VietTTS/flow/flow.py

import logging
import random
from typing import Dict, Optional
import torch
import torch.nn as nn
from torch.nn import functional as F
from omegaconf import DictConfig
from VietTTS.utils.mask import make_pad_mask


class MaskedDiffWithXvec(torch.nn.Module):
    def __init__(self,
                input_size: int = 512,
                output_size: int = 80,
                spk_embed_dim: int = 192,
                output_type: str = "mel",
                vocab_size: int = 4096,
                input_frame_rate: int = 50,
                only_mask_loss: bool = True,
                encoder: torch.nn.Module = None,
                length_regulator: torch.nn.Module = None,
                decoder: torch.nn.Module = None,
                decoder_conf: Dict = {
                    'in_channels': 240,
                    'out_channel': 80,
                    'spk_emb_dim': 80,
                    'n_spks': 1,
                    'cfm_params': DictConfig({
                        'sigma_min': 1e-06,
                        'solver': 'euler',
                        't_scheduler': 'cosine',
                        'training_cfg_rate': 0.2,
                        'inference_cfg_rate': 0.7,
                        'reg_loss_type': 'l1'
                    }),
                    'decoder_params': {
                        'channels': [256, 256],
                        'dropout': 0.0,
                        'attention_head_dim': 64,
                        'n_blocks': 4,
                        'num_mid_blocks': 12,
                        'num_heads': 8,
                        'act_fn': 'gelu'
                    }
                },
                mel_feat_conf: Dict = {
                    'n_fft': 1024,
                    'num_mels': 80,
                    'sampling_rate': 22050,
                    'hop_size': 256,
                    'win_size': 1024,
                    'fmin': 0,
                    'fmax': 8000
                }
            ):
        super().__init__()
        self.input_size = input_size
        self.output_size = output_size
        self.decoder_conf = decoder_conf
        self.mel_feat_conf = mel_feat_conf
        self.vocab_size = vocab_size
        self.output_type = output_type
        self.input_frame_rate = input_frame_rate
        logging.info(f"input frame rate={self.input_frame_rate}")
        self.input_embedding = nn.Embedding(vocab_size, input_size)
        self.spk_embed_affine_layer = torch.nn.Linear(spk_embed_dim, output_size)
        self.encoder = encoder
        self.encoder_proj = torch.nn.Linear(self.encoder.output_size(), output_size)
        self.decoder = decoder
        self.length_regulator = length_regulator
        self.only_mask_loss = only_mask_loss

    def forward(
            self,
            batch: dict,
            device: torch.device,
    ) -> Dict[str, Optional[torch.Tensor]]:
        token = batch['speech_token'].to(device)
        token_len = batch['speech_token_len'].to(device)
        feat = batch['speech_feat'].to(device)
        feat_len = batch['speech_feat_len'].to(device)
        embedding = batch['embedding'].to(device)

        # xvec projection
        embedding = F.normalize(embedding, dim=1)
        embedding = self.spk_embed_affine_layer(embedding)

        # concat text and prompt_text
        mask = (~make_pad_mask(token_len)).float().unsqueeze(-1).to(device)
        token = self.input_embedding(torch.clamp(token, min=0)) * mask

        # text encode
        h, h_lengths = self.encoder(token, token_len)
        h = self.encoder_proj(h)
        h, h_lengths = self.length_regulator(h, feat_len)

        # get conditions
        conds = torch.zeros(feat.shape, device=token.device)
        for i, j in enumerate(feat_len):
            if random.random() < 0.5:
                continue
            index = random.randint(0, int(0.3 * j))
            conds[i, :index] = feat[i, :index]
        conds = conds.transpose(1, 2)

        mask = (~make_pad_mask(feat_len)).to(h)
        feat = F.interpolate(feat.unsqueeze(dim=1), size=h.shape[1:], mode="nearest").squeeze(dim=1)
        loss, _ = self.decoder.compute_loss(
            feat.transpose(1, 2).contiguous(),
            mask.unsqueeze(1),
            h.transpose(1, 2).contiguous(),
            embedding,
            cond=conds
        )
        return {'loss': loss}

    @torch.inference_mode()
    def inference(self,
                  token,
                  token_len,
                  prompt_token,
                  prompt_token_len,
                  prompt_feat,
                  prompt_feat_len,
                  embedding):
        assert token.shape[0] == 1
        # xvec projection
        embedding = F.normalize(embedding, dim=1)
        embedding = self.spk_embed_affine_layer(embedding)

        # concat text and prompt_text
        token_len1, token_len2 = prompt_token.shape[1], token.shape[1]
        token, token_len = torch.concat([prompt_token, token], dim=1), prompt_token_len + token_len
        mask = (~make_pad_mask(token_len)).unsqueeze(-1).to(embedding)
        token = self.input_embedding(torch.clamp(token, min=0)) * mask

        # text encode
        h, h_lengths = self.encoder(token, token_len)
        h = self.encoder_proj(h)
        mel_len1, mel_len2 = prompt_feat.shape[1], int(token_len2 / self.input_frame_rate * 22050 / 256)
        h, h_lengths = self.length_regulator.inference(h[:, :token_len1], h[:, token_len1:], mel_len1, mel_len2, self.input_frame_rate)

        # get conditions
        conds = torch.zeros([1, mel_len1 + mel_len2, self.output_size], device=token.device)
        conds[:, :mel_len1] = prompt_feat
        conds = conds.transpose(1, 2)

        mask = (~make_pad_mask(torch.tensor([mel_len1 + mel_len2]))).to(h)
        feat = self.decoder(
            mu=h.transpose(1, 2).contiguous(),
            mask=mask.unsqueeze(1),
            spks=embedding,
            cond=conds,
            n_timesteps=10
        )
        feat = feat[:, :, mel_len1:]
        assert feat.shape[2] == mel_len2
        return feat