forked from mrq/DL-Art-School
80 lines
3.0 KiB
Python
80 lines
3.0 KiB
Python
import torch
|
|
from librosa.filters import mel as librosa_mel_fn
|
|
from models.tacotron2.audio_processing import dynamic_range_compression
|
|
from models.tacotron2.audio_processing import dynamic_range_decompression
|
|
from models.tacotron2.stft import STFT
|
|
|
|
|
|
class LinearNorm(torch.nn.Module):
|
|
def __init__(self, in_dim, out_dim, bias=True, w_init_gain='linear'):
|
|
super(LinearNorm, self).__init__()
|
|
self.linear_layer = torch.nn.Linear(in_dim, out_dim, bias=bias)
|
|
|
|
torch.nn.init.xavier_uniform_(
|
|
self.linear_layer.weight,
|
|
gain=torch.nn.init.calculate_gain(w_init_gain))
|
|
|
|
def forward(self, x):
|
|
return self.linear_layer(x)
|
|
|
|
|
|
class ConvNorm(torch.nn.Module):
|
|
def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,
|
|
padding=None, dilation=1, bias=True, w_init_gain='linear'):
|
|
super(ConvNorm, self).__init__()
|
|
if padding is None:
|
|
assert(kernel_size % 2 == 1)
|
|
padding = int(dilation * (kernel_size - 1) / 2)
|
|
|
|
self.conv = torch.nn.Conv1d(in_channels, out_channels,
|
|
kernel_size=kernel_size, stride=stride,
|
|
padding=padding, dilation=dilation,
|
|
bias=bias)
|
|
|
|
torch.nn.init.xavier_uniform_(
|
|
self.conv.weight, gain=torch.nn.init.calculate_gain(w_init_gain))
|
|
|
|
def forward(self, signal):
|
|
conv_signal = self.conv(signal)
|
|
return conv_signal
|
|
|
|
|
|
class TacotronSTFT(torch.nn.Module):
|
|
def __init__(self, filter_length=1024, hop_length=256, win_length=1024,
|
|
n_mel_channels=80, sampling_rate=22050, mel_fmin=0.0,
|
|
mel_fmax=8000.0):
|
|
super(TacotronSTFT, self).__init__()
|
|
self.n_mel_channels = n_mel_channels
|
|
self.sampling_rate = sampling_rate
|
|
self.stft_fn = STFT(filter_length, hop_length, win_length)
|
|
mel_basis = librosa_mel_fn(
|
|
sampling_rate, filter_length, n_mel_channels, mel_fmin, mel_fmax)
|
|
mel_basis = torch.from_numpy(mel_basis).float()
|
|
self.register_buffer('mel_basis', mel_basis)
|
|
|
|
def spectral_normalize(self, magnitudes):
|
|
output = dynamic_range_compression(magnitudes)
|
|
return output
|
|
|
|
def spectral_de_normalize(self, magnitudes):
|
|
output = dynamic_range_decompression(magnitudes)
|
|
return output
|
|
|
|
def mel_spectrogram(self, y):
|
|
"""Computes mel-spectrograms from a batch of waves
|
|
PARAMS
|
|
------
|
|
y: Variable(torch.FloatTensor) with shape (B, T) in range [-1, 1]
|
|
|
|
RETURNS
|
|
-------
|
|
mel_output: torch.FloatTensor of shape (B, n_mel_channels, T)
|
|
"""
|
|
assert(torch.min(y.data) >= -1)
|
|
assert(torch.max(y.data) <= 1)
|
|
|
|
magnitudes, phases = self.stft_fn.transform(y)
|
|
magnitudes = magnitudes.data
|
|
mel_output = torch.matmul(self.mel_basis, magnitudes)
|
|
mel_output = self.spectral_normalize(mel_output)
|
|
return mel_output |