import math import random import torch from model import monotonic_align from model.base import BaseModule from model.text_encoder import TextEncoder from model.diffusion import Diffusion from model.utils import sequence_mask, generate_path, duration_loss, fix_len_compatibility class GradTTSWithEmo(BaseModule): def __init__(self, n_vocab=148, n_spks=1,n_emos=5, spk_emb_dim=64, n_enc_channels=192, filter_channels=768, filter_channels_dp=256, n_heads=2, n_enc_layers=6, enc_kernel=3, enc_dropout=0.1, window_size=4, n_feats=80, dec_dim=64, beta_min=0.05, beta_max=20.0, pe_scale=1000, use_classifier_free=False, dummy_spk_rate=0.5, **kwargs): super(GradTTSWithEmo, self).__init__() self.n_vocab = n_vocab self.n_spks = n_spks self.n_emos = n_emos self.spk_emb_dim = spk_emb_dim self.n_enc_channels = n_enc_channels self.filter_channels = filter_channels self.filter_channels_dp = filter_channels_dp self.n_heads = n_heads self.n_enc_layers = n_enc_layers self.enc_kernel = enc_kernel self.enc_dropout = enc_dropout self.window_size = window_size self.n_feats = n_feats self.dec_dim = dec_dim self.beta_min = beta_min self.beta_max = beta_max self.pe_scale = pe_scale self.use_classifier_free = use_classifier_free # if n_spks > 1: self.spk_emb = torch.nn.Embedding(n_spks, spk_emb_dim) self.emo_emb = torch.nn.Embedding(n_emos, spk_emb_dim) self.merge_spk_emo = torch.nn.Sequential( torch.nn.Linear(spk_emb_dim*2, spk_emb_dim), torch.nn.ReLU(), torch.nn.Linear(spk_emb_dim, spk_emb_dim) ) self.encoder = TextEncoder(n_vocab, n_feats, n_enc_channels, filter_channels, filter_channels_dp, n_heads, n_enc_layers, enc_kernel, enc_dropout, window_size, spk_emb_dim=spk_emb_dim, n_spks=n_spks) self.decoder = Diffusion(n_feats, dec_dim, spk_emb_dim, beta_min, beta_max, pe_scale) if self.use_classifier_free: self.dummy_xv = torch.nn.Parameter(torch.randn(size=(spk_emb_dim, ))) self.dummy_rate = dummy_spk_rate print(f"Using classifier free with rate {self.dummy_rate}") @torch.no_grad() def forward(self, x, x_lengths, n_timesteps, temperature=1.0, stoc=False, spk=None, emo=None, length_scale=1.0, classifier_free_guidance=1., force_dur=None): """ Generates mel-spectrogram from text. Returns: 1. encoder outputs 2. decoder outputs 3. generated alignment Args: x (torch.Tensor): batch of texts, converted to a tensor with phoneme embedding ids. x_lengths (torch.Tensor): lengths of texts in batch. n_timesteps (int): number of steps to use for reverse diffusion in decoder. temperature (float, optional): controls variance of terminal distribution. stoc (bool, optional): flag that adds stochastic term to the decoder sampler. Usually, does not provide synthesis improvements. length_scale (float, optional): controls speech pace. Increase value to slow down generated speech and vice versa. """ x, x_lengths = self.relocate_input([x, x_lengths]) # Get speaker embedding spk = self.spk_emb(spk) emo = self.emo_emb(emo) if self.use_classifier_free: emo = emo / torch.sqrt(torch.sum(emo**2, dim=1, keepdim=True)) # unit norm spk_merged = self.merge_spk_emo(torch.cat([spk, emo], dim=-1)) # Get encoder_outputs `mu_x` and log-scaled token durations `logw` mu_x, logw, x_mask = self.encoder(x, x_lengths, spk_merged) w = torch.exp(logw) * x_mask w_ceil = torch.ceil(w) * length_scale if force_dur is not None: w_ceil = force_dur.unsqueeze(1) # [1, 1, Ltext] y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long() y_max_length = int(y_lengths.max()) y_max_length_ = fix_len_compatibility(y_max_length) # Using obtained durations `w` construct alignment map `attn` y_mask = sequence_mask(y_lengths, y_max_length_).unsqueeze(1).to(x_mask.dtype) attn_mask = x_mask.unsqueeze(-1) * y_mask.unsqueeze(2) attn = generate_path(w_ceil.squeeze(1), attn_mask.squeeze(1)).unsqueeze(1) # Align encoded text and get mu_y mu_y = torch.matmul(attn.squeeze(1).transpose(1, 2), mu_x.transpose(1, 2)) mu_y = mu_y.transpose(1, 2) encoder_outputs = mu_y[:, :, :y_max_length] # Sample latent representation from terminal distribution N(mu_y, I) z = mu_y + torch.randn_like(mu_y, device=mu_y.device) / temperature # print(z) # Generate sample by performing reverse dynamics unit_dummy_emo = self.dummy_xv / torch.sqrt(torch.sum(self.dummy_xv**2)) if self.use_classifier_free else None dummy_spk = self.merge_spk_emo(torch.cat([spk, unit_dummy_emo.unsqueeze(0).repeat(len(spk), 1)], dim=-1)) if self.use_classifier_free else None decoder_outputs = self.decoder(z, y_mask, mu_y, n_timesteps, stoc, spk_merged, use_classifier_free=self.use_classifier_free, classifier_free_guidance=classifier_free_guidance, dummy_spk=dummy_spk) decoder_outputs = decoder_outputs[:, :, :y_max_length] return encoder_outputs, decoder_outputs, attn[:, :, :y_max_length] def classifier_guidance_decode(self, x, x_lengths, n_timesteps, temperature=1.0, stoc=False, spk=None, emo=None, length_scale=1.0, classifier_func=None, guidance=1.0, classifier_type='conformer'): x, x_lengths = self.relocate_input([x, x_lengths]) # Get speaker embedding spk = self.spk_emb(spk) dummy_emo = self.emo_emb(torch.zeros_like(emo).long()) # this is for feeding the text encoder. spk_merged = self.merge_spk_emo(torch.cat([spk, dummy_emo], dim=-1)) # Get encoder_outputs `mu_x` and log-scaled token durations `logw` mu_x, logw, x_mask = self.encoder(x, x_lengths, spk_merged) w = torch.exp(logw) * x_mask # print("w shape is ", w.shape) w_ceil = torch.ceil(w) * length_scale y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long() y_max_length = int(y_lengths.max()) if classifier_type == 'CNN' or classifier_type == 'CNN-with-time' : y_max_length = max(y_max_length, 180) # NOTE: added for CNN classifier y_max_length_ = fix_len_compatibility(y_max_length) # Using obtained durations `w` construct alignment map `attn` y_mask = sequence_mask(y_lengths, y_max_length_).unsqueeze(1).to(x_mask.dtype) attn_mask = x_mask.unsqueeze(-1) * y_mask.unsqueeze(2) attn = generate_path(w_ceil.squeeze(1), attn_mask.squeeze(1)).unsqueeze(1) # Align encoded text and get mu_y mu_y = torch.matmul(attn.squeeze(1).transpose(1, 2), mu_x.transpose(1, 2)) mu_y = mu_y.transpose(1, 2) encoder_outputs = mu_y[:, :, :y_max_length] # Sample latent representation from terminal distribution N(mu_y, I) z = mu_y + torch.randn_like(mu_y, device=mu_y.device) / temperature # Generate sample by performing reverse dynamics decoder_outputs = self.decoder.classifier_decode(z, y_mask, mu_y, n_timesteps, stoc, spk_merged, classifier_func, guidance, control_emo=emo, classifier_type=classifier_type) decoder_outputs = decoder_outputs[:, :, :y_max_length] return encoder_outputs, decoder_outputs, attn[:, :, :y_max_length] def classifier_guidance_decode_DPS(self, x, x_lengths, n_timesteps, temperature=1.0, stoc=False, spk=None, emo=None, length_scale=1.0, classifier_func=None, guidance=1.0, classifier_type='conformer'): x, x_lengths = self.relocate_input([x, x_lengths]) # Get speaker embedding spk = self.spk_emb(spk) dummy_emo = self.emo_emb(torch.zeros_like(emo).long()) # this is for feeding the text encoder. spk_merged = self.merge_spk_emo(torch.cat([spk, dummy_emo], dim=-1)) # Get encoder_outputs `mu_x` and log-scaled token durations `logw` mu_x, logw, x_mask = self.encoder(x, x_lengths, spk_merged) w = torch.exp(logw) * x_mask w_ceil = torch.ceil(w) * length_scale y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long() y_max_length = int(y_lengths.max()) if classifier_type == 'CNN' or classifier_type == 'CNN-with-time' : y_max_length = max(y_max_length, 180) # NOTE: added for CNN classifier y_max_length_ = fix_len_compatibility(y_max_length) # Using obtained durations `w` construct alignment map `attn` y_mask = sequence_mask(y_lengths, y_max_length_).unsqueeze(1).to(x_mask.dtype) attn_mask = x_mask.unsqueeze(-1) * y_mask.unsqueeze(2) attn = generate_path(w_ceil.squeeze(1), attn_mask.squeeze(1)).unsqueeze(1) # Align encoded text and get mu_y mu_y = torch.matmul(attn.squeeze(1).transpose(1, 2), mu_x.transpose(1, 2)) mu_y = mu_y.transpose(1, 2) encoder_outputs = mu_y[:, :, :y_max_length] # Sample latent representation from terminal distribution N(mu_y, I) z = mu_y + torch.randn_like(mu_y, device=mu_y.device) / temperature # Generate sample by performing reverse dynamics decoder_outputs = self.decoder.classifier_decode_DPS(z, y_mask, mu_y, n_timesteps, stoc, spk_merged, classifier_func, guidance, control_emo=emo, classifier_type=classifier_type) decoder_outputs = decoder_outputs[:, :, :y_max_length] return encoder_outputs, decoder_outputs, attn[:, :, :y_max_length] def classifier_guidance_decode_two_mixture(self, x, x_lengths, n_timesteps, temperature=1.0, stoc=False, spk=None, emo1=None, emo2=None, emo1_weight=None, length_scale=1.0, classifier_func=None, guidance=1.0, classifier_type='conformer'): x, x_lengths = self.relocate_input([x, x_lengths]) # Get speaker embedding spk = self.spk_emb(spk) dummy_emo = self.emo_emb(torch.zeros_like(emo1).long()) # this is for feeding the text encoder. spk_merged = self.merge_spk_emo(torch.cat([spk, dummy_emo], dim=-1)) # Get encoder_outputs `mu_x` and log-scaled token durations `logw` mu_x, logw, x_mask = self.encoder(x, x_lengths, spk_merged) w = torch.exp(logw) * x_mask w_ceil = torch.ceil(w) * length_scale y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long() y_max_length = int(y_lengths.max()) if classifier_type == 'CNN' or classifier_type == 'CNN-with-time' : y_max_length = max(y_max_length, 180) # NOTE: added for CNN classifier y_max_length_ = fix_len_compatibility(y_max_length) # Using obtained durations `w` construct alignment map `attn` y_mask = sequence_mask(y_lengths, y_max_length_).unsqueeze(1).to(x_mask.dtype) attn_mask = x_mask.unsqueeze(-1) * y_mask.unsqueeze(2) attn = generate_path(w_ceil.squeeze(1), attn_mask.squeeze(1)).unsqueeze(1) # Align encoded text and get mu_y mu_y = torch.matmul(attn.squeeze(1).transpose(1, 2), mu_x.transpose(1, 2)) mu_y = mu_y.transpose(1, 2) encoder_outputs = mu_y[:, :, :y_max_length] # Sample latent representation from terminal distribution N(mu_y, I) z = mu_y + torch.randn_like(mu_y, device=mu_y.device) / temperature # Generate sample by performing reverse dynamics decoder_outputs = self.decoder.classifier_decode_mixture(z, y_mask, mu_y, n_timesteps, stoc, spk_merged, classifier_func, guidance, control_emo1=emo1, control_emo2=emo2, emo1_weight=emo1_weight, classifier_type=classifier_type) decoder_outputs = decoder_outputs[:, :, :y_max_length] return