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import tensorflow as tf
import numpy as np
import joblib
import os
import sys
module_path = os.path.abspath(os.path.join('./lib'))
if module_path not in sys.path:
sys.path.append(module_path)
from MusicXMLParser import MusicXMLDocument as Parser
model = tf.keras.models.load_model('./content/model.h5')
tokenizer = joblib.load("./content/tokenizer.pkl")
class Generator:
NOTES = {
"Cb": 11,
"C": 0,
"C#": 1,
"Db": 1,
"D": 2,
"D#": 3,
"Eb": 3,
"E": 4,
"E#": 5,
"Fb": 4,
"F": 5,
"F#": 6,
"Gb": 6,
"G": 7,
"G#": 8,
"Ab": 8,
"A": 9,
"A#": 10,
"Bb": 10,
"B": 11,
"B#": 0,
}
NUM_TO_NOTE = {
0: "C",
1: "Db",
2: "D",
3: "Eb",
4: "E",
5: "F",
6: "Gb",
7: "G",
8: "Ab",
9: "A",
10: "Bb",
11: "B",
-1: "",
}
NUM_TO_QUALITY = {
1: "^7",
2: "-7",
3: "7",
-1: "",
}
def decode_tokens(self, numpy_array):
total = []
for i in numpy_array:
total.append([i])
return tokenizer.sequences_to_texts(total)
def decode_chords(self, chords):
res = []
for chord in chords:
res.append(
Generator.NUM_TO_NOTE[chord[0]] + Generator.NUM_TO_QUALITY[chord[1]])
return res
def encode_chords(self, chords):
res = []
for chord in chords:
if not chord:
continue
c = []
if len(chord) > 2 and chord[0] + chord[1] in Generator.NOTES:
c.append(Generator.NOTES[chord[:2]])
else:
c.append(Generator.NOTES[chord[0]])
if "^" in chord or "maj" in chord:
c.append(1)
elif "-" in chord or "m" in chord:
c.append(2)
elif "7" in chord or "alt" in chord or "11" in chord:
c.append(3)
else:
c.append(1)
res.append(c)
return res
def generateChords(self, chordsBefore, numGenerate = 10):
chordsBefore = self.decode_chords(self.encode_chords(chordsBefore))
input_eval = [tokenizer.word_index[chord] for chord in chordsBefore]
input_eval = tf.expand_dims(input_eval, 0)
temperature = .1
newChords = []
for i in range(int(numGenerate)):
predictions = model(input_eval)
predictions = tf.squeeze(predictions, 0)
predictions = predictions / temperature
predicted_id = tf.random.categorical(
predictions, num_samples=1)[-1, 0].numpy()
input_eval = tf.expand_dims([predicted_id], 0)
newChords.append(predicted_id)
return self.decode_tokens(newChords)
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