345 lines
14 KiB
Python
345 lines
14 KiB
Python
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)
|
||
|
||
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.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.BatchNorm2d(out_channels, momentum=momentum),
|
||
nn.ReLU(),
|
||
)
|
||
if in_channels != out_channels:
|
||
self.shortcut = nn.Conv2d(in_channels, out_channels, (1, 1))
|
||
self.is_shortcut = True
|
||
else:
|
||
self.is_shortcut = False
|
||
|
||
def forward(self, x):
|
||
if self.is_shortcut:
|
||
return self.conv(x) + self.shortcut(x)
|
||
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):
|
||
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.latent_channels.append([out_channels, in_size])
|
||
in_channels = out_channels
|
||
out_channels *= 2
|
||
in_size //= 2
|
||
self.out_size = in_size
|
||
self.out_channel = out_channels
|
||
|
||
def forward(self, x):
|
||
concat_tensors = []
|
||
x = self.bn(x)
|
||
for i in range(self.n_encoders):
|
||
_, 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):
|
||
super(ResEncoderBlock, self).__init__()
|
||
self.n_blocks = n_blocks
|
||
self.conv = nn.ModuleList()
|
||
self.conv.append(ConvBlockRes(in_channels, out_channels, momentum))
|
||
for i in range(n_blocks - 1):
|
||
self.conv.append(ConvBlockRes(out_channels, out_channels, momentum))
|
||
self.kernel_size = kernel_size
|
||
if self.kernel_size is not None:
|
||
self.pool = nn.AvgPool2d(kernel_size=kernel_size)
|
||
|
||
def forward(self, x):
|
||
for i in range(self.n_blocks):
|
||
x = self.conv[i](x)
|
||
if self.kernel_size is not None:
|
||
return x, self.pool(x)
|
||
else:
|
||
return x
|
||
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))
|
||
|
||
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.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):
|
||
self.conv2.append(ConvBlockRes(out_channels, out_channels, momentum))
|
||
|
||
def forward(self, x, concat_tensor):
|
||
x = self.conv1(x)
|
||
x = torch.cat((x, concat_tensor), dim=1)
|
||
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__()
|
||
self.layers = nn.ModuleList()
|
||
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))
|
||
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])
|
||
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):
|
||
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)
|
||
|
||
def forward(self, x):
|
||
x, concat_tensors = self.encoder(x)
|
||
x = self.intermediate(x)
|
||
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):
|
||
super(E2E, self).__init__()
|
||
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()
|
||
)
|
||
else:
|
||
self.fc = nn.Sequential(
|
||
nn.Linear(3 * N_MELS, N_CLASS),
|
||
nn.Dropout(0.25),
|
||
nn.Sigmoid()
|
||
)
|
||
|
||
def forward(self, mel):
|
||
mel = mel.transpose(-1, -2).unsqueeze(1)
|
||
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
|
||
):
|
||
super().__init__()
|
||
n_fft = win_length if n_fft is None else n_fft
|
||
self.hann_window = {}
|
||
mel_basis = mel(
|
||
sr=sampling_rate,
|
||
n_fft=n_fft,
|
||
n_mels=n_mel_channels,
|
||
fmin=mel_fmin,
|
||
fmax=mel_fmax,
|
||
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
|
||
self.hop_length = hop_length
|
||
self.win_length = win_length
|
||
self.sampling_rate = sampling_rate
|
||
self.n_mel_channels = n_mel_channels
|
||
self.clamp = clamp
|
||
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)
|
||
if keyshift_key not in self.hann_window:
|
||
self.hann_window[keyshift_key] = torch.hann_window(win_length_new).to(audio.device)
|
||
fft = torch.stft(
|
||
audio,
|
||
n_fft=n_fft_new,
|
||
hop_length=hop_length_new,
|
||
win_length=win_length_new,
|
||
window=self.hann_window[keyshift_key],
|
||
center=center,
|
||
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
|
||
mel_output = torch.matmul(self.mel_basis, magnitude)
|
||
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):
|
||
self.resample_kernel = {}
|
||
model = E2E(4, 1, (2, 2))
|
||
ckpt = torch.load(model_path,map_location="cpu")
|
||
model.load_state_dict(ckpt)
|
||
model.eval()
|
||
if(is_half==True):model=model.half()
|
||
self.model = model
|
||
self.resample_kernel = {}
|
||
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)
|
||
self.model = self.model.to(device)
|
||
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')
|
||
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 = np.array([10 * (2 ** (cent_pred / 1200)) if cent_pred else 0 for cent_pred in cents_pred])
|
||
return f0
|
||
|
||
def infer_from_audio(self, audio, thred=0.03):
|
||
audio = torch.from_numpy(audio).float().to(self.device).unsqueeze(0)
|
||
# torch.cuda.synchronize()
|
||
# t0=ttime()
|
||
mel = self.mel_extractor(audio, center=True)
|
||
# torch.cuda.synchronize()
|
||
# t1=ttime()
|
||
hidden = self.mel2hidden(mel)
|
||
# torch.cuda.synchronize()
|
||
# t2=ttime()
|
||
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):
|
||
# t0 = ttime()
|
||
center = np.argmax(salience, axis=1) # 帧长#index
|
||
salience = np.pad(salience, ((0, 0), (4, 4))) # 帧长,368
|
||
# t1 = ttime()
|
||
center += 4
|
||
todo_salience = []
|
||
todo_cents_mapping = []
|
||
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]])
|
||
# t2 = ttime()
|
||
todo_salience = np.array(todo_salience) # 帧长，9
|
||
todo_cents_mapping = np.array(todo_cents_mapping) # 帧长，9
|
||
product_sum = np.sum(todo_salience * todo_cents_mapping, 1)
|
||
weight_sum = np.sum(todo_salience, 1) # 帧长
|
||
devided = product_sum / weight_sum # 帧长
|
||
# t3 = ttime()
|
||
maxx = np.max(salience, axis=1) # 帧长
|
||
devided[maxx <= thred] = 0
|
||
# t4 = ttime()
|
||
# print("decode:%s\t%s\t%s\t%s" % (t1 - t0, t2 - t1, t3 - t2, t4 - t3))
|
||
return devided
|
||
|
||
|
||
|
||
|
||
# if __name__ == '__main__':
|
||
# audio, sampling_rate = sf.read("卢本伟语录~1.wav")
|
||
# if len(audio.shape) > 1:
|
||
# audio = librosa.to_mono(audio.transpose(1, 0))
|
||
# audio_bak = audio.copy()
|
||
# if sampling_rate != 16000:
|
||
# audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
|
||
# model_path = "/bili-coeus/jupyter/jupyterhub-liujing04/vits_ch/test-RMVPE/weights/rmvpe_llc_half.pt"
|
||
# thred = 0.03 # 0.01
|
||
# device = 'cuda' if torch.cuda.is_available() else 'cpu'
|
||
# rmvpe = RMVPE(model_path,is_half=False, device=device)
|
||
# t0=ttime()
|
||
# f0 = rmvpe.infer_from_audio(audio, thred=thred)
|
||
# f0 = rmvpe.infer_from_audio(audio, thred=thred)
|
||
# f0 = rmvpe.infer_from_audio(audio, thred=thred)
|
||
# f0 = rmvpe.infer_from_audio(audio, thred=thred)
|
||
# f0 = rmvpe.infer_from_audio(audio, thred=thred)
|
||
# t1=ttime()
|
||
# print(f0.shape,t1-t0)
|