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 class Vits_models_only_decoder(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() #.................................... 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 return spectrogram