def analyse_sound(path):
x, sr = librosa.load(path)
td = librosa.get_duration(x)
print("Time duration of audio: ", td)
x = np.reshape(x, [1, -1, 1, 1])
print("Shape of input waveform: ", x.shape)
# Setup visible device
os.environ["CUDA_VISIBLE_DEVICES"] = args.cuda_device
# Load pre-trained model
G_name = './models/sound8.npy'
param_G = np.load(G_name, encoding='latin1').item()
# Init. Session
sess_config = tf.ConfigProto()
sess_config.allow_soft_placement = True
sess_config.gpu_options.allow_growth = True
with tf.Session(config=sess_config) as session:
# Build model
model = Model(session, config=local_config, param_G=param_G)
init = tf.global_variables_initializer()
session.run(init)
model.load()
features = ef.extract_feat(model, x, local_config)
print("Shape of feature: ", features.shape)
def main():
args = parse_args()
local_config['phase'] = args.phase
# Setup visible device
os.environ["CUDA_VISIBLE_DEVICES"] = args.cuda_device
# Make path
if not os.path.exists(args.outpath):
os.mkdir(args.outpath)
# Load pre-trained model
param_G = np.load(local_config['param_g_dir'], encoding='latin1').item() \
if args.phase in ['finetune', 'extract'] \
else None
# Init. Session
sess_config = tf.ConfigProto()
sess_config.allow_soft_placement = True
sess_config.gpu_options.allow_growth = True
with tf.Session(config=sess_config) as session:
# Build model
model = Model(session, config=local_config, param_G=param_G)
if args.phase in ['train', 'finetune']:
# Training phase
model.train()
elif args.phase == 'extract':
# import when we need
from extract_feat import extract_feat
# Feature extractor
#sound_sample = np.reshape(np.load('./data/demo.npy', encoding='latin1'), [local_config['batch_size'], -1, 1, 1])
import librosa
audio_path = './data/demo.mp3'
sound_sample, _ = load_audio(audio_path)
sound_sample = preprocess(sound_sample, config=local_config)
output = extract_feat(model, sound_sample, args)
def extract_feat_from_link(model, audio_path, config):
sound_sample = load_from_link(audio_path)
features = extract_feat(model, sound_sample)
return features
# coding: utf-8
import pandas as pd
from extract_feat import extract_feat
dataset = pd.read_csv("extract_dataset/extract_feat_dataset_15.csv")
feat = dataset[dataset.time >= "t5"][[
"user_id", "item_id", "item_category", "label"
]]
feat = feat.drop_duplicates()
feat = extract_feat(feat, dataset, '2014-12-15 00', '2014-12-16 00',
'2014-12-14 00', '2014-12-12 00', '2014-12-09 00',
'2014-11-18 00')
feat.to_csv("feature/feature1.csv", index=False)
# coding: utf-8
import pandas as pd
from extract_feat import extract_feat
dataset = pd.read_csv("extract_dataset/extract_feat_dataset_18.csv")
feat = dataset[dataset.time >= "t5"][["user_id", "item_id", "item_category"]]
feat = feat.drop_duplicates()
feat = extract_feat(feat, dataset, '2014-12-18 00',
'2014-12-19 00',
'2014-12-17 00',
'2014-12-15 00',
'2014-12-12 00',
'2014-11-18 00')
feat.to_csv("feature/feature4.csv",index=False)
# coding: utf-8
import pandas as pd
from extract_feat import extract_feat
dataset = pd.read_csv("extract_dataset/extract_feat_dataset_17.csv")
feat = dataset[dataset.time >= "t5"][[
"user_id", "item_id", "item_category", "label"
]]
feat = feat.drop_duplicates()
feat = extract_feat(feat, dataset, '2014-12-17 00', '2014-12-18 00',
'2014-12-16 00', '2014-12-14 00', '2014-12-11 00',
'2014-11-18 00')
feat.to_csv("feature/feature3.csv", index=False)
