def main():
file_path = search_number('755205')
if 'Error' not in file_path:
record, status = process(file_path)
return record, status
else:
return 'Error', 'Not Found'
def main(wf):
import parser
import moment_format
args = wf.args
if len(args) == 0:
return
action = args[0]
if action == 'parse':
parser.process(wf, args[1:])
elif action == 'format':
moment_format.process(wf, args[1:])
elif action == 'addFormat':
moment_format.add_format(wf, args[1:])
elif action == 'delFormat':
moment_format.del_format(wf, args[1:])
def process(self):
parser = Parser(self.source, self.filename)
self.content = content = []
self.write = write = self.content.append
for state, s in parser.process():
getattr(self, 'process_'+state)(s)
content.append('\n')
source = ''.join(content)
if isinstance(source, unicode):
_compile = compile_unicode
else:
_compile = compile
try:
return _compile(source, self.filename, 'exec')
except SyntaxError, exc:
raise CompileError(exc.msg, self.filename, exc.lineno)
def generate_long(
text="",
numeric_translation=True
): # slower, but can translate numeric details and longer sentences
"""
params: text :: a str (long)
numeric_translation :: phonetic translation will be performed before speech generation [slightly slower]
** will be saved as out.wav **
"""
# the weights couldn't be stored directly in github
if not os.path.exists("model1/model_gs_301k.data-00000-of-00001"):
print('--------------------------------------------------------------')
print('--------------------------------------------------------------')
print("No weights found for first model. Downloading ...")
wget.download(
"https://gitlab.com/zabir-nabil/bangla_tts_weights/raw/master/model_gs_301k.data-00000-of-00001"
)
shutil.move("model_gs_301k.data-00000-of-00001",
"model1/model_gs_301k.data-00000-of-00001")
if not os.path.exists("model2/model_gs_300k.data-00000-of-00001"):
print('--------------------------------------------------------------')
print('--------------------------------------------------------------')
print("No weights found for second model. Downloading ...")
wget.download(
"https://gitlab.com/zabir-nabil/bangla_tts_weights/raw/master/model_gs_300k.data-00000-of-00001"
)
shutil.move("model_gs_300k.data-00000-of-00001",
"model2/model_gs_300k.data-00000-of-00001")
text_arr = process(text)
print(text_arr)
# Load data
L = load_data(text_arr)
# Load graph
g = Graph()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
# Restore parameters
var_list = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
'Text2Mel')
saver1 = tf.train.Saver(var_list=var_list)
# check for the weights
saver1.restore(sess, tf.train.latest_checkpoint("model1"))
print("Model 1 loaded!")
var_list = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, 'SSRN') + \
tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, 'gs')
saver2 = tf.train.Saver(var_list=var_list)
saver2.restore(sess, tf.train.latest_checkpoint("model2"))
print("Model 2 loaded!")
t1 = time.time()
## mel generation
Y = np.zeros((len(L), max_T, n_mels), np.float32)
prev_max_attentions = np.zeros((len(L), ), np.int32)
for j in tqdm(range(max_T)):
_gs, _Y, _max_attentions, _alignments = \
sess.run([g.global_step, g.Y, g.max_attentions, g.alignments],
{g.L: L,
g.mels: Y,
g.prev_max_attentions: prev_max_attentions})
Y[:, j, :] = _Y[:, j, :]
prev_max_attentions = _max_attentions[:, j]
# Get magnitude spectrum
Z = sess.run(g.Z, {g.Y: Y})
generated_wav = np.array(
[]) # a tuple, wav numpy array and sampling rate
for i, mag in enumerate(Z):
#mag = upsample2(mag)
wav = spectrogram2wav(mag) # griffin-lim speech generation
generated_wav = np.append(generated_wav, wav)
t_needed = time.time() - t1
print(f'Total time taken {t_needed} secs.')
write("out.wav", sr, generated_wav)
def test_parse_symgiza_output(self):
data = process()
print sys.path
self.assertEqual(data, [('Y', 'and'), ('sus', 'their'),
('children', 'niños')])
def from_file(filename):
"""Parses a .frac file into a Program."""
return Program(process(filename))
