使用python实现语音文件的特征提取方法

from pydub import AudioSegmentimport pydubdef MP32WAV(mp3_path,wav_path): """ 这是MP3文件转化成WAV文件的函数 :param mp3_path: MP3文件的地址 :param wav_path: WAV文件的地址 """ pydub.AudioSegment.converter = "D://ffmpeg//bin//ffmpeg.exe" MP3_File = AudioSegment.from_mp3(file=mp3_path) MP3_File.export(wav_path,format="wav")

import waveimport jsondef Read_WAV(wav_path): """ 这是读取wav文件的函数，音频数据是单通道的。返回json :param wav_path: WAV文件的地址 """ wav_file = wave.open(wav_path,'r') numchannel = wav_file.getnchannels()   # 声道数 samplewidth = wav_file.getsampwidth()  # 量化位数 framerate = wav_file.getframerate()  # 采样频率 numframes = wav_file.getnframes()   # 采样点数 print("channel", numchannel) print("sample_width", samplewidth) print("framerate", framerate) print("numframes", numframes) Wav_Data = wav_file.readframes(numframes) Wav_Data = np.fromstring(Wav_Data,dtype=np.int16) Wav_Data = Wav_Data*1.0/(max(abs(Wav_Data)))  #对数据进行归一化 # 生成音频数据,ndarray不能进行json化，必须转化为list，生成JSON dict = {"channel":numchannel,   "samplewidth":samplewidth,   "framerate":framerate,   "numframes":numframes,   "WaveData":list(Wav_Data)} return json.dumps(dict)

from matplotlib import pyplot as pltdef DrawSpectrum(wav_data,framerate): """ 这是画音频的频谱函数 :param wav_data: 音频数据 :param framerate: 采样频率 """ Time = np.linspace(0,len(wav_data)/framerate*1.0,num=len(wav_data)) plt.figure(1) plt.plot(Time,wav_data) plt.grid(True) plt.show() plt.figure(2) Pxx, freqs, bins, im = plt.specgram(wav_data,NFFT=1024,Fs = 16000,noverlap=900) plt.show() print(Pxx) print(freqs) print(bins) print(im)

#!/usr/bin/python3# -*- coding: utf-8 -*-# @Time : 2018/7/5 13:11# @Author : DaiPuwei# @FileName: VoiceExtract.py# @Software: PyCharm# @E-mail ：771830171@qq.com# @Blog ：https://blog.csdn.net/qq_30091945import numpy as npfrom pydub import AudioSegmentimport pydubimport osimport waveimport jsonfrom matplotlib import pyplot as pltdef MP32WAV(mp3_path,wav_path): """ 这是MP3文件转化成WAV文件的函数 :param mp3_path: MP3文件的地址 :param wav_path: WAV文件的地址 """ pydub.AudioSegment.converter = "D://ffmpeg//bin//ffmpeg.exe"   #说明ffmpeg的地址 MP3_File = AudioSegment.from_mp3(file=mp3_path) MP3_File.export(wav_path,format="wav")def Read_WAV(wav_path): """ 这是读取wav文件的函数，音频数据是单通道的。返回json :param wav_path: WAV文件的地址 """ wav_file = wave.open(wav_path,'r') numchannel = wav_file.getnchannels()   # 声道数 samplewidth = wav_file.getsampwidth()  # 量化位数 framerate = wav_file.getframerate()  # 采样频率 numframes = wav_file.getnframes()   # 采样点数 print("channel", numchannel) print("sample_width", samplewidth) print("framerate", framerate) print("numframes", numframes) Wav_Data = wav_file.readframes(numframes) Wav_Data = np.fromstring(Wav_Data,dtype=np.int16) Wav_Data = Wav_Data*1.0/(max(abs(Wav_Data)))  #对数据进行归一化 # 生成音频数据,ndarray不能进行json化，必须转化为list，生成JSON dict = {"channel":numchannel,   "samplewidth":samplewidth,   "framerate":framerate,   "numframes":numframes,   "WaveData":list(Wav_Data)} return json.dumps(dict)def DrawSpectrum(wav_data,framerate): """ 这是画音频的频谱函数 :param wav_data: 音频数据 :param framerate: 采样频率 """ Time = np.linspace(0,len(wav_data)/framerate*1.0,num=len(wav_data)) plt.figure(1) plt.plot(Time,wav_data) plt.grid(True) plt.show() plt.figure(2) Pxx, freqs, bins, im = plt.specgram(wav_data,NFFT=1024,Fs = 16000,noverlap=900) plt.show() print(Pxx) print(freqs) print(bins) print(im)def run_main(): """  这是主函数 """ # MP3文件和WAV文件的地址 path1 = './MP3_File' path2 = "./WAV_File" paths = os.listdir(path1) mp3_paths = [] # 获取mp3文件的相对地址 for mp3_path in paths:  mp3_paths.append(path1+"/"+mp3_path) print(mp3_paths) # 得到MP3文件对应的WAV文件的相对地址 wav_paths = [] for mp3_path in mp3_paths:  wav_path = path2+"/"+mp3_path[1:].split('.')[0].split('/')[-1]+'.wav'  wav_paths.append(wav_path) print(wav_paths) # 将MP3文件转化成WAV文件 for(mp3_path,wav_path) in zip(mp3_paths,wav_paths):  MP32WAV(mp3_path,wav_path) for wav_path in wav_paths:  Read_WAV(wav_path) # 开始对音频文件进行数据化 for wav_path in wav_paths:  wav_json = Read_WAV(wav_path)  print(wav_json)  wav = json.loads(wav_json)  wav_data = np.array(wav['WaveData'])  framerate = int(wav['framerate'])  DrawSpectrum(wav_data,framerate)if __name__ == '__main__': run_main()