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0.6. Visualization
Jim Schwoebel edited this page Aug 17, 2018
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This section covers all the scripts in the Chapter 6: Visualizations folder.
audio_stream.py
import sounddevice as sd
import soundfile as sf
import random, time, librosa, os
import numpy as np
def visualize_data(sample, minimum, maximum):
difference=maximum-minimum
delta=difference/10
bar='==.==.'
if sample <= minimum:
# 1 bar
output=bar
elif minimum+delta >= sample > minimum:
# 1 bar
output=bar
elif minimum+delta*2 >= sample > minimum+delta:
# 2 bars
output=bar*2
elif minimum+delta*3 >= sample >= minimum+delta*2:
# 3 bars
output=bar*3
elif minimum+delta*4 >= sample > minimum+delta*3:
# 4 bars
output=bar*4
elif minimum+delta*5 >= sample > minimum+delta*4:
# 5 bars
output=bar*5
elif minimum+delta*6 >= sample > minimum+delta*5:
# 6 bars
output=bar*6
elif minimum+delta*7 >= sample > minimum+delta*6:
# 7 bars
output=bar*7
elif minimum+delta*8 >= sample > minimum+delta*7:
# 8 bards
output=bar*8
elif minimum+delta*9 >= sample > minimum+delta*8:
# 9 bars
output=bar*9
elif maximum > sample >= minimum+delta*9:
# 10 bars
output=bar*10
elif sample >= maximum:
# 10 bars
output=bar*10
else:
print(sample)
output='error'
# plot bars based on a min and max
return output[0:-1]
def record_data(filename, duration, fs, channels):
# synchronous recording
myrecording = sd.rec(int(duration * fs), samplerate=fs, channels=channels)
sd.wait()
sf.write(filename, myrecording, fs)
y, sr = librosa.load(filename)
rmse=np.mean(librosa.feature.rmse(y)[0])
os.remove(filename)
return rmse*1000
# take a streaming sample and then put that data as it is being recorded
minimum=0
maximum=70
samples=list()
for i in range(100):
# record 20ms of data
sample=record_data('sample.wav',0.02, 44100, 1)
if sample > maximum:
maximum=sample
print('new max is %s'%(maximum))
samples.append(sample)
#print(sample)
print(visualize_data(sample,minimum,maximum))
# other stuff - if you'd like to sleep or generate random samples
# keep going streaming
# randomize data
# sample=random.randint(0,30)
#time.sleep(0.2)
samples=np.array(samples)
minval=np.amin(samples)
maxval=np.amax(samples)
print('minimum val: %s'%(str(minval)))
print('max val: %s'%(str(maxval)))audio_path.py
import sounddevice as sd
import soundfile as sf
import random, time, librosa, os
import numpy as np
import matplotlib.pyplot as plt
from drawnow import drawnow
def make_fig():
plt.scatter(x, y)
def record_data(filename, duration, fs, channels):
# synchronous recording
myrecording = sd.rec(int(duration * fs), samplerate=fs, channels=channels)
sd.wait()
sf.write(filename, myrecording, fs)
y, sr = librosa.load(filename)
rmse=np.mean(librosa.feature.rmse(y)[0])
os.remove(filename)
return rmse*1000
# initialize plot
plt.ion() # enable interactivity
fig = plt.figure() # make a figure
x = list()
y = list()
for i in range(100):
# record 20ms of data
sample=record_data('sample.wav',0.02, 44100, 1)
x.append(i)
y.append(sample)
drawnow(make_fig)
plt.savefig('stream.png')
os.system('open stream.png')audio_spectrograms.py
import librosa, os
import matplotlib.pyplot as plt
import numpy as np
import librosa.display
import audio_plot as ap
# load file
data_dir=os.getcwd()+'/data'
os.chdir(data_dir)
filename='Voice.wav'
imgfile=ap.plot_spectrogram(filename)
audio_plotmany.py image
audio_plotspd.py image
audio_cluster.py
import os, librosa
import scipy.io.wavfile as wav
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import numpy as np
########################################################################
## MAIN CODE BASE ##
########################################################################
