def main():
filename = sys.argv[1]
model = sys.argv[2]
sound = AudioSegment.from_file(filename)
lenght_in_miliseconds = len(sound)
sound = sound.set_frame_rate(16000)
sound = sound.apply_gain(-18 - sound.dBFS)
# Trying to normalize to 85 db
# Note: When REPLAYGAIN_REFERENCE_LOUDNESS is not provided the *_GAIN tags are interpreted relative to an assumed target of -18 dBFS.
max_db = 20 * math.log(sound.apply_gain(-18 - sound.dBFS).max, 10)
print("The file was normalized so now it has {} db as peak value".format(
round(max_db), 2))
silence = AudioSegment.silent(duration=5000)
sound = silence + sound + silence
sound.export(filename, format="wav")
netA_path = "{}trained_model/{}/generator_ab.npz".format(ROOT_PATH, model)
netB_path = "{}trained_model/{}/generator_ba.npz".format(ROOT_PATH, model)
convert(netA_path, netB_path, filename)
sound = AudioSegment.from_file(filename)
sound = sound[len(silence):-len(silence)]
padding_audio = AudioSegment.silent(duration=lenght_in_miliseconds -
len(sound))
sound = sound + padding_audio
sound.export(filename, format="wav")
def test_success(self):
"""
successful scenario
"""
import convertor, StringIO
io = StringIO.StringIO()
args = ["convertor.py", "sample.py", "--reports=n", "--output-format=parseable"]
expected_result = "%s/sample.py:6:0: [C] More than one statement on a single line\n" % PROJECT_FOLDER
convertor.convert(args, io)
result = io.getvalue()
self.assertEqual(expected_result, result)
def test_error(self):
import convertor, StringIO
help_text = "This is help test"
convertor.HELP_TEXT = help_text
original_parse_module_name = convertor.parse_module_name
convertor.parse_module_name = MainTest.parse_module_name_mock
io = StringIO.StringIO()
convertor.convert([], io)
result = io.getvalue()
convertor.parse_module_name = original_parse_module_name
expected_result = "Error: exception\n" + help_text
self.assertEqual(expected_result, result)
def test_error(self):
import convertor, StringIO
help_text = "This is help test"
convertor.HELP_TEXT = help_text
original_parse_module_name = convertor.parse_module_name
convertor.parse_module_name = MainTest.parse_module_name_mock
io = StringIO.StringIO()
convertor.convert([], io)
result = io.getvalue()
convertor.parse_module_name = original_parse_module_name
expected_result = "Error: exception\n"+help_text
self.assertEqual(expected_result, result)
def test_success(self):
"""
successful scenario
"""
import convertor, StringIO
io = StringIO.StringIO()
args = ["convertor.py", "sample.py", "--reports=n"]
expected_result = """************* Module pylint_pycharm.sample
%s/sample.py:6:6: [C0321(multiple-statements), ] More than one statement on a single line
""" % PROJECT_FOLDER
convertor.convert(args, io)
result = io.getvalue()
self.assertEqual(expected_result, result)
def test_success(self):
"""
successful scenario
"""
import convertor, StringIO
io = StringIO.StringIO()
args = [
"convertor.py", "sample.py", "--reports=n",
"--output-format=parseable"
]
expected_result = "%s/sample.py:6:0: [C] More than one statement on a single line\n" % PROJECT_FOLDER
convertor.convert(args, io)
result = io.getvalue()
self.assertEqual(expected_result, result)
def main():
print("start to clean data")
# load data file
Csv_Dics = readfile()
#print(Csv_Dics.keys())
Lists_Labeled_data = {}
i = 0
for key in Csv_Dics.keys():
# data["Read_time_Bin"]=CategorizeData(data['x0_time_read_time_total'], cut_points, labels)
print(key)
data_binded = CategorizeData(Csv_Dics[key], 0)
nameOfthefile = path_leaf(key)
print(nameOfthefile)
