def main():
# from model settings
model_params = {
'nonlinearity': 'tanh',
'layer_size': 600,
'embedding_size': 40,
'normalize': 'False'
}
uid_settings = '/local_data/magnolia/pipeline_data/date_2017_09_27_time_13_25/settings/assign_uids_LibriSpeech_UrbanSound8K.json'
model_save_base = '/local_data/magnolia/experiment_data/date_2017_09_28_time_13_14/aux/model_saves/large_l41'
model_location = '/cpu:0'
model_settings = ''
mixes = [
'/local_data/magnolia/pipeline_data/date_2017_09_27_time_13_25/settings/mixing_LibriSpeech_UrbanSound8K_test_in_sample.json'
]
from_disk = True
mix_number = 1
output_path = '/local_data/magnolia/experiment_data/date_2017_09_28_time_13_14/aux/sample_wav_files/large_lab41'
os.makedirs(output_path, exist_ok=True)
mixer = MixIterator(mixes_settings_filenames=mixes,
batch_size=1,
from_disk=from_disk)
# get frequency dimension
frequency_dim = mixer.sample_dimensions()[0]
# get number of sources
settings = json.load(open(uid_settings))
uid_file = settings['output_file']
uid_csv = pd.read_csv(uid_file)
number_of_sources = uid_csv['uid'].max() + 1
model = L41Model(**model_params,
num_speakers=number_of_sources,
F=frequency_dim,
device=model_location)
model.load(model_save_base)
assert (mix_number <= mixer.epoch_size())
mix_settings = json.load(open(mixes[0]))
signal = mix_settings['signals'][0]
preprocessing_settings = json.load(open(signal['preprocessing_settings']))
stft_args = preprocessing_settings['processing_parameters']['stft_args']
istft_args = convert_preprocessing_parameters(stft_args)
preemphasis_coeff = preprocessing_settings['processing_parameters'][
'preemphasis_coeff']
n_fft = 2048
if 'n_fft' in stft_args:
n_fft = stft_args['n_fft']
for i in range(mix_number):
spec, bin_masks, source_specs, uids, snrs = next(mixer)
model_spec = spec
spec = spec[0]
bin_masks = bin_masks[0]
source_specs = source_specs[0]
uids = uids[0]
snrs = snrs[0]
print('SNR of this mix: {}'.format(snrs))
y_mix = undo_preprocessing(spec,
mixer.sample_length_in_bits(),
preemphasis_coeff=preemphasis_coeff,
istft_args=istft_args)
# NOTE: this is only to make comparisons to the reconstructed waveforms later
y_mix[-n_fft:] = 0.0
y_mix = standardize_waveform(y_mix)
# print('Mixed sample')
lr.output.write_wav(os.path.join(output_path,
'mix_{}.wav'.format(mix_number)),
y_mix,
mixer.sample_rate(),
norm=True)
for i, source_spec in enumerate(source_specs):
y = undo_preprocessing(source_spec,
mixer.sample_length_in_bits(),
preemphasis_coeff=preemphasis_coeff,
istft_args=istft_args)
# NOTE: this is only to make comparisons to the reconstructed waveforms later
y[-n_fft:] = 0.0
y = standardize_waveform(y)
# print('Sample for source {}'.format(i + 1))
lr.output.write_wav(os.path.join(
output_path,
'mix_{}_original_source_{}.wav'.format(mix_number, i + 1)),
y,
mixer.sample_rate(),
norm=True)
source_specs = l41_clustering_separate(
model_spec, model,
mixer.number_of_samples_in_mixes()) #, binary_mask=False)
for i, source_spec in enumerate(source_specs):
y = undo_preprocessing(source_spec,
mixer.sample_length_in_bits(),
preemphasis_coeff=preemphasis_coeff,
istft_args=istft_args)
# NOTE: this is only because the masking creates a chirp in the last
# fft frame (likely due to the mask)
y[-n_fft:] = 0.0
y = standardize_waveform(y)
# print('Separated sample for source {}'.format(i + 1))
lr.output.write_wav(os.path.join(
output_path, 'mix_{}_separated_{}.wav'.format(mix_number, i + 1)),
y,
mixer.sample_rate(),
norm=True)
def main():
# from model settings
params = {}
params['cf'] = 'kl'
params['sparsity'] = 5
params['R'] = 1000
params['conv_eps'] = 1e-3
params['verbose'] = False
