Пери пожалуйста, что и как это работает, как мне использовать это в моем проекте, как передать аудиофайл, что вы получите на выходе другой аудиофайл. ```import torch.nn as nn
import torch.nn.functional as F
import torch
import numpy as np
from librosa.filters import mel

from mods.log_control import VoiceChangaerLogger

logger = VoiceChangaerLogger.get_instance().getLogger()

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

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
    if self.mel_basis.device != magnitude.device:
        logger.warn(f"[RMVPE] Device is not same. mel_basis:{self.mel_basis.device}, magnitude:{magnitude.device}")
        self.mel_basis.to(magnitude.device)
    mel_output = torch.matmul(self.mel_basis, magnitude)
    if self.is_half is 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 is 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 is 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 infer_from_audio_t(self, audio, thred=0.03):
    # audio = torch.from_numpy(audio).float().to(self.device).unsqueeze(0)
    mel = self.mel_extractor(audio.unsqueeze(0), center=True)
    hidden = self.mel2hidden(mel)
    hidden = hidden.squeeze(0).cpu().numpy()
    if self.is_half is True:
        hidden = hidden.astype("float32")
    f0 = self.decode(hidden, thred=thred)
    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])  # NOQA
        todo_cents_mapping.append(self.cents_mapping[starts[idx] : ends[idx]])  # NOQA
    # 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