433 lines
15 KiB
Python
433 lines
15 KiB
Python
import sys, torch, numpy as np, traceback, pdb
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import torch.nn as nn
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from time import time as ttime
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import torch.nn.functional as F
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class BiGRU(nn.Module):
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def __init__(self, input_features, hidden_features, num_layers):
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super(BiGRU, self).__init__()
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self.gru = nn.GRU(
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input_features,
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hidden_features,
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num_layers=num_layers,
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batch_first=True,
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bidirectional=True,
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)
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def forward(self, x):
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return self.gru(x)[0]
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class ConvBlockRes(nn.Module):
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def __init__(self, in_channels, out_channels, momentum=0.01):
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super(ConvBlockRes, self).__init__()
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self.conv = nn.Sequential(
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nn.Conv2d(
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in_channels=in_channels,
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out_channels=out_channels,
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kernel_size=(3, 3),
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stride=(1, 1),
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padding=(1, 1),
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bias=False,
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),
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nn.BatchNorm2d(out_channels, momentum=momentum),
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nn.ReLU(),
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nn.Conv2d(
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in_channels=out_channels,
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out_channels=out_channels,
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kernel_size=(3, 3),
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stride=(1, 1),
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padding=(1, 1),
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bias=False,
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),
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nn.BatchNorm2d(out_channels, momentum=momentum),
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nn.ReLU(),
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)
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if in_channels != out_channels:
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self.shortcut = nn.Conv2d(in_channels, out_channels, (1, 1))
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self.is_shortcut = True
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else:
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self.is_shortcut = False
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def forward(self, x):
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if self.is_shortcut:
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return self.conv(x) + self.shortcut(x)
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else:
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return self.conv(x) + x
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class Encoder(nn.Module):
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def __init__(
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self,
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in_channels,
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in_size,
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n_encoders,
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kernel_size,
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n_blocks,
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out_channels=16,
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momentum=0.01,
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):
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super(Encoder, self).__init__()
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self.n_encoders = n_encoders
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self.bn = nn.BatchNorm2d(in_channels, momentum=momentum)
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self.layers = nn.ModuleList()
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self.latent_channels = []
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for i in range(self.n_encoders):
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self.layers.append(
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ResEncoderBlock(
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in_channels, out_channels, kernel_size, n_blocks, momentum=momentum
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)
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)
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self.latent_channels.append([out_channels, in_size])
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in_channels = out_channels
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out_channels *= 2
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in_size //= 2
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self.out_size = in_size
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self.out_channel = out_channels
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def forward(self, x):
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concat_tensors = []
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x = self.bn(x)
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for i in range(self.n_encoders):
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_, x = self.layers[i](x)
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concat_tensors.append(_)
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return x, concat_tensors
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class ResEncoderBlock(nn.Module):
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def __init__(
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self, in_channels, out_channels, kernel_size, n_blocks=1, momentum=0.01
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):
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super(ResEncoderBlock, self).__init__()
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self.n_blocks = n_blocks
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self.conv = nn.ModuleList()
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self.conv.append(ConvBlockRes(in_channels, out_channels, momentum))
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for i in range(n_blocks - 1):
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self.conv.append(ConvBlockRes(out_channels, out_channels, momentum))
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self.kernel_size = kernel_size
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if self.kernel_size is not None:
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self.pool = nn.AvgPool2d(kernel_size=kernel_size)
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def forward(self, x):
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for i in range(self.n_blocks):
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x = self.conv[i](x)
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if self.kernel_size is not None:
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return x, self.pool(x)
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else:
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return x
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class Intermediate(nn.Module): #
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def __init__(self, in_channels, out_channels, n_inters, n_blocks, momentum=0.01):
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super(Intermediate, self).__init__()
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self.n_inters = n_inters
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self.layers = nn.ModuleList()
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self.layers.append(
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ResEncoderBlock(in_channels, out_channels, None, n_blocks, momentum)
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)
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for i in range(self.n_inters - 1):
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self.layers.append(
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ResEncoderBlock(out_channels, out_channels, None, n_blocks, momentum)
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)
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def forward(self, x):
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for i in range(self.n_inters):
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x = self.layers[i](x)
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return x
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class ResDecoderBlock(nn.Module):
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def __init__(self, in_channels, out_channels, stride, n_blocks=1, momentum=0.01):
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super(ResDecoderBlock, self).__init__()
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out_padding = (0, 1) if stride == (1, 2) else (1, 1)
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self.n_blocks = n_blocks
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self.conv1 = nn.Sequential(
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nn.ConvTranspose2d(
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in_channels=in_channels,
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out_channels=out_channels,
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kernel_size=(3, 3),
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stride=stride,
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padding=(1, 1),
