import os import yaml import torch import torch.nn as nn import numpy as np from .diffusion import GaussianDiffusion from .wavenet import WaveNet from .vocoder import Vocoder class DotDict(dict): def __getattr__(*args): val = dict.get(*args) return DotDict(val) if type(val) is dict else val __setattr__ = dict.__setitem__ __delattr__ = dict.__delitem__ def load_model_vocoder( model_path, device='cpu', config_path = None ): if config_path is None: config_file = os.path.join(os.path.split(model_path)[0], 'config.yaml') else: config_file = config_path with open(config_file, "r") as config: args = yaml.safe_load(config) args = DotDict(args) # load vocoder vocoder = Vocoder(args.vocoder.type, args.vocoder.ckpt, device=device) # load model model = Unit2Mel( args.data.encoder_out_channels, args.model.n_spk, args.model.use_pitch_aug, vocoder.dimension, args.model.n_layers, args.model.n_chans, args.model.n_hidden) print(' [Loading] ' + model_path) ckpt = torch.load(model_path, map_location=torch.device(device)) model.to(device) model.load_state_dict(ckpt['model']) model.eval() return model, vocoder, args class Unit2Mel(nn.Module): def __init__( self, input_channel, n_spk, use_pitch_aug=False, out_dims=128, n_layers=20, n_chans=384, n_hidden=256): super().__init__() self.unit_embed = nn.Linear(input_channel, n_hidden) self.f0_embed = nn.Linear(1, n_hidden) self.volume_embed = nn.Linear(1, n_hidden) if use_pitch_aug: self.aug_shift_embed = nn.Linear(1, n_hidden, bias=False) else: self.aug_shift_embed = None self.n_spk = n_spk if n_spk is not None and n_spk > 1: self.spk_embed = nn.Embedding(n_spk, n_hidden) self.n_hidden = n_hidden # diffusion self.decoder = GaussianDiffusion(WaveNet(out_dims, n_layers, n_chans, n_hidden), out_dims=out_dims) self.input_channel = input_channel def init_spkembed(self, units, f0, volume, spk_id = None, spk_mix_dict = None, aug_shift = None, gt_spec=None, infer=True, infer_speedup=10, method='dpm-solver', k_step=300, use_tqdm=True): ''' input: B x n_frames x n_unit return: dict of B x n_frames x feat ''' x = self.unit_embed(units) + self.f0_embed((1+ f0 / 700).log()) + self.volume_embed(volume) if self.n_spk is not None and self.n_spk > 1: if spk_mix_dict is not None: spk_embed_mix = torch.zeros((1,1,self.hidden_size)) for k, v in spk_mix_dict.items(): spk_id_torch = torch.LongTensor(np.array([[k]])).to(units.device) spk_embeddd = self.spk_embed(spk_id_torch) self.speaker_map[k] = spk_embeddd spk_embed_mix = spk_embed_mix + v * spk_embeddd x = x + spk_embed_mix else: x = x + self.spk_embed(spk_id - 1) self.speaker_map = self.speaker_map.unsqueeze(0) self.speaker_map = self.speaker_map.detach() return x.transpose(1, 2) def init_spkmix(self, n_spk): self.speaker_map = torch.zeros((n_spk,1,1,self.n_hidden)) hubert_hidden_size = self.input_channel n_frames = 10 hubert = torch.randn((1, n_frames, hubert_hidden_size)) mel2ph = torch.arange(end=n_frames).unsqueeze(0).long() f0 = torch.randn((1, n_frames)) volume = torch.randn((1, n_frames)) spks = {} for i in range(n_spk): spks.update({i:1.0/float(self.n_spk)}) orgouttt = self.init_spkembed(hubert, f0.unsqueeze(-1), volume.unsqueeze(-1), spk_mix_dict=spks) def forward(self, units, f0, volume, spk_id = None, spk_mix_dict = None, aug_shift = None, gt_spec=None, infer=True, infer_speedup=10, method='dpm-solver', k_step=300, use_tqdm=True): ''' input: B x n_frames x n_unit return: dict of B x n_frames x feat ''' x = self.unit_embed(units) + self.f0_embed((1+ f0 / 700).log()) + self.volume_embed(volume) if self.n_spk is not None and self.n_spk > 1: if spk_mix_dict is not None: for k, v in spk_mix_dict.items(): spk_id_torch = torch.LongTensor(np.array([[k]])).to(units.device) x = x + v * self.spk_embed(spk_id_torch) else: if spk_id.shape[1] > 1: g = spk_id.reshape((spk_id.shape[0], spk_id.shape[1], 1, 1, 1)) # [N, S, B, 1, 1] g = g * self.speaker_map # [N, S, B, 1, H] g = torch.sum(g, dim=1) # [N, 1, B, 1, H] g = g.transpose(0, -1).transpose(0, -2).squeeze(0) # [B, H, N] x = x + g else: x = x + self.spk_embed(spk_id) if self.aug_shift_embed is not None and aug_shift is not None: x = x + self.aug_shift_embed(aug_shift / 5) x = self.decoder(x, gt_spec=gt_spec, infer=infer, infer_speedup=infer_speedup, method=method, k_step=k_step, use_tqdm=use_tqdm) return x