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# import gin
import numpy as np
import torch
import torch.nn as nn
from torch.nn.utils import weight_norm
from .pcmer import PCmer
def split_to_dict(tensor, tensor_splits):
"""Split a tensor into a dictionary of multiple tensors."""
labels = []
sizes = []
for k, v in tensor_splits.items():
labels.append(k)
sizes.append(v)
tensors = torch.split(tensor, sizes, dim=-1)
return dict(zip(labels, tensors))
def get_min_shape(*args):
"""Get the minimum size along each dimension of multiple tensors."""
if not args:
return [] # 如果没有传入任何张量，返回空列表
# 初始化最小形状为第一个张量的形状
min_shape = list(args[0].shape)
# 遍历所有张量，更新每个维度的最小值
for tensor in args:
# 确保张量的形状可以与当前最小形状进行比较
if len(tensor.shape) != len(min_shape):
print("All tensors must have the same number of dimensions")
# 更新每个维度的最小值
min_shape = [min(dim_size, min_dim_size) for dim_size, min_dim_size in zip(tensor.shape, min_shape)]
return min_shape
class Unit2Control(nn.Module):
def __init__(
self,
input_channel,
n_spk,
output_splits,
use_pitch_aug=False,
pcmer_norm=False):
super().__init__()
self.output_splits = output_splits
self.f0_embed = nn.Linear(1, 256)
self.phase_embed = nn.Linear(1, 256)
self.volume_embed = nn.Linear(1, 256)
self.n_spk = n_spk
if n_spk is not None and n_spk > 1:
self.spk_embed = nn.Embedding(n_spk, 256)
if use_pitch_aug:
self.aug_shift_embed = nn.Linear(1, 256, bias=False)
else:
self.aug_shift_embed = None
# conv in stack
self.stack = nn.Sequential(
nn.Conv1d(input_channel, 256, 3, 1, 1),
nn.GroupNorm(4, 256),
nn.LeakyReLU(),
nn.Conv1d(256, 256, 3, 1, 1))
# transformer
self.decoder = PCmer(
num_layers=3,
num_heads=8,
dim_model=256,
dim_keys=256,
dim_values=256,
residual_dropout=0.1,
attention_dropout=0.1,
pcmer_norm=pcmer_norm)
self.norm = nn.LayerNorm(256)
# out
self.n_out = sum([v for k, v in output_splits.items()])
self.dense_out = weight_norm(
nn.Linear(256, self.n_out))
def forward(self, units, f0, phase, volume, spk_id = None, spk_mix_dict = None, aug_shift = None):
'''
input:
B x n_frames x n_unit
return:
dict of B x n_frames x feat
'''
x = self.stack(units.transpose(1,2)).transpose(1,2)
try:
x = x + self.f0_embed((1+ f0 / 700).log()) + self.phase_embed(phase / np.pi) + self.volume_embed(volume)
except:
f0_dim2, phase_dim2, volume_dim2 = f0.shape[1], phase.shape[1], volume.shape[1]
x = x[:, :f0_dim2, :]
x = x + self.f0_embed((1+ f0 / 700).log()) + self.phase_embed(phase / np.pi) + 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 - 1)
else:
x = x + self.spk_embed(spk_id - 1)
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)
x = self.norm(x)
e = self.dense_out(x)
controls = split_to_dict(e, self.output_splits)
return controls, x
class Unit2ControlFac(nn.Module):
def __init__(
self,
input_channel,
output_splits,
use_pitch_aug=False,
pcmer_norm=False):
super().__init__()
self.output_splits = output_splits
self.f0_embed = nn.Linear(1, 256)
self.phase_embed = nn.Linear(1, 256)
self.volume_embed = nn.Linear(1, 256)
# if n_spk is not None and n_spk > 1:
# self.spk_embed = nn.Embedding(n_spk, 256)
if use_pitch_aug:
self.aug_shift_embed = nn.Linear(1, 256, bias=False)
else:
self.aug_shift_embed = None
# conv in stack
self.stack = nn.Sequential(
nn.Conv1d(input_channel, 256, 3, 1, 1),
nn.GroupNorm(4, 256),
nn.LeakyReLU(),
nn.Conv1d(256, 256, 3, 1, 1))
# transformer
self.decoder = PCmer(
num_layers=3,
num_heads=8,
dim_model=256,
dim_keys=256,
dim_values=256,
residual_dropout=0.1,
attention_dropout=0.1,
pcmer_norm=pcmer_norm)
self.norm = nn.LayerNorm(256)
# out
self.n_out = sum([v for k, v in output_splits.items()])
self.dense_out = weight_norm(
nn.Linear(256, self.n_out))
def forward(self, units, f0, phase, volume, spk, aug_shift = None):
'''
input:
B x n_frames x n_unit
return:
dict of B x n_frames x feat
'''
x = self.stack(units.transpose(1,2)).transpose(1,2)
try:
x = x + self.f0_embed((1+ f0 / 700).log()) + self.phase_embed(phase / np.pi) + self.volume_embed(volume)
except:
f0_dim2, phase_dim2, volume_dim2 = f0.shape[1], phase.shape[1], volume.shape[1]
x = x[:, :f0_dim2, :]
x = x + self.f0_embed((1+ f0 / 700).log()) + self.phase_embed(phase / np.pi) + 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 - 1)
# else:
# x = x + self.spk_embed(spk_id - 1)
n_frame = x.shape[1]
spk = spk.unsqueeze(1).repeat(1, n_frame, 1)
x = x + spk
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)
x = self.norm(x)
e = self.dense_out(x)
controls = split_to_dict(e, self.output_splits)
return controls, x |