def text_featurize(filename, jsondump):
# transcribe with sphinx
transcript = ts.transcribe_sphinx('test.wav')
# now put transcript through various feature engines
nltk_featureset, nltk_labels = nf.nltk_featurize(transcript)
spacy_featureset, spacy_labels = spf.spacy_featurize(transcript)
# make gensim embedding on alice and wonderland text
# (or any text corpus you'd like)
modelname = 'alice.pickle'
if modelname not in os.listdir():
text = open('alice.txt').read()
gf.w2v_train(text, 100, modelname)
gensim_featureset = gf.sentence_embedding(transcript, 100, modelname)
data = {
'transcript': transcript,
'transcript type': 'sphinx',
'nltk': np.array(nltk_featureset).tolist(),
'spacy': np.array(spacy_featureset).tolist(),
'gensim': np.array(gensim_featureset).tolist(),
}
if jsondump == True:
jsonfilename = filename[0:-4] + '.json'
jsonfile = open(jsonfilename, 'w')
json.dump(data, jsonfile)
jsonfile.close()
return data
def nltk_featurize(file):
# get transcript
if file[-4:] == '.wav':
transcript = ts.transcribe_sphinx('test.wav')
else:
transcript = file
#alphabetical features
a = transcript.count('a')
b = transcript.count('b')
c = transcript.count('c')
d = transcript.count('d')
e = transcript.count('e')
f = transcript.count('f')
g_ = transcript.count('g')
h = transcript.count('h')
i = transcript.count('i')
j = transcript.count('j')
k = transcript.count('k')
l = transcript.count('l')
m = transcript.count('m')
n = transcript.count('n')
o = transcript.count('o')
p = transcript.count('p')
q = transcript.count('q')
r = transcript.count('r')
s = transcript.count('s')
t = transcript.count('t')
u = transcript.count('u')
v = transcript.count('v')
w = transcript.count('w')
x = transcript.count('x')
y = transcript.count('y')
z = transcript.count('z')
space = transcript.count(' ')
#numerical features and capital letters
num1 = transcript.count('0') + transcript.count('1') + transcript.count(
'2') + transcript.count('3') + transcript.count(
'4') + transcript.count('5') + transcript.count(
'6') + transcript.count('7') + transcript.count(
'8') + transcript.count('9')
num2 = transcript.count('zero') + transcript.count(
'one') + transcript.count('two') + transcript.count(
'three') + transcript.count('four') + transcript.count(
'five') + transcript.count('six') + transcript.count(
'seven') + transcript.count('eight') + transcript.count(
'nine') + transcript.count('ten')
number = num1 + num2
capletter = sum(1 for c in transcript if c.isupper())
#part of speech
text = word_tokenize(transcript)
g = nltk.pos_tag(transcript)
cc = 0
cd = 0
dt = 0
ex = 0
in_ = 0
jj = 0
jjr = 0
jjs = 0
ls = 0
md = 0
nn = 0
nnp = 0
nns = 0
pdt = 0
pos = 0
prp = 0
prp2 = 0
rb = 0
rbr = 0
rbs = 0
rp = 0
to = 0
uh = 0
vb = 0
vbd = 0
vbg = 0
vbn = 0
vbp = 0
vbp = 0
vbz = 0
wdt = 0
wp = 0
wrb = 0
for i in range(len(g)):
if g[i][1] == 'CC':
cc = cc + 1
elif g[i][1] == 'CD':
cd = cd + 1
elif g[i][1] == 'DT':
dt = dt + 1
elif g[i][1] == 'EX':
ex = ex + 1
elif g[i][1] == 'IN':
in_ = in_ + 1
elif g[i][1] == 'JJ':
jj = jj + 1
elif g[i][1] == 'JJR':
jjr = jjr + 1
elif g[i][1] == 'JJS':
jjs = jjs + 1
elif g[i][1] == 'LS':
ls = ls + 1
elif g[i][1] == 'MD':
md = md + 1
elif g[i][1] == 'NN':
nn = nn + 1
elif g[i][1] == 'NNP':
nnp = nnp + 1
elif g[i][1] == 'NNS':
nns = nns + 1
elif g[i][1] == 'PDT':
pdt = pdt + 1
elif g[i][1] == 'POS':
pos = pos + 1
elif g[i][1] == 'PRP':
prp = prp + 1
elif g[i][1] == 'PRP$':
prp2 = prp2 + 1
elif g[i][1] == 'RB':
rb = rb + 1
elif g[i][1] == 'RBR':
rbr = rbr + 1
elif g[i][1] == 'RBS':
rbs = rbs + 1
elif g[i][1] == 'RP':
rp = rp + 1
elif g[i][1] == 'TO':
to = to + 1
elif g[i][1] == 'UH':
uh = uh + 1
elif g[i][1] == 'VB':
vb = vb + 1
elif g[i][1] == 'VBD':
vbd = vbd + 1
elif g[i][1] == 'VBG':
vbg = vbg + 1
elif g[i][1] == 'VBN':
vbn = vbn + 1
elif g[i][1] == 'VBP':
vbp = vbp + 1
elif g[i][1] == 'VBZ':
vbz = vbz + 1
elif g[i][1] == 'WDT':
wdt = wdt + 1
elif g[i][1] == 'WP':
wp = wp + 1
elif g[i][1] == 'WRB':
wrb = wrb + 1
#sentiment
tblob = TextBlob(transcript)
polarity = float(tblob.sentiment[0])
subjectivity = float(tblob.sentiment[1])
#word repeats
words = transcript.split()
newlist = transcript.split()
repeat = 0
for i in range(len(words)):
newlist.remove(words[i])
if words[i] in newlist:
repeat = repeat + 1
features = np.array([
a, b, c, d, e, f, g_, h, i, j, k, l, m, n, o, p, q, r, s, t, u, v, w,
x, y, z, space, number, capletter, cc, cd, dt, ex, in_, jj, jjr, jjs,
ls, md, nn, nnp, nns, pdt, pos, prp, prp2, rbr, rbs, rp, to, uh, vb,
vbd, vbg, vbn, vbp, vbz, wdt, wp, wrb, polarity, subjectivity, repeat
])
labels = [
'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n',
'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z', 'space',
'numbers', 'capletters', 'cc', 'cd', 'dt', 'ex', 'in', 'jj', 'jjr',
'jjs', 'ls', 'md', 'nn', 'nnp', 'nns', 'pdt', 'pos', 'prp', 'prp2',
'rbr', 'rbs', 'rp', 'to', 'uh', 'vb', 'vbd', 'vbg', 'vbn', 'vbp',
'vbz', 'wdt', 'wp', 'wrb', 'polarity', 'subjectivity', 'repeat'
]
return features, labels
# transcribe with pocketsphinx
#features, labels = nltk_featurize('test.wav')
import sounddevice as sd
import soundfile as sf
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')
# record a test file
sync_record('test.wav', 10, 44100, 2)
# fetch a google transcript (note: you need environment vars setup)
try:
google_transcript = ts.transcribe_google('test.wav')
except:
print('do not have proper environment variables setup')
# transcribe with pocketsphinx
sphinx_transcript = ts.transcribe_sphinx('test.wav')
# transcribe with deepspeech (can be CPU intensive)
deepspeech_transcript = ts.transcribe_deepspeech('test.wav')
# record a sample file and transcribe using all transcription methods
# then, output all the transcripts into a jsonfile ('test.json')
jsonfile = ts.transcribe_all('test.wav')