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output_padding=out_padding,
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bias=False,
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),
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nn.BatchNorm2d(out_channels, momentum=momentum),
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nn.ReLU(),
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)
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self.conv2 = nn.ModuleList()
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self.conv2.append(ConvBlockRes(out_channels * 2, out_channels, momentum))
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for i in range(n_blocks - 1):
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self.conv2.append(ConvBlockRes(out_channels, out_channels, momentum))
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def forward(self, x, concat_tensor):
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x = self.conv1(x)
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x = torch.cat((x, concat_tensor), dim=1)
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for i in range(self.n_blocks):
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x = self.conv2[i](x)
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return x
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class Decoder(nn.Module):
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def __init__(self, in_channels, n_decoders, stride, n_blocks, momentum=0.01):
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super(Decoder, self).__init__()
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self.layers = nn.ModuleList()
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self.n_decoders = n_decoders
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for i in range(self.n_decoders):
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out_channels = in_channels // 2
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self.layers.append(
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ResDecoderBlock(in_channels, out_channels, stride, n_blocks, momentum)
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)
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in_channels = out_channels
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def forward(self, x, concat_tensors):
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for i in range(self.n_decoders):
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x = self.layers[i](x, concat_tensors[-1 - i])
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return x
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class DeepUnet(nn.Module):
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def __init__(
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self,
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kernel_size,
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n_blocks,
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en_de_layers=5,
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inter_layers=4,
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in_channels=1,
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en_out_channels=16,
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):
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super(DeepUnet, self).__init__()
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self.encoder = Encoder(
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in_channels, 128, en_de_layers, kernel_size, n_blocks, en_out_channels
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)
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self.intermediate = Intermediate(
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self.encoder.out_channel // 2,
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self.encoder.out_channel,
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inter_layers,
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n_blocks,
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)
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self.decoder = Decoder(
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self.encoder.out_channel, en_de_layers, kernel_size, n_blocks
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)
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def forward(self, x):
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x, concat_tensors = self.encoder(x)
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x = self.intermediate(x)
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x = self.decoder(x, concat_tensors)
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return x
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class E2E(nn.Module):
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def __init__(
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self,
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n_blocks,
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n_gru,
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kernel_size,
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en_de_layers=5,
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inter_layers=4,
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in_channels=1,
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en_out_channels=16,
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):
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super(E2E, self).__init__()
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self.unet = DeepUnet(
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kernel_size,
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n_blocks,
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en_de_layers,
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inter_layers,
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in_channels,
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en_out_channels,
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)
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self.cnn = nn.Conv2d(en_out_channels, 3, (3, 3), padding=(1, 1))
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if n_gru:
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self.fc = nn.Sequential(
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BiGRU(3 * 128, 256, n_gru),
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nn.Linear(512, 360),
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nn.Dropout(0.25),
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nn.Sigmoid(),
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)
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else:
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self.fc = nn.Sequential(
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nn.Linear(3 * N_MELS, N_CLASS), nn.Dropout(0.25), nn.Sigmoid()
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)
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def forward(self, mel):
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mel = mel.transpose(-1, -2).unsqueeze(1)
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x = self.cnn(self.unet(mel)).transpose(1, 2).flatten(-2)
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x = self.fc(x)
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return x
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from librosa.filters import mel
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class MelSpectrogram(torch.nn.Module):
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def __init__(
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self,
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is_half,
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n_mel_channels,
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sampling_rate,
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win_length,
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hop_length,
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n_fft=None,
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mel_fmin=0,
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mel_fmax=None,
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clamp=1e-5,
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):
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super().__init__()
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n_fft = win_length if n_fft is None else n_fft
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self.hann_window = {}
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mel_basis = mel(
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sr=sampling_rate,
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n_fft=n_fft,
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n_mels=n_mel_channels,
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fmin=mel_fmin,
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fmax=mel_fmax,
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htk=True,
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)
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mel_basis = torch.from_numpy(mel_basis).float()
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self.register_buffer("mel_basis", mel_basis)
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self.n_fft = win_length if n_fft is None else n_fft
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self.hop_length = hop_length
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self.win_length = win_length
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self.sampling_rate = sampling_rate
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self.n_mel_channels = n_mel_channels
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self.clamp = clamp
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self.is_half = is_half
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def forward(self, audio, keyshift=0, speed=1, center=True):
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factor = 2 ** (keyshift / 12)
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n_fft_new = int(np.round(self.n_fft * factor))
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win_length_new = int(np.round(self.win_length * factor))
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hop_length_new = int(np.round(self.hop_length * speed))
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keyshift_key = str(keyshift) + "_" + str(audio.device)
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if keyshift_key not in self.hann_window:
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self.hann_window[keyshift_key] = torch.hann_window(win_length_new).to(
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audio.device
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)
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fft = torch.stft(
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audio,
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n_fft=n_fft_new,
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hop_length=hop_length_new,
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win_length=win_length_new,
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window=self.hann_window[keyshift_key],
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center=center,
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return_complex=True,
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)
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magnitude = torch.sqrt(fft.real.pow(2) + fft.imag.pow(2))
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if keyshift != 0:
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size = self.n_fft // 2 + 1
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resize = magnitude.size(1)
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if resize < size:
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magnitude = F.pad(magnitude, (0, 0, 0, size - resize))
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magnitude = magnitude[:, :size, :] * self.win_length / win_length_new
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mel_output = torch.matmul(self.mel_basis, magnitude)
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if self.is_half == True:
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mel_output = mel_output.half()
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log_mel_spec = torch.log(torch.clamp(mel_output, min=self.clamp))
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return log_mel_spec
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class RMVPE:
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def __init__(self, model_path, is_half, device=None):
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self.resample_kernel = {}
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model = E2E(4, 1, (2, 2))
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ckpt = torch.load(model_path, map_location="cpu")
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model.load_state_dict(ckpt)
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model.eval()
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if is_half == True:
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model = model.half()
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self.model = model
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self.resample_kernel = {}
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self.is_half = is_half
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if device is None:
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device = "cuda" if torch.cuda.is_available() else "cpu"
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self.device = device
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self.mel_extractor = MelSpectrogram(
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is_half, 128, 16000, 1024, 160, None, 30, 8000
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).to(device)
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self.model = self.model.to(device)
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cents_mapping = 20 * np.arange(360) + 1997.3794084376191
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self.cents_mapping = np.pad(cents_mapping, (4, 4)) # 368
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def mel2hidden(self, mel):
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with torch.no_grad():
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n_frames = mel.shape[-1]
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mel = F.pad(
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mel, (0, 32 * ((n_frames - 1) // 32 + 1) - n_frames), mode="reflect"
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)
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hidden = self.model(mel)
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return hidden[:, :n_frames]
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def decode(self, hidden, thred=0.03):
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cents_pred = self.to_local_average_cents(hidden, thred=thred)
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f0 = 10 * (2 ** (cents_pred / 1200))
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f0[f0 == 10] = 0
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# f0 = np.array([10 * (2 ** (cent_pred / 1200)) if cent_pred else 0 for cent_pred in cents_pred])
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return f0
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def infer_from_audio(self, audio, thred=0.03):
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audio = torch.from_numpy(audio).float().to(self.device).unsqueeze(0)
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# torch.cuda.synchronize()
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# t0=ttime()
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mel = self.mel_extractor(audio, center=True)
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# torch.cuda.synchronize()
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# t1=ttime()
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hidden = self.mel2hidden(mel)
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# torch.cuda.synchronize()
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# t2=ttime()
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hidden = hidden.squeeze(0).cpu().numpy()
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if self.is_half == True:
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hidden = hidden.astype("float32")
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f0 = self.decode(hidden, thred=thred)
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# torch.cuda.synchronize()
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# t3=ttime()
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# print("hmvpe:%s\t%s\t%s\t%s"%(t1-t0,t2-t1,t3-t2,t3-t0))
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return f0
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def to_local_average_cents(self, salience, thred=0.05):
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# t0 = ttime()
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center = np.argmax(salience, axis=1) # 帧长#index
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salience = np.pad(salience, ((0, 0), (4, 4))) # 帧长,368
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# t1 = ttime()
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center += 4
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todo_salience = []
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todo_cents_mapping = []
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starts = center - 4
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ends = center + 5
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for idx in range(salience.shape[0]):
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todo_salience.append(salience[:, starts[idx] : ends[idx]][idx])
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todo_cents_mapping.append(self.cents_mapping[starts[idx] : ends[idx]])
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# t2 = ttime()
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todo_salience = np.array(todo_salience) # 帧长，9
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todo_cents_mapping = np.array(todo_cents_mapping) # 帧长，9
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product_sum = np.sum(todo_salience * todo_cents_mapping, 1)
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weight_sum = np.sum(todo_salience, 1) # 帧长
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devided = product_sum / weight_sum # 帧长
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# t3 = ttime()
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maxx = np.max(salience, axis=1) # 帧长
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devided[maxx <= thred] = 0
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# t4 = ttime()
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# print("decode:%s\t%s\t%s\t%s" % (t1 - t0, t2 - t1, t3 - t2, t4 - t3))
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return devided
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# if __name__ == '__main__':
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# audio, sampling_rate = sf.read("卢本伟语录~1.wav")
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# if len(audio.shape) > 1:
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# audio = librosa.to_mono(audio.transpose(1, 0))
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# audio_bak = audio.copy()
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# if sampling_rate != 16000:
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# audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
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# model_path = "/bili-coeus/jupyter/jupyterhub-liujing04/vits_ch/test-RMVPE/weights/rmvpe_llc_half.pt"
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# thred = 0.03 # 0.01
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# device = 'cuda' if torch.cuda.is_available() else 'cpu'
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# rmvpe = RMVPE(model_path,is_half=False, device=device)
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# t0=ttime()
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# f0 = rmvpe.infer_from_audio(audio, thred=thred)
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# f0 = rmvpe.infer_from_audio(audio, thred=thred)
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# f0 = rmvpe.infer_from_audio(audio, thred=thred)
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# f0 = rmvpe.infer_from_audio(audio, thred=thred)
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# f0 = rmvpe.infer_from_audio(audio, thred=thred)
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# t1=ttime()
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# print(f0.shape,t1-t0)
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