def evaluation():
with torch.no_grad():
#
# initialize network
#
np.random.seed(0)
torch.manual_seed(0)
network_params = 0
network = EnsembleNetwork(
filepath_gating=file_gating,
filepaths_denoising=files_specialists,
g_hs=hidden_size_gating,
g_nl=num_layers_gating,
s_hs=hidden_size_specialist,
s_nl=num_layers_specialist,
ct=args.latent_space,
).to(device=args.device_id)
F.write_data(filename=os.path.join(output_directory,
'files_gating.txt'),
data=str(file_gating))
F.write_data(filename=os.path.join(output_directory,
'files_specialist.txt'),
data=str(files_specialists))
with torch.cuda.device(args.device_id):
torch.cuda.empty_cache()
if args.latent_space == 'gender':
te_sisdr = {str(k): 0 for k in C.gender_all}
for te_gender in C.gender_all:
te_batch_durations = list()
te_batch_sisdr = list()
files_speech = np.random.choice(F.filter_by_gender(
te_utterances, te_gender),
size=C.te_batch_size)
files_noise = np.random.choice(te_noises, size=C.te_batch_size)
for (i, fs, fn) in zip(range(C.te_batch_size), files_speech,
files_noise):
source = F.load_audio(fs,
duration=None,
random_offset=False,
device=args.device_id)
noise = F.load_audio(fn,
duration=None,
random_offset=False,
device=args.device_id)
min_length = min(len(source), len(noise))
stft_frames = ceil(min_length / C.hop_size)
source = source[:min_length]
noise = noise[:min_length]
(x, s, n) = F.mix_signals(source, noise, snr_db=C.snr_all)
(X, X_mag) = F.stft(x)
X = X.permute(
1, 0,
2)[:stft_frames] # (seq_len, num_features, channel)
X_mag = X_mag.permute(
1, 0)[:stft_frames] # (seq_len, num_features)
X = torch.unsqueeze(X, dim=0)
X_mag = torch.unsqueeze(X_mag, dim=0)
s = torch.unsqueeze(s, dim=0)
x = torch.unsqueeze(x, dim=0)
actual_sisdr = float(F.calculate_sisdr(s, x).item())
# feed-forward
M_hat = network(X_mag)
s_hat = F.istft(X, mask=M_hat)
te_batch_sisdr.append(
(F.calculate_sisdr(s, s_hat,
offset=actual_sisdr).mean().item()))
te_batch_durations.append(min_length)
# store the weighted average results
te_sisdr[str(te_gender)] = np.average(
te_batch_sisdr, weights=te_batch_durations)
elif args.latent_space == 'snr':
te_sisdr = {str(k): 0 for k in C.snr_all}
for te_snr in C.snr_all:
te_batch_durations = list()
te_batch_sisdr = list()
files_speech = np.random.choice(te_utterances,
size=C.te_batch_size)
files_noise = np.random.choice(te_noises, size=C.te_batch_size)
for (i, fs, fn) in zip(range(C.te_batch_size), files_speech,
files_noise):
source = F.load_audio(fs,
duration=None,
random_offset=False,
device=args.device_id)
noise = F.load_audio(fn,
duration=None,
random_offset=False,
device=args.device_id)
min_length = min(len(source), len(noise))
stft_frames = ceil(min_length / C.hop_size)
source = source[:min_length]
noise = noise[:min_length]
(x, s, n) = F.mix_signals(source, noise, snr_db=te_snr)
(X, X_mag) = F.stft(x)
X = X.permute(
1, 0,
2)[:stft_frames] # (seq_len, num_features, channel)
X_mag = X_mag.permute(
1, 0)[:stft_frames] # (seq_len, num_features)
X = torch.unsqueeze(X, dim=0)
X_mag = torch.unsqueeze(X_mag, dim=0)
s = torch.unsqueeze(s, dim=0)
x = torch.unsqueeze(x, dim=0)
actual_sisdr = float(F.calculate_sisdr(s, x).item())
# feed-forward
M_hat = network(X_mag)
s_hat = F.istft(X, mask=M_hat)
te_batch_sisdr.append(
(F.calculate_sisdr(s, s_hat,
offset=actual_sisdr).mean().item()))
te_batch_durations.append(min_length)
# store the weighted average results
te_sisdr[str(te_snr)] = np.average(te_batch_sisdr,
weights=te_batch_durations)
te_sisdr['mean'] = np.mean(list(te_sisdr.values()))
logging.info(json.dumps(te_sisdr, sort_keys=True, indent=4))
F.write_data(filename=os.path.join(output_directory,
f'test_results.txt'),
data=te_sisdr)
return
def experiment():
#
# initialize network
#
np.random.seed(0)
torch.manual_seed(0)
network = M.DenoisingNetwork(args.hidden_size,
args.num_layers).to(device=args.device_id)
network_params = F.count_parameters(network)
optimizer = torch.optim.Adam(
params=network.parameters(),
lr=args.learning_rate,
)
criterion = F.loss_sisdr
F.write_data(filename=os.path.join(output_directory, 'num_parameters.txt'),
data=network_params)
with torch.cuda.device(args.device_id):
torch.cuda.empty_cache()
#
# log experiment configuration
#
os.system('cls' if os.name == 'nt' else 'clear')
logging.info(f'Training Denoising network' + (
f' specializing in {F.fmt_specialty(args.specialization)} mixtures'
if args.specialization else '') + '...')
logging.info(f'\u2022 {args.hidden_size} hidden units')
logging.info(f'\u2022 {args.num_layers} layers')
logging.info(f'\u2022 {network_params} learnable parameters')
logging.info(f'\u2022 {args.learning_rate:.3e} learning rate')
logging.info(f'Results will be saved in "{output_directory}".')
logging.info(f'Using GPU device {args.device_id}...')
