Python库 | musicgeneration-0.0.7.tar.gz

import os import regex as re import subprocess import urllib import numpy as np import tensorflow as tf from IPython.display import Audio cwd = os.path.dirname(__file__) def load_training_data(): with open(os.path.join(cwd, "irish.abc"), "r") as f: text = f.read() songs = extract_song_snippet(text) return songs load_training_data() def extract_song_snippet(text): pattern = '(^|\n\n)(.*?)\n\n' search_results = re.findall(pattern, text, overlapped=True, flags=re.DOTALL) songs = [song[1] for song in search_results] print("Found {} songs in text".format(len(songs))) return songs def save_song_to_abc(song, filename="tmp"): save_name = "{}.abc".format(filename) with open(save_name, "w") as f: f.write(song) return filename def abc2wav(abc_file): path_to_tool = os.path.join(cwd, 'bin', 'abc2wav') cmd = "{} {}".format(path_to_tool, abc_file) return os.system(cmd) def play_wav(wav_file): return Audio(wav_file) def play_song(song): basename = save_song_to_abc(song) ret = abc2wav(basename+'.abc') if ret == 0: #did not suceed return play_wav(basename+'.wav') return None def play_generated_song(generated_text): songs = extract_song_snippet(generated_text) if len(songs) == 0: print("No valid songs found in generated text. Try training the \ model longer or increasing the amount of generated music to \ ensure complete songs are generated!") for song in songs: play_song(song) print("None of the songs were valid, try training longer to improve \ syntax.") def test_batch_func_types(func, args): ret = func(*args) assert len(ret) == 2, "[FAIL] get_batch must return two arguments (input and label)" assert type(ret[0]) == np.ndarray, "[FAIL] test_batch_func_types: x is not np.array" assert type(ret[1]) == np.ndarray, "[FAIL] test_batch_func_types: y is not np.array" print("[PASS] test_batch_func_types") return True def test_batch_func_shapes(func, args): dataset, seq_length, batch_size = args x, y = func(*args) correct = (batch_size, seq_length) assert x.shape == correct, "[FAIL] test_batch_func_shapes: x {} is not correct shape {}".format(x.shape, correct) assert y.shape == correct, "[FAIL] test_batch_func_shapes: y {} is not correct shape {}".format(y.shape, correct) print("[PASS] test_batch_func_shapes") return True def test_batch_func_next_step(func, args): x, y = func(*args) assert (x[:,1:] == y[:,:-1]).all(), "[FAIL] test_batch_func_next_step: x_{t} must equal y_{t-1} for all t" print("[PASS] test_batch_func_next_step") return True def test_custom_dense_layer_output(y): true_y = np.array([[0.2697859, 0.45750418, 0.66536945]],dtype='float32') assert tf.shape(y).numpy().tolist() == list(true_y.shape), "[FAIL] output is of incorrect shape. expected {} but got {}".format(true_y.shape, y.numpy().shape) np.testing.assert_almost_equal(y.numpy(), true_y, decimal=7, err_msg="[FAIL] output is of incorrect value. expected {} but got {}".format(y.numpy(), true_y), verbose=True) print("[PASS] test_custom_dense_layer_output") return True