encoder_outputs, decoder_outputs, attn[:, :, :y_max_length] def classifier_guidance_decode_two_mixture_DPS(self, x, x_lengths, n_timesteps, temperature=1.0, stoc=False, spk=None, emo1=None, emo2=None, emo1_weight=None, length_scale=1.0, classifier_func=None, guidance=1.0, classifier_type='conformer'): x, x_lengths = self.relocate_input([x, x_lengths]) # Get speaker embedding spk = self.spk_emb(spk) dummy_emo = self.emo_emb(torch.zeros_like(emo1).long()) # this is for feeding the text encoder. spk_merged = self.merge_spk_emo(torch.cat([spk, dummy_emo], dim=-1)) # Get encoder_outputs `mu_x` and log-scaled token durations `logw` mu_x, logw, x_mask = self.encoder(x, x_lengths, spk_merged) w = torch.exp(logw) * x_mask w_ceil = torch.ceil(w) * length_scale y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long() y_max_length = int(y_lengths.max()) if classifier_type == 'CNN' or classifier_type == 'CNN-with-time' : y_max_length = max(y_max_length, 180) # NOTE: added for CNN classifier y_max_length_ = fix_len_compatibility(y_max_length) # Using obtained durations `w` construct alignment map `attn` y_mask = sequence_mask(y_lengths, y_max_length_).unsqueeze(1).to(x_mask.dtype) attn_mask = x_mask.unsqueeze(-1) * y_mask.unsqueeze(2) attn = generate_path(w_ceil.squeeze(1), attn_mask.squeeze(1)).unsqueeze(1) # Align encoded text and get mu_y mu_y = torch.matmul(attn.squeeze(1).transpose(1, 2), mu_x.transpose(1, 2)) mu_y = mu_y.transpose(1, 2) encoder_outputs = mu_y[:, :, :y_max_length] # Sample latent representation from terminal distribution N(mu_y, I) z = mu_y + torch.randn_like(mu_y, device=mu_y.device) / temperature # Generate sample by performing reverse dynamics decoder_outputs = self.decoder.classifier_decode_mixture_DPS(z, y_mask, mu_y, n_timesteps, stoc, spk_merged, classifier_func, guidance, control_emo1=emo1, control_emo2=emo2, emo1_weight=emo1_weight, classifier_type=classifier_type) decoder_outputs = decoder_outputs[:, :, :y_max_length] return encoder_outputs, decoder_outputs, attn[:, :, :y_max_length] def compute_loss(self, x, x_lengths, y, y_lengths, spk=None, emo=None, out_size=None, use_gt_dur=False, durs=None): """ Computes 3 losses: 1. duration loss: loss between predicted token durations and those extracted by Monotinic Alignment Search (MAS). 2. prior loss: loss between mel-spectrogram and encoder outputs. 3. diffusion loss: loss between gaussian noise and its reconstruction by diffusion-based decoder. Args: x (torch.Tensor): batch of texts, converted to a tensor with phoneme embedding ids. x_lengths (torch.Tensor): lengths of texts in batch. y (torch.Tensor): batch of corresponding mel-spectrograms. y_lengths (torch.Tensor): lengths of mel-spectrograms in batch. out_size (int, optional): length (in mel's sampling rate) of segment to cut, on which decoder will be trained. Should be divisible by 2^{num of UNet downsamplings}. Needed to increase batch size. use_gt_dur: bool durs: gt duration """ x, x_lengths, y, y_lengths = self.relocate_input([x, x_lengths, y, y_lengths]) # y: B, 80, L spk = self.spk_emb(spk) emo = self.emo_emb(emo) # [B, D] if self.use_classifier_free: emo = emo / torch.sqrt(torch.sum(emo ** 2, dim=1, keepdim=True)) # unit norm use_dummy_per_sample = torch.distributions.Binomial(1, torch.tensor( [self.dummy_rate] * len(emo))).sample().bool() # [b, ] True/False where True accords to rate emo[use_dummy_per_sample] = (self.dummy_xv / torch.sqrt( torch.sum(self.dummy_xv ** 2))) # substitute