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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 UDS2W2LDS00(BaseModel):
def __init__(self,num_features_input,load_best=False):
super(UDS2W2LDS00, self).__init__('UDS2W2LDS00')
dropout = 0.2
self.conv = nn.Sequential(
nn.Conv2d(1, 32, kernel_size=(41, 11), stride=(2, 1), padding=(20, 5), bias=False),
nn.BatchNorm2d(32),
nn.ReLU(inplace=True),
nn.Dropout(dropout),
nn.Conv2d(32, 32, kernel_size=(21, 11), stride=(2, 2), padding=(10, 5), bias=False),
nn.BatchNorm2d(32),
nn.ReLU(inplace=True),
nn.Dropout(dropout),
)
#self.lstm1 = nn.GRU(1024, 256, num_layers=1 , batch_first=True, bidirectional=True)
self.cnn1 = nn.Sequential(
nn.Conv1d(1024, 256, 11, 1,5),
nn.BatchNorm1d(256),
nn.ReLU(),
nn.Dropout(0.2),
ResB(256,11,5,0.2),
ResB(256,11,5,0.2),
ResB(256,11,5,0.2),
ResB(256,11,5,0.2)
)
#self.lstm2 = nn.GRU(256, 384, num_layers=1 , batch_first=True, bidirectional=True)
self.cnn2 = nn.Sequential(
nn.Conv1d(256, 384, 13, 1,6),
nn.BatchNorm1d(384),
nn.ReLU(),
nn.Dropout(0.2),
ResB(384,13,6,0.2),
ResB(384,13,6,0.2),
ResB(384,13,6,0.2),
nn.Conv1d(384, 512, 17, 1,8),
nn.BatchNorm1d(512),
nn.ReLU(),
nn.Dropout(0.2),
ResB(512,17,8,0.3),
ResB(512,17,8,0.3),
nn.Conv1d(512, 768, 25, 1,12),
nn.BatchNorm1d(768),
nn.ReLU(),
nn.Dropout(0.3),
ResB(768,25,12,0.3),
nn.Conv1d(768, 1024, 1, 1),
nn.BatchNorm1d(1024),
nn.ReLU(),
nn.Dropout(0.3),
ResB(1024,1,0,0.0),
)
self.outlayer = nn.Conv1d(1024, uyghur_latin.vocab_size, 1, 1)
self.softMax = nn.LogSoftmax(dim=1)
self.checkpoint = 'results/' + self.ModelName
self._loadfrom()
print(f'The model has {self.parameters_count(self):,} trainable parameters')
def smooth_labels(self, x):
return (1.0 - self.smoothing) * x + self.smoothing / x.size(-1)
def forward(self, x, lengths):
x.unsqueeze_(1)
out = self.conv(x)
b, c, h, w = out.size()
out = out.view(b, c*h, w).contiguous()
out_lens = lengths//2
out = self.cnn1(out)
out = self.cnn2(out)
out = self.outlayer(out)
out = self.softMax(out)
return out, out_lens
class ResB(nn.Module):
def __init__(self, num_filters, kernel, pad, d = 0.4):
super().__init__()
self.conv = nn.Sequential(
nn.Conv1d(num_filters, num_filters, kernel_size = kernel, stride = 1 , padding=pad),
nn.BatchNorm1d(num_filters)
)
self.relu = nn.ReLU()
self.bn = nn.BatchNorm1d(num_filters)
self.drop =nn.Dropout(d)
def forward(self, x):
identity = x
out = self.conv(x)
out += identity
out = self.bn(out)
out = self.relu(out)
out = self.drop(out)
return out
if __name__ == "__main__":
from data import featurelen, melfuture
device ="cpu"
net = UDS2W2LDS00(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(0)
melf = melfuture("test3.wav")
melf.unsqueeze_(0)
conv0 = nn.Conv1d(featurelen,256,11,2, 5, 1)
conv1 = nn.Conv1d(256,256,11,1, 5, 1)
conv3 = nn.Conv1d(256,256,11,1, 5*2, 2)
conv5 = nn.Conv1d(256,256,11,1, 5*3, 3)
out0 = conv0(melf)
out1 = conv1(out0)
out3 = conv3(out0)
out5 = conv5(out0)
print(out1.size())
print(out3.size())
print(out5.size())
out = out1 * out3 * out5
print(out.size())
#net = GCGCRes(featurelen).to(device)
#net.save(1)
#text = net.predict("test1.wav",device)
#print(text)
#text = net.predict("test2.wav",device)
#print(text)