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import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from librosa.filters import mel
from typing import List
# Constants for readability
N_MELS = 128
N_CLASS = 360
# Define a helper function for creating convolutional blocks
class ConvBlockRes(nn.Module):
"""
A convolutional block with residual connection.
Args:
in_channels (int): Number of input channels.
out_channels (int): Number of output channels.
momentum (float): Momentum for batch normalization.
"""
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
# Define a class for residual encoder blocks
class ResEncoderBlock(nn.Module):
"""
A residual encoder block.
Args:
in_channels (int): Number of input channels.
out_channels (int): Number of output channels.
kernel_size (tuple): Size of the average pooling kernel.
n_blocks (int): Number of convolutional blocks in the block.
momentum (float): Momentum for batch normalization.
"""
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 _ 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
# Define a class for the encoder
class Encoder(nn.Module):
"""
The encoder part of the DeepUnet.
Args:
in_channels (int): Number of input channels.
in_size (int): Size of the input tensor.
n_encoders (int): Number of encoder blocks.
kernel_size (tuple): Size of the average pooling kernel.
n_blocks (int): Number of convolutional blocks in each encoder block.
out_channels (int): Number of output channels for the first encoder block.
momentum (float): Momentum for batch normalization.
"""
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_channel = out_channels
def forward(self, x: torch.Tensor):
concat_tensors: List[torch.Tensor] = []
x = self.bn(x)
for i in range(self.n_encoders):
t, x = self.layers[i](x)
concat_tensors.append(t)
return x, concat_tensors
# Define a class for the intermediate layer
class Intermediate(nn.Module):
"""
The intermediate layer of the DeepUnet.
Args:
in_channels (int): Number of input channels.
out_channels (int): Number of output channels.
n_inters (int): Number of convolutional blocks in the intermediate layer.
n_blocks (int): Number of convolutional blocks in each intermediate block.
momentum (float): Momentum for batch normalization.
"""
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 _ 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
# Define a class for residual decoder blocks
class ResDecoderBlock(nn.Module):
"""
A residual decoder block.
Args:
in_channels (int): Number of input channels.
out_channels (int): Number of output channels.
stride (tuple): Stride for transposed convolution.
n_blocks (int): Number of convolutional blocks in the block.
momentum (float): Momentum for batch normalization.
"""
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 _ 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
# Define a class for the decoder
class Decoder(nn.Module):
"""
The decoder part of the DeepUnet.
Args:
in_channels (int): Number of input channels.
n_decoders (int): Number of decoder blocks.
stride (tuple): Stride for transposed convolution.
n_blocks (int): Number of convolutional blocks in each decoder block.
momentum (float): Momentum for batch normalization.
"""
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 _ 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
# Define a class for the DeepUnet architecture
class DeepUnet(nn.Module):
"""
The DeepUnet architecture.
Args:
kernel_size (tuple): Size of the average pooling kernel.
n_blocks (int): Number of convolutional blocks in each encoder/decoder block.
en_de_layers (int): Number of encoder/decoder layers.
inter_layers (int): Number of convolutional blocks in the intermediate layer.
in_channels (int): Number of input channels.
en_out_channels (int): Number of output channels for the first encoder block.
"""
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
# Define a class for the end-to-end model
class E2E(nn.Module):
"""
The end-to-end model.
Args:
n_blocks (int): Number of convolutional blocks in each encoder/decoder block.
n_gru (int): Number of GRU layers.
kernel_size (tuple): Size of the average pooling kernel.
en_de_layers (int): Number of encoder/decoder layers.
inter_layers (int): Number of convolutional blocks in the intermediate layer.
in_channels (int): Number of input channels.
en_out_channels (int): Number of output channels for the first encoder block.
"""
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, N_CLASS),
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
# Define a class for the MelSpectrogram extractor
class MelSpectrogram(torch.nn.Module):
"""
Extracts Mel-spectrogram features from audio.
Args:
is_half (bool): Whether to use half-precision floating-point numbers.
n_mel_channels (int): Number of Mel-frequency bands.
sample_rate (int): Sampling rate of the audio.
win_length (int): Length of the window function in samples.
hop_length (int): Hop size between frames in samples.
n_fft (int, optional): Length of the FFT window. Defaults to None, which uses win_length.
mel_fmin (int, optional): Minimum frequency for the Mel filter bank. Defaults to 0.
mel_fmax (int, optional): Maximum frequency for the Mel filter bank. Defaults to None.
clamp (float, optional): Minimum value for clamping the Mel-spectrogram. Defaults to 1e-5.
"""
def __init__(
self,
is_half,
n_mel_channels,
sample_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=sample_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.sample_rate = sample_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:
mel_output = mel_output.half()
log_mel_spec = torch.log(torch.clamp(mel_output, min=self.clamp))
return log_mel_spec
# Define a class for the RMVPE0 predictor
class RMVPE0Predictor:
"""
A predictor for fundamental frequency (F0) based on the RMVPE0 model.
Args:
model_path (str): Path to the RMVPE0 model file.
is_half (bool): Whether to use half-precision floating-point numbers.
device (str, optional): Device to use for computation. Defaults to None, which uses CUDA if available.
"""
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:
model = model.half()
self.model = model
self.resample_kernel = {}
self.is_half = is_half
self.device = device
self.mel_extractor = MelSpectrogram(
is_half, N_MELS, 16000, 1024, 160, None, 30, 8000
).to(device)
self.model = self.model.to(device)
cents_mapping = 20 * np.arange(N_CLASS) + 1997.3794084376191
self.cents_mapping = np.pad(cents_mapping, (4, 4))
def mel2hidden(self, mel):
"""
Converts Mel-spectrogram features to hidden representation.
Args:
mel (torch.Tensor): Mel-spectrogram features.
"""
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):
"""
Decodes hidden representation to F0.
Args:
hidden (np.ndarray): Hidden representation.
thred (float, optional): Threshold for salience. Defaults to 0.03.
"""
cents_pred = self.to_local_average_cents(hidden, thred=thred)
f0 = 10 * (2 ** (cents_pred / 1200))
f0[f0 == 10] = 0
return f0
def infer_from_audio(self, audio, thred=0.03):
"""
Infers F0 from audio.
Args:
audio (np.ndarray): Audio signal.
thred (float, optional): Threshold for salience. Defaults to 0.03.
"""
audio = torch.from_numpy(audio).float().to(self.device).unsqueeze(0)
mel = self.mel_extractor(audio, center=True)
hidden = self.mel2hidden(mel)
hidden = hidden.squeeze(0).cpu().numpy()
if self.is_half == True:
hidden = hidden.astype("float32")
f0 = self.decode(hidden, thred=thred)
return f0
def to_local_average_cents(self, salience, thred=0.05):
"""
Converts salience to local average cents.
Args:
salience (np.ndarray): Salience values.
thred (float, optional): Threshold for salience. Defaults to 0.05.
"""
center = np.argmax(salience, axis=1)
salience = np.pad(salience, ((0, 0), (4, 4)))
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]])
todo_salience = np.array(todo_salience)
todo_cents_mapping = np.array(todo_cents_mapping)
product_sum = np.sum(todo_salience * todo_cents_mapping, 1)
weight_sum = np.sum(todo_salience, 1)
devided = product_sum / weight_sum
maxx = np.max(salience, axis=1)
devided[maxx <= thred] = 0
return devided
# Define a class for BiGRU (bidirectional GRU)
class BiGRU(nn.Module):
"""
A bidirectional GRU layer.
Args:
input_features (int): Number of input features.
hidden_features (int): Number of hidden features.
num_layers (int): Number of GRU layers.
"""
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]