T_L = 8
T_R = 0
random_seed = 1234567890
uid_settings = '/local_data/magnolia/pipeline_data/date_2017_09_27_time_13_25/settings/assign_uids_LibriSpeech_UrbanSound8K.json'
library_output_file = '/local_data/magnolia/experiment_data/date_2017_09_28_time_13_14/aux/model_saves/snmf/library_weights.hdf5'
# library_output_file = '/local_data/magnolia/experiment_data/date_2017_09_28_time_13_14/aux/model_saves/snmf/REMOVE_library_weights.hdf5'
model_settings = ''
params['max_iter'] = 25
mixes = [
'/local_data/magnolia/pipeline_data/date_2017_09_27_time_13_25/settings/mixing_LibriSpeech_UrbanSound8K_test_out_of_sample.json'
]
# mixes = ['/local_data/magnolia/pipeline_data/date_2017_09_27_time_13_25/settings/mixing_LibriSpeech_UrbanSound8K_test_in_sample.json']
from_disk = True
output_path = '/local_data/magnolia/experiment_data/date_2017_09_28_time_13_14/aux/evaluations/snmf/out_of_sample_test'
# output_path = '/local_data/magnolia/experiment_data/date_2017_09_28_time_13_14/aux/evaluations/snmf/in_sample_test'
eval_sr = 8000
params['rng'] = np.random.RandomState(random_seed)
mixer = MixIterator(mixes_settings_filenames=mixes,
batch_size=1,
from_disk=from_disk)
# get frequency dimension
frequency_dim = mixer.sample_dimensions()[0]
# get number of sources
settings = json.load(open(uid_settings))
uid_file = settings['output_file']
uid_csv = pd.read_csv(uid_file)
number_of_sources = uid_csv['uid'].max() + 1
model = SNMF(T_L, T_R, params['R'], params['sparsity'], params['cf'])
model.load(library_output_file)
mix_settings = json.load(open(mixes[0]))
signal = mix_settings['signals'][0]
preprocessing_settings = json.load(open(signal['preprocessing_settings']))
stft_args = preprocessing_settings['processing_parameters']['stft_args']
istft_args = convert_preprocessing_parameters(stft_args)
preemphasis_coeff = preprocessing_settings['processing_parameters'][
'preemphasis_coeff']
n_fft = 2048
if 'n_fft' in stft_args:
n_fft = stft_args['n_fft']
os.makedirs(output_path, exist_ok=True)
mix_count = 0
for _ in tqdm.trange(mixer.epoch_size()):
spec, bin_masks, source_specs, uids, snrs = next(mixer)
spec = spec[0]
bin_masks = bin_masks[0]
source_specs = source_specs[0]
uids = uids[0]
snrs = snrs[0]
# print('SNR of mix {}: {}'.format(mix_count + 1, snrs))
y_mix = undo_preprocessing(spec,
mixer.sample_length_in_bits(),
preemphasis_coeff=preemphasis_coeff,
istft_args=istft_args)
# NOTE: this is only to make comparisons to the reconstructed waveforms later
y_mix[-n_fft:] = 0.0
y_mix = lr.core.resample(y_mix,
mixer.sample_rate(),
eval_sr,
scale=True)
y_mix = standardize_waveform(y_mix)
filename = os.path.join(
output_path, 'mix_{}_snr_{:.2f}.wav'.format(mix_count + 1, snrs))
lr.output.write_wav(filename, y_mix, eval_sr, norm=True)
originals = {}
for i, source_spec in enumerate(source_specs):
y = undo_preprocessing(source_spec,
mixer.sample_length_in_bits(),
preemphasis_coeff=preemphasis_coeff,
istft_args=istft_args)
# NOTE: this is only to make comparisons to the reconstructed waveforms later
y[-n_fft:] = 0.0
y = lr.core.resample(y, mixer.sample_rate(), eval_sr, scale=True)
y = standardize_waveform(y)
originals[i] = y
# use model to source-separate the spectrogram
source_specs = model.source_separate(spec,
max_iter=params['max_iter'],
conv_eps=params['conv_eps'],
rng=params['rng'],
verbose=params['verbose'])
# for i, source_spec in enumerate(source_specs):
for i, source_spec in enumerate(source_specs.keys()):
y = undo_preprocessing(source_specs[source_spec],
mixer.sample_length_in_bits(),
preemphasis_coeff=preemphasis_coeff,
istft_args=istft_args)