with dummy xv(unit norm too) spk = self.merge_spk_emo(torch.cat([spk, emo], dim=-1)) # [B, D] # Get encoder_outputs `mu_x` and log-scaled token durations `logw` mu_x, logw, x_mask = self.encoder(x, x_lengths, spk) y_max_length = y.shape[-1] y_mask = sequence_mask(y_lengths, y_max_length).unsqueeze(1).to(x_mask) attn_mask = x_mask.unsqueeze(-1) * y_mask.unsqueeze(2) # Use MAS to find most likely alignment `attn` between text and mel-spectrogram if use_gt_dur: attn = generate_path(durs, attn_mask.squeeze(1)).detach() else: with torch.no_grad(): const = -0.5 * math.log(2 * math.pi) * self.n_feats factor = -0.5 * torch.ones(mu_x.shape, dtype=mu_x.dtype, device=mu_x.device) y_square = torch.matmul(factor.transpose(1, 2), y ** 2) y_mu_double = torch.matmul(2.0 * (factor * mu_x).transpose(1, 2), y) mu_square = torch.sum(factor * (mu_x ** 2), 1).unsqueeze(-1) log_prior = y_square - y_mu_double + mu_square + const # it's actually the log likelihood of y given the Gaussian with (mu_x, I) attn = monotonic_align.maximum_path(log_prior, attn_mask.squeeze(1)) attn = attn.detach() # Compute loss between predicted log-scaled durations and those obtained from MAS logw_ = torch.log(1e-8 + torch.sum(attn.unsqueeze(1), -1)) * x_mask dur_loss = duration_loss(logw, logw_, x_lengths) # print(attn.shape) # Cut a small segment of mel-spectrogram in order to increase batch size if not isinstance(out_size, type(None)): clip_size = min(out_size, y_max_length) # when out_size > max length, do not actually perform clipping clip_size = -fix_len_compatibility(-clip_size) # this is to ensure dividable max_offset = (y_lengths - clip_size).clamp(0) offset_ranges = list(zip([0] * max_offset.shape[0], max_offset.cpu().numpy())) out_offset = torch.LongTensor([ torch.tensor(random.choice(range(start, end)) if end > start else 0) for start, end in offset_ranges ]).to(y_lengths) attn_cut = torch.zeros(attn.shape[0], attn.shape[1], clip_size, dtype=attn.dtype, device=attn.device) y_cut = torch.zeros(y.shape[0], self.n_feats, clip_size, dtype=y.dtype, device=y.device) y_cut_lengths = [] for i, (y_, out_offset_) in enumerate(zip(y, out_offset)): y_cut_length = clip_size + (y_lengths[i] - clip_size).clamp(None, 0) y_cut_lengths.append(y_cut_length) cut_lower, cut_upper = out_offset_, out_offset_ + y_cut_length y_cut[i, :, :y_cut_length] = y_[:, cut_lower:cut_upper] attn_cut[i, :, :y_cut_length] = attn[i, :, cut_lower:cut_upper] y_cut_lengths = torch.LongTensor(y_cut_lengths) y_cut_mask = sequence_mask(y_cut_lengths).unsqueeze(1).to(y_mask) attn = attn_cut # attn -> [B, text_length, cut_length]. It does not begin from top left corner y = y_cut y_mask = y_cut_mask # Align encoded text with mel-spectrogram and get mu_y segment mu_y = torch.matmul(attn.squeeze(1).transpose(1, 2), mu_x.transpose(1, 2)) # here mu_x is not cut. mu_y = mu_y.transpose(1, 2) # B, 80, cut_length # Compute loss of score-based decoder # print(y.shape, y_mask.shape, mu_y.shape) diff_loss, xt = self.decoder.compute_loss(y, y_mask, mu_y, spk) # Compute loss between aligned encoder outputs and mel-spectrogram prior_loss = torch.sum(0.5 * ((y - mu_y) ** 2 + math.log(2 * math.pi)) * y_mask) prior_loss = prior_loss / (torch.sum(y_mask) * self.n_feats) return dur_loss, prior_loss, diff_loss class GradTTSXvector(BaseModule): def __init__(self, n_vocab=148, spk_emb_dim=64, n_enc_channels=192, filter_channels=768, filter_channels_dp=256, n_heads=2, n_enc_layers=6, enc_kernel=3, enc_dropout=0.1, window_size=4, n_feats=80, dec_dim=64, beta_min=0.05, beta_max=20.0, pe_scale=1000, xvector_dim=512, **kwargs): super(GradTTSXvector, self).