path = "/Users/rezakhoshkangini/Documents/Drexel_Documents/Work/Mat-Code/newExperiment_Trento/Sections/Sections_new_features/Labeled/"
#data_binded=CategorizeData(data)
#Clustring
data_labaled, data_confid = Clustering_new(data_binded, i,
nameOfthefile)
WritCsv(data_confid, path + str('Conf_' + nameOfthefile))
# data_labaled=Culstring_Players(data_binded)
TmpContiner = pd.DataFrame(data_labaled)
Lists_Labeled_data[
i] = TmpContiner # adding all the labled data into a list
#writing bindeded data to CSV file
WritCsv(data_labaled, path + str('Result_' + nameOfthefile))
i = i + 1
#
#calling classification class
# Sub_Machine()
Difrentiate_Labled_Data(Lists_Labeled_data)
#converting csv file into arff
Converted_Data_arff = convert()
def post(self):
newtext = ""
text = self.request.get('text')
if text:
newtext = convert(str(text))
self.render("convert.html", text=newtext)
def final_handler(callback_query):
message = callback_query.message
data = callback_query.data
VALUTE_DICT['cur_to'] = data
set_cur_to(cbr_currencies[data])
result = convert(int(RESULT_DICT["amount"]), RESULT_DICT["cur_from"],
RESULT_DICT["cur_to"])
bot.send_message(
message.chat.id,
text=f'{RESULT_DICT["amount"]} {VALUTE_DICT["cur_from"]} в'
f' {VALUTE_DICT["cur_to"]}:\n'
f'{result} {VALUTE_DICT["cur_to"]}')
bot.send_message(
message.chat.id,
text='Перевод завершен, для нового перевода отправь мне любое сообщение'
)
update_state(message, START)
def main(**kwargs):
if "lyrics" in kwargs:
lyrics = kwargs['lyrics']
else:
lyrics = "oo "
if "notes" in kwargs:
notes = list(map(int, kwargs['notes'].split(",")))
else:
notes = [0]
if "dur" in kwargs:
durations = list(map(float, kwargs['dur'].split(",")))
else:
durations = [1]
if 'file' in kwargs:
filename = kwargs['file']
else:
filename = 'output.wav'
if 'tempo' in kwargs:
tempo = kwargs['tempo']
else:
tempo = 100
print(kwargs)
if 'lang' in kwargs:
languageCode = kwargs['lang']
else:
languageCode = "es"
if 'scale' in kwargs:
scale = list(map(int, kwargs['scale'].split(",")))
else:
scale = [0, 2, 4, 5, 7, 9, 11] # Major Scale
if 'root' in kwargs:
root_note = int(kwargs['root'])
else:
root_note = 0 # C is the root note
if 'octave' in kwargs:
octave = int(kwargs['octave'])
else:
octave = 5 # C is the root note
if 'model' in kwargs:
renderizeVoice(filename, lyrics, notes, durations, tempo, scale,
root_note, octave, languageCode)
model = kwargs['model']
sound = AudioSegment.from_file(filename)
lenght_in_miliseconds = len(sound)
silence = AudioSegment.silent(duration=5000)
sound = silence + sound + silence
sound = sound.set_frame_rate(16000)
sound = sound.apply_gain(-18 - sound.dBFS)
# Trying to normalize to 85 db
# Note: When REPLAYGAIN_REFERENCE_LOUDNESS is not provided the *_GAIN tags are interpreted relative to an assumed target of -18 dBFS.
max_db = 20 * math.log(sound.apply_gain(-18 - sound.dBFS).max, 10)
print(
"The file was normalized so now it has {} db as peak value".format(
round(max_db), 2))
sound.export(filename, format="wav")
netA_path = "{}trained_model/{}/generator_ab.npz".format(
ROOT_PATH, model)
netB_path = "{}trained_model/{}/generator_ba.npz".format(
ROOT_PATH, model)
convert(netA_path, netB_path, filename)
sound = AudioSegment.from_file(filename)
sound = sound[len(silence):-len(silence)]
padding_audio = AudioSegment.silent(duration=lenght_in_miliseconds -
len(sound))
sound = sound + padding_audio
sound.export(filename, format="wav")
else:
renderizeVoice(filename, lyrics, notes, durations, tempo, scale,
root_note, octave, languageCode)