os.chdir('data/samples')
listdir=os.listdir()
wavfiles=list()
for i in range(len(listdir)):
if listdir[i][-4:]=='.wav':
wavfiles.append(listdir[i])
wavfiles=sorted(wavfiles)
samples=list()
for i in range(len(wavfiles)):
y, sr = librosa.core.load(wavfiles[i])
rmse=np.mean(librosa.feature.rmse(y)[0])
mfcc=np.mean(librosa.feature.mfcc(y)[0])
samples.append(np.array([rmse, mfcc]))
kmeans = KMeans(3, max_iter = 1000, n_init = 100)
kmeans.fit_transform(samples)
predictions = kmeans.predict(samples)
x=list()
y=list()
for i in range(len(predictions)):
x.append(i)
y.append(predictions[i])
x0=list()
x1=list()
x2=list()
y0=list()
y1=list()
y2=list()
for i in range(len(y)):
if y[i] == 0:
x0.append(x[i])
y0.append(y[i])
elif y[i] == 1:
x1.append(x[i])
y1.append(y[i])
elif y[i] == 2:
x2.append(x[i])
y2.append(y[i])
plt.scatter(x0, y0, marker='o', c='black')
plt.scatter(x1, y1, marker='o', c='blue')
plt.scatter(x2, y2, marker='o', c='red')
plt.xlabel('sample number')
plt.ylabel('k means class')
plt.savefig('kmeans.png')text_stream.py
import os, json, shutil
import speech_recognition as sr_audio
import sounddevice as sd
import soundfile as sf
def transcribe_pocket(filename):
# transcribe the audio (note this is only done if a voice sample)
r=sr_audio.Recognizer()
with sr_audio.AudioFile(filename) as source:
audio = r.record(source)
text=r.recognize_sphinx(audio)
return text
def sync_record(filename, duration, fs, channels):
#print('recording')
myrecording = sd.rec(int(duration * fs), samplerate=fs, channels=channels)
sd.wait()
sf.write(filename, myrecording, fs)
#print('done recording')
# set recording params
duration=1
fs=44100
channels=1
try:
os.mkdir('recordings')
os.chdir(os.getcwd()+'/recordings')
except:
shutil.rmtree('recordings')
os.mkdir('recordings')
os.chdir(os.getcwd()+'/recordings')
filenames=list()
transcripts=list()
for i in range(30):
filename='%s.wav'%(str(i))
sync_record(filename, duration, fs, channels)
transcript=transcribe_pocket(filename)
# print transcript on screen
print(transcript)
filenames.append(filename)
transcripts.append(transcript)
data={
'filenames':filenames,
'transcripts':transcripts
}
jsonfile=open('recordings.json','w')
json.dump(data,jsonfile)
jsonfile.close()text_path.py
text_freqplot.py
text_wordcloud.py
text_tree.py
text_entity.py
text_network.py
text_tsne.py image text_tsne_many.py image
mixed_stream.py (sample output)
==.==.==.==.==.==.==.==.==.== as things were
==.==
==.==.==.==.==.==.==.==.==.== not sure who
==.==.==.==.==.== and it's all gone
==.==
==.==
==.== good
==.==.==.==.==.==.==.==.==.== exactly what
==.== air
==.== oh
==.==
==.==
==.==.==.==.==.==.==.==.==.==.==.== i was too good at all
==.==
==.==
==.==.==.==.==.==.==.==.==.== uh huh
==.==.==.==.==.==.==.==.==.==.==.== in the mud
==.==.==.==.==.==.==.==.==.== aren't you
==.==
==.==.==.==.==.==.==.==.==.==.==.== so
==.== mm
==.==
==.==
==.==
==.==.==.==.==.== this is
==.== oh
==.==
==.==.==.==.==.== oh
==.==
==.==
minimum val: 0.05180590960662812
max val: 39.191748946905136
transcripts:
['as things were', '', 'not sure who', "and it's all gone", '', '', 'good', 'exactly what', 'air', 'oh', '', '', 'i was too good at all', '', '', 'uh huh', 'in the mud', "aren't you", '', 'so', 'mm', '', '', '', 'this is', 'oh', '', 'oh', '', '']
mixed_path.py visual mixed_video.py visual
meta_stream.py output
meta_multi.py
meta_nonstream.py
If you are interested to read more on any of these topics, check out the documentation below. Visualization libraries Matplotlib Seaborn Ggplot Bokeh Pygal Plotly Dash Geoplotlib Gleam Missingno Leather Audio data Librosa Soundfile Sounddevice Text data Sounddevice NLTK Spacy NetworkX Mixed data Sounddevice OpenCV Meta data Keras Scikit-learn