Cleanup and reorganization

.gitignore

@@ -6,10 +6,12 @@
 !extractAss.sh
 !karaUtils.py
 !autokara.py
-!segment.py
 !assUtils.py
-!music_processor.py
-!model.py
 !process_train_data.sh
-!infer.py
+!cnn_prepare_data.py
+!cnn_train.py
+!*/cnn/segment.py
+!*/cnn/music_processor.py
+!*/cnn/model.py
+!rosa/*.py
 media/
\ No newline at end of file

autokara.py

@@ -6,8 +6,7 @@ import shlex
 from pathlib import Path
 from assUtils import AssWriter
 
-from segment import Segment
-import infer
+from cnn.segment import segment
 
 
 parser = argparse.ArgumentParser(description='AutoKara - Automatic karaoke timing tool')
@@ -42,7 +41,7 @@ else:
 
 
 print("Identifying syl starts...")
-onsets = infer.segment(sys.argv[1])
+onsets = segment(sys.argv[1])
 syls = [[t, ''] for t in onsets]
 
 print("Syls found, writing ASS file...")

model.py → cnn/model.py

@@ -4,7 +4,7 @@ import torch.nn.functional as F
 import torch.optim as optim
 import numpy as np
 from tqdm import tqdm
-from music_processor import *
+from cnn.music_processor import *
 
 """
 On the paper,

infer.py → cnn/segment.py

-from model import *
-from music_processor import *
+from cnn.model import *
+from cnn.music_processor import *
 from assUtils import AssWriter
 import pickle
 import numpy as np

cnn_prepare_data.py

+from cnn.music_processor import *
+
+
+
+if sys.argv[1] == 'train':
+    print("preparing all train data processing...")
+    serv = f'./{sys.argv[2]:s}/*'
+    music_for_train(serv, verbose=True)
+    print("all train data processing done!")    
+
+if sys.argv[1] == 'test':
+    print("test data proccesing...")
+    serv = f'./{sys.argv[2]:s}/*'
+    music_for_test(serv)
+    print("test data processing done!")
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cnn_train.py

+from cnn.model import *
+from cnn.music_processor import *
+
+
+device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
+net = convNet()
+net = net.to(device)
+        
+with open('./data/pickles/train_data.pickle', mode='rb') as f:
+    songs = pickle.load(f)
+
+minibatch = 128
+soundlen = 15
+epoch = 100
+
+
+net.train(songs=songs, minibatch=minibatch, val_song=None, epoch=epoch, device=device, soundlen=soundlen, save_place='./models/model.pth', log='./data/log/log.txt')
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