File size: 2,424 Bytes
f320de7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 | from music21 import stream, chord
import tensorflow as tf
import keras
import numpy as np
import random
import glob
def predict_next_token(model, input_sequence, temperature=1, seed=42):
"predict next token given a context, sample from a categorical distribution controllable via temperature"
assert keras.ops.ndim(input_sequence) == 2, "function expects input_sequence to be (batch_size, sequence_len)"
logits = model.predict_on_batch(input_sequence)[:, -1, :]
scaled_logits = logits / temperature
return tf.random.categorical(scaled_logits, num_samples=1, seed=seed)
def generate_sequence(init_context, model, include_init_context=False, max_len=25, temperature=1 ,seed=42):
"""Generates a continuation of a given seed sequence by autoregressively sampling from the trained model."""
assert keras.ops.ndim(init_context) == 2, "function expects init_context to be (batch_size, sequence_len)"
seq_len_init_context = init_context.shape[1]
context = init_context
for _ in range(max_len * 4):
next_token = predict_next_token(model, context, temperature=temperature, seed=seed)
context = keras.ops.concatenate([context, next_token], axis=1)
return context if include_init_context else context[:,seq_len_init_context:]
def generate_chorale(model, sample_seed_path,note2id,id2note, file_name= "samples/chorale.mid", max_len=25, temperature=1,
sample_seed_rows: slice = slice(0,100), include_init_context=False, seed=42):
"""Generates a Bach-style MIDI chorale from a random seed sequence using the trained model."""
sample_seed = np.loadtxt(sample_seed_path, skiprows=1, delimiter=",").flatten()[sample_seed_rows].reshape(1,-1)
sample_seed = note2id(sample_seed)
chorale = generate_sequence(sample_seed, model, include_init_context=include_init_context,
max_len=max_len, temperature=temperature ,seed=seed)
chorale = keras.ops.convert_to_numpy(keras.ops.reshape(id2note(chorale), (-1,4)))
strm = stream.Stream([chord.Chord(chorale[s].tolist()) for s in range(len(chorale))])
strm.write('midi', fp=file_name)
print(f"chorale saved as {file_name}")
def draw_random_sample(csv_dir, seed=42):
"""Selects and returns a random CSV file path from the given directory for sampling."""
files = glob.glob(csv_dir + '/*.csv')
random.seed(seed)
random.shuffle(files)
return files[0] |