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readdata_14.py
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readdata_14.py
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#-*- coding:utf-8 -*-
#author:zhangwei
'''
此脚本是用来测试8层mccnn模型，模型的结构架构为(16-16-32-32-64-64-64-64)*3-512-1024-1422
'''
import numpy as np
from general_function.file_wav import *
from general_function.file_dict import *
from general_function.feature_extract import *
import random
class DataSpeech():
def __init__(self , path , type):
self.datapath = path
self.type = type
self.slash = '/'
if self.slash != self.datapath[-1]:
self.datapath = self.datapath + self.slash
self.dic_wavlist_thchs30 = {}
self.dic_symbollist_thchs30 = {}
self.symbolnum = 0
self.datanum = 0
self.wavs_data = []
self.list_wavnum_thchs30 = []
self.list_symbolnum_thchs30 = []
self.load_datalist()
self.list_symbol = self.get_symbollist()
pass
def load_datalist(self):
if self.type == 'train':
filename_wavlist_thchs30 = 'thchs30' + self.slash + 'train.wav.lst'
filename_symbollist_thchs30 = 'thchs30' + self.slash + 'train.syllable.txt'
elif self.type == 'dev':
filename_wavlist_thchs30 = 'thchs30' + self.slash + 'cv.wav.lst'
filename_symbollist_thchs30 = 'thchs30' + self.slash + 'cv.syllable.txt'
elif self.type == 'test':
filename_wavlist_thchs30 = 'thchs30' + self.slash + 'test.wav.lst'
filename_symbollist_thchs30 = 'thchs30' + self.slash + 'test.syllable.txt'
else:
pass
self.dic_wavlist_thchs30 , self.list_wavnum_thchs30 = get_wav_list(self.datapath + filename_wavlist_thchs30)
self.dic_symbollist_thchs30 , self.list_symbolnum_thchs30 = get_wav_symbol(self.datapath + filename_symbollist_thchs30)
self.datanum = self.get_datanum()
def get_datanum(self):
num_wavlist_thchs30 = len(self.dic_wavlist_thchs30)
num_symbollist_thchs30 = len(self.dic_symbollist_thchs30)
if num_wavlist_thchs30 == num_symbollist_thchs30:
datanum = num_wavlist_thchs30
else:
datanum = -1
return datanum
def get_data(self , n_start):
filename = self.dic_wavlist_thchs30[self.list_wavnum_thchs30[n_start]]
list_symbol = self.dic_symbollist_thchs30[self.list_symbolnum_thchs30[n_start]]
wavsignal , fs = read_wav_data(self.datapath + filename)
feat_out = []
for i in list_symbol:
n = self.symbol_to_num(i)
feat_out.append(n)
data_input = get_frequency_feature(wavsignal , fs)
data_input = data_input.reshape(data_input.shape[0] , data_input.shape[1] , 1)
data_label = np.array(feat_out)
return data_input , data_label
def data_generator(self , batch_size=8 , audio_length=1600):
labels = []
for i in range(0 , batch_size):
labels.append([0.0])
labels = np.array(labels , dtype=np.float)
while True:
X = np.zeros([batch_size , audio_length , 200 , 1] , dtype=np.float)
y = np.zeros([batch_size , 64] , dtype=np.int16)
input_length = []
label_length = []
for i in range(batch_size):
ran_num = random.randint(0 , self.datanum - 1)
data_input , data_labels = self.get_data(ran_num)
input_length.append([data_input.shape[0]//16])
X[i , 0 : len(data_input)] = data_input
y[i , 0 : len(data_labels)] = data_labels
label_length.append([len(data_labels)])
label_length = np.array(label_length)
input_length = np.array(input_length)
yield [X , y , input_length , label_length] , labels
pass
def get_symbollist(self):
list_symbol = []
with open('dict.txt' , 'r') as fr:
lines = fr.readlines()
for line in lines:
res = line.split()
list_symbol.append(res[0])
list_symbol.append('_')
self.symbolnum = len(list_symbol)
return list_symbol
def symbol_to_num(self , symbol):
if symbol != '':
return self.list_symbol.index(symbol)
else:
return self.symbolnum
def get_symbol_num(self):
return len(self.list_symbol)
if __name__ == '__main__':
datapath = '/home/zhangwei/PycharmProjects/ASR_Thchs30/data_list/'
Data = DataSpeech(path=datapath , type='train')
data_input , data_labels = Data.get_data(0)
print(data_labels.shape)