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2023-07-13 14:35:24 +08:00
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parent 6c13f1fe52
commit 9739f3085d
5 changed files with 418 additions and 184 deletions
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rmvpe.py
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@@ -1,34 +1,46 @@
import sys,torch,numpy as np,traceback,pdb
import sys, torch, numpy as np, traceback, pdb
import torch.nn as nn
from time import time as ttime
import torch.nn.functional as F
class BiGRU(nn.Module):
def __init__(self, input_features, hidden_features, num_layers):
super(BiGRU, self).__init__()
self.gru = nn.GRU(input_features, hidden_features, num_layers=num_layers, batch_first=True, bidirectional=True)
self.gru = nn.GRU(
input_features,
hidden_features,
num_layers=num_layers,
batch_first=True,
bidirectional=True,
)
def forward(self, x):
return self.gru(x)[0]
class ConvBlockRes(nn.Module):
def __init__(self, in_channels, out_channels, momentum=0.01):
super(ConvBlockRes, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(in_channels=in_channels,
out_channels=out_channels,
kernel_size=(3, 3),
stride=(1, 1),
padding=(1, 1),
bias=False),
nn.Conv2d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=(3, 3),
stride=(1, 1),
padding=(1, 1),
bias=False,
),
nn.BatchNorm2d(out_channels, momentum=momentum),
nn.ReLU(),
nn.Conv2d(in_channels=out_channels,
out_channels=out_channels,
kernel_size=(3, 3),
stride=(1, 1),
padding=(1, 1),
bias=False),
nn.Conv2d(
in_channels=out_channels,
out_channels=out_channels,
kernel_size=(3, 3),
stride=(1, 1),
padding=(1, 1),
bias=False,
),
nn.BatchNorm2d(out_channels, momentum=momentum),
nn.ReLU(),
)
@@ -44,15 +56,29 @@ class ConvBlockRes(nn.Module):
else:
return self.conv(x) + x
class Encoder(nn.Module):
def __init__(self, in_channels, in_size, n_encoders, kernel_size, n_blocks, out_channels=16, momentum=0.01):
def __init__(
self,
in_channels,
in_size,
n_encoders,
kernel_size,
n_blocks,
out_channels=16,
momentum=0.01,
):
super(Encoder, self).__init__()
self.n_encoders = n_encoders
self.bn = nn.BatchNorm2d(in_channels, momentum=momentum)
self.layers = nn.ModuleList()
self.latent_channels = []
for i in range(self.n_encoders):
self.layers.append(ResEncoderBlock(in_channels, out_channels, kernel_size, n_blocks, momentum=momentum))
self.layers.append(
ResEncoderBlock(
in_channels, out_channels, kernel_size, n_blocks, momentum=momentum
)
)
self.latent_channels.append([out_channels, in_size])
in_channels = out_channels
out_channels *= 2
@@ -67,8 +93,12 @@ class Encoder(nn.Module):
_, x = self.layers[i](x)
concat_tensors.append(_)
return x, concat_tensors
class ResEncoderBlock(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, n_blocks=1, momentum=0.01):
def __init__(
self, in_channels, out_channels, kernel_size, n_blocks=1, momentum=0.01
):
super(ResEncoderBlock, self).__init__()
self.n_blocks = n_blocks
self.conv = nn.ModuleList()
@@ -86,38 +116,48 @@ class ResEncoderBlock(nn.Module):
return x, self.pool(x)
else:
return x
class Intermediate(nn.Module):#
class Intermediate(nn.Module): #
def __init__(self, in_channels, out_channels, n_inters, n_blocks, momentum=0.01):
