def _main():
"""
Test model.
"""
from pyasv.data_manage import DataManage
from pyasv import Config
sys.path.append("../..")
con = Config(name='MFM',
n_speaker=100,
batch_size=64,
n_gpu=2,
max_step=5,
is_big_dataset=False,
learning_rate=0.001,
save_path='./save')
x = np.random.random([6400, 50, 40, 1])
y = np.random.randint(0, 100, [6400, 1])
train = DataManage(x, y, con)
enroll = train
x = np.random.random([640, 50, 40, 1])
y = np.random.randint(0, 100, [640, 1])
validation = DataManage(x, y, con)
test = validation
run(con, train, validation)
restore(con, enroll, test)
def _main():
"""
Test model.
"""
from pyasv.data_manage import DataManage
from pyasv import Config
sys.path.append("../..")
con = Config(name='ctdnn',
n_speaker=100,
batch_size=64 * 2,
n_gpu=2,
max_step=5,
is_big_dataset=False,
learning_rate=0.001,
save_path='./save')
#con.save('ctdnn')
x = np.random.random([6500, 9, 40, 1])
y = np.random.randint(0, 99, [6500, 1])
enroll = DataManage(x, y, con)
train = enroll
x = np.random.random([1500, 9, 40, 1])
y = np.random.randint(0, 99, [1500, 1])
test = DataManage(x, y, con)
validation = test
run(con, train, validation, False)
restore(con, enroll, test)
def _main():
"""
Test model.
"""
from pyasv.data_manage import DataManage
from pyasv import Config
import sys
sys.path.append("../..")
print("Model test")
print("input n_gpu", end="")
a = int(eval(input()))
con = Config(name='deepspeaker',
n_speaker=100,
batch_size=32 * max(1, a),
n_gpu=a,
max_step=5,
is_big_dataset=False,
learning_rate=0.001,
save_path='./save',
conv_weight_decay=0.01,
fc_weight_decay=0.01,
bn_epsilon=1e-3,
deep_speaker_out_channel=[32, 64])
x = np.random.random([320, 100, 64, 1])
y = np.random.randint(0, 99, [320, 1])
train = DataManage(x, y, con)
x = np.random.random([64, 100, 64, 1])
y = np.random.randint(0, 99, [64, 1])
validation = DataManage(x, y, con)
# run(con, train, validation)
restore(con, train, validation)
def _main():
"""
Test model.
"""
"""
Test model.
"""
from pyasv.data_manage import DataManage
from pyasv import Config
import sys
sys.path.append("../..")
print("Model test")
print("input n_gpu", end="")
a = int(eval(input()))
con = Config(name='deepspeaker', n_speaker=100, batch_size=32*max(1, a), n_gpu=a, max_step=2, is_big_dataset=True,
url_of_bigdataset_temp_file='./',
learning_rate=0.001, save_path='./save', conv_weight_decay=0.01, fc_weight_decay=0.01,
bn_epsilon=1e-3, deep_speaker_out_channel=[64, 128, 256, 512])
x = np.random.random([320, 100, 64, 1])
y = np.random.randint(0, 99, [320, 1])
train = DataManage4BigData(con, 'train')
train.write_file(x, y)
x = np.random.random([64, 100, 64, 1])
y = np.random.randint(0, 99, [64, 1])
validation = DataManage4BigData(con, 'validation')
validation.write_file(x, y)
run(con, train, validation)
train.clear()
validation.clear()
def _main():
"""
Test model.
"""
from pyasv.data_manage import DataManage
from pyasv import Config
import sys
sys.path.append("../..")
con = Config(name='deepspeaker', n_speaker=100, batch_size=64, n_gpu=4, max_step=20, is_big_dataset=False,
learning_rate=0.001, save_path='./save', conv_weight_decay=0.01, fc_weight_decay=0.01, bn_epsilon=1e-3)
x = np.random.random([6400, 100, 64, 1])
y = np.random.randint(0, 100, [6400, 1])
train = DataManage(x, y, con)
x = np.random.random([640, 100, 64, 1])
y = np.random.randint(0, 100, [640, 1])
validation = DataManage(x, y, con)
run(con, train, validation)
import pyasv.speech_processing
from pyasv.model import CTDnn
from pyasv import Config
root = "/opt/user1/fhq/asr/data-url/c863/"
config = Config(config_path="./test.json")
train_frames, train_labels = pyasv.speech_processing.ext_fbank_feature(root +
'train')
enroll_frames, enroll_labels = pyasv.speech_processing.ext_fbank_feature(
root + 'enroll')
test_frames, test_labels = pyasv.speech_processing.ext_fbank_feature(root +
'test')
m = CTDnn(config)
m.run(train_frames, train_labels, enroll_frames, enroll_labels, test_frames,
test_labels)
if i == j else tf.reduce_sum(mean_per_spkr[i, :] *
embeddings[j, :, :],
axis=1,
keep_dims=True)
for i in range(X)
],
axis=1) for j in range(X)
],
axis=0)
# data shape is [spkr_num, utt_per_spkr, embedding_dim]
# The shape of S is [spkr_num, utt_per_spkr, spkr_num]
#S = ops.cosine(embeddings, mean_per_spkr, w=w, b=b)
self.logger.debug(S.get_shape().as_list())
S_per_spkr = tf.reduce_sum(S, axis=-1)
self.logger.debug(S_per_spkr.get_shape().as_list())
L = 2 * S - S_per_spkr
self.logger.debug(L.get_shape().as_list())
return tf.reduce_sum(L)
if __name__ == '__main__':
x = tf.placeholder(dtpye=tf.float32, shape=[32, 150, 64])
config = Config('lstmp.yaml')
model = LSTMP(config, 400, 3)
out = model.inference(x)
model.loss(out)