# NOTE: this is only because the masking creates a chirp in the last
# fft frame (likely due to the binary mask)
y[-n_fft:] = 0.0
y = lr.core.resample(y, mixer.sample_rate(), eval_sr, scale=True)
y = standardize_waveform(y)
# match this waveform with an original source waveform
min_key = 0
min_mse = np.inf
for key in originals:
mse = np.mean((y - originals[key])**2)
if mse < min_mse:
min_key = key
min_mse = mse
# print('Separated sample for source {}'.format(i + 1))
filename = os.path.join(
output_path,
'mix_{}_original_source_{}.wav'.format(mix_count + 1,
min_key + 1))
lr.output.write_wav(filename,
originals[min_key],
eval_sr,
norm=True)
filename = os.path.join(
output_path, 'mix_{}_separated_source_{}.wav'.format(
mix_count + 1, min_key + 1))
lr.output.write_wav(filename, y, eval_sr, norm=True)
y_original = originals.pop(min_key, None)
if y_original is None:
print("something went horribly wrong")
mix_count += 1
def main():
# from model settings
model_params = {
'nonlinearity': 'tanh',
'layer_size': 600,
'embedding_size': 40,
'normalize': 'False'
}
uid_settings = '/local_data/magnolia/pipeline_data/date_2017_09_27_time_13_25/settings/assign_uids_LibriSpeech_UrbanSound8K.json'
model_save_base = '/local_data/magnolia/experiment_data/date_2017_09_28_time_13_14/aux/model_saves/l41'
model_location = '/cpu:0'
model_settings = ''
mixes = [
'/local_data/magnolia/pipeline_data/date_2017_09_27_time_13_25/settings/mixing_LibriSpeech_UrbanSound8K_test_in_sample.json'
]
from_disk = True
mix_number = 1
output_path = '/local_data/magnolia/experiment_data/date_2017_09_28_time_13_14/aux'
mixer = MixIterator(mixes_settings_filenames=mixes,
batch_size=1,
from_disk=from_disk)
# get frequency dimension
frequency_dim = mixer.sample_dimensions()[0]
# get number of sources
settings = json.load(open(uid_settings))
uid_file = settings['output_file']
uid_csv = pd.read_csv(uid_file)
number_of_sources = uid_csv['uid'].max() + 1
model = L41Model(**model_params,
num_speakers=number_of_sources,
F=frequency_dim,
device=model_location)
model.load(model_save_base)
assert (mix_number <= mixer.epoch_size())
mix_settings = json.load(open(mixes[0]))
signal = mix_settings['signals'][0]
preprocessing_settings = json.load(open(signal['preprocessing_settings']))
istft_args = convert_preprocessing_parameters(
preprocessing_settings['processing_parameters']['stft_args'])
preemphasis_coeff = preprocessing_settings['processing_parameters'][
'preemphasis_coeff']
for i in range(mix_number):
spec, bin_masks, source_specs, uids, snrs = next(mixer)
model_spec = spec
spec = spec[0]
bin_masks = bin_masks[0]
source_specs = source_specs[0]
uids = uids[0]
snrs = snrs[0]
print('SNR of this mix: {}'.format(snrs))
y_mix = undo_preprocessing(spec,
mixer.sample_length_in_bits(),
preemphasis_coeff=preemphasis_coeff,
istft_args=istft_args)
# print('Mixed sample')
lr.output.write_wav('{}_mix.wav'.format(output_path),
y_mix,
mixer.sample_rate(),
norm=True)
for i, source_spec in enumerate(source_specs):
y = undo_preprocessing(source_spec,
mixer.sample_length_in_bits(),
preemphasis_coeff=preemphasis_coeff,
istft_args=istft_args)
# print('Sample for source {}'.format(i + 1))
lr.output.write_wav('{}_original_source_{}.wav'.format(output_path, i),
y,
mixer.sample_rate(),
norm=True)
source_specs = l41_clustering_separate(model_spec, model,
mixer.number_of_samples_in_mixes())
for i, source_spec in enumerate(source_specs):
y = undo_preprocessing(source_spec,
mixer.sample_length_in_bits(),
preemphasis_coeff=preemphasis_coeff,
istft_args=istft_args)
# print('Separated sample for source {}'.format(i + 1))
lr.output.write_wav('{}_separated_source_{}.wav'.format(