super(Intermediate, self).__init__()
self.n_inters = n_inters
self.layers = nn.ModuleList()
self.layers.append(ResEncoderBlock(in_channels, out_channels, None, n_blocks, momentum))
for i in range(self.n_inters-1):
self.layers.append(ResEncoderBlock(out_channels, out_channels, None, n_blocks, momentum))
self.layers.append(
ResEncoderBlock(in_channels, out_channels, None, n_blocks, momentum)
)
for i in range(self.n_inters - 1):
self.layers.append(
ResEncoderBlock(out_channels, out_channels, None, n_blocks, momentum)
)
def forward(self, x):
for i in range(self.n_inters):
x = self.layers[i](x)
return x
class ResDecoderBlock(nn.Module):
def __init__(self, in_channels, out_channels, stride, n_blocks=1, momentum=0.01):
super(ResDecoderBlock, self).__init__()
out_padding = (0, 1) if stride == (1, 2) else (1, 1)
self.n_blocks = n_blocks
self.conv1 = nn.Sequential(
nn.ConvTranspose2d(in_channels=in_channels,
out_channels=out_channels,
kernel_size=(3, 3),
stride=stride,
padding=(1, 1),
output_padding=out_padding,
bias=False),
nn.ConvTranspose2d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=(3, 3),
stride=stride,
padding=(1, 1),
output_padding=out_padding,
bias=False,
),
nn.BatchNorm2d(out_channels, momentum=momentum),
nn.ReLU(),
)
self.conv2 = nn.ModuleList()
self.conv2.append(ConvBlockRes(out_channels * 2, out_channels, momentum))
for i in range(n_blocks-1):
for i in range(n_blocks - 1):
self.conv2.append(ConvBlockRes(out_channels, out_channels, momentum))
def forward(self, x, concat_tensor):
@@ -126,6 +166,8 @@ class ResDecoderBlock(nn.Module):
for i in range(self.n_blocks):
x = self.conv2[i](x)
return x
class Decoder(nn.Module):
def __init__(self, in_channels, n_decoders, stride, n_blocks, momentum=0.01):
super(Decoder, self).__init__()
@@ -133,20 +175,40 @@ class Decoder(nn.Module):
self.n_decoders = n_decoders
for i in range(self.n_decoders):
out_channels = in_channels // 2
self.layers.append(ResDecoderBlock(in_channels, out_channels, stride, n_blocks, momentum))
self.layers.append(
ResDecoderBlock(in_channels, out_channels, stride, n_blocks, momentum)
)
in_channels = out_channels
def forward(self, x, concat_tensors):
for i in range(self.n_decoders):
x = self.layers[i](x, concat_tensors[-1-i])
x = self.layers[i](x, concat_tensors[-1 - i])
return x
class DeepUnet(nn.Module):
def __init__(self, kernel_size, n_blocks, en_de_layers=5, inter_layers=4, in_channels=1, en_out_channels=16):
def __init__(
self,
kernel_size,
n_blocks,
en_de_layers=5,
inter_layers=4,
in_channels=1,
en_out_channels=16,
):
super(DeepUnet, self).__init__()
self.encoder = Encoder(in_channels, 128, en_de_layers, kernel_size, n_blocks, en_out_channels)
self.intermediate = Intermediate(self.encoder.out_channel // 2, self.encoder.out_channel, inter_layers, n_blocks)
self.decoder = Decoder(self.encoder.out_channel, en_de_layers, kernel_size, n_blocks)
self.encoder = Encoder(
in_channels, 128, en_de_layers, kernel_size, n_blocks, en_out_channels
)
self.intermediate = Intermediate(
self.encoder.out_channel // 2,
self.encoder.out_channel,
inter_layers,
n_blocks,
)
self.decoder = Decoder(
self.encoder.out_channel, en_de_layers, kernel_size, n_blocks
