Seich
/
mayavoz
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

fix shapes

This commit is contained in:
shahules786 2022-09-06 20:44:19 +05:30
parent 8a43354cb0
commit b42ca28851
1 changed files with 16 additions and 14 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

30
enhancer/models/demucs.py
View File

@ -1,6 +1,5 @@
from typing import bool
from torch import nn
import torch.functional as F
import torch.nn.functional as F
import math
from enhancer.utils.io import Audio as audio
@ -14,6 +13,7 @@ class DeLSTM(nn.Module):
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)
@ -25,7 +25,7 @@ class DeLSTM(nn.Module):
return output
class Demus(nn.Module):
class Demucs(nn.Module):
def __init__(
self,
@ -35,10 +35,10 @@ class Demus(nn.Module):
kernel_size:int=8,
stride:int=4,
growth_factor:int=2,
depth:int = 6,
depth:int = 5,
glu:bool = True,
bidirectional:bool=True,
resample:int=2,
resample:int=4,
sampling_rate = 16000
):
@ -65,8 +65,8 @@ class Demus(nn.Module):
nn.ReLU(),
nn.Conv1d(hidden, hidden*multi_factor,kernel_size,1),
self.activation]
encoder_layer = nn.Sequential(encoder_layer)
self.encoder.append(*encoder_layer)
encoder_layer = nn.Sequential(*encoder_layer)
self.encoder.append(encoder_layer)
decoder_layer = [nn.Conv1d(hidden,hidden*multi_factor,kernel_size,1),
self.activation,
@ -87,25 +87,27 @@ class Demus(nn.Module):
def forward(self,mixed_signal):
length = mixed_signal.shape[-1]
x = F.pad((0,self.get_padding_length(length) - length))
x = F.pad(mixed_signal, (0,self.get_padding_length(length) - length))
if self.resample>1:
x = audio.resample_audio(audio=x,
sampling_rate = int(self.sampling_rate * self.resample))
x = audio.pt_resample_audio(audio=x, sr=self.sampling_rate,
target_sr=int(self.sampling_rate * self.resample))
print("resampled->",x.shape)
encoder_outputs = []
for encoder in self.encoder:
x = encoder(x)
print(x.shape)
encoder_outputs.append(x)
x = x.permute(0,2,1)
x = self.de_lstm(x)
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.resample > 1:
x = audio.resample_audio(x,int(self.sampling_rate * self.resample),
x = audio.pt_resample_audio(x,int(self.sampling_rate * self.resample),
self.sampling_rate)
return x