output_path, i),
y,
mixer.sample_rate(),
norm=True)
def main():
# from model settings
params = {}
params['cf'] = 'kl'
params['sparsity'] = 5
params['R'] = 1000
params['conv_eps'] = 1e-3
params['verbose'] = False
T_L = 8
T_R = 0
random_seed = 1234567890
uid_settings = '/local_data/magnolia/pipeline_data/date_2017_09_27_time_13_25/settings/assign_uids_LibriSpeech_UrbanSound8K.json'
library_output_file = '/local_data/magnolia/experiment_data/date_2017_09_28_time_13_14/aux/model_saves/snmf/library_weights.hdf5'
# library_output_file = '/local_data/magnolia/experiment_data/date_2017_09_28_time_13_14/aux/model_saves/snmf/REMOVE_library_weights.hdf5'
params['max_iter'] = 25
mixes = ['/local_data/magnolia/pipeline_data/date_2017_09_27_time_13_25/settings/mixing_LibriSpeech_UrbanSound8K_test_in_sample.json']
from_disk = True
mix_number = 1
output_path = '/local_data/magnolia/experiment_data/date_2017_09_28_time_13_14/aux/sample_wav_files/snmf'
os.makedirs(output_path, exist_ok=True)
params['rng'] = np.random.RandomState(random_seed)
mixer = MixIterator(mixes_settings_filenames=mixes,
batch_size=1,
from_disk=from_disk)
# get frequency dimension
frequency_dim = mixer.sample_dimensions()[0]
# get number of sources
settings = json.load(open(uid_settings))
uid_file = settings['output_file']
uid_csv = pd.read_csv(uid_file)
number_of_sources = uid_csv['uid'].max() + 1
model = SNMF(T_L, T_R, params['R'], params['sparsity'], params['cf'])
model.load(library_output_file)
assert(mix_number <= mixer.epoch_size())
mix_settings = json.load(open(mixes[0]))
signal = mix_settings['signals'][0]
preprocessing_settings = json.load(open(signal['preprocessing_settings']))
stft_args = preprocessing_settings['processing_parameters']['stft_args']
istft_args = convert_preprocessing_parameters(stft_args)
preemphasis_coeff = preprocessing_settings['processing_parameters']['preemphasis_coeff']
n_fft = 2048
if 'n_fft' in stft_args:
n_fft = stft_args['n_fft']
for i in range(mix_number):
spec, bin_masks, source_specs, uids, snrs = next(mixer)
spec = spec[0]
bin_masks = bin_masks[0]
source_specs = source_specs[0]
uids = uids[0]
snrs = snrs[0]
print('SNR of this mix: {}'.format(snrs))
y_mix = undo_preprocessing(spec, mixer.sample_length_in_bits(),
preemphasis_coeff=preemphasis_coeff,
istft_args=istft_args)
# NOTE: this is only to make comparisons to the reconstructed waveforms later
y_mix[-n_fft:] = 0.0
y_mix = standardize_waveform(y_mix)
lr.output.write_wav(os.path.join(output_path, 'mix_{}.wav'.format(mix_number)), y_mix, mixer.sample_rate(), norm=True)
for i, source_spec in enumerate(source_specs):
y = undo_preprocessing(source_spec, mixer.sample_length_in_bits(),
preemphasis_coeff=preemphasis_coeff,
istft_args=istft_args)
# NOTE: this is only to make comparisons to the reconstructed waveforms later
y[-n_fft:] = 0.0
y = standardize_waveform(y)
lr.output.write_wav(os.path.join(output_path, 'mix_{}_original_source_{}.wav'.format(mix_number, i + 1)), y, mixer.sample_rate(), norm=True)
source_specs = model.source_separate(spec,
max_iter=params['max_iter'],
conv_eps=params['conv_eps'],
rng=params['rng'],
verbose=params['verbose'])
for source_spec in source_specs:
y = undo_preprocessing(source_specs[source_spec], mixer.sample_length_in_bits(),
preemphasis_coeff=preemphasis_coeff,
istft_args=istft_args)
# NOTE: this is only because the masking creates a chirp in the last
# fft frame (likely due to the mask)
y[-n_fft:] = 0.0
y = standardize_waveform(y)
lr.output.write_wav(os.path.join(output_path, 'mix_{}_separated_{}.wav'.format(mix_number, source_spec)), y, mixer.sample_rate(), norm=True)
def main():