__init__() self.n_vocab = n_vocab # self.n_spks = n_spks self.spk_emb_dim = spk_emb_dim self.n_enc_channels = n_enc_channels self.filter_channels = filter_channels self.filter_channels_dp = filter_channels_dp self.n_heads = n_heads self.n_enc_layers = n_enc_layers self.enc_kernel = enc_kernel self.enc_dropout = enc_dropout self.window_size = window_size self.n_feats = n_feats self.dec_dim = dec_dim self.beta_min = beta_min self.beta_max = beta_max self.pe_scale = pe_scale self.xvector_proj = torch.nn.Linear(xvector_dim, spk_emb_dim) self.encoder = TextEncoder(n_vocab, n_feats, n_enc_channels, filter_channels, filter_channels_dp, n_heads, n_enc_layers, enc_kernel, enc_dropout, window_size, spk_emb_dim=spk_emb_dim, n_spks=999) # NOTE: not important `n_spk` self.decoder = Diffusion(n_feats, dec_dim, spk_emb_dim, beta_min, beta_max, pe_scale) @torch.no_grad() def forward(self, x, x_lengths, n_timesteps, temperature=1.0, stoc=False, spk=None, length_scale=1.0): """ Generates mel-spectrogram from text. Returns: 1. encoder outputs 2. decoder outputs 3. generated alignment Args: x (torch.Tensor): batch of texts, converted to a tensor with phoneme embedding ids. x_lengths (torch.Tensor): lengths of texts in batch. n_timesteps (int): number of steps to use for reverse diffusion in decoder. temperature (float, optional): controls variance of terminal distribution. stoc (bool, optional): flag that adds stochastic term to the decoder sampler. Usually, does not provide synthesis improvements. length_scale (float, optional): controls speech pace. Increase value to slow down generated speech and vice versa. spk: actually the xvectors """ x, x_lengths = self.relocate_input([x, x_lengths]) spk = self.xvector_proj(spk) # NOTE: use x-vectors instead of speaker embedding # Get encoder_outputs `mu_x` and log-scaled token durations `logw` mu_x, logw, x_mask = self.encoder(x, x_lengths, spk) w = torch.exp(logw) * x_mask w_ceil = torch.ceil(w) * length_scale y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long() y_max_length = int(y_lengths.max()) y_max_length_ = fix_len_compatibility(y_max_length) # Using obtained durations `w` construct alignment map `attn` y_mask = sequence_mask(y_lengths, y_max_length_).unsqueeze(1).to(x_mask.dtype) attn_mask = x_mask.unsqueeze(-1) * y_mask.unsqueeze(2) attn = generate_path(w_ceil.squeeze(1), attn_mask.squeeze(1)).unsqueeze(1) # Align encoded text and get mu_y mu_y = torch.matmul(attn.squeeze(1).transpose(1, 2), mu_x.transpose(1, 2)) mu_y = mu_y.transpose(1, 2) encoder_outputs = mu_y[:, :, :y_max_length] # Sample latent representation from terminal distribution N(mu_y, I) z = mu_y + torch.randn_like(mu_y, device=mu_y.device) / temperature # Generate sample by performing reverse dynamics decoder_outputs = self.decoder(z, y_mask, mu_y, n_timesteps, stoc, spk) decoder_outputs = decoder_outputs[:, :, :y_max_length] return encoder_outputs, decoder_outputs, attn[:, :, :y_max_length] def compute_loss(self, x, x_lengths, y, y_lengths, spk=None, out_size=None, use_gt_dur=False, durs=None): """ Computes 3 losses: 1. duration loss: loss between predicted token durations and those extracted by Monotonic Alignment Search (MAS). 2. prior loss: loss between mel-spectrogram and encoder outputs. 