import numpy as np import torch from torch import nn import math from typing import Any, Callable, Optional, Tuple, Union from torch.cuda.amp import autocast, GradScaler from .vits_config import VitsConfig,VitsPreTrainedModel from .flow import VitsResidualCouplingBlock from .duration_predictor import VitsDurationPredictor, VitsStochasticDurationPredictor from .encoder import VitsTextEncoder from .decoder import VitsHifiGan from .posterior_encoder import VitsPosteriorEncoder from .discriminator import VitsDiscriminator from .vits_output import VitsModelOutput, VitsTrainingOutput from .dataset_features_collector import FeaturesCollectionDataset from .feature_extraction import VitsFeatureExtractor import os import sys from typing import Optional import tempfile from torch.cuda.amp import autocast, GradScaler from IPython.display import clear_output from transformers import set_seed import wandb import logging import copy Lst=['input_ids', 'attention_mask', 'waveform', 'labels', 'labels_attention_mask', 'mel_scaled_input_features'] def covert_cuda_batch(d): #return d for key in Lst: d[key]=d[key].cuda(non_blocking=True) # for key in d['text_encoder_output']: # d['text_encoder_output'][key]=d['text_encoder_output'][key].cuda(non_blocking=True) for key in d['posterior_encode_output']: d['posterior_encode_output'][key]=d['posterior_encode_output'][key].cuda(non_blocking=True) return d def generator_loss(disc_outputs): total_loss = 0 gen_losses = [] for disc_output in disc_outputs: disc_output = disc_output loss = torch.mean((1 - disc_output) ** 2) gen_losses.append(loss) total_loss += loss return total_loss, gen_losses def discriminator_loss(disc_real_outputs, disc_generated_outputs): loss = 0 real_losses = 0 generated_losses = 0 for disc_real, disc_generated in zip(disc_real_outputs, disc_generated_outputs): real_loss = torch.mean((1 - disc_real) ** 2) generated_loss = torch.mean(disc_generated**2) loss += real_loss + generated_loss real_losses += real_loss generated_losses += generated_loss return loss, real_losses, generated_losses def feature_loss(feature_maps_real, feature_maps_generated): loss = 0 for feature_map_real, feature_map_generated in zip(feature_maps_real, feature_maps_generated): for real, generated in zip(feature_map_real, feature_map_generated): real = real.detach() loss += torch.mean(torch.abs(real - generated)) return loss * 2 def kl_loss(z_p, logs_q, m_p, logs_p, z_mask): """ z_p, logs_q: [b, h, t_t] m_p, logs_p: [b, h, t_t] """ z_p = z_p.float() logs_q = logs_q.float() m_p = m_p.float() logs_p = logs_p.float() z_mask = z_mask.float() kl = logs_p - logs_q - 0.5 kl += 0.5 * ((z_p - m_p)**2) * torch.exp(-2. * logs_p) kl = torch.sum(kl * z_mask) l = kl / torch.sum(z_mask) return l #............................................. # def kl_loss(prior_latents, posterior_log_variance, prior_means, prior_log_variance, labels_mask): # kl = prior_log_variance - posterior_log_variance - 0.5 # kl += 0.5 * ((prior_latents - prior_means) ** 2) * torch.exp(-2.0 * prior_log_variance) # kl = torch.sum(kl * labels_mask) # loss = kl / torch.sum(labels_mask) # return loss def get_state_grad_loss(k1=True, mel=True, duration=True, generator=True, discriminator=True): return {'k1':k1,'mel':mel,'duration':duration,'generator':generator,'discriminator':discriminator} def clip_grad_value_(parameters, clip_value, norm_type=2): if isinstance(parameters, torch.Tensor): parameters = [parameters] parameters = list(filter(lambda p: p.grad is not None, parameters)) norm_type = float(norm_type) if clip_value is not None: clip_value = float(clip_value) total_norm = 0 for p in parameters: param_norm = p.grad.data.norm(norm_type) total_norm += param_norm.item() ** norm_type if clip_value is not None: p.grad.data.clamp_(min=-clip_value, max=clip_value) total_norm = total_norm ** (1. / norm_type) return total_norm class VitsModel(VitsPreTrainedModel): def __init__(self, config: VitsConfig): super().__init__(config) self.config = config self.text_encoder = VitsTextEncoder(config) self.flow = VitsResidualCouplingBlock(config) self.decoder = VitsHifiGan(config) if config.use_stochastic_duration_prediction: self.duration_predictor = VitsStochasticDurationPredictor(config) else: self.duration_predictor = VitsDurationPredictor(config) if config.num_speakers > 1: self.embed_speaker = nn.Embedding(config.num_speakers, config.speaker_embedding_size) # This is used only for training. self.posterior_encoder = VitsPosteriorEncoder(config) self.discriminator = VitsDiscriminator(config) # These parameters control the synthesised speech properties self.speaking_rate = config.speaking_rate self.noise_scale = config.noise_scale self.noise_scale_duration = config.noise_scale_duration self.segment_size = self.config.segment_size // self.config.hop_length # Initialize weights and apply final processing self.post_init() self.monotonic_alignment_function=self.monotonic_align_max_path #.................................... def setMfA(self,fn): self.monotonic_alignment_function=fn def monotonic_align_max_path(self,log_likelihoods, mask): # used for training - awfully slow # an alternative is proposed in examples/pytorch/text-to-speech/run_vits_finetuning.py path = torch.zeros_like(log_likelihoods) text_length_maxs = mask.sum(1)[:, 0] latent_length_maxs = mask.sum(2)[:, 0] indexes = latent_length_maxs - 1 max_neg_val = -1e9 for batch_id in range(len(path)): index = int(indexes[batch_id].item()) text_length_max = int(text_length_maxs[batch_id].item()) latent_length_max = int(latent_length_maxs[batch_id].item()) for y in range(text_length_max): for x in range(max(0, latent_length_max + y - text_length_max), min(latent_length_max, y + 1)): if x == y: v_cur = max_neg_val else: v_cur = log_likelihoods[batch_id, y - 1, x] if x == 0: if y == 0: v_prev = 0.0 else: v_prev = max_neg_val else: v_prev = log_likelihoods[batch_id, y - 1, x - 1] log_likelihoods[batch_id, y, x] += max(v_prev, v_cur) for y in range(text_length_max - 1, -1, -1): path[batch_id, y, index] = 1 if index != 0 and ( index == y or log_likelihoods[batch_id, y - 1, index] < log_likelihoods[batch_id, y - 1, index - 1] ): index = index - 1 return path #.................................... def slice_segments(self,hidden_states, ids_str, segment_size=4): batch_size, channels, _ = hidden_states.shape # 1d tensor containing the indices to keep indices = torch.arange(segment_size).to(ids_str.device) # extend the indices to match the shape of hidden_states indices = indices.view(1, 1, -1).expand(batch_size, channels, -1) # offset indices with ids_str indices = indices + ids_str.view(-1, 1, 1) # gather indices output = torch.gather(hidden_states, dim=2, index=indices) return output #.................................... def rand_slice_segments(self,hidden_states, sample_lengths=None, segment_size=4): batch_size, _, seq_len = hidden_states.size() if sample_lengths is None: sample_lengths = seq_len ids_str_max = sample_lengths - segment_size + 1 ids_str = (torch.rand([batch_size]).to(device=hidden_states.device) * ids_str_max).to(dtype=torch.long) ret = self.slice_segments(hidden_states, ids_str, segment_size) return ret, ids_str #.................................... def resize_speaker_embeddings( self, new_num_speakers: int, speaker_embedding_size: Optional[int] = None, pad_to_multiple_of: Optional[int] = 2, ): if pad_to_multiple_of is not None: new_num_speakers = ((new_num_speakers + pad_to_multiple_of - 1) // pad_to_multiple_of) * pad_to_multiple_of # first, take care of embed_speaker if self.config.num_speakers <= 1: if speaker_embedding_size is None: raise ValueError( "The current model had no previous speaker embedding, but `speaker_embedding_size` is not specified. Pass `speaker_embedding_size` to this method." ) # create new embedding layer new_embeddings = nn.Embedding( new_num_speakers, speaker_embedding_size, device=self.device, ) # initialize all new embeddings self._init_weights(new_embeddings) else: new_embeddings = self._get_resized_embeddings(self.embed_speaker, new_num_speakers) self.embed_speaker = new_embeddings # then take care of sub-models self.flow.resize_speaker_embeddings(speaker_embedding_size) for flow in self.flow.flows: self._init_weights(flow.wavenet.cond_layer) self.decoder.resize_speaker_embedding(speaker_embedding_size) self._init_weights(self.decoder.cond) self.duration_predictor.resize_speaker_embeddings(speaker_embedding_size) self._init_weights(self.duration_predictor.cond) self.posterior_encoder.resize_speaker_embeddings(speaker_embedding_size) self._init_weights(self.posterior_encoder.wavenet.cond_layer) self.config.num_speakers = new_num_speakers self.config.speaker_embedding_size = speaker_embedding_size #.................................... def get_input_embeddings(self): return self.text_encoder.get_input_embeddings() #.................................... def set_input_embeddings(self, value): self.text_encoder.set_input_embeddings(value) #.................................... def apply_weight_norm(self): self.decoder.apply_weight_norm() self.flow.apply_weight_norm() self.posterior_encoder.apply_weight_norm() #.................................... def remove_weight_norm(self): self.decoder.remove_weight_norm() self.flow.remove_weight_norm() self.posterior_encoder.remove_weight_norm() #.................................... def discriminate(self, hidden_states): return self.discriminator(hidden_states) #.................................... def get_encoder(self): return self.text_encoder #.................................... def _inference_forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, speaker_embeddings: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, padding_mask: Optional[torch.Tensor] = None, ): text_encoder_output = self.text_encoder( input_ids=input_ids, padding_mask=padding_mask, attention_mask=attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = text_encoder_output[0] if not return_dict else text_encoder_output.last_hidden_state hidden_states = hidden_states.transpose(1, 2) input_padding_mask = padding_mask.transpose(1, 2) prior_means = text_encoder_output[1] if not return_dict else text_encoder_output.prior_means prior_log_variances = text_encoder_output[2] if not return_dict else text_encoder_output.prior_log_variances if self.config.use_stochastic_duration_prediction: log_duration = self.duration_predictor( hidden_states, input_padding_mask, speaker_embeddings, reverse=True, noise_scale=self.noise_scale_duration, ) else: log_duration = self.duration_predictor(hidden_states, input_padding_mask, speaker_embeddings) length_scale = 1.0 / self.speaking_rate duration = torch.ceil(torch.exp(log_duration) * input_padding_mask * length_scale) predicted_lengths = torch.clamp_min(torch.sum(duration, [1, 2]), 1).long() # Create a padding mask for the output lengths of shape (batch, 1, max_output_length) indices = torch.arange(predicted_lengths.max(), dtype=predicted_lengths.dtype, device=predicted_lengths.device) output_padding_mask = indices.unsqueeze(0) < predicted_lengths.unsqueeze(1) output_padding_mask = output_padding_mask.unsqueeze(1).to(input_padding_mask.dtype) # Reconstruct an attention tensor of shape (batch, 1, out_length, in_length) attn_mask = torch.unsqueeze(input_padding_mask, 2) * torch.unsqueeze(output_padding_mask, -1) batch_size, _, output_length, input_length = attn_mask.shape cum_duration = torch.cumsum(duration, -1).view(batch_size * input_length, 1) indices = torch.arange(output_length, dtype=duration.dtype, device=duration.device) valid_indices = indices.unsqueeze(0) < cum_duration valid_indices = valid_indices.to(attn_mask.dtype).view(batch_size, input_length, output_length) padded_indices = valid_indices - nn.functional.pad(valid_indices, [0, 0, 1, 0, 0, 0])[:, :-1] attn = padded_indices.unsqueeze(1).transpose(2, 3) * attn_mask # Expand prior distribution prior_means = torch.matmul(attn.squeeze(1), prior_means).transpose(1, 2) prior_log_variances = torch.matmul(attn.squeeze(1), prior_log_variances).transpose(1, 2) prior_latents = prior_means + torch.randn_like(prior_means) * torch.exp(prior_log_variances) * self.noise_scale latents = self.flow(prior_latents, output_padding_mask, speaker_embeddings, reverse=True) spectrogram = latents * output_padding_mask waveform = self.decoder(spectrogram, speaker_embeddings) waveform = waveform.squeeze(1) sequence_lengths = predicted_lengths * np.prod(self.config.upsample_rates) if not return_dict: outputs = (waveform, sequence_lengths, spectrogram) + text_encoder_output[3:] return outputs return VitsModelOutput( waveform=waveform, sequence_lengths=sequence_lengths, spectrogram=spectrogram, hidden_states=text_encoder_output.hidden_states, attentions=text_encoder_output.attentions, ) #.................................... def forward_k( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, speaker_id: Optional[int] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, labels: Optional[torch.FloatTensor] = None, labels_attention_mask: Optional[torch.Tensor] = None, monotonic_alignment_function: Optional[Callable] = None, ) -> Union[Tuple[Any], VitsModelOutput]: output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict monotonic_alignment_function = ( self.monotonic_align_max_path if monotonic_alignment_function is None else monotonic_alignment_function ) if attention_mask is not None: input_padding_mask = attention_mask.unsqueeze(-1).float() else: input_padding_mask = torch.ones_like(input_ids).unsqueeze(-1).float() if self.config.num_speakers > 1 and speaker_id is not None: if isinstance(speaker_id, int): speaker_id = torch.full(size=(1,), fill_value=speaker_id, device=self.device) elif isinstance(speaker_id, (list, tuple, np.ndarray)): speaker_id = torch.tensor(speaker_id, device=self.device) if not ((0 <= speaker_id).all() and (speaker_id < self.config.num_speakers).all()).item(): raise ValueError(f"Set `speaker_id` in the range 0-{self.config.num_speakers - 1}.") if not (len(speaker_id) == 1 or len(speaker_id == len(input_ids))): raise ValueError( f"You passed {len(speaker_id)} `speaker_id` but you should either pass one speaker id or `batch_size` `speaker_id`." ) speaker_embeddings = self.embed_speaker(speaker_id).unsqueeze(-1) else: speaker_embeddings = None # if inference, return inference forward of VitsModel if labels is None: return self._inference_forward( input_ids, attention_mask, speaker_embeddings, output_attentions, output_hidden_states, return_dict, input_padding_mask, ) if labels_attention_mask is not None: labels_padding_mask = labels_attention_mask.unsqueeze(1).float() else: labels_attention_mask = torch.ones((labels.shape[0], labels.shape[2])).float().to(self.device) labels_padding_mask = labels_attention_mask.unsqueeze(1) text_encoder_output = self.text_encoder( input_ids=input_ids, padding_mask=input_padding_mask, attention_mask=attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = text_encoder_output[0] if not return_dict else text_encoder_output.last_hidden_state hidden_states = hidden_states.transpose(1, 2) input_padding_mask = input_padding_mask.transpose(1, 2) prior_means = text_encoder_output[1] if not return_dict