def main():
files = glob(config.data_path+'/*.wav') # + glob('data/*.mp3') # try ffmpeg -i input.mp3 output.wav
data, meta = [], []
for file_id, file in enumerate(files):
print(f'reading: {file}')
d, sample_rate = load(file, config.sample_rate)
data.append(d)
# synthesis
# signal_recons = data_to_audio(data)
# write(f'{file.split("/")[-1]}_{file_id}.wav', config.sample_rate, signal_recons)
# signal_recons, sample_rate = load(f'{file.split("/")[-1]}_{file_id}.wav', config.sample_rate)
data_min = min([min(d) for d in data])
data_max = max([max(d) for d in data])
data = [(d-data_min)/(data_max-data_min) for d in data]
meta.extend([data_min, data_max])
pickle_save([data,meta], config.data_path+('.pk' if config.data_path[-3:]!='.pk' else ''))
print('saved data.')
def save_model(model, path=None, py_serialize=True):
if not path: path = config.model_save_path
if py_serialize:
model = [type(layer)(*[getattr(layer,field).detach().numpy() for field in layer._fields]) for layer in model]
meta = [[[e.detach().numpy() for e in ee] for ee in moments],[[e.detach().numpy() for e in ee] for ee in variances]]
else:
meta = [moments,variances]
pickle_save([model,meta],path+'.pk')
def save_model(model, path=None):
from warnings import filterwarnings
filterwarnings("ignore")
if not path: path = config.model_path
if path[-3:] != '.pk': path += '.pk'
if config.use_gpu:
moments_ = [[e2.detach().cuda() for e2 in e1] for e1 in moments]
variances_ = [[e2.detach().cuda() for e2 in e1] for e1 in variances]
meta = [moments_, variances_]
model = pull_copy_from_gpu(model)
else:
meta = [moments, variances]
configs = [[field, getattr(config, field)] for field in dir(config)
if field in config.config_to_save]
pickle_save([model, meta, configs], path)
def main():
files = glob(config.data_path + '*/*.wav')
shuffle(files)
data = []
for file_id, file in enumerate(files):
print(f'reading: {file}')
signal, sample_rate = load(file, config.sample_rate)
data.append(audio_to_data(signal))
# signal_recons = data_to_audio(data,1)
# write(f'{file.split("/")[-1]}_{file_id}.wav', config.sample_rate, signal_recons)
# signal_recons, sample_rate = load(f'{file.split("/")[-1]}_{file_id}.wav', config.sample_rate)
meta = [max([max(d) for d in data]), min([min(d) for d in data])]
data = [(d - meta[1]) / (meta[0] - meta[1]) for d in data]
pickle_save([data, meta], config.data_path + '.pk')
print('saved data.')
def main():
files = glob(config.data_path+'/*.wav') # + glob('data/*.mp3') # try ffmpeg -i input.mp3 output.wav
converted = []
for file_id, file in enumerate(files):
print(f'reading: {file}')
song_id = [0 if i == file_id else 1 for i in range(len(files))]
# analysis
signal, sample_rate = load(file, config.sample_rate)
data, meta = audio_to_data(signal, song_id)
converted.append([data,meta])
# synthesis
signal_recons = data_to_audio(data,meta)
write(f'{file.split("/")[-1]}_{file_id}.wav', config.sample_rate, signal_recons)
signal_recons, sample_rate = load(f'{file.split("/")[-1]}_{file_id}.wav', config.sample_rate)
pickle_save(converted, config.data_path+'.pk')
print('saved data.')
def save_data(data, path=None):
if not path: path = config.data_path
if path[-3:] != '.pk': path += '.pk'
pickle_save(data, path)
def save_data():
pickle_save([
load(file, config.sample_rate)[0]
for file in glob(config.data_path + '/*.wav')
], config.data_path + '.pk')
def save_data(data, path=None):
if not path: path = config.data_path
pickle_save(data, path + '.pk')
def main():
files = glob(
config.data_path +
'/*.wav') # + glob('data/*.mp3') # try ffmpeg -i input.mp3 output.wav
if not config.frequencies_to_pick:
# gather initial info from all files
frequency_strengths = zeros(len(config.frequencies_of_bins))
for file in files:
signal = load(file, config.sample_rate)[0]
spec = abs(
stft(signal, config.fft_bins, config.fft_hop_len,
config.fft_window_len))
# print('\tmax min initially:', max(spec), min(spec))
# show(specshow(spec, sr=config.sample_rate, hop_length=config.fft_hop_len))
frequency_strengths += spec.sum(1) / spec.shape[1]
max_strength = max(frequency_strengths)
strength_thr = max_strength / config.frequency_strength_thr
band_low_hz = 999_999
band_high_hz = -1
for frequency, strength in zip(config.frequencies_of_bins,
frequency_strengths):
if strength >= strength_thr:
config.frequencies_to_pick.append(frequency)
if frequency < band_low_hz: band_low_hz = frequency
if frequency > band_high_hz: band_low_hz = frequency
# spec = cat([spec[config.frequencies_of_bins.index(i),:] for i in config.frequencies_to_pick], 0)
# print('\tmax min after bandpass:'******'with bandpass, timestep size: {len(config.frequencies_of_bins)} -> {len(config.frequencies_to_pick)}'
)
print(
f'copy paste this line into frequencies_to_pick @ config: \n{config.frequencies_to_pick}'
)
# proceed to separately processing each file
converted = []
for file_id, file in enumerate(files):
print(f'reading: {file}')
song_id = [0 if i == file_id else 1 for i in range(len(files))]
# analysis
signal, sample_rate = load(file, config.sample_rate)
data, meta = audio_to_data(signal, song_id)
converted.append([data, meta])
# synthesis
signal_recons = data_to_audio(data, meta)
write(f'{file.split("/")[-1]}_{file_id}.wav', config.sample_rate,
signal_recons)
signal_recons, sample_rate = load(
f'{file.split("/")[-1]}_{file_id}.wav', config.sample_rate)
pickle_save(converted, config.data_path + '.pk')
print('saved data.')