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import math
import torch
import torch.nn as nn
from torch.nn.functional import log_softmax
import torch.nn.functional as F
from torch.nn.parameter import Parameter
from data import melfuture
from uyghur import uyghur_latin
from BaseModel import BaseModel
class Swish(nn.Module):
def forward(self, x):
return x * x.sigmoid()
class Mish(nn.Module):
def forward(self, x):
#inlining this saves 1 second per epoch (V100 GPU) vs having a temp x and then returning x(!)
return x *( torch.tanh(F.softplus(x)))
class UDS2W2LGLU8(BaseModel):
def __init__(self,num_features_input,load_best=False):
super(UDS2W2LGLU8, self).__init__('UDS2W2LGLU8')
self.smoothing = 0.01
self.conv = nn.Sequential(
nn.Conv2d(1, 32, kernel_size=(41, 11), stride=(2, 2), padding=(20, 5), bias=False),
nn.BatchNorm2d(32),
nn.Hardtanh(0, 20, inplace=True),
nn.Conv2d(32, 32, kernel_size=(21, 11), stride=(2, 1), padding=(10, 5),bias=False),
nn.BatchNorm2d(32),
nn.Hardtanh(0, 20, inplace=True),
)
self.lstm1 = nn.GRU(1024, 256, num_layers=1 , batch_first=True, bidirectional=True)
self.cnn1 = nn.Sequential(
ResBGLU(256, 256, 11, 0.2, 2),
ResBGLU(256, 256, 11, 0.2),
ResBGLU(256, 256, 11, 0.2),
ResBGLU(256, 256, 11, 0.2),
ResBGLU(256, 256, 11, 0.2),
)
self.lstm2 = nn.GRU(256, 384, num_layers=1 , batch_first=True, bidirectional=True)
self.cnn2 = nn.Sequential(
ResBGLU(384, 384, 13, 0.2),
ResBGLU(384, 384, 13, 0.2),
ResBGLU(384, 384, 13, 0.2),
ResBGLU(384, 512, 17, 0.2),
ResBGLU(512, 512, 17, 0.3),
ResBGLU(512, 512, 1, 0.3),
)
self.outlayer = nn.Conv1d(512, uyghur_latin.vocab_size, 1, 1)
self.softMax = nn.LogSoftmax(dim=1)
self.checkpoint = 'results/' + self.ModelName
self._load(load_best)
print(f'The model has {self.parameters_count(self):,} trainable parameters')
def smooth_labels(self, x):
sl = x.size(1)
return (1.0 - self.smoothing) * x + self.smoothing / sl
def forward(self, x, lengths):
out_lens = lengths//2
x.unsqueeze_(1)
out = self.conv(x)
b, c, h, w = out.size()
out = out.view(b, c*h, w).contiguous() #.permute(0,2,1)
out = out.permute(0,2,1)
out = nn.utils.rnn.pack_padded_sequence(out, out_lens, batch_first=True)
out, _ = self.lstm1(out)
out, _ = nn.utils.rnn.pad_packed_sequence(out, batch_first=True)
out = (out[:, :, :self.lstm1.hidden_size] + out[:, :, self.lstm1.hidden_size:]).contiguous()
out = self.cnn1(out.permute(0,2,1))
out_lens = out_lens//2
out = out.permute(0,2,1)
out = nn.utils.rnn.pack_padded_sequence(out, out_lens, batch_first=True)
out,_ = self.lstm2(out)
out, _ = nn.utils.rnn.pad_packed_sequence(out, batch_first=True)
out = (out[:, :, :self.lstm2.hidden_size] + out[:, :, self.lstm2.hidden_size:]).contiguous()
out = self.cnn2(out.permute(0,2,1))
out = self.outlayer(out)
#out = self.smooth_labels(out)
out = self.softMax(out)
return out, out_lens
class ResBGLU(nn.Module):
def __init__(self, in_channel, out_channel, kernel, d = 0.4, stride = 1):
super().__init__()
self.isRes = (in_channel == out_channel and stride == 1)
pad = (kernel-1)//2
self.conv = nn.Sequential(
nn.Conv1d(in_channel, out_channel*2, kernel_size = kernel, stride = stride , padding=pad, bias=False),
nn.BatchNorm1d(out_channel*2),
nn.GLU(dim=1)
)
self.fc = nn.Sequential(
nn.BatchNorm1d(out_channel),
Mish(),
)
self.drop = nn.Dropout(d)
def forward(self, x):
out = self.conv(x)
if self.isRes:
out = self.fc(out+x)
out = self.drop(out)
return out
if __name__ == "__main__":
from data import featurelen, melfuture
device ="cpu"
net = UDS2W2LGLU8(featurelen).to(device)
text = net.predict("test1.wav",device)
print(text)
text = net.predict("test2.wav",device)
print(text)
#net.best_cer = 1.0
#net.save(78)