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RVC_V2_Huggingface_Version / rmvpe.py

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	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)