else text_encoder_output.prior_means prior_log_variances = text_encoder_output[2] if not return_dict else text_encoder_output.prior_log_variances latents, posterior_means, posterior_log_variances = self.posterior_encoder( labels, labels_padding_mask, speaker_embeddings ) prior_latents = self.flow(latents, labels_padding_mask, speaker_embeddings, reverse=False) prior_means, prior_log_variances = prior_means.transpose(1, 2), prior_log_variances.transpose(1, 2) with torch.no_grad(): # negative cross-entropy # [batch_size, d, latent_length] prior_variances = torch.exp(-2 * prior_log_variances) # [batch_size, 1, latent_length] neg_cent1 = torch.sum(-0.5 * math.log(2 * math.pi) - prior_log_variances, [1], keepdim=True) # [batch_size, text_length, d] x [batch_size, d, latent_length] = [batch_size, text_length, latent_length] neg_cent2 = torch.matmul(-0.5 * (prior_latents**2).transpose(1, 2), prior_variances) # [batch_size, text_length, d] x [batch_size, d, latent_length] = [batch_size, text_length, latent_length] neg_cent3 = torch.matmul(prior_latents.transpose(1, 2), (prior_means * prior_variances)) # [batch_size, 1, latent_length] neg_cent4 = torch.sum(-0.5 * (prior_means**2) * prior_variances, [1], keepdim=True) # [batch_size, text_length, latent_length] neg_cent = neg_cent1 + neg_cent2 + neg_cent3 + neg_cent4 attn_mask = torch.unsqueeze(input_padding_mask, 2) * torch.unsqueeze(labels_padding_mask, -1) attn = monotonic_alignment_function(neg_cent, attn_mask.squeeze(1)).unsqueeze(1).detach() durations = attn.sum(2) if self.config.use_stochastic_duration_prediction: log_duration = self.duration_predictor( hidden_states, input_padding_mask, speaker_embeddings, durations=durations, reverse=False ) log_duration = log_duration / torch.sum(input_padding_mask) else: log_duration_padded = torch.log(durations + 1e-6) * input_padding_mask log_duration = self.duration_predictor(hidden_states, input_padding_mask, speaker_embeddings) log_duration = torch.sum((log_duration - log_duration_padded) ** 2, [1, 2]) / torch.sum(input_padding_mask) # expand priors prior_means = torch.matmul(attn.squeeze(1), prior_means.transpose(1, 2)).transpose(1, 2) prior_log_variances = torch.matmul(attn.squeeze(1), prior_log_variances.transpose(1, 2)).transpose(1, 2) label_lengths = labels_attention_mask.sum(dim=1) latents_slice, ids_slice = self.rand_slice_segments(latents, label_lengths, segment_size=self.segment_size) waveform = self.decoder(latents_slice, speaker_embeddings) if not return_dict: outputs = ( waveform, log_duration, attn, ids_slice, input_padding_mask, labels_padding_mask, latents, prior_latents, prior_means, prior_log_variances, posterior_means, posterior_log_variances, ) return outputs return VitsTrainingOutput( waveform=waveform, log_duration=log_duration, attn=attn, ids_slice=ids_slice, input_padding_mask=input_padding_mask, labels_padding_mask=labels_padding_mask, latents=latents, prior_latents=prior_latents, prior_means=prior_means, prior_log_variances=prior_log_variances, posterior_means=posterior_means, posterior_log_variances=posterior_log_variances, ) def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, speaker_id: Optional[int] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, labels: Optional[torch.FloatTensor] = None, labels_attention_mask: Optional[torch.Tensor] = None, text_encoder_output=None, posterior_encode_output=None, monotonic_alignment_function: Optional[Callable] = None, speaker_embeddings=None ) -> Union[Tuple[Any], VitsModelOutput]: #output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states# if output_hidden_states is not None else self.config.output_hidden_states ) # return_dict = return_dict if return_dict is not None else self.config.use_return_dict # if attention_mask is not None: input_padding_mask = attention_mask.unsqueeze(-1).float() #else: # input_padding_mask = torch.ones_like(input_ids).unsqueeze(-1).float() # speaker_embeddings=None # if labels_attention_mask is not None: labels_padding_mask = labels_attention_mask.unsqueeze(1).float() # else: # labels_attention_mask = torch.ones((labels.shape[0], labels.shape[2])).float().to(self.device) # labels_padding_mask = labels_attention_mask.unsqueeze(1) if text_encoder_output is None: text_encoder_output = self.text_encoder( input_ids=input_ids, padding_mask=input_padding_mask, attention_mask=attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) #hidden_states = text_encoder_output[0] #if not return_dict else text_encoder_output.last_hidden_state hidden_states = text_encoder_output[0].transpose(1, 2) input_padding_mask = input_padding_mask.transpose(1, 2) prior_means = text_encoder_output[1].transpose(1, 2) #if not return_dict else text_encoder_output.prior_means prior_log_variances = text_encoder_output[2].transpose(1, 2) #if not return_dict else text_encoder_output.prior_log_variances # if posterior_encode_output is None: # latents, posterior_means, posterior_log_variances = self.posterior_encoder( # labels, labels_padding_mask, speaker_embeddings # ) # else: latents=posterior_encode_output['posterior_latents'] posterior_means=posterior_encode_output['posterior_means'] posterior_log_variances=posterior_encode_output['posterior_log_variances'] prior_latents = self.flow(latents, labels_padding_mask, speaker_embeddings, reverse=False) # prior_means, prior_log_variances = prior_means.transpose(1, 2), prior_log_variances.transpose(1, 2) with torch.no_grad(): # negative cross-entropy # [batch_size, d, latent_length] prior_variances = torch.exp(-2 * prior_log_variances) # [batch_size, 1, latent_length] neg_cent1 = torch.sum(-0.5 * math.log(2 * math.pi) - prior_log_variances, [1], keepdim=True) # [batch_size, text_length, d] x [batch_size, d, latent_length] = [batch_size, text_length, latent_length] neg_cent2 = torch.matmul(-0.5 * (prior_latents**2).transpose(1, 2), prior_variances) # [batch_size, text_length, d] x [batch_size, d, latent_length] = [batch_size, text_length, latent_length] neg_cent3 = torch.matmul(prior_latents.transpose(1, 2), (prior_means * prior_variances)) # [batch_size, 1, latent_length] neg_cent4 = torch.sum(-0.5 * (prior_means**2) * prior_variances, [1], keepdim=True) # [batch_size, text_length, latent_length] neg_cent = neg_cent1 + neg_cent2 + neg_cent3 + neg_cent4 attn_mask = torch.unsqueeze(input_padding_mask, 2) * torch.unsqueeze(labels_padding_mask, -1) attn = monotonic_alignment_function(neg_cent, attn_mask.squeeze(1)).unsqueeze(1).detach() durations = attn.sum(2) #if self.config.use_stochastic_duration_prediction: log_duration = self.duration_predictor( hidden_states, input_padding_mask, speaker_embeddings, durations=durations, reverse=False ) log_duration = log_duration / torch.sum(input_padding_mask) # else: # log_duration_padded = torch.log(durations + 1e-6) * input_padding_mask # log_duration = self.duration_predictor(hidden_states, input_padding_mask, speaker_embeddings) # log_duration = torch.sum((log_duration - log_duration_padded) ** 2, [1, 2]) / torch.sum(input_padding_mask) # expand priors prior_means = torch.matmul(attn.squeeze(1), prior_means.transpose(1, 2)).transpose(1, 2) prior_log_variances = torch.matmul(attn.squeeze(1), prior_log_variances.transpose(1, 2)).transpose(1, 2) label_lengths = labels_attention_mask.sum(dim=1) latents_slice, ids_slice = self.rand_slice_segments(latents, label_lengths, segment_size=self.segment_size) waveform = self.decoder(latents_slice, speaker_embeddings) return waveform,ids_slice,log_duration,prior_latents,posterior_log_variances,prior_means,prior_log_variances,labels_padding_mask