#
# experiment loop
#
(iteration, iteration_best) = (0, 0)
sisdr_best = 0
while not C.stopping_criteria(iteration, iteration_best):
network.train()
np.random.seed(iteration)
torch.manual_seed(iteration)
# training
for batch_index in range(100):
# forward propagation
batch = F.generate_batch(
np.random.choice(tr_utterances, size=C.tr_batch_size),
np.random.choice(tr_noises, size=C.tr_batch_size),
mixture_snr=tr_snr,
device=args.device_id,
)
M_hat = network(batch.X_mag)
s_hat = F.istft(batch.X, mask=M_hat)
# backward propagation
optimizer.zero_grad()
F.loss_sisdr(batch.s, s_hat).backward()
torch.nn.utils.clip_grad_norm_(network.parameters(), max_norm=1e-4)
optimizer.step()
network.eval()
np.random.seed(0)
torch.manual_seed(0)
# validation
with torch.no_grad():
if args.latent_space == 'gender':
sisdr_batch = {k: 0 for k in C.gender_all}
for vl_gender in C.gender_all:
vl_filtered_files = F.filter_by_gender(
vl_utterances, vl_gender)
batch = F.generate_batch(
np.random.choice(vl_filtered_files,
size=C.vl_batch_size),
np.random.choice(vl_noises, size=C.vl_batch_size),
device=args.device_id,
)
M_hat = network(batch.X_mag)
s_hat = F.istft(batch.X, mask=M_hat)
sisdr_batch[vl_gender] = float(
F.calculate_sisdr(
batch.s, s_hat,
offset=batch.actual_sisdr).mean().item())
else:
sisdr_batch = {k: 0 for k in C.snr_all}
for vl_snr in C.snr_all:
batch = F.generate_batch(
np.random.choice(vl_utterances, size=C.vl_batch_size),
np.random.choice(vl_noises, size=C.vl_batch_size),
mixture_snr=vl_snr,
device=args.device_id,
)
M_hat = network(batch.X_mag)
s_hat = F.istft(batch.X, mask=M_hat)
sisdr_batch[vl_snr] = float(
F.calculate_sisdr(
batch.s, s_hat,
offset=batch.actual_sisdr).mean().item())
sisdr_batch['mean'] = np.mean(list(sisdr_batch.values()))
# print results
if sisdr_batch['mean' if args.
latent_space != 'snr' else tr_snr] > sisdr_best:
sisdr_best = sisdr_batch['mean' if args.
latent_space != 'snr' else tr_snr]
iteration_best = iteration
F.write_data(filename=os.path.join(output_directory,
'validation_sisdr.txt'),
data=f'{sisdr_best:%}')
torch.save(network.state_dict(),
os.path.join(output_directory, 'model.pt'))
checkmark = ' | \033[32m\u2714\033[39m'
else:
checkmark = ''
status = ''
for (k, v) in sisdr_batch.items():
status += f'\033[33m{k}: {v:>6.3f} dB\033[39m, '
ts_end = int(round(time.time())) - ts_start
status += f'Time Elapsed: {int(ts_end/60)} minutes' + checkmark
logging.info(status)
iteration += 1
return
def evaluation():
with torch.no_grad():
sum_num_params = 0
#
# initialize gating network
#
gating = GatingNetwork(hidden_size_gating, num_layers_gating, len(gender_all)).to(device=args.device_id)
gating.load_state_dict(torch.load(
args.state_dict_file_gating, map_location=torch.device(args.device_id))
)
gating.eval()
sum_num_params += F.count_parameters(gating)
#
# initialize specialist networks (as a hashed list of networks)
#
specialists = {
i: SpecialistNetwork(hidden_size_specialist, num_layers_specialist).to(device=args.device_id)
for i in range(len(gender_all))
}
for i in range(len(gender_all)):
assert (re.search(r'gender\_[MF]', args.state_dict_file_specialist) is not None)
filepath = re.sub(r'gender\_[MF]', F.fmt_gender(gender_all[i]), args.state_dict_file_specialist)
specialists[i].load_state_dict(torch.load(
filepath, map_location=torch.device(args.device_id))
)
specialists[i].eval()
sum_num_params += F.count_parameters(specialists[i])
F.write_data(filename=os.path.join(output_directory, 'num_parameters.txt'),
data=sum_num_params)
with torch.cuda.device(args.device_id):
torch.cuda.empty_cache()
#
# log experiment configuration
#
logging.info('All results will be stored in "{}".'.format(
output_directory))
logging.info('Testing {} model (with Gating architecture {} and Specialist architecture {}) to denoise {} gendered mixtures...'.format(
