168 lines
6.3 KiB
Python
168 lines
6.3 KiB
Python
import os
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import numpy as np
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import torch
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import torch.nn as nn
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import yaml
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from .diffusion import GaussianDiffusion
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from .vocoder import Vocoder
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from .wavenet import WaveNet
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class DotDict(dict):
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def __getattr__(*args):
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val = dict.get(*args)
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return DotDict(val) if type(val) is dict else val
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__setattr__ = dict.__setitem__
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__delattr__ = dict.__delitem__
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def load_model_vocoder(
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model_path,
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device='cpu',
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config_path = None
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):
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if config_path is None:
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config_file = os.path.join(os.path.split(model_path)[0], 'config.yaml')
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else:
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config_file = config_path
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with open(config_file, "r") as config:
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args = yaml.safe_load(config)
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args = DotDict(args)
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# load vocoder
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vocoder = Vocoder(args.vocoder.type, args.vocoder.ckpt, device=device)
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# load model
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model = Unit2Mel(
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args.data.encoder_out_channels,
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args.model.n_spk,
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args.model.use_pitch_aug,
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vocoder.dimension,
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args.model.n_layers,
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args.model.n_chans,
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args.model.n_hidden,
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args.model.timesteps,
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args.model.k_step_max
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)
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print(' [Loading] ' + model_path)
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ckpt = torch.load(model_path, map_location=torch.device(device))
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model.to(device)
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model.load_state_dict(ckpt['model'])
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model.eval()
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print(f'Loaded diffusion model, sampler is {args.infer.method}, speedup: {args.infer.speedup} ')
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return model, vocoder, args
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class Unit2Mel(nn.Module):
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def __init__(
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self,
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input_channel,
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n_spk,
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use_pitch_aug=False,
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out_dims=128,
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n_layers=20,
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n_chans=384,
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n_hidden=256,
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timesteps=1000,
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k_step_max=1000
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):
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super().__init__()
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self.unit_embed = nn.Linear(input_channel, n_hidden)
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self.f0_embed = nn.Linear(1, n_hidden)
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self.volume_embed = nn.Linear(1, n_hidden)
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if use_pitch_aug:
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self.aug_shift_embed = nn.Linear(1, n_hidden, bias=False)
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else:
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self.aug_shift_embed = None
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self.n_spk = n_spk
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if n_spk is not None and n_spk > 1:
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self.spk_embed = nn.Embedding(n_spk, n_hidden)
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self.timesteps = timesteps if timesteps is not None else 1000
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self.k_step_max = k_step_max if k_step_max is not None and k_step_max>0 and k_step_max<self.timesteps else self.timesteps
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self.n_hidden = n_hidden
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# diffusion
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self.decoder = GaussianDiffusion(WaveNet(out_dims, n_layers, n_chans, n_hidden),timesteps=self.timesteps,k_step=self.k_step_max, out_dims=out_dims)
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self.input_channel = input_channel
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def init_spkembed(self, units, f0, volume, spk_id = None, spk_mix_dict = None, aug_shift = None,
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gt_spec=None, infer=True, infer_speedup=10, method='dpm-solver', k_step=300, use_tqdm=True):
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'''
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input:
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B x n_frames x n_unit
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return:
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dict of B x n_frames x feat
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'''
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x = self.unit_embed(units) + self.f0_embed((1+ f0 / 700).log()) + self.volume_embed(volume)
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if self.n_spk is not None and self.n_spk > 1:
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if spk_mix_dict is not None:
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spk_embed_mix = torch.zeros((1,1,self.hidden_size))
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for k, v in spk_mix_dict.items():
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spk_id_torch = torch.LongTensor(np.array([[k]])).to(units.device)
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spk_embeddd = self.spk_embed(spk_id_torch)
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self.speaker_map[k] = spk_embeddd
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spk_embed_mix = spk_embed_mix + v * spk_embeddd
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x = x + spk_embed_mix
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else:
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x = x + self.spk_embed(spk_id - 1)
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self.speaker_map = self.speaker_map.unsqueeze(0)
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self.speaker_map = self.speaker_map.detach()
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return x.transpose(1, 2)
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def init_spkmix(self, n_spk):
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self.speaker_map = torch.zeros((n_spk,1,1,self.n_hidden))
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hubert_hidden_size = self.input_channel
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n_frames = 10
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hubert = torch.randn((1, n_frames, hubert_hidden_size))
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f0 = torch.randn((1, n_frames))
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volume = torch.randn((1, n_frames))
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spks = {}
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for i in range(n_spk):
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spks.update({i:1.0/float(self.n_spk)})
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self.init_spkembed(hubert, f0.unsqueeze(-1), volume.unsqueeze(-1), spk_mix_dict=spks)
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def forward(self, units, f0, volume, spk_id = None, spk_mix_dict = None, aug_shift = None,
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gt_spec=None, infer=True, infer_speedup=10, method='dpm-solver', k_step=300, use_tqdm=True):
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'''
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input:
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B x n_frames x n_unit
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return:
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dict of B x n_frames x feat
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'''
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if not self.training and gt_spec is not None and k_step>self.k_step_max:
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raise Exception("The shallow diffusion k_step is greater than the maximum diffusion k_step(k_step_max)!")
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if not self.training and gt_spec is None and self.k_step_max!=self.timesteps:
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raise Exception("This model can only be used for shallow diffusion and can not infer alone!")
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x = self.unit_embed(units) + self.f0_embed((1+ f0 / 700).log()) + self.volume_embed(volume)
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if self.n_spk is not None and self.n_spk > 1:
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if spk_mix_dict is not None:
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for k, v in spk_mix_dict.items():
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spk_id_torch = torch.LongTensor(np.array([[k]])).to(units.device)
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x = x + v * self.spk_embed(spk_id_torch)
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else:
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if spk_id.shape[1] > 1:
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g = spk_id.reshape((spk_id.shape[0], spk_id.shape[1], 1, 1, 1)) # [N, S, B, 1, 1]
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g = g * self.speaker_map # [N, S, B, 1, H]
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g = torch.sum(g, dim=1) # [N, 1, B, 1, H]
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g = g.transpose(0, -1).transpose(0, -2).squeeze(0) # [B, H, N]
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x = x + g
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else:
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x = x + self.spk_embed(spk_id)
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if self.aug_shift_embed is not None and aug_shift is not None:
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x = x + self.aug_shift_embed(aug_shift / 5)
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x = self.decoder(x, gt_spec=gt_spec, infer=infer, infer_speedup=infer_speedup, method=method, k_step=k_step, use_tqdm=use_tqdm)
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return x
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