При выполнении этой программы для извлечения речевых функций из файла wav у меня возникла проблема с кодом, скажем, ошибка IndexError: список индексов вне диапазона
Файл "C: / Users / KALEEM / PycharmProjects /Speech_Processing / 2-Speech_Signal_Processing_and_Classification-master / feature_extraction_techniques / mgca.py ", строка 77, в файле mel_Generalized () (файл" C: /Users/KALEEM/PycharmProjects/Speech_Processing/2., в mel_Generalized mgca_feature_extraction (wav) Файл "C: /Users/KALEEM/PycharmProjects/Speech_Processing/2-Speech_Signal_Processing_and_Classification-master/feature_extraction_techniques/mgca.py" файл_файла/KALEEM/PycharmProjects/Speech_Processing/2-Speech_Signal_Processing_and_Classification-master/feature_extraction_techniques/mgca.py ", строка 46, в файле writeFeatures wav = makeFormat (wav), файл" C: / Users / KALEEM / PycharmPro "ects / Speech_Processing / 2-Speech_Signal_Processing_and_Classification-master / feature_extraction_techniques / mgca.py ", строка 53, в makeFormat wav = wav.split ('/') [1] .split ('-') [1] IndexError: вывести индекс из спискадиапазона
Процесс завершен с кодом выхода 1
#!usr/bin/python
from pysptk import *
from scipy import hamming
import numpy.matlib
import scipy
import scipy.io.wavfile as wav
import numpy as np
import wave
from python_speech_features.sigproc import *
from math import *
from six.moves import input as raw_input
def readWavFile(wav):
#given a path from the keyboard to read a .wav file
#wav = raw_input('Give me the path of the .wav file you want to read: ')
inputWav = 'C:/Users/KALEEM/PycharmProjects/Speech_Processing/2-Speech_Signal_Processing_and_Classification-master/feature_extraction_techniques'+wav
return inputWav
#reading the .wav file (signal file) and extract the information we need
def initialize(inputWav):
rate , signal = wav.read(readWavFile(inputWav)) # returns a wave_read object , rate: sampling frequency
sig = wave.open(readWavFile(inputWav))
# signal is the numpy 2D array with the date of the .wav file
# len(signal) number of samples
sampwidth = sig.getsampwidth()
print ('The sample rate of the audio is: ',rate)
print ('Sampwidth: ',sampwidth)
return signal , rate
#implementation of the low-pass filter
def lowPassFilter(signal, coeff=0.97):
return np.append(signal[0], signal[1:] - coeff * signal[:-1]) #y[n] = x[n] - a*x[n-1] , a = 0.97 , a>0 for low-pass filters
def preEmphasis(wav):
#taking the signal
signal , rate = initialize(wav)
#Pre-emphasis Stage
preEmphasis = 0.97
emphasizedSignal = lowPassFilter(signal)
Time=np.linspace(0, len(signal)/rate, num=len(signal))
EmphasizedTime=np.linspace(0, len(emphasizedSignal)/rate, num=len(emphasizedSignal))
return emphasizedSignal, signal , rate
def writeFeatures(mgca_features,wav):
#write in a txt file the output vectors of every sample
f = open('mel_generalized_features.txt','a')#sample ID
#f = open('mfcc_featuresLR.txt','a')#only to initiate the input for the ROC curve
wav = makeFormat(wav)
np.savetxt(f,mgca_features,newline=",")
f.write(wav)
f.write('\n')
def makeFormat(wav):
#if i want to keep only the gender (male,female)
wav = wav.split('/')[1].split('-')[1]
#only to make the format for Logistic Regression
if (wav=='Female'):
wav='1'
else:
wav='0'
return wav
def mgca_feature_extraction(wav):
#I pre-emphasized the signal with a low pass filter
emphasizedSignal,signal,rate = preEmphasis(wav)
#and now I have the signal windowed
emphasizedSignal*=np.hamming(len(emphasizedSignal))
mgca_features = 'mgcep(emphasizedSignal,order=12)'
writeFeatures(mgca_features,wav)
def mel_Generalized():
folder = raw_input('Give the name of the folder that you want to read data: ')
amount = raw_input('Give the number of samples in the specific folder: ')
for x in range(1,int(amount)+1):
wav = '/'+folder+'/'+str(x)+'.wav'
print (wav)
mgca_feature_extraction(wav)
#def main():
mel_Generalized()
#main()