155 lines
4.8 KiB
Python
155 lines
4.8 KiB
Python
import math
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import torch
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import torch.nn as nn
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from torch.nn.functional import log_softmax
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import torch.nn.functional as F
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from torch.nn.parameter import Parameter
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from data import melfuture
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from uyghur import uyghur_latin
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from BaseModel import BaseModel
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class UDS2W2LG(BaseModel):
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def __init__(self,num_features_input,load_best=False):
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super(UDS2W2LG, self).__init__('UDS2W2LG')
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dropout = 0.1
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self.conv = nn.Sequential(
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nn.Conv2d(1, 32, kernel_size=(11, 11), stride=(2, 2), padding=(5, 5), bias=False),
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nn.BatchNorm2d(32), nn.ReLU(inplace=True), nn.Dropout(dropout),
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nn.Conv2d(32, 32, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), bias=False),
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nn.BatchNorm2d(32), nn.ReLU(inplace=True), nn.Dropout(dropout)
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)
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self.lstm1 = nn.GRU(1024, 256, num_layers=1 , batch_first=True, bidirectional=True)
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self.cnn1 = nn.Sequential(
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ResB(256,11,5,0.2),
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ResB(256,11,5,0.2),
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ResB(256,11,5,0.2),
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ResB(256,11,5,0.2),
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ResB(256,11,5,0.2)
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)
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self.lstm2 = nn.GRU(256, 384, num_layers=2 , batch_first=True, bidirectional=True)
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self.cnn2 = nn.Sequential(
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ResB(384,13,6,0.2),
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ResB(384,13,6,0.2),
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ResB(384,13,6,0.2),
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nn.Conv1d(384, 512, 17, 1,8,bias=False),
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nn.BatchNorm1d(512),
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nn.ReLU(),
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nn.Dropout(0.2),
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ResB(512,17,8,0.3),
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ResB(512,17,8,0.3),
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nn.Conv1d(512, 1024, 1, 1,bias=False),
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nn.BatchNorm1d(1024),
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nn.ReLU(),
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nn.Dropout(0.3),
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ResB(1024,1,0,0.0),
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)
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self.outlayer = nn.Conv1d(1024, uyghur_latin.vocab_size, 1, 1)
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self.softMax = nn.LogSoftmax(dim=1)
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print(" Model Name:", self.ModelName)
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self.checkpoint = 'results/' + self.ModelName
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self._loadfrom()
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print(f'The model has {self.parameters_count(self):,} trainable parameters')
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def smooth_labels(self, x):
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return (1.0 - self.smoothing) * x + self.smoothing / x.size(-1)
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def forward(self, x, lengths):
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out_lens = lengths//4
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x.unsqueeze_(1)
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out = self.conv(x)
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b, c, h, w = out.size()
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out = out.view(b, c*h, w).contiguous() #.permute(0,2,1)
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out = out.permute(0,2,1)
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out = nn.utils.rnn.pack_padded_sequence(out, out_lens, batch_first=True)
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out, _ = self.lstm1(out)
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out, _ = nn.utils.rnn.pad_packed_sequence(out, batch_first=True)
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out = (out[:, :, :self.lstm1.hidden_size] + out[:, :, self.lstm1.hidden_size:]).contiguous()
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out = self.cnn1(out.permute(0,2,1))
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out = out.permute(0,2,1)
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out = nn.utils.rnn.pack_padded_sequence(out, out_lens, batch_first=True)
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out,_ = self.lstm2(out)
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out, _ = nn.utils.rnn.pad_packed_sequence(out, batch_first=True)
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out = (out[:, :, :self.lstm2.hidden_size] + out[:, :, self.lstm2.hidden_size:]).contiguous()
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out = self.cnn2(out.permute(0,2,1))
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out = self.outlayer(out)
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out = self.softMax(out)
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return out, out_lens
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class ResB(nn.Module):
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def __init__(self, num_filters, kernel, pad, d = 0.4):
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super().__init__()
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self.conv = nn.Sequential(
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nn.Conv1d(num_filters, num_filters, kernel_size = kernel, stride = 1 , padding=pad, bias=False),
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nn.BatchNorm1d(num_filters)
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)
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self.relu = nn.ReLU()
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self.bn = nn.BatchNorm1d(num_filters)
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self.drop =nn.Dropout(d)
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def forward(self, x):
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identity = x
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out = self.conv(x)
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out += identity
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out = self.bn(out)
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out = self.relu(out)
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out = self.drop(out)
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return out
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if __name__ == "__main__":
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from data import featurelen, melfuture
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device ="cpu"
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net = UDS2W2LG(featurelen).to(device)
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text = net.predict("test1.wav",device)
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print(text)
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text = net.predict("test2.wav",device)
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print(text)
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#net.best_cer = 1.0
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#net.save(0)
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melf = melfuture("test3.wav")
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melf.unsqueeze_(0)
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conv0 = nn.Conv1d(featurelen,256,11,2, 5, 1)
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conv1 = nn.Conv1d(256,256,11,1, 5, 1)
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conv3 = nn.Conv1d(256,256,11,1, 5*2, 2)
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conv5 = nn.Conv1d(256,256,11,1, 5*3, 3)
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out0 = conv0(melf)
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out1 = conv1(out0)
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out3 = conv3(out0)
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out5 = conv5(out0)
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print(out1.size())
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print(out3.size())
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print(out5.size())
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out = out1 * out3 * out5
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print(out.size())
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#net = GCGCRes(featurelen).to(device)
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#net.save(1)
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#text = net.predict("test1.wav",device)
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#print(text)
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#text = net.predict("test2.wav",device)
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#print(text) |