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import torch
import numpy as np
from torch.nn.utils import remove_weight_norm
from torch.nn.utils.parametrizations import weight_norm
from typing import Optional
from rvc.lib.algorithm.residuals import LRELU_SLOPE, ResBlock1, ResBlock2
from rvc.lib.algorithm.commons import init_weights
class Generator(torch.nn.Module):
"""Generator for synthesizing audio.
Args:
initial_channel (int): Number of channels in the initial convolutional layer.
resblock (str): Type of residual block to use (1 or 2).
resblock_kernel_sizes (list): Kernel sizes of the residual blocks.
resblock_dilation_sizes (list): Dilation rates of the residual blocks.
upsample_rates (list): Upsampling rates.
upsample_initial_channel (int): Number of channels in the initial upsampling layer.
upsample_kernel_sizes (list): Kernel sizes of the upsampling layers.
gin_channels (int, optional): Number of channels for the global conditioning input. Defaults to 0.
"""
def __init__(
self,
initial_channel,
resblock,
resblock_kernel_sizes,
resblock_dilation_sizes,
upsample_rates,
upsample_initial_channel,
upsample_kernel_sizes,
gin_channels=0,
):
super(Generator, self).__init__()
self.num_kernels = len(resblock_kernel_sizes)
self.num_upsamples = len(upsample_rates)
self.conv_pre = torch.nn.Conv1d(
initial_channel, upsample_initial_channel, 7, 1, padding=3
)
resblock = ResBlock1 if resblock == "1" else ResBlock2
self.ups = torch.nn.ModuleList()
self.resblocks = torch.nn.ModuleList()
for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
self.ups.append(
weight_norm(
torch.nn.ConvTranspose1d(
upsample_initial_channel // (2**i),
upsample_initial_channel // (2 ** (i + 1)),
k,
u,
padding=(k - u) // 2,
)
)
)
ch = upsample_initial_channel // (2 ** (i + 1))
for j, (k, d) in enumerate(
zip(resblock_kernel_sizes, resblock_dilation_sizes)
):
self.resblocks.append(resblock(ch, k, d))
self.conv_post = torch.nn.Conv1d(ch, 1, 7, 1, padding=3, bias=False)
self.ups.apply(init_weights)
if gin_channels != 0:
self.cond = torch.nn.Conv1d(gin_channels, upsample_initial_channel, 1)
def forward(self, x: torch.Tensor, g: Optional[torch.Tensor] = None):
x = self.conv_pre(x)
if g is not None:
x = x + self.cond(g)
for i in range(self.num_upsamples):
x = torch.nn.functional.leaky_relu(x, LRELU_SLOPE)
x = self.ups[i](x)
xs = None
for j in range(self.num_kernels):
if xs == None:
xs = self.resblocks[i * self.num_kernels + j](x)
else:
xs += self.resblocks[i * self.num_kernels + j](x)
x = xs / self.num_kernels
x = torch.nn.functional.leaky_relu(x)
x = self.conv_post(x)
x = torch.tanh(x)
return x
def __prepare_scriptable__(self):
"""Prepares the module for scripting."""
for l in self.ups_and_resblocks:
for hook in l._forward_pre_hooks.values():
if (
hook.__module__ == "torch.nn.utils.parametrizations.weight_norm"
and hook.__class__.__name__ == "WeightNorm"
):
torch.nn.utils.remove_weight_norm(l)
return self
def remove_weight_norm(self):
"""Removes weight normalization from the upsampling and residual blocks."""
for l in self.ups:
remove_weight_norm(l)
for l in self.resblocks:
l.remove_weight_norm()
class SineGenerator(torch.nn.Module):
"""
A sine wave generator that synthesizes waveforms with optional harmonic overtones and noise.
Args:
sampling_rate (int): The sampling rate in Hz.
num_harmonics (int, optional): The number of harmonic overtones to include. Defaults to 0.
sine_amplitude (float, optional): The amplitude of the sine waveform. Defaults to 0.1.
noise_stddev (float, optional): The standard deviation of Gaussian noise. Defaults to 0.003.
voiced_threshold (float, optional): F0 threshold for distinguishing voiced/unvoiced frames. Defaults to 0.
