setup_logging(logging.getLogger())
data_source = []
for f in sorted(os.listdir(env.dataset())):
if f.endswith(".wav"):
data_source.append(env.dataset(f))
cm = ConvModel(
batch_size = 30000,
filter_len = 150,
filters_num = 100,
target_sr = 3000,
gamma = 1e-03,
strides = 8,
avg_window = 5,
lrate = 1e-04
)
sess = tf.Session()
dataset = cm.form_dataset(data_source, proportion = 0.1)
cm.train(sess, dataset, 10000)
cm.evaluate_and_save(sess, dataset)
cm.serialize(sess)
from matplotlib import pyplot as plt
from os.path import join as pj
from util import setup_logging
from conv_model import ConvModel
from env import current as env
setup_logging(logging.getLogger())
data_source = []
for f in sorted(os.listdir(env.dataset())):
if f.endswith(".wav"):
data_source.append(env.dataset(f))
cm = ConvModel(batch_size=30000,
filter_len=150,
filters_num=100,
target_sr=3000,
gamma=1e-03,
strides=8,
avg_window=5,
lrate=1e-04)
sess = tf.Session()
dataset = cm.form_dataset(data_source, proportion=0.1)
cm.train(sess, dataset, 10000)
cm.evaluate_and_save(sess, dataset)
cm.serialize(sess)