model_name, architecture_gating, architecture_specialist, gender_all))
logging.info('Using GPU device {}...'.format(
args.device_id))
fields = ['snr_val','num_mixtures','sdr','sisdr','mse','bce','accuracy']
#
# validation
#
results_validation = []
np.random.seed(0)
torch.manual_seed(0)
for gender_val in gender_all:
# construct a batch
batch = F.generate_batch(
vl_utterances, vl_noises,
batch_size=vl_batch_size,
gender=gender_val,
device=args.device_id,
)
Y = batch.index_gender
# compute batch-wise specialist probabilities
Y_hat = gating(batch.X_mag)
# pick the best specialist to apply to the whole batch (based on batch probabilities sum)
k = int(Y_hat.sum(dim=0).argmax().item())
# apply the best specialist to the entire batch
M_hat = specialists[k](batch.X_mag)
s_hat = F.istft(batch.X, mask=M_hat)
results_validation.append([
gender_val,
vl_batch_size,
float(F.calculate_sdr(batch.s, s_hat, offset=batch.actual_sdr).mean().item()),
float(F.calculate_sisdr(batch.s, s_hat, offset=batch.actual_sisdr).mean().item()),
float(F.calculate_mse(batch.M, M_hat).item()),
float(F.calculate_bce(batch.M, M_hat).item()),
float(F.calculate_accuracy(Y, Y_hat)),
])
status = (
f'Validation Data (Gender={gender_val}) -- ' + \
f'SDR: {results_validation[-1][2]:>6.3f} dB, ' + \
f'\033[33mSISDR: {results_validation[-1][3]:>6.3f} dB\033[39m, ' + \
f'MSE: {results_validation[-1][4]:>6.3f}, ' + \
f'BCE: {results_validation[-1][5]:>6.3f}, ' + \
f'Accuracy: {results_validation[-1][6]:>6.3f}'
)
logging.info(status)
F.write_table(filename=os.path.join(output_directory, f'validation_results.txt'),
table_data=results_validation, headers=fields)
#
# testing
#
results_testing = []
for gender_val in gender_all:
np.random.seed(0)
torch.manual_seed(0)
te_utterances_filtered = te_utterances[np.array([(F.get_gender(row) in gender_val) for row in te_utterances])]
files_speech = np.random.choice(te_utterances_filtered, size=te_batch_size)
files_noise = np.random.choice(te_noises, size=te_batch_size)
te_m_durations = list()
te_m_sdr = list()
te_m_sisdr = list()
te_m_mse = list()
te_m_bce = list()
te_m_accuracy = list()
for (i, fs, fn) in zip(range(te_batch_size), files_speech, files_noise):
source = F.load_audio(fs, duration=None, random_offset=False, device=args.device_id)
noise = F.load_audio(fn, duration=None, random_offset=False, device=args.device_id)
min_length = min(len(source), len(noise))
stft_frames = ceil(min_length/hop_size)
source = source[:min_length]
noise = noise[:min_length]
(x, s, n) = F.mix_signals(source, noise, snr_db=snr_all)
(S, S_mag) = F.stft(s)
(N, N_mag) = F.stft(n)
(X, X_mag) = F.stft(x)
(M) = F.calculate_masking_target(S_mag, N_mag)
X = X.permute(1, 0, 2)[:stft_frames] # (seq_len, num_features, channel)
S_mag = S_mag.permute(1, 0)[:stft_frames] # (seq_len, num_features)
N_mag = N_mag.permute(1, 0)[:stft_frames] # (seq_len, num_features)
X_mag = X_mag.permute(1, 0)[:stft_frames] # (seq_len, num_features)
M = M.permute(1, 0)[:stft_frames] # (seq_len, num_features)
actual_sdr = float(F.calculate_sdr(s, x).item())
actual_sisdr = float(F.calculate_sisdr(s, x).item())
gender_index = int(F.get_gender(fs)=='F')
Y = torch.zeros(1, len(gender_all), device=args.device_id)
Y[..., gender_index] = 1
# add a fake batch axis to everything
x = torch.unsqueeze(x, dim=0)
s = torch.unsqueeze(s, dim=0)
n = torch.unsqueeze(n, dim=0)
S = torch.unsqueeze(S, dim=0)
S_mag = torch.unsqueeze(S_mag, dim=0)
N = torch.unsqueeze(N, dim=0)
N_mag = torch.unsqueeze(N_mag, dim=0)
X = torch.unsqueeze(X, dim=0)
X_mag = torch.unsqueeze(X_mag, dim=0)
M = torch.unsqueeze(M, dim=0)
# compute batch-wise specialist probabilities
Y_hat = gating(X_mag)
# pick the best specialist to apply to the whole batch (based on batch probabilities sum)
k = int(Y_hat.sum(dim=0).argmax().item())
# apply the best specialist to the entire batch
M