Python实现简单的语音识别系统

 import wave import numpy as np import matplotlib.pyplot as plt from python_speech_features import mfcc from math import cos,sin,sqrt,pi def read_file(file_name): with wave.open(file_name,'r') as file: params = file.getparams() _, _, framerate, nframes = params[:4] str_data = file.readframes(nframes) wave_data = np.fromstring(str_data, dtype = np.short) time = np.arange(0, nframes) * (1.0/framerate) return wave_data, time return index1,index2 def find_point(data): count1,count2 = 0,0 for index,val in enumerate(data): if count1 0.15 else 0 index1 = index if count1==40 and count2 <5: count2=count2+1 if abs(val)-1 and k2>-1) #if _i==0 and _j==1 :print(_i,_j,'\n',Dist,len(Dist[0]),len(Dist[1])) yield _i,_j,Dist def search_path(n): comparison = np.array([[0]*10 for i in range(n)],dtype = np.double) for _i,_j,Dist in create_Dist(): N = len(Dist) cut_off = 5 row = [(d,N-1,j) for j,d in enumerate(Dist[N-1]) if abs(N-1-j)<=cut_off] col = [(d,i,N-1) for i,d in enumerate(Dist[:,N-1]) if abs(N-1-i)<=cut_off] min_d,min_i,min_j = min(row+col ) comparison[_i][_j] = min_d optimal_path_x,optimal_path_y = [min_i],[min_j] while min_i and min_j: optimal_path_x.append(min_i) optimal_path_y.append(min_j) pos = [(min_i-1,min_j),(min_i,min_j-1),(min_i-1,min_j-1)] #try: min_d,min_i,min_j = min(((Dist[int(k1)][int(k2)],k1,k2) for k1,k2 in pos\ if abs(k1-k2)<=cut_off)) if _i==_j and _i==4: plt.scatter(optimal_path_x[::-1],optimal_path_y[::-1],color = 'red') plt.show() return comparison mfcc_coff_input = [] mfcc_coff = [] def match(pre): global mfcc_coff_input global mfcc_coff mfcc_coff_input = compute_mfcc_coff(pre) compare = np.array([[0]*10 for i in range(len(mfcc_coff_input))],dtype = np.double) for prefix in ['','B']: mfcc_coff = compute_mfcc_coff(prefix) compare += search_path(len(mfcc_coff_input)) for l in compare: print([int(x) for x in l]) print(min(((val,index)for index,val in enumerate(l)))[1]) data,time = read_file('8.wav') match('I') match('II')