At first,I am a python beginner. I need to read my clean.wav(cut to 50 slices to haha0~haha49 .wav) and noise.wav ,doing a snr [5,10,15]. Then output the before and after's waveform and Spectrogram. But,teriminal always tell me RuntimeError: Error opening 'D:\python\pythonnoisy\add_white\haha0_white_snr5.wav': System error. However, I can see this file in my folder.
import os
import re
import sys
import wave
import librosa
import matplotlib
import numpy as np
import pylab as pl
import soundfile as sf
import matplotlib.pyplot as plt
from scipy.fftpack import fft
wavedir = r"D:\python"
noisydir = wavedir+"\\pythonnoisy"
noisedir = wavedir+"\\pythonnoise"
cleandir = wavedir+"\\pythonclean"
def add_noise(noisydir, noisedir, cleandir, snr): # noisy
noisewav = "white.wav"
noise, fs = sf.read(noisedir+"\\"+noisewav) # 讀取白雜訊.wav
noisy_splitdir = noisydir+"\\"+"add_"+noisewav[:-4]+"\\"
# 迴圈取原始wav檔資料夾裡所有檔案
for cleanwav in os.listdir(cleandir):
clean, Fs = sf.read(cleandir+"\\"+cleanwav) # 讀取原始.wav檔
# 取樣頻率:原始音檔==白雜訊&&時長:原始音檔<白雜訊
if fs == Fs and len(clean) <= len(noise):
cleanenergy = np.sum(np.power(clean, 2)) # 原始音檔Power(=振幅^2)
# 1<隨機生成長度<noise長-clean長+1
ind = np.random.randint(1, len(noise) - len(clean) + 1)
noiselen = noise[ind:len(clean) + ind]
noiseenergy = np.sum(np.power(noiselen, 2)) # 白雜訊Power
ratio2 = np.sqrt(
(cleanenergy / noiseenergy) / (np.power(10, snr * 0.1)))
noisyAudio = clean + noiselen * ratio2 # 混音振幅
# 混音路徑+檔名
noisywavname = noisy_splitdir + \
cleanwav[:-4]+"_"+noisewav[:-4]+"_snr"+str(snr)+".wav"
sf.write(noisywavname, noisyAudio, 44100) # 生成混音.wav
def draw_clean(cleandir, j):
f = wave.open(
r"D:\python\pythonclean\haha" + str(j) + ".wav", "rb")
params = f.getparams()
nchannels, sampwidth, framerate, nframes = params[:4]
str_data = f.readframes(nframes)
f.close()
wave_data = np.fromstring(str_data, dtype=np.short)
wave_data.shape = -1, 2
wave_data = wave_data.T
time = np.arange(0, nframes) * (1.0 / framerate)
plt.subplot(4, 2, 7)
plt.plot(time, wave_data[0])
plt.xlabel("time(s)")
plt.subplot(4, 2, 8)
plt.specgram(wave_data[0], Fs=framerate)
plt.xlabel("time(s)")
def draw_noisy(noisydir, j):
noisydirr = noisydir+"\\add_white\\haha"
for k in range(5, 20, 5):
f = wave.open(r"D:\python\pythonnoisy\add_white\haha" +
str(j)+"_white_snr"+str(k)+".wav", "rb")
params = f.getparams()
nchannels, sampwidth, framerate, nframes = params[:4]
str_data = f.readframes(nframes)
wave_data = np.fromstring(str_data, dtype=np.short)
wave_data.shape = -1, 2
wave_data = wave_data.T
time = np.arange(0, nframes) * (1.0 / framerate)
plt.subplot(4, 2, k/5*2-1)
plt.plot(time, wave_data[0])
plt.subplot(4, 2, k/5*2)
plt.specgram(wave_data[0], Fs=framerate)
wavedir = r"D:\python"
noisydir = wavedir+"\\pythonnoisy"
noisedir = wavedir+"\\pythonnoise"
cleandir = wavedir+"\\pythonclean"
level = [5, 10, 15]
for snr in level:
add_noise(noisydir, noisedir, cleandir, snr)
for j in range(0, 51):
draw_clean(cleandir, j)
draw_noisy(noisydir, j)
picture = noisydir+"\picture\hahawhite"+str(j)+".png"
plt.savefig(picture)
plt.close()
