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fastai_rnn.py
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fastai_rnn.py
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"""Uses language models architecture from the fastai library.
"""
from fastai.text import *
import numpy as np
import pandas as pd
config = awd_lstm_lm_config.copy()
config['n_hid'] = 300
epochs = 40
bs = 10
lr = .001
def collect_sequences(filename):
sequences = np.load(filename)
return sequences
def convert_lists_to_strings(sequences):
sequences = [list(map(str, sequence)) for sequence in sequences]
str_seqs = list()
for sequence in sequences:
str_seqs.append(' '.join(sequence))
return str_seqs
def create_train_val_sets(str_seqs):
train_val_ind = int(len(str_seqs) * .8)
train, val = pd.DataFrame(), pd.DataFrame()
train['seq'] = str_seqs[:train_val_ind]
val['seq'] = str_seqs[train_val_ind:]
return train, val
def create_databunch(train, val, bs):
tok = Tokenizer(pre_rules=list(), post_rules=list(), special_cases=[UNK])
data_lm = TextLMDataBunch.from_df("data", train_df=train, valid_df=val, text_cols='seq', tokenizer=tok, bs=bs)
return data_lm
def create_model_and_train(data_lm, config, epochs, output_file="fastai_rnn"):
learner = language_model_learner(data=data_lm, arch=AWD_LSTM, pretrained=False, config=config)
learner.fit(epochs=epochs, lr=lr)
learner.save(output_file)
return learner
def predict(learner, start, beats_length):
if not start.startswith('xxbox'):
start = "xxbos " + start
return learner.predict(start, n_words=beats_length * 12)
def main():
# The following code is used for training and predicting using notes without timing.
"""
sequences = collect_sequences("data/train_sequences/cello_sequences.npy")
str_seqs = convert_lists_to_strings(sequences)
train, val = create_train_val_sets(str_seqs)
data_lm = create_databunch(train, val, bs)
learner = create_model_and_train(data_lm, config, epochs)
print(predict(learner, "60", 10))
"""
# The following code is used for training and predicting using chord_sequences
"""
sequences = np.load("data/train_sequences/chord_sequences.npy", allow_pickle=True)
train, val = create_train_val_sets(sequences)
data_lm = create_databunch(train, val, bs)
learner = create_model_and_train(data_lm, config, epochs)
test_sequence = ["0" for _ in range(48)]
test_sequence[12], test_sequence[24], test_sequence[28] = "1", "1", "1"
test_sequence = "".join(test_sequence)
print(predict(learner, test_sequence, 50))
"""
sequences = np.load("data/train_sequences/chorale_note_sequences.npy", allow_pickle=True)
train, val = create_train_val_sets(sequences)
data_lm = create_databunch(train, val, bs)
learner = create_model_and_train(data_lm, config, epochs)
learner.export('model.pkl')
test_sequence = "xxbos 12 24 31 step step step step step step"
predict(learner, test_sequence, 100)
if __name__ == "__main__":
main()