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dataops.py
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import os
from tqdm import tqdm
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from scipy.io import wavfile
import librosa
from python_speech_features import mfcc, logfbank
plt.style.use('ggplot')
plt.grid(True)
def plot_signals(signals):
fig, axes = plt.subplots(nrows=2, ncols=4, sharex=False,
sharey=True, figsize=(20, 5))
fig.suptitle('Time Series', size=16)
i = 0
for x in range(2):
for y in range(4):
axes[x,y].set_title(list(signals.keys())[i])
axes[x,y].plot(list(signals.values())[i])
axes[x,y].get_xaxis().set_visible(False)
axes[x,y].get_yaxis().set_visible(False)
i += 1
def plot_fft(fft):
fig, axes = plt.subplots(nrows=2, ncols=4, sharex=False,
sharey=True, figsize=(20,5))
fig.suptitle('Fourier Transforms', size=16)
i = 0
for x in range(2):
for y in range(4):
data = list(fft.values())[i]
Y, freq = data[0], data[1]
axes[x,y].set_title(list(fft.keys())[i])
axes[x,y].plot(freq, Y)
axes[x,y].get_xaxis().set_visible(False)
axes[x,y].get_yaxis().set_visible(False)
i += 1
def plot_fbank(fbank):
fig, axes = plt.subplots(nrows=2, ncols=4, sharex=False,
sharey=True, figsize=(20,5))
fig.suptitle('Filter Bank Coefficients', size=16)
i = 0
for x in range(2):
for y in range(4):
axes[x,y].set_title(list(fbank.keys())[i])
axes[x,y].imshow(list(fbank.values())[i],
cmap='hot', interpolation='nearest')
axes[x,y].get_xaxis().set_visible(False)
axes[x,y].get_yaxis().set_visible(False)
i += 1
def plot_mfccs(mfccs):
fig, axes = plt.subplots(nrows=2, ncols=4, sharex=False,
sharey=True, figsize=(20,4))
fig.suptitle('Mel Frequency Cepstrum Coefficients', size=16)
i = 0
for x in range(2):
for y in range(4):
axes[x,y].set_title(list(mfccs.keys())[i])
axes[x,y].imshow(list(mfccs.values())[i],
cmap='hot', interpolation='nearest')
axes[x,y].get_xaxis().set_visible(False)
axes[x,y].get_yaxis().set_visible(False)
i += 1
def envelope(y, rate, threshold):
mask = []
y = pd.Series(y).apply(np.abs)
y_mean = y.rolling(window=int(rate/10), min_periods=1, center=True).mean()
for mean in y_mean:
if mean > threshold:
mask.append(True)
else:
mask.append(False)
return mask
def calc_fft(y, rate):
n = len(y)
freq = np.fft.rfftfreq(n, d=1/rate)
Y = abs(np.fft.rfft(y)/n)
return (Y, freq)
df = pd.read_csv('test.csv')
df.set_index('fname', inplace=True)
for f in df.index:
rate, signal = wavfile.read('wavfiles/'+f)
df.at[f, 'length'] = signal.shape[0] / rate
classes = list(np.unique(df.label))
class_dist = df.groupby(['label'])['length'].mean()
fig, ax = plt.subplots()
ax.set_title('Class Distributions', y=1.08)
ax.pie(class_dist, labels=class_dist.index, autopct='%1.1f%%',
shadow=False,startangle=90)
ax.axis('equal')
plt.show()
df.reset_index(inplace=True)
signals = {}
fft = {}
fbank ={}
mfccs = {}
for c in classes:
wav_file = df[df.label == c].iloc[0, 0]
signal, rate = librosa.load('wavfiles/'+wav_file, sr=44100)
mask = envelope(signal, rate, threshold=0.0005)
signal = signal[mask]
signals[c] = signal
fft[c] = calc_fft(signal, rate)
bank = logfbank(signal[:rate], rate, nfilt=26, nfft=1103).T
fbank[c] =bank
mel = mfcc(signal[:rate], rate, numcep=13, nfilt=26, nfft=1103).T
mfccs[c] = mel
plot_signals(signals)
plt.show()
plot_fft(fft)
plt.show()
plot_fbank(fbank)
plt.show()
plot_mfccs(mfccs)
plt.show()
if len(os.listdir('clean')) == 0:
for f in tqdm(df.fname):
signal, rate = librosa.load('wavfiles/'+f, sr=16000)
mask = envelope(signal, rate, 0.0005)
wavfile.write(filename='clean/'+f, rate=rate, data=signal[mask])