# from model settings
model_params = {
}
uid_settings = '/local_data/magnolia/pipeline_data/date_2017_09_27_time_13_25/settings/assign_uids_LibriSpeech_UrbanSound8K.json'
model_save_base = '/local_data/magnolia/experiment_data/date_2017_09_28_time_13_14/aux/model_saves/chimera'
model_location = '/cpu:0'
model_settings = ''
# mixes = ['/local_data/magnolia/pipeline_data/date_2017_09_27_time_13_25/settings/mixing_LibriSpeech_UrbanSound8K_test_in_sample.json']
mixes = ['/local_data/magnolia/pipeline_data/date_2017_09_27_time_13_25/settings/mixing_LibriSpeech_UrbanSound8K_test_out_of_sample.json']
from_disk = True
# output_path = '/local_data/magnolia/experiment_data/date_2017_09_28_time_13_14/aux/evaluations/chimera/in_sample_test'
output_path = '/local_data/magnolia/experiment_data/date_2017_09_28_time_13_14/aux/evaluations/chimera/out_of_sample_test'
eval_sr = 8000
mixer = MixIterator(mixes_settings_filenames=mixes,
batch_size=1,
from_disk=from_disk)
# get frequency dimension
frequency_dim = mixer.sample_dimensions()[0]
# get number of sources
settings = json.load(open(uid_settings))
uid_file = settings['output_file']
uid_csv = pd.read_csv(uid_file)
number_of_sources = uid_csv['uid'].max() + 1
model = Chimera(**model_params,
F=frequency_dim,
device=model_location)
model.load(model_save_base)
mix_settings = json.load(open(mixes[0]))
signal = mix_settings['signals'][0]
preprocessing_settings = json.load(open(signal['preprocessing_settings']))
stft_args = preprocessing_settings['processing_parameters']['stft_args']
istft_args = convert_preprocessing_parameters(stft_args)
preemphasis_coeff = preprocessing_settings['processing_parameters']['preemphasis_coeff']
n_fft = 2048
if 'n_fft' in stft_args:
n_fft = stft_args['n_fft']
os.makedirs(output_path, exist_ok=True)
mix_count = 0
for _ in tqdm.trange(mixer.epoch_size()):
spec, bin_masks, source_specs, uids, snrs = next(mixer)
model_spec = spec
spec = spec[0]
bin_masks = bin_masks[0]
source_specs = source_specs[0]
uids = uids[0]
snrs = snrs[0]
# print('SNR of mix {}: {}'.format(mix_count + 1, snrs))
y_mix = undo_preprocessing(spec, mixer.sample_length_in_bits(),
preemphasis_coeff=preemphasis_coeff,
istft_args=istft_args)
# NOTE: this is only to make comparisons to the reconstructed waveforms later
y_mix[-n_fft:] = 0.0
y_mix = lr.core.resample(y_mix, mixer.sample_rate(), eval_sr, scale=True)
y_mix = standardize_waveform(y_mix)
filename = os.path.join(output_path, 'mix_{}_snr_{:.2f}.wav'.format(mix_count + 1, snrs))
lr.output.write_wav(filename, y_mix, eval_sr, norm=True)
originals = {}
for i, source_spec in enumerate(source_specs):
y = undo_preprocessing(source_spec, mixer.sample_length_in_bits(),
preemphasis_coeff=preemphasis_coeff,
istft_args=istft_args)
# NOTE: this is only to make comparisons to the reconstructed waveforms later
y[-n_fft:] = 0.0
y = lr.core.resample(y, mixer.sample_rate(), eval_sr, scale=True)
y = standardize_waveform(y)
originals[i] = y
# use dc-head of model + clustering to source-separate the spectrogram
source_specs = chimera_clustering_separate(model_spec, model, mixer.number_of_samples_in_mixes())
for i, source_spec in enumerate(source_specs):
y = undo_preprocessing(source_spec, mixer.sample_length_in_bits(),
preemphasis_coeff=preemphasis_coeff,
istft_args=istft_args)
# NOTE: this is only because the masking creates a chirp in the last
# fft frame (likely due to the binary mask)
y[-n_fft:] = 0.0
y = lr.core.resample(y, mixer.sample_rate(), eval_sr, scale=True)
y = standardize_waveform(y)
# match this waveform with an original source waveform
min_key = 0
min_mse = np.inf
for key in originals:
