VitsModelSplit/vits_model_only_d.py

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

    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,
        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,
        )