"""
def __init__(
self,
sampling_rate: int,
num_harmonics: int = 0,
sine_amplitude: float = 0.1,
noise_stddev: float = 0.003,
voiced_threshold: float = 0.0,
):
super(SineGenerator, self).__init__()
self.sampling_rate = sampling_rate
self.num_harmonics = num_harmonics
self.sine_amplitude = sine_amplitude
self.noise_stddev = noise_stddev
self.voiced_threshold = voiced_threshold
self.waveform_dim = self.num_harmonics + 1 # fundamental + harmonics
def _compute_voiced_unvoiced(self, f0: torch.Tensor) -> torch.Tensor:
"""
Generate a binary mask to indicate voiced/unvoiced frames.
Args:
f0 (torch.Tensor): Fundamental frequency tensor (batch_size, length).
"""
uv_mask = (f0 > self.voiced_threshold).float()
return uv_mask
def _generate_sine_wave(
self, f0: torch.Tensor, upsampling_factor: int
) -> torch.Tensor:
"""
Generate sine waves for the fundamental frequency and its harmonics.
Args:
f0 (torch.Tensor): Fundamental frequency tensor (batch_size, length, 1).
upsampling_factor (int): Upsampling factor.
"""
batch_size, length, _ = f0.shape
# Create an upsampling grid
upsampling_grid = torch.arange(
1, upsampling_factor + 1, dtype=f0.dtype, device=f0.device
)
# Calculate phase increments
phase_increments = (f0 / self.sampling_rate) * upsampling_grid
phase_remainder = torch.fmod(phase_increments[:, :-1, -1:] + 0.5, 1.0) - 0.5
cumulative_phase = phase_remainder.cumsum(dim=1).fmod(1.0).to(f0.dtype)
phase_increments += torch.nn.functional.pad(
cumulative_phase, (0, 0, 1, 0), mode="constant"
)
# Reshape to match the sine wave shape
phase_increments = phase_increments.reshape(batch_size, -1, 1)
# Scale for harmonics
harmonic_scale = torch.arange(
1, self.waveform_dim + 1, dtype=f0.dtype, device=f0.device
).reshape(1, 1, -1)
phase_increments *= harmonic_scale
# Add random phase offset (except for the fundamental)
random_phase = torch.rand(1, 1, self.waveform_dim, device=f0.device)
random_phase[..., 0] = 0 # Fundamental frequency has no random offset
phase_increments += random_phase
# Generate sine waves
sine_waves = torch.sin(2 * np.pi * phase_increments)
return sine_waves
def forward(self, f0: torch.Tensor, upsampling_factor: int):
"""
Forward pass to generate sine waveforms with noise and voiced/unvoiced masking.
Args:
f0 (torch.Tensor): Fundamental frequency tensor (batch_size, length, 1).
upsampling_factor (int): Upsampling factor.
"""
with torch.no_grad():
# Expand `f0` to include waveform dimensions
f0 = f0.unsqueeze(-1)
# Generate sine waves
sine_waves = (
self._generate_sine_wave(f0, upsampling_factor) * self.sine_amplitude
)
# Compute voiced/unvoiced mask
voiced_mask = self._compute_voiced_unvoiced(f0)
# Upsample voiced/unvoiced mask
voiced_mask = torch.nn.functional.interpolate(
voiced_mask.transpose(2, 1),
scale_factor=float(upsampling_factor),
mode="nearest",
).transpose(2, 1)
# Compute noise amplitude
noise_amplitude = voiced_mask * self.noise_stddev + (1 - voiced_mask) * (
self.sine_amplitude / 3
)
# Add Gaussian noise
noise = noise_amplitude * torch.randn_like(sine_waves)
# Combine sine waves and noise
sine_waveforms = sine_waves * voiced_mask + noise
return sine_waveforms, voiced_mask, noise
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