mse = np.mean((y - originals[key])**2)
if mse < min_mse:
min_key = key
min_mse = mse
# print('Separated sample for source {}'.format(i + 1))
filename = os.path.join(output_path, 'mix_{}_original_source_{}.wav'.format(mix_count + 1, min_key + 1))
lr.output.write_wav(filename, originals[min_key], eval_sr, norm=True)
filename = os.path.join(output_path, 'mix_{}_dc_separated_source_{}.wav'.format(mix_count + 1, min_key + 1))
lr.output.write_wav(filename, y, eval_sr, norm=True)
y_original = originals.pop(min_key, None)
if y_original is None:
print("something went horribly wrong")
# use mi-head of model to source-separate the spectrogram
source_specs = chimera_mask(model_spec, model)[0]
for i in range(source_specs.shape[2]):
source_spec = source_specs[:, :, i]
y = undo_preprocessing(source_spec, mixer.sample_length_in_bits(),
preemphasis_coeff=preemphasis_coeff,
istft_args=istft_args)
# NOTE: this is only because the masking creates a chirp in the last
# fft frame (likely due to the binary mask)
y[-n_fft:] = 0.0
y = lr.core.resample(y, mixer.sample_rate(), eval_sr, scale=True)
y = standardize_waveform(y)
filename = os.path.join(output_path, 'mix_{}_mi_separated_source_{}.wav'.format(mix_count + 1, i + 1))
lr.output.write_wav(filename, y, eval_sr, norm=True)
mix_count += 1
def main():
# parse command line arguments
parser = argparse.ArgumentParser(
description='Reconstruct waveforms from mixes.')
parser.add_argument('--sample',
'-n',
default=1,
type=int,
help='sample number to write to file (1-indexed)')
parser.add_argument('--output_file',
'-o',
default='mix.wav',
help='output file name (wav format)')
parser.add_argument('--settings',
'-s',
default='../../settings/mixing_template.json',
help='sample mixing settings JSON file')
parser.add_argument('--logger_settings',
'-l',
default='../../settings/logging.conf',
help='logging configuration file')
args = parser.parse_args()
# Load logging configuration
logging.config.fileConfig(args.logger_settings)
logger = logging.getLogger('iteration')
mixer = MixIterator([args.settings], batch_size=1)
mixer_iter = iter(mixer)
with open(args.settings) as settings_file:
settings = json.load(settings_file)
total_number_of_mixed_samples = settings['number_of_mixed_samples']
assert (args.sample <= total_number_of_mixed_samples
and args.sample > 0)
signal = settings['signals'][0]
preprocessing_settings = json.load(
open(signal['preprocessing_settings']))
istft_args = convert_preprocessing_parameters(
preprocessing_settings['processing_parameters']['stft_args'])
preemphasis_coeff = preprocessing_settings['processing_parameters'][
'preemphasis_coeff']
sample_rate = preprocessing_settings['processing_parameters'][
'target_sample_rate']
sample_length = settings['target_sample_length']
total_length = int(sample_length * sample_rate)
for i in range(args.sample):
spec, bin_masks, source_specs, uids, snrs = next(mixer_iter)
spec = spec[0]
bin_masks = bin_masks[0]
uids = uids[0]
snrs = snrs[0]
print('SNR of this mix: {}'.format(snrs))
mix_file_name = '{}_mix.wav'.format(
os.path.splitext(args.output_file)[0])
y = undo_preprocessing(spec,
total_length,
preemphasis_coeff=preemphasis_coeff,
istft_args=istft_args)
lr.output.write_wav(mix_file_name, y, sample_rate, norm=True)
for i in range(bin_masks.shape[0]):
source_file_name = '{}_{}.wav'.format(
os.path.splitext(args.output_file)[0], uids[i])
source_spec = apply_binary_mask(bin_masks[i], spec)
source_y = undo_preprocessing(source_spec,
total_length,
preemphasis_coeff=preemphasis_coeff,
istft_args=istft_args)
lr.output.write_wav(source_file_name,
source_y,
sample_rate,
norm=True)