pyhton 画出音频文件的波形图和频谱图
# -*- coding:utf-8 -*-import waveimport structimport numpy as npimport matplotlib.pyplot as pltdef oscillogram_spectrum(audio_path):"""画出音频文件audio_path的声波图和频谱图:param audio_path:音频文件路径:return:"""# 读取wav文件filename = audio_pathwavefile = wave.open(filename, 'r') # open for writing# 读取wav文件的四种信息的函数。期中numframes表示一共读取了几个frames。nchannels = wavefile.getnchannels()sample_width = wavefile.getsampwidth()framerate = wavefile.getframerate()numframes = wavefile.getnframes()print("channel", nchannels)print("sample_width", sample_width)print("framerate", framerate)print("numframes", numframes)# 建一个y的数列，用来保存后面读的每个frame的amplitude。y = np.zeros(numframes)# for循环，readframe(1)每次读一个frame，取其前两位，是左声道的信息。右声道就是后两位啦。# unpack是struct里的一个函数，简单说来就是把＃packed的string转换成原来的数据，无论是什么样的数据都返回一个tuple。这里返回的是长度为一的一个# tuple，所以我们取它的第零位。for i in range(numframes):val = wavefile.readframes(1)left = val[0:2]# right = val[2:4]v = struct.unpack('h', left)[0]y[i] = v# framerate就是声音的采用率，文件初读取的值。Fs = frameratetime = np.arange(0, numframes) * (1.0 / framerate)# 显示时域图(波形图)plt.subplot(211)plt.plot(time, y)# 显示频域图(频谱图)plt.subplot(212)plt.specgram(y, NFFT=1024, Fs=Fs, noverlap=900)plt.show()if __name__ == '__main__':audio_path = '1.wav'oscillogram_spectrum(audio_path)
import waveimport matplotlib.pyplot as pltimport numpy as npimport osf = wave.open('play.wav', 'rb')params = f.getparams()nchannels, sampwidth, framerate, nframes = params[:4]print("nchannels:", nchannels)print("sampwidth:", sampwidth)print("framerate:", framerate)print("nframes:", nframes)strData = f.readframes(nframes) # 读取音频，字符串格式print("strData:", strData)waveData = np.fromstring(strData, dtype=np.int16) # 将字符串转化为intprint("waveData:", waveData)# waveData = waveData * 1.0 / (max(abs(waveData))) # wave幅值归一化# plot the wavetime = np.arange(0, nframes) * (1.0 / framerate)# 调节横竖坐标长度（单位：英寸）# figure [ˈfɪɡə(r)] fig 做 + ure 名词 = 图形plt.figure(figsize=(6, 3))plt.plot(time, waveData)plt.xlabel("Time(s)")plt.ylabel("Amplitude")plt.title("Single channel wavedata")plt.grid('on') # 标尺，on：有，off:无。plt.show()
import librosa, librosa.displayimport matplotlib.pyplot as pltfile = 'play.wav'signal, sr = librosa.load(file, sr=22050)librosa.display.waveplot(signal, sr=sr)plt.xlabel('time')plt.ylabel('amplitude')plt.show()
