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from typing import Dict
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchlibrosa.stft import ISTFT, STFT, magphase
from bytesep.models.pytorch_modules import Base, init_bn, init_layer
from bytesep.models.subband_tools.pqmf import PQMF
from bytesep.models.unet import ConvBlock, DecoderBlock, EncoderBlock
class UNetSubbandTime(nn.Module, Base):
def __init__(self, input_channels: int, target_sources_num: int):
r"""Subband waveform UNet."""
super(UNetSubbandTime, self).__init__()
self.input_channels = input_channels
self.target_sources_num = target_sources_num
window_size = 512 # 2048 // 4
hop_size = 110 # 441 // 4
center = True
pad_mode = "reflect"
window = "hann"
activation = "leaky_relu"
momentum = 0.01
self.subbands_num = 4
self.K = 3 # outputs: |M|, cos∠M, sin∠M
self.downsample_ratio = 2 ** 6 # This number equals 2^{#encoder_blcoks}
self.pqmf = PQMF(
N=self.subbands_num,
M=64,
project_root='bytesep/models/subband_tools/filters',
)
self.stft = STFT(
n_fft=window_size,
hop_length=hop_size,
win_length=window_size,
window=window,
center=center,
pad_mode=pad_mode,
freeze_parameters=True,
)
self.istft = ISTFT(
n_fft=window_size,
hop_length=hop_size,
win_length=window_size,
window=window,
center=center,
pad_mode=pad_mode,
freeze_parameters=True,
)
self.bn0 = nn.BatchNorm2d(window_size // 2 + 1, momentum=momentum)
self.encoder_block1 = EncoderBlock(
in_channels=input_channels * self.subbands_num,
out_channels=32,
kernel_size=(3, 3),
downsample=(2, 2),
activation=activation,
momentum=momentum,
)
self.encoder_block2 = EncoderBlock(
in_channels=32,
out_channels=64,
kernel_size=(3, 3),
downsample=(2, 2),
activation=activation,
momentum=momentum,
)
self.encoder_block3 = EncoderBlock(
in_channels=64,
out_channels=128,
kernel_size=(3, 3),
downsample=(2, 2),
activation=activation,
momentum=momentum,
)
self.encoder_block4 = EncoderBlock(
in_channels=128,
out_channels=256,
kernel_size=(3, 3),
downsample=(2, 2),
activation=activation,
momentum=momentum,
)
self.encoder_block5 = EncoderBlock(
in_channels=256,
out_channels=384,
kernel_size=(3, 3),
downsample=(2, 2),
activation=activation,
momentum=momentum,
)
self.encoder_block6 = EncoderBlock(
in_channels=384,
out_channels=384,
kernel_size=(3, 3),
downsample=(2, 2),
activation=activation,
momentum=momentum,
)
self.conv_block7 = ConvBlock(
in_channels=384,
out_channels=384,
kernel_size=(3, 3),
activation=activation,
momentum=momentum,
)
self.decoder_block1 = DecoderBlock(
in_channels=384,
out_channels=384,
kernel_size=(3, 3),
upsample=(2, 2),
activation=activation,
momentum=momentum,
)
self.decoder_block2 = DecoderBlock(
in_channels=384,
out_channels=384,
kernel_size=(3, 3),
upsample=(2, 2),
activation=activation,
momentum=momentum,
)
self.decoder_block3 = DecoderBlock(
in_channels=384,
out_channels=256,
kernel_size=(3, 3),
upsample=(2, 2),
activation=activation,
momentum=momentum,
)
self.decoder_block4 = DecoderBlock(
in_channels=256,
out_channels=128,
kernel_size=(3, 3),
upsample=(2, 2),
activation=activation,
momentum=momentum,
)
self.decoder_block5 = DecoderBlock(
in_channels=128,
out_channels=64,
kernel_size=(3, 3),
upsample=(2, 2),
activation=activation,
momentum=momentum,
)
self.decoder_block6 = DecoderBlock(
in_channels=64,
out_channels=32,
kernel_size=(3, 3),
upsample=(2, 2),
activation=activation,
momentum=momentum,
)
self.after_conv_block1 = ConvBlock(
in_channels=32,
out_channels=32,
kernel_size=(3, 3),
activation=activation,
momentum=momentum,
)
self.after_conv2 = nn.Conv2d(
in_channels=32,
out_channels=target_sources_num
* input_channels
* self.K
* self.subbands_num,
kernel_size=(1, 1),
stride=(1, 1),
padding=(0, 0),
bias=True,
)
self.init_weights()
def init_weights(self):
r"""Initialize weights."""
init_bn(self.bn0)
init_layer(self.after_conv2)
def feature_maps_to_wav(
self,
input_tensor: torch.Tensor,
sp: torch.Tensor,
sin_in: torch.Tensor,
cos_in: torch.Tensor,
audio_length: int,
) -> torch.Tensor:
r"""Convert feature maps to waveform.
