def POST(self):
t = Tropo()
answer = self.get_answer()
if re.match("[0-9]{5}", answer):
pitch_id = answer
# test that the pitch exists...
pitch.get_pitch(pitch_id)
t.say("You want to hear pitch")
for c in pitch_id:
t.say(c)
elif answer in ("0", "random"):
pitch_id = pitch.random_pitch()['pitch_id']
if pitch_id:
# test that the pitch exists...
pitch.get_pitch(pitch_id)
t.say("Your random pitch is pitch number")
for c in pitch_id:
t.say(c)
else:
t.say("Sorry, no pitches are available yet.")
elif answer == None:
return self.do_bad_choice()
else:
raise Exception("unexpected answer %s" % answer)
if pitch_id:
url = self.s3_url(pitch_id)
t.say(url)
t.on(event="continue", next=self.MAIN_MENU)
return t.RenderJson()
def do_happy(self, pitch_id):
t = Tropo()
pitch.confirm_pitch(pitch_id)
t.say("Great, your pitch has been saved.")
p = pitch.get_pitch(pitch_id)
### @export "send-info-sms"
call_id = json.loads(web.data())['result']['callId']
session = pitch.session_info(call_id)
is_sip = session['caller_network'] == 'SIP'
is_number = re.match("^(\+)?[0-9]+$", session['caller_id'])
if is_sip and is_number:
t.message(
"""Thank you for using pitchlift!
Your Pitch ID is %s.
Your PIN is %s.""" % (pitch_id, p['pin']),
to=session['caller_id'],
channel='TEXT'
)
### @export "say-info-aloud"
t.say("Your pitch eye dee is")
for c in pitch_id:
t.say(c)
t.say("Your pin is")
for c in "%s" % p['pin']:
t.say(c)
t.say("once again")
t.say("Your pitch eye dee is")
for c in pitch_id:
t.say(c)
t.say("Your pin is")
for c in "%s" % p['pin']:
t.say(c)
### @export "finish"
t.on(event="continue", next=self.MAIN_MENU)
return t.RenderJson()
def extract_features(path):
df = pd.DataFrame()
print('Extracting features')
freq_col = ['pitch']
mfcc_col = ['mfcc' + str(i + 1) for i in list(range(110))]
col = freq_col + mfcc_col
directory = os.listdir(path + "recorded_audio\\")
print(directory)
for wav_file in directory:
write_features = []
y, sr = librosa.load(path + "recorded_audio\\" + wav_file)
fs, x = wav.read(path + "recorded_audio\\" + wav_file)
pitch = get_pitch(fs, x)
mfcc_features = get_mfcc(y, sr)
write_features = [pitch] + mfcc_features.tolist()[0]
df = df.append([write_features])
df.columns = col
df.to_csv('recorded_audio_features.csv')
def main(path, gender):
df = pd.DataFrame()
print('Extracting features for ' + gender)
directory = os.listdir(path)
for wav_file in directory:
write_features = []
y, sr = librosa.load(path + wav_file)
fs, x = wav.read(path + wav_file)
print(wav_file)
pitch = get_pitch(fs, x)
#frequencies=get_frequencies(y,sr)
#freq_features=get_features(frequencies)
mfcc_features = get_mfcc(y, sr)
#write_features=[pitch]+freq_features+mfcc_features.tolist()[0]+[gender]
write_features = [pitch] + mfcc_features.tolist()[0] + [gender]
df = df.append([write_features])
#if wav_file=='00001.wav':
#break #remove break to execute for all files
df.columns = col
df.to_csv(gender + '_features.csv')