3. diffusion loss: loss between gaussian noise and its reconstruction by diffusion-based decoder. Args: x (torch.Tensor): batch of texts, converted to a tensor with phoneme embedding ids. x_lengths (torch.Tensor): lengths of texts in batch. y (torch.Tensor): batch of corresponding mel-spectrograms. y_lengths (torch.Tensor): lengths of mel-spectrograms in batch. out_size (int, optional): length (in mel's sampling rate) of segment to cut, on which decoder will be trained. Should be divisible by 2^{num of UNet downsamplings}. Needed to increase batch size. spk: xvector use_gt_dur: bool durs: gt duration """ x, x_lengths, y, y_lengths = self.relocate_input([x, x_lengths, y, y_lengths]) spk = self.xvector_proj(spk) # NOTE: use x-vectors instead of speaker embedding # Get encoder_outputs `mu_x` and log-scaled token durations `logw` mu_x, logw, x_mask = self.encoder(x, x_lengths, spk) y_max_length = y.shape[-1] y_mask = sequence_mask(y_lengths, y_max_length).unsqueeze(1).to(x_mask) attn_mask = x_mask.unsqueeze(-1) * y_mask.unsqueeze(2) # Use MAS to find most likely alignment `attn` between text and mel-spectrogram if not use_gt_dur: with torch.no_grad(): const = -0.5 * math.log(2 * math.pi) * self.n_feats factor = -0.5 * torch.ones(mu_x.shape, dtype=mu_x.dtype, device=mu_x.device) y_square = torch.matmul(factor.transpose(1, 2), y ** 2) y_mu_double = torch.matmul(2.0 * (factor * mu_x).transpose(1, 2), y) mu_square = torch.sum(factor * (mu_x ** 2), 1).unsqueeze(-1) log_prior = y_square - y_mu_double + mu_square + const attn = monotonic_align.maximum_path(log_prior, attn_mask.squeeze(1)) attn = attn.detach() else: with torch.no_grad(): attn = generate_path(durs, attn_mask.squeeze(1)).detach() # Compute loss between predicted log-scaled durations and those obtained from MAS logw_ = torch.log(1e-8 + torch.sum(attn.unsqueeze(1), -1)) * x_mask dur_loss = duration_loss(logw, logw_, x_lengths) # print(attn.shape) # Cut a small segment of mel-spectrogram in order to increase batch size if not isinstance(out_size, type(None)): max_offset = (y_lengths - out_size).clamp(0) offset_ranges = list(zip([0] * max_offset.shape[0], max_offset.cpu().numpy())) out_offset = torch.LongTensor([ torch.tensor(random.choice(range(start, end)) if end > start else 0) for start, end in offset_ranges ]).to(y_lengths) attn_cut = torch.zeros(attn.shape[0], attn.shape[1], out_size, dtype=attn.dtype, device=attn.device) y_cut = torch.zeros(y.shape[0], self.n_feats, out_size, dtype=y.dtype, device=y.device) y_cut_lengths = [] for i, (y_, out_offset_) in enumerate(zip(y, out_offset)): y_cut_length = out_size + (y_lengths[i] - out_size).clamp(None, 0) y_cut_lengths.append(y_cut_length) cut_lower, cut_upper = out_offset_, out_offset_ + y_cut_length y_cut[i, :, :y_cut_length] = y_[:, cut_lower:cut_upper] attn_cut[i, :, :y_cut_length] = attn[i, :, cut_lower:cut_upper] y_cut_lengths = torch.LongTensor(y_cut_lengths) y_cut_mask = sequence_mask(y_cut_lengths).unsqueeze(1).to(y_mask) attn = attn_cut y = y_cut y_mask = y_cut_mask # Align encoded text with mel-spectrogram and get mu_y segment mu_y = torch.matmul(attn.squeeze(1).transpose(1, 2), mu_x.transpose(1, 2)) mu_y = mu_y.transpose(1, 2) # Compute loss of score-based decoder diff_loss, xt = self.decoder.compute_loss(y, y_mask, mu_y, spk) # Compute loss between aligned encoder outputs and mel-spectrogram prior_loss = torch.sum(0.5 * ((y - mu_y) ** 2 + math.log(2 * math.pi)) * y_mask) prior_loss = prior_loss / (torch.sum(y_mask) * self.n_feats) return dur_loss, prior_loss, diff_loss