Args:
input_tensor: (batch_size, target_sources_num * input_channels * self.K, time_steps, freq_bins)
sp: (batch_size, target_sources_num * input_channels, time_steps, freq_bins)
sin_in: (batch_size, target_sources_num * input_channels, time_steps, freq_bins)
cos_in: (batch_size, target_sources_num * input_channels, time_steps, freq_bins)
Outputs:
waveform: (batch_size, target_sources_num * input_channels, segment_samples)
"""
batch_size, _, time_steps, freq_bins = input_tensor.shape
x = input_tensor.reshape(
batch_size,
self.target_sources_num,
self.input_channels,
self.K,
time_steps,
freq_bins,
)
# x: (batch_size, target_sources_num, input_channles, K, time_steps, freq_bins)
mask_mag = torch.sigmoid(x[:, :, :, 0, :, :])
_mask_real = torch.tanh(x[:, :, :, 1, :, :])
_mask_imag = torch.tanh(x[:, :, :, 2, :, :])
_, mask_cos, mask_sin = magphase(_mask_real, _mask_imag)
# mask_cos, mask_sin: (batch_size, target_sources_num, input_channles, time_steps, freq_bins)
# Y = |Y|cos∠Y + j|Y|sin∠Y
# = |Y|cos(∠X + ∠M) + j|Y|sin(∠X + ∠M)
# = |Y|(cos∠X cos∠M - sin∠X sin∠M) + j|Y|(sin∠X cos∠M + cos∠X sin∠M)
out_cos = (
cos_in[:, None, :, :, :] * mask_cos - sin_in[:, None, :, :, :] * mask_sin
)
out_sin = (
sin_in[:, None, :, :, :] * mask_cos + cos_in[:, None, :, :, :] * mask_sin
)
# out_cos: (batch_size, target_sources_num, input_channles, time_steps, freq_bins)
# out_sin: (batch_size, target_sources_num, input_channles, time_steps, freq_bins)
# Calculate |Y|.
out_mag = F.relu_(sp[:, None, :, :, :] * mask_mag)
# out_mag: (batch_size, target_sources_num, input_channles, time_steps, freq_bins)
# Calculate Y_{real} and Y_{imag} for ISTFT.
out_real = out_mag * out_cos
out_imag = out_mag * out_sin
# out_real, out_imag: (batch_size, target_sources_num, input_channles, time_steps, freq_bins)
# Reformat shape to (n, 1, time_steps, freq_bins) for ISTFT.
shape = (
batch_size * self.target_sources_num * self.input_channels,
1,
time_steps,
freq_bins,
)
out_real = out_real.reshape(shape)
out_imag = out_imag.reshape(shape)
# ISTFT.
x = self.istft(out_real, out_imag, audio_length)
# (batch_size * target_sources_num * input_channels, segments_num)
# Reshape.
waveform = x.reshape(
batch_size, self.target_sources_num * self.input_channels, audio_length
)
# (batch_size, target_sources_num * input_channels, segments_num)
return waveform
def forward(self, input_dict: Dict) -> Dict:
"""Forward data into the module.
Args:
input_dict: dict, e.g., {
waveform: (batch_size, input_channels, segment_samples),
...,
}
Outputs:
output_dict: dict, e.g., {
'waveform': (batch_size, input_channels, segment_samples),
...,
}
"""
mixtures = input_dict['waveform']
# (batch_size, input_channels, segment_samples)
if self.subbands_num > 1:
subband_x = self.pqmf.analysis(mixtures)
# -- subband_x: (batch_size, input_channels * subbands_num, segment_samples)
# -- subband_x: (batch_size, subbands_num * input_channels, segment_samples)
else:
subband_x = mixtures
# from IPython import embed; embed(using=False); os._exit(0)
# import soundfile
# soundfile.write(file='_zz.wav', data=subband_x.data.cpu().numpy()[0, 2], samplerate=11025)
mag, cos_in, sin_in = self.wav_to_spectrogram_phase(subband_x)
# mag, cos_in, sin_in: (batch_size, input_channels * subbands_num, time_steps, freq_bins)
# Batch normalize on individual frequency bins.
x = mag.transpose(1, 3)
x = self.bn0(x)
x = x.transpose(1, 3)
# (batch_size, input_channels * subbands_num, time_steps, freq_bins)
# Pad spectrogram to be evenly divided by downsample ratio.
origin_len = x.shape[2]
pad_len = (
int(np.ceil(x.shape[2] / self.downsample_ratio)) * self.downsample_ratio
- origin_len
)
x = F.pad(x, pad=(0, 0, 0, pad_len))
# x: (batch_size, input_channels * subbands_num, padded_time_steps, freq_bins)
# Let frequency bins be evenly divided by 2, e.g., 257 -> 256
x = x[..., 0 : x.shape[-1] - 1] # (bs, input_channels, T, F)
# x: (batch_size, input_channels * subbands_num, padded_time_steps, freq_bins)
# UNet
(x1_pool, x1) = self.encoder_block1(x) # x1_pool: (bs, 32, T / 2, F' / 2)
(x2_pool, x2) = self.encoder_block2(x1_pool) # x2_pool: (bs, 64, T / 4, F' / 4)
(x3_pool, x3) = self.encoder_block3(
x2_pool
) # x3_pool: (bs, 128, T / 8, F' / 8)
(x4_pool, x4) = self.encoder_block4(
x3_pool
) # x4_pool: (bs, 256, T / 16, F' / 16)
(x5_pool, x5) = self.encoder_block5(
x4_pool
) # x5_pool: (bs, 384, T / 32, F' / 32)
(x6_pool, x6) = self.encoder_block6(
x5_pool
) # x6_pool: (bs, 384, T / 64, F' / 64)
x_center = self.conv_block7(x6_pool) # (bs, 384, T / 64, F' / 64)
x7 = self.decoder_block1(x_center, x6) # (bs, 384, T / 32, F' / 32)
x8 = self.decoder_block2(x7, x5) # (bs, 384, T / 16, F' / 16)
x9 = self.decoder_block3(x8, x4) # (bs, 256, T / 8, F' / 8)
x10 = self.decoder_block4(x9, x3) # (bs, 128, T / 4, F' / 4)
x11 = self.decoder_block5(x10, x2) # (bs, 64, T / 2, F' / 2)
x12 = self.decoder_block6(x11, x1) # (bs, 32, T, F')
x = self.after_conv_block1(x12) # (bs, 32, T, F')
x = self.after_conv2(x)
# (batch_size, subbands_num * target_sources_num * input_channles * self.K, T, F')
# Recover shape
x = F.pad(x, pad=(0, 1)) # Pad frequency, e.g., 256 -> 257.
x = x[:, :, 0:origin_len, :]
# (batch_size, subbands_num * target_sources_num * input_channles * self.K, T, F')
audio_length = subband_x.shape[2]
# Recover each subband spectrograms to subband waveforms. Then synthesis
# the subband waveforms to a waveform.
C1 = x.shape[1] // self.subbands_num
C2 = mag.shape[1] // self.subbands_num
separated_subband_audio = torch.cat(
[
self.feature_maps_to_wav(
input_tensor=x[:, j * C1 : (j + 1) * C1, :, :],
sp=mag[:, j * C2 : (j + 1) * C2, :, :],
sin_in=sin_in[:, j * C2 : (j + 1) * C2, :, :],
cos_in=cos_in[:, j * C2 : (j + 1) * C2, :, :],
audio_length=audio_length,
)
for j in range(self.subbands_num)
],
dim=1,
)
# （batch_size, subbands_num * target_sources_num * input_channles, segment_samples)
if self.subbands_num > 1:
separated_audio = self.pqmf.synthesis(separated_subband_audio)
# (batch_size, target_sources_num * input_channles, segment_samples)
else:
separated_audio = separated_subband_audio
output_dict = {'waveform': separated_audio}
return output_dict
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