def midi_to_token_ids(self, midi_name):
"""
Helper function to encode midis from a dataset. See
data_utils.py's encode() for the specifics.
"""
path = os.path.join(self.data_dir, midi_name)
try:
stream = m21.converter.parse(path)
encoding, measures = encode(stream)
track_id = self.get_track_id(path)
track_ids = [track_id] * len(encoding)
return encoding, measures, track_ids
except:
return [[], [], []]
yvocab_size = xvocab_size
emb_dim = 1024
import seq2seq_wrapper
import importlib
importlib.reload(seq2seq_wrapper)
model = seq2seq_wrapper.Seq2Seq(xseq_len=xseq_len,
yseq_len=yseq_len,
xvocab_size=xvocab_size,
yvocab_size=yvocab_size,
ckpt_path='ckpt/opensubtitle/',
emb_dim=emb_dim,
num_layers=3)
sess = model.restore_last_session()
while True:
query = input('Input:\t')
if query == 'quit' or query == 'exit':
exit(0)
ids = data_utils.encode(sequence=query, lookup=metadata['w2idx'])
output = model.predict(sess, ids)
reply = data_utils.decode(sequence=output[0],
lookup=metadata['idx2w'],
separator=' ')
print('Output:' + reply)
def respond(msg):
encoded_msg = data_utils.encode(msg, w2idx, limit['maxq'])
response = model.predict(sess, encoded_msg)[0]
return data_utils.decode(response, idx2w)
from data_utils import encode
from loader import load_file_content, dump_to_file
train = load_file_content('data/original/train.txt')
dev = load_file_content('data/original/dev.txt')
train_data = encode(train)
dev_data = encode(dev)
dump_to_file(train_data, 'data/example.train', 'w')
dump_to_file(dev_data, 'data/example.dev', 'w')
dump_to_file(dev_data, 'data/example.test', 'w')