| 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] |