)
def forward(self, x):
x, concat_tensors = self.encoder(x)
@@ -154,24 +216,38 @@ class DeepUnet(nn.Module):
x = self.decoder(x, concat_tensors)
return x
class E2E(nn.Module):
def __init__(self, n_blocks, n_gru, kernel_size, en_de_layers=5, inter_layers=4, in_channels=1,
en_out_channels=16):
def __init__(
self,
n_blocks,
n_gru,
kernel_size,
en_de_layers=5,
inter_layers=4,
in_channels=1,
en_out_channels=16,
):
super(E2E, self).__init__()
self.unet = DeepUnet(kernel_size, n_blocks, en_de_layers, inter_layers, in_channels, en_out_channels)
self.unet = DeepUnet(
kernel_size,
n_blocks,
en_de_layers,
inter_layers,
in_channels,
en_out_channels,
)
self.cnn = nn.Conv2d(en_out_channels, 3, (3, 3), padding=(1, 1))
if n_gru:
self.fc = nn.Sequential(
BiGRU(3 * 128, 256, n_gru),
nn.Linear(512, 360),
nn.Dropout(0.25),
nn.Sigmoid()
nn.Sigmoid(),
)
else:
self.fc = nn.Sequential(
nn.Linear(3 * N_MELS, N_CLASS),
nn.Dropout(0.25),
nn.Sigmoid()
nn.Linear(3 * N_MELS, N_CLASS), nn.Dropout(0.25), nn.Sigmoid()
)
def forward(self, mel):
@@ -179,19 +255,23 @@ class E2E(nn.Module):
x = self.cnn(self.unet(mel)).transpose(1, 2).flatten(-2)
x = self.fc(x)
return x
from librosa.filters import mel
class MelSpectrogram(torch.nn.Module):
def __init__(
self,
is_half,
n_mel_channels,
sampling_rate,
win_length,
hop_length,
n_fft=None,
mel_fmin=0,
mel_fmax=None,
clamp=1e-5
self,
is_half,
n_mel_channels,
sampling_rate,
win_length,
hop_length,
n_fft=None,
mel_fmin=0,
mel_fmax=None,
clamp=1e-5,
):
super().__init__()
n_fft = win_length if n_fft is None else n_fft
@@ -202,7 +282,8 @@ class MelSpectrogram(torch.nn.Module):
n_mels=n_mel_channels,
fmin=mel_fmin,
fmax=mel_fmax,
htk=True)
htk=True,
)
mel_basis = torch.from_numpy(mel_basis).float()
self.register_buffer("mel_basis", mel_basis)
self.n_fft = win_length if n_fft is None else n_fft
@@ -211,16 +292,18 @@ class MelSpectrogram(torch.nn.Module):
self.sampling_rate = sampling_rate
self.n_mel_channels = n_mel_channels
self.clamp = clamp
self.is_half=is_half
self.is_half = is_half
def forward(self, audio, keyshift=0, speed=1, center=True):
factor = 2 ** (keyshift / 12)
n_fft_new = int(np.round(self.n_fft * factor))
win_length_new = int(np.round(self.win_length * factor))
hop_length_new = int(np.round(self.hop_length * speed))
keyshift_key = str(keyshift) + '_' + str(audio.device)
keyshift_key = str(keyshift) + "_" + str(audio.device)
if keyshift_key not in self.hann_window:
self.hann_window[keyshift_key] = torch.hann_window(win_length_new).to(audio.device)
self.hann_window[keyshift_key] = torch.hann_window(win_length_new).to(
audio.device
)
fft = torch.stft(
audio,
n_fft=n_fft_new,
@@ -228,51 +311,57 @@ class MelSpectrogram(torch.nn.Module):
win_length=win_length_new,
window=self.hann_window[keyshift_key],
center=center,
return_complex=True)
return_complex=True,
)
magnitude = torch.sqrt(fft.real.pow(2) + fft.imag.pow(2))
if keyshift != 0:
size = self.n_fft // 2 + 1
resize = magnitude.size(1)
if resize < size:
magnitude = F.pad(magnitude, (0, 0, 0, size - resize))
magnitude = magnitude[:, :size, :]* self.win_length / win_length_new
magnitude = magnitude[:, :size, :] * self.win_length / win_length_new
