raise ValueError('Unknown flist {}'.format(args.flist))
for env in ['caf', 'bus', 'str', 'ped']:
for beamformer in beamformers:
mkdir_p(os.path.join(args.output_dir, beamformer,'{}05_{}_{}'.format(
stage, env, scenario
)))
t_io = 0
t_net = 0
t_beamform = 0
# Beamform loop
for cur_line in tqdm(flist):
with Timer() as t:
if scenario == 'simu':
audio_data = get_audio_data(cur_line)
context_samples = 0
elif scenario == 'real':
audio_data, context_samples = get_audio_data_with_context(
cur_line[0], cur_line[1], cur_line[2])
t_io += t.msecs
Y = stft(audio_data, time_dim=1).transpose((1, 0, 2))
Y_var = Variable(np.abs(Y).astype(np.float32))
if args.gpu >= 0:
Y_var.to_gpu(args.gpu)
with Timer() as t:
N_masks, X_masks = model.calc_masks(Y_var)
N_masks.to_cpu()
X_masks.to_cpu()
t_net += t.msecs
else:
raise ValueError('Unknown flist {}'.format(args.flist))
for env in ['caf', 'bus', 'str', 'ped']:
mkdir_p(os.path.join(args.output_dir, '{}05_{}_{}'.format(
stage, env, scenario
)))
t_io = 0
t_net = 0
t_beamform = 0
# Beamform loop
for cur_line in tqdm(flist):
with Timer() as t:
if scenario == 'simu':
audio_data = get_audio_data(cur_line)
context_samples = 0
elif scenario == 'real':
audio_data, context_samples = get_audio_data_with_context(
cur_line[0], cur_line[1], cur_line[2])
t_io += t.msecs
Y = stft(audio_data, time_dim=1).transpose((1, 0, 2))
Y_var = Variable(np.abs(Y).astype(np.float32), True)
if args.gpu >= 0:
Y_var.to_gpu(args.gpu)
with Timer() as t:
N_masks, X_masks = model.calc_masks(Y_var)
N_masks.to_cpu()
X_masks.to_cpu()
t_net += t.msecs
