3 and padding=2 # For example (kernel_size=3, stride=2, padding=2): # 0 0 x x x x x 0 0 -> Input = 5, 0 is zero padding, x is valid values coming from padding=2 in conv2d # _____ # x _____ # x _____ # x ____ # x # x x x x -> Output valid length = 4 # Since every example in te batch is zero padded and therefore have separate valid_lengths, # we need to mask off all the values AFTER the valid length for each example in the batch. # Otherwise, the convolutions create noise and a lot of not real information valid_lengths = (valid_lengths / 2).float() valid_lengths = torch.ceil(valid_lengths).to(dtype=torch.int64) + 1 # 2 is stride -- size: [batch_size] post_conv_max_width = x.size(2) mask = torch.arange(post_conv_max_width).to(inputs.device).expand( len(valid_lengths), post_conv_max_width ) < valid_lengths.unsqueeze(1) mask = mask.expand(1, 1, -1, -1).transpose(2, 0).transpose(-1, 2) # [batch_size, 1, post_conv_max_width, 1] x = x * mask x = x.transpose(1, 2) # x: 4D tensor [batch_size, post_conv_width, # num_channels==128, post_conv_height] post_conv_width = x.size(1) x = x.contiguous().view(batch_size, post_conv_width, -1) # x: 3D tensor [batch_size, post_conv_width, # num_channels*post_conv_height] # Routine for fetching the last valid output of a dynamic LSTM with varying input lengths and padding post_conv_input_lengths = valid_lengths packed_seqs = nn.utils.rnn.pack_padded_sequence( x, post_conv_input_lengths.tolist(), batch_first=True, enforce_sorted=False ) # dynamic rnn sequence padding self.recurrence.flatten_parameters() _, (ht, _) = self.recurrence(packed_seqs) last_output = ht[-1] return last_output.to(inputs.device) # [B, 128] @staticmethod def calculate_post_conv_height(height, kernel_size, stride, pad, n_convs): """Height of spec after n convolutions with fixed kernel/stride/pad.""" for _ in range(n_convs): height = (height - kernel_size + 2 * pad) // stride + 1 return height class TextSummary(nn.Module): def __init__(self, embedding_dim, encoder_output_dim): super().__init__() self.lstm = nn.LSTM( encoder_output_dim, # text embedding dimension from the text encoder embedding_dim, # fixed length output summary the lstm creates from the input batch_first=True, bidirectional=False, ) def forward(self, inputs, input_lengths): # Routine for fetching the last valid output of a dynamic LSTM with varying input lengths and padding packed_seqs = nn.utils.rnn.pack_padded_sequence( inputs, input_lengths.tolist(), batch_first=True, enforce_sorted=False ) # dynamic rnn sequence padding self.lstm.flatten_parameters() _, (ht, _) = self.lstm(packed_seqs) last_output = ht[-1] return last_output class PostEncoderMLP(nn.Module): def __init__(self, input_size, hidden_size): super().__init__() self.hidden_size = hidden_size modules = [ nn.Linear(input_size, hidden_size), # Hidden Layer nn.Tanh(), nn.Linear(hidden_size, hidden_size * 2), ] # Output layer twice the size for mean and variance self.net = nn.Sequential(*modules) self.softplus = nn.Softplus() def forward(self, _input): mlp_output = self.net(_input) # The mean parameter is unconstrained mu = mlp_output[:, : self.hidden_size] # The standard deviation must be positive. Parameterise with a softplus sigma = self.softplus(mlp_output[:, self.hidden_size :]) return mu, sigma