0.6. Visualization
This section documents all the scripts in the Chapter_6_visualization 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)))results in a stream like this:
new max is 157.97308087348938
==.==.==.==.==.==.==.==.==.==.==.==.==.==.==.==.==.==.==.==
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minimum val: 0.057202301832148805
max val: 157.97308087348938
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')results in an image like this:
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)results in an image like this:
audio_plotmany.py
results in an image like this:
audio_plotspd.py
results in an image like this:
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')results in an image like this:
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()results in a .JSON output like this:
1 lines (1 sloc) 672 Bytes
{"filenames": ["0.wav", "1.wav", "2.wav", "3.wav", "4.wav", "5.wav", "6.wav", "7.wav", "8.wav", "9.wav", "10.wav", "11.wav", "12.wav", "13.wav", "14.wav", "15.wav", "16.wav", "17.wav", "18.wav", "19.wav", "20.wav", "21.wav", "22.wav", "23.wav", "24.wav", "25.wav", "26.wav", "27.wav", "28.wav", "29.wav"], "transcripts": ["oh", "it is gorgeous", "testing on this", "return to go", "oh good and it's", "", "", "where", "it can be", "", "", "runny nose", "", "", "the", "oh", "the", "we're then were", "so good", "", "", "", "resting to", "testing testing", "asking because too", "testing testing", "interesting times", "testing testing", "deemed resting", "talking to us"]}
text_path.py
results an image like this:
text_freqplot.py
results in an image like this:
text_wordcloud.py
results in an image like this:
text_tree.py
results in an image like this:
text_entity.py
results in an image like this (in browser):
text_network.py
text_tsne.py
results in an image like this:
text_tsne_many.py
results in an image like this:
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
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==.==
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==.==.==.==.==.== this is
==.== oh
==.==
==.==.==.==.==.== oh
==.==
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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
results in an image like this:
mixed_video.py
results in a video as shown here
Looks like this:
meta_stream.py
Looks like this:
meta_multi.py
Looks like this:
meta_nonstream.py
Looks like this:
If you are interested to read more on any of these topics, check out the documentation below.
Visualization libraries
Audio data
Text data
Mixed data
Meta data