Delete UDS2W2LG1.py
This commit is contained in:
Uyghur
GitHub
parent
93b305c53f
commit
1c6fa107dc
157
UDS2W2LG1.py
157
UDS2W2LG1.py
@ -1,157 +0,0 @@
|
|||||||
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 UDS2W2LG1(BaseModel):
|
|
||||||
def __init__(self,num_features_input,load_best=False):
|
|
||||||
super(UDS2W2LG1, self).__init__('UDS2W2LG1')
|
|
||||||
dropout = 0.1
|
|
||||||
#UDS2W2LG ning bu yerila ozgerdi
|
|
||||||
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, 2), 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(
|
|
||||||
ResB(256,11,5,0.2),
|
|
||||||
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=2 , batch_first=True, bidirectional=True)
|
|
||||||
self.cnn2 = nn.Sequential(
|
|
||||||
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,bias=False),
|
|
||||||
nn.BatchNorm1d(512),
|
|
||||||
nn.ReLU(),
|
|
||||||
nn.Dropout(0.2),
|
|
||||||
ResB(512,17,8,0.3),
|
|
||||||
ResB(512,17,8,0.3),
|
|
||||||
nn.Conv1d(512, 1024, 1, 1,bias=False),
|
|
||||||
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._load()
|
|
||||||
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):
|
|
||||||
out_lens = lengths//4
|
|
||||||
|
|
||||||
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 = 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.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, bias=False),
|
|
||||||
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 = UDS2W2LG1(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)
|
|
||||||
Loading…
Reference in New Issue
Block a user