152 lines
4.5 KiB
Python
152 lines
4.5 KiB
Python
from tkinter import wantobjects
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import wave
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from typing import Optional, Union, List
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from enhancer.models.model import Model
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from enhancer.data.dataset import EnhancerDataset
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class WavenetDecoder(nn.Module):
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def __init__(
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self,
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in_channels:int,
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out_channels:int,
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kernel_size:int=5,
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padding:int=2,
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stride:int=1,
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dilation:int=1,
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):
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super(WavenetDecoder,self).__init__()
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self.decoder = nn.Sequential(
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nn.Conv1d(in_channels,out_channels,kernel_size,stride=stride,padding=padding,dilation=dilation),
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nn.BatchNorm1d(out_channels),
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nn.LeakyReLU(negative_slope=0.1)
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)
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def forward(self,waveform):
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return self.decoder(waveform)
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class WavenetEncoder(nn.Module):
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def __init__(
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self,
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in_channels:int,
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out_channels:int,
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kernel_size:int=15,
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padding:int=7,
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stride:int=1,
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dilation:int=1,
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):
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super(WavenetEncoder,self).__init__()
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self.encoder = nn.Sequential(
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nn.Conv1d(in_channels,out_channels,kernel_size,stride=stride,padding=padding,dilation=dilation),
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nn.BatchNorm1d(out_channels),
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nn.LeakyReLU(negative_slope=0.1)
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)
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def forward(
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self,
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waveform
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):
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return self.encoder(waveform)
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class WaveUnet(Model):
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def __init__(
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self,
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num_channels:int=1,
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depth:int=12,
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initial_output_channels:int=24,
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sampling_rate:int=16000,
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lr:float=1e-3,
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dataset:Optional[EnhancerDataset]=None,
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duration:Optional[float]=None,
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loss: Union[str, List] = "mse",
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metric:Union[str,List] = "mse"
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):
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super().__init__(num_channels=num_channels,
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sampling_rate=sampling_rate,lr=lr,
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dataset=dataset,duration=duration,loss=loss, metric=metric
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)
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self.save_hyperparameters("depth")
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self.encoders = nn.ModuleList()
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self.decoders = nn.ModuleList()
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out_channels = initial_output_channels
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for layer in range(depth):
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encoder = WavenetEncoder(num_channels,out_channels)
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self.encoders.append(encoder)
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num_channels = out_channels
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out_channels += initial_output_channels
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if layer == depth -1 :
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decoder = WavenetDecoder(depth * initial_output_channels + num_channels,num_channels)
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else:
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decoder = WavenetDecoder(num_channels+out_channels,num_channels)
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self.decoders.insert(0,decoder)
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bottleneck_dim = depth * initial_output_channels
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self.bottleneck = nn.Sequential(
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nn.Conv1d(bottleneck_dim,bottleneck_dim, 15, stride=1,
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padding=7),
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nn.BatchNorm1d(bottleneck_dim),
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nn.LeakyReLU(negative_slope=0.1, inplace=True)
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)
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self.final = nn.Sequential(
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nn.Conv1d(1 + initial_output_channels, 1, kernel_size=1, stride=1),
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nn.Tanh()
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)
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def forward(
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self,waveform
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):
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if waveform.dim() == 2:
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waveform = waveform.unsqueeze(1)
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if waveform.size(1)!=1:
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raise TypeError(f"Wave-U-Net can only process mono channel audio, input has {waveform.size(1)} channels")
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encoder_outputs = []
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out = waveform
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for encoder in self.encoders:
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out = encoder(out)
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encoder_outputs.insert(0,out)
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out = out[:,:,::2]
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out = self.bottleneck(out)
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for layer,decoder in enumerate(self.decoders):
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out = F.interpolate(out, scale_factor=2, mode="linear")
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out = self.fix_last_dim(out,encoder_outputs[layer])
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out = torch.cat([out,encoder_outputs[layer]],dim=1)
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out = decoder(out)
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out = torch.cat([out, waveform],dim=1)
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out = self.final(out)
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return out
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def fix_last_dim(self,x,target):
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"""
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trying to do centre crop along last dimension
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"""
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assert x.shape[-1] >= target.shape[-1], "input dimension cannot be larger than target dimension"
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if x.shape[-1] == target.shape[-1]:
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return x
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diff = x.shape[-1] - target.shape[-1]
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if diff%2!=0:
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x = F.pad(x,(0,1))
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diff += 1
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crop = diff//2
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return x[:,:,crop:-crop]
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