mel_output = torch.matmul(self.mel_basis, magnitude)
if(self.is_half==True):mel_output=mel_output.half()
if self.is_half == True:
mel_output = mel_output.half()
log_mel_spec = torch.log(torch.clamp(mel_output, min=self.clamp))
return log_mel_spec
class RMVPE:
def __init__(self, model_path,is_half, device=None):
def __init__(self, model_path, is_half, device=None):
self.resample_kernel = {}
model = E2E(4, 1, (2, 2))
ckpt = torch.load(model_path,map_location="cpu")
ckpt = torch.load(model_path, map_location="cpu")
model.load_state_dict(ckpt)
model.eval()
if(is_half==True):model=model.half()
if is_half == True:
model = model.half()
self.model = model
self.resample_kernel = {}
self.is_half=is_half
self.is_half = is_half
if device is None:
device = 'cuda' if torch.cuda.is_available() else 'cpu'
self.device=device
self.mel_extractor = MelSpectrogram(is_half,128, 16000, 1024, 160, None, 30, 8000).to(device)
device = "cuda" if torch.cuda.is_available() else "cpu"
self.device = device
self.mel_extractor = MelSpectrogram(
is_half, 128, 16000, 1024, 160, None, 30, 8000
).to(device)
self.model = self.model.to(device)
cents_mapping = (20 * np.arange(360) + 1997.3794084376191)
cents_mapping = 20 * np.arange(360) + 1997.3794084376191
self.cents_mapping = np.pad(cents_mapping, (4, 4)) # 368
def mel2hidden(self, mel):
with torch.no_grad():
n_frames = mel.shape[-1]
mel = F.pad(mel, (0, 32 * ((n_frames - 1) // 32 + 1) - n_frames), mode='reflect')
mel = F.pad(
mel, (0, 32 * ((n_frames - 1) // 32 + 1) - n_frames), mode="reflect"
)
hidden = self.model(mel)
return hidden[:, :n_frames]
def decode(self, hidden, thred=0.03):
cents_pred = self.to_local_average_cents(hidden, thred=thred)
f0 = 10 * (2 ** (cents_pred / 1200))
f0[f0==10]=0
f0[f0 == 10] = 0
# f0 = np.array([10 * (2 ** (cent_pred / 1200)) if cent_pred else 0 for cent_pred in cents_pred])
return f0
@@ -286,15 +375,16 @@ class RMVPE:
hidden = self.mel2hidden(mel)
# torch.cuda.synchronize()
# t2=ttime()
hidden=hidden.squeeze(0).cpu().numpy()
if(self.is_half==True):hidden=hidden.astype("float32")
hidden = hidden.squeeze(0).cpu().numpy()
if self.is_half == True:
hidden = hidden.astype("float32")
f0 = self.decode(hidden, thred=thred)
# torch.cuda.synchronize()
# t3=ttime()
# print("hmvpe:%s\t%s\t%s\t%s"%(t1-t0,t2-t1,t3-t2,t3-t0))
return f0
def to_local_average_cents(self,salience, thred=0.05):
def to_local_average_cents(self, salience, thred=0.05):
# t0 = ttime()
center = np.argmax(salience, axis=1) # 帧长#index
salience = np.pad(salience, ((0, 0), (4, 4))) # 帧长,368
@@ -305,8 +395,8 @@ class RMVPE:
starts = center - 4
ends = center + 5
for idx in range(salience.shape[0]):
todo_salience.append(salience[:, starts[idx]:ends[idx]][idx])
todo_cents_mapping.append(self.cents_mapping[starts[idx]:ends[idx]])
todo_salience.append(salience[:, starts[idx] : ends[idx]][idx])
todo_cents_mapping.append(self.cents_mapping[starts[idx] : ends[idx]])
# t2 = ttime()
todo_salience = np.array(todo_salience) # 帧长，9
todo_cents_mapping = np.array(todo_cents_mapping) # 帧长，9
@@ -321,8 +411,6 @@ class RMVPE:
return devided
# if __name__ == '__main__':
# audio, sampling_rate = sf.read("卢本伟语录~1.wav")
# if len(audio.shape) > 1: