def convert_gen_to_out(data_dir):
gen_dir = os.path.join(data_dir, 'gen')
out_dir = os.path.join(data_dir, 'out')
datatools.ensure_dir_exists(gen_dir)
datatools.ensure_dir_exists(out_dir)
gen_glob_file_path = os.path.join(gen_dir, '*.npy')
write_flush('-- Converting gen to out...')
for npy_data_file in glob.glob(gen_glob_file_path):
blocks = []
filename = npy_data_file.split('/')[-1]
wav_filename = os.path.join(out_dir, filename.replace('.npy','.wav'))
data_as_fft = np.load(npy_data_file)
for fft_block in data_as_fft:
real_imag_split = fft_block.shape[0] // 2
real = fft_block[0:real_imag_split]
imag = fft_block[real_imag_split:]
time_domain = np.fft.ifft(real + 1.0j * imag)
blocks.append(time_domain)
song_blocks = np.concatenate(blocks)
song_blocks = song_blocks * 32767.0
song_blocks = song_blocks.astype('int16')
wavfile.write(wav_filename, 44100, song_blocks)
write_flush('finished. \n')
def convert_wav_to_fft(data_dir, block_size, seql):
fft_dir = os.path.join(data_dir, 'fft')
datatools.ensure_dir_exists(fft_dir)
# Split into chunks
wav_dir = os.path.join(data_dir, 'wav')
wav_glob_path = os.path.join(wav_dir, '*.wav')
wav_files = glob.glob(wav_glob_path)
block_lists = []
if len(wav_files) > 0:
write_flush('-- Converting wav to fft...')
for wav_data_file in glob.glob(wav_glob_path):
np_filename = wav_data_file.split('/')[-1].replace('.wav','')
np_data_file = os.path.join(fft_dir, np_filename)
wav_np_array = wavfile.read(wav_data_file)[1].astype('float32') / 32767.0
block_lists = []
total_samples = wav_np_array.shape[0]
num_samples_so_far = 0
while(num_samples_so_far < total_samples):
block = wav_np_array[num_samples_so_far:num_samples_so_far+block_size]
if(block.shape[0] < block_size):
padding = np.zeros((block_size - block.shape[0],))
block = np.concatenate((block, padding))
fft_block = np.fft.fft(block)
new_block = np.concatenate((np.real(fft_block), np.imag(fft_block)))
block_lists.append(new_block)
num_samples_so_far += block_size
# Create X and Y training sets
start_index = 0
training_instances = len(block_lists)-seql
freq_bins = len(block_lists[0])
X_train = np.zeros((training_instances, seql, freq_bins))
Y_train = np.zeros((training_instances, freq_bins))
while True:
if start_index+seql+1 < len(block_lists):
progress_str = '\r-- Converting wav to fft... ({}/{})'.format(start_index, len(block_lists)-seql-1)
write_flush(progress_str)
else:
write_flush('\r-- Converting wav to fft...finished. \n')
break
for i in range(seql):
for j in range(freq_bins):
X_train[start_index][i][j] = block_lists[start_index+i][j]
for j in range(freq_bins):
Y_train[start_index][j] = block_lists[start_index+seql+1][j]
start_index = start_index+1
write_flush('-- Saving fft file...')
np.save(np_data_file+'_x', X_train)
np.save(np_data_file+'_y', Y_train)
write_flush('finished. \n')
return block_lists
def convert_mp3s_to_wav(data_dir):
tmp_dir = os.path.join(data_dir, 'tmp')
wav_dir = os.path.join(data_dir, 'wav')
# Ensure that our output dirs exist
datatools.ensure_dir_exists(tmp_dir)
datatools.ensure_dir_exists(wav_dir)
# Begin MP3 processing
data_glob_path = os.path.join(data_dir, '*.mp3')
mp3_data_files = glob.glob(data_glob_path)
if len(mp3_data_files) > 0:
write_flush('-- Converting stereo to mono...')
for data_filename in mp3_data_files:
name = data_filename.split('/')[-1]
tmp_filename = os.path.join(tmp_dir, name)
cmd = 'lame --quiet -a -m m {} {}'.format(data_filename, tmp_filename)
os.system(cmd)
write_flush('finished. \n')
tmp_data_glob_path = os.path.join(tmp_dir, '*.mp3')
tmp_data_files = glob.glob(tmp_data_glob_path)
if len(tmp_data_files) > 0:
write_flush('-- Converting to mono to wav...')
for tmp_filename in tmp_data_files:
name = tmp_filename.split('/')[-1].replace('.mp3','.wav')
wav_filename = os.path.join(wav_dir, name)
cmd = 'lame --quiet --decode {} {} --resample 44.1'.format(tmp_filename, wav_filename)
os.system(cmd)
write_flush('finished. \n')
data_folder_wav_glob_path = os.path.join(data_dir, '*.wav')
wav_files_in_data_folder = glob.glob(data_folder_wav_glob_path)
if len(wav_files_in_data_folder) > 0:
write_flush('-- Moving existing wavs...')
for wav_filename in wav_files_in_data_folder:
name = wav_filename.split('/')[-1]
new_wav_filename = os.path.join(wav_dir, name)
cmd = 'cp {} {}'.format(wav_filename, new_wav_filename)
os.system(cmd)
write_flush('finished. \n')
def generate_from_dnn(data_dir, seql, generate_x_blocks, hidden_dim):
fft_dir = os.path.join(data_dir, 'fft')
fft_glob = os.path.join(fft_dir, '*.npy')
weights_dir = os.path.join(data_dir, 'weights')
gen_dir = os.path.join(data_dir, 'gen')
datatools.ensure_dir_exists(weights_dir)
datatools.ensure_dir_exists(gen_dir)
filenames = Set()
for g in glob.glob(fft_glob):
filenames.add(g.replace('_x.npy','').replace('_y.npy',''))
for f in filenames:
X_train = np.load(f+'_x.npy')
filename = f.split('/')[-1]
weight_file = os.path.join(weights_dir, filename+'.hdf5')
trained_file_location = os.path.join(gen_dir, filename)
write_flush("# Generating for '{}'\n\n".format(filename))
write_flush("-- Preparing data...")
output = np.zeros((generate_x_blocks+seql, X_train.shape[1], X_train.shape[2]))
output = np.append(X_train[0:seql], output, axis=0)
fft_output = np.zeros((generate_x_blocks, X_train.shape[2]))
write_flush("finished.\n")
write_flush("-- Building dnn...")
model = nntools.get_current_model(X_train.shape[2], hidden_dim)
write_flush("finished.\n")
write_flush("-- Loading weights...")
if os.path.exists(weight_file):
model.load_weights(weight_file)
write_flush("finished.\n")
i = 0
write_flush("-- Generating...")
while True:
write_flush("\r-- Generating... ({}/{})".format(i, fft_output.shape[0]))
if i >= fft_output.shape[0]:
break
next_val = model.predict(output[i:i+seql])
for k in range(0, seql-1):
for x in range(0, output.shape[2]):
output[i+seql+1][k][x] = next_val[k][x]
fft_output[i][x] = next_val[seql-1][x]
i = i + 1
write_flush("\r-- Generating... finished.\n")
write_flush("-- Saving numpy array...")
np.save(trained_file_location, fft_output)
write_flush("finished.\n\n")
def train_dnn(data_dir, epochs_per_round, max_training_iterations, hidden_dim):
fft_dir = os.path.join(data_dir, 'fft')
fft_glob = os.path.join(fft_dir, '*.npy')
weights_dir = os.path.join(data_dir, 'weights')
datatools.ensure_dir_exists(weights_dir)
filenames = Set()
for g in glob.glob(fft_glob):
filenames.add(g.replace('_x.npy','').replace('_y.npy',''))
for f in filenames:
X_train = np.load(f+'_x.npy')
y_train = np.load(f+'_y.npy')
filename = f.split('/')[-1]
weight_file = os.path.join(weights_dir, filename+'.hdf5')
write_flush('# Training on \'{}\'\n\n'.format(filename))
write_flush('-- Building dnn...')
model = nntools.get_current_model(X_train.shape[2], hidden_dim)
write_flush('finished. \n')
checkpointer = ModelCheckpoint(filepath=weight_file, verbose=0, save_best_only=True)
i = 0
write_flush('-- Training model... ({}/{})'.format(i, max_training_iterations))
while True:
if i >= max_training_iterations:
break
if os.path.exists(weight_file):
model.load_weights(weight_file)
model.fit(X_train, y_train, nb_epoch=epochs_per_round, verbose=0, validation_split=0.1, callbacks=[checkpointer])
i = i + epochs_per_round
write_flush('\r-- Training model... ({}/{})'.format(i, max_training_iterations))
write_flush('\r-- Training model...finished.\n')