153 lines
4.9 KiB
Python
153 lines
4.9 KiB
Python
from typing import Optional, Union, List
|
|
from torch import nn
|
|
import torch.nn.functional as F
|
|
import math
|
|
|
|
from enhancer.models.model import Model
|
|
from enhancer.data.dataset import EnhancerDataset
|
|
from enhancer.utils.io import Audio as audio
|
|
from enhancer.utils.utils import merge_dict
|
|
|
|
class DeLSTM(nn.Module):
|
|
def __init__(
|
|
self,
|
|
input_size:int,
|
|
hidden_size:int,
|
|
num_layers:int,
|
|
bidirectional:bool=True
|
|
|
|
):
|
|
super().__init__()
|
|
self.lstm = nn.LSTM(input_size, hidden_size, num_layers, bidirectional=bidirectional)
|
|
dim = 2 if bidirectional else 1
|
|
self.linear = nn.Linear(dim*hidden_size,hidden_size)
|
|
|
|
def forward(self,x):
|
|
|
|
output,(h,c) = self.lstm(x)
|
|
output = self.linear(output)
|
|
|
|
return output,(h,c)
|
|
|
|
class Demucs(Model):
|
|
|
|
ED_DEFAULTS = {
|
|
"intial_output_channels":48,
|
|
"kernel_size":8,
|
|
"stride":1,
|
|
"depth":5,
|
|
"glu":True,
|
|
"growth_factor":2,
|
|
}
|
|
LSTM_DEFAULTS = {
|
|
"bidirectional":True,
|
|
"num_layers":2,
|
|
}
|
|
|
|
def __init__(
|
|
self,
|
|
encoder_decoder:Optional[dict]=None,
|
|
lstm:Optional[dict]=None,
|
|
num_channels:int=1,
|
|
resample:int=4,
|
|
sampling_rate = 16000,
|
|
lr:float=1e-3,
|
|
dataset:Optional[EnhancerDataset]=None,
|
|
loss:Union[str, List] = "mse"
|
|
|
|
):
|
|
super().__init__(num_channels=num_channels,
|
|
sampling_rate=sampling_rate,lr=lr,
|
|
dataset=dataset,loss=loss)
|
|
|
|
encoder_decoder = merge_dict(self.ED_DEFAULTS,encoder_decoder)
|
|
lstm = merge_dict(self.LSTM_DEFAULTS,lstm)
|
|
self.save_hyperparameters("encoder_decoder","lstm","resample")
|
|
|
|
hidden = encoder_decoder["initial_channel_output"]
|
|
activation = nn.GLU(1) if encoder_decoder["glu"] else nn.ReLU()
|
|
multi_factor = 2 if encoder_decoder["glu"] else 1
|
|
|
|
self.encoder = nn.ModuleList()
|
|
self.decoder = nn.ModuleList()
|
|
|
|
for layer in range(encoder_decoder["depth"]):
|
|
|
|
encoder_layer = [nn.Conv1d(num_channels,hidden,encoder_decoder["kernel_size"],encoder_decoder["stride"]),
|
|
nn.ReLU(),
|
|
nn.Conv1d(hidden, hidden*multi_factor,encoder_decoder["kernel_size"],1),
|
|
activation]
|
|
encoder_layer = nn.Sequential(*encoder_layer)
|
|
self.encoder.append(encoder_layer)
|
|
|
|
decoder_layer = [nn.Conv1d(hidden,hidden*multi_factor,encoder_decoder["kernel_size"],1),
|
|
activation,
|
|
nn.ConvTranspose1d(hidden,num_channels,encoder_decoder["kernel_size"],encoder_decoder["stride"])
|
|
]
|
|
if layer>0:
|
|
decoder_layer.append(nn.ReLU())
|
|
decoder_layer = nn.Sequential(*decoder_layer)
|
|
self.decoder.insert(0,decoder_layer)
|
|
|
|
num_channels = hidden
|
|
hidden = self.growth_factor * hidden
|
|
|
|
|
|
self.de_lstm = DeLSTM(input_size=num_channels,hidden_size=num_channels,num_layers=lstm["num_layers"],bidirectional=lstm["bidirectional"])
|
|
|
|
def forward(self,mixed_signal):
|
|
|
|
if mixed_signal.dim() == 2:
|
|
mixed_signal = mixed_signal.unsqueeze(1)
|
|
|
|
length = mixed_signal.shape[-1]
|
|
x = F.pad(mixed_signal, (0,self.get_padding_length(length) - length))
|
|
if self.hparams.resample>1:
|
|
x = audio.pt_resample_audio(audio=x, sr=self.hparams.sampling_rate,
|
|
target_sr=int(self.hparams.sampling_rate * self.hparams.resample))
|
|
|
|
encoder_outputs = []
|
|
for encoder in self.encoder:
|
|
x = encoder(x)
|
|
encoder_outputs.append(x)
|
|
x = x.permute(0,2,1)
|
|
x,_ = self.de_lstm(x)
|
|
|
|
x = x.permute(0,2,1)
|
|
for decoder in self.decoder:
|
|
skip_connection = encoder_outputs.pop(-1)
|
|
x += skip_connection[..., :x.shape[-1]]
|
|
x = decoder(x)
|
|
|
|
if self.hparams.resample > 1:
|
|
x = audio.pt_resample_audio(x,int(self.hparams.sampling_rate * self.hparams.resample),
|
|
self.hparams.sampling_rate)
|
|
|
|
return x
|
|
|
|
def get_padding_length(self,input_length):
|
|
|
|
input_length = math.ceil(input_length * self.hparams.resample)
|
|
|
|
|
|
for layer in range(self.hparams.encoder_decoder["depth"]): # encoder operation
|
|
input_length = math.ceil((input_length - self.kernel_size)/self.stride)+1
|
|
input_length = max(1,input_length)
|
|
for layer in range(self.hparams.encoder_decoder["depth"]): # decoder operaration
|
|
input_length = (input_length-1) * self.stride + self.kernel_size
|
|
input_length = math.ceil(input_length/self.hparams.resample)
|
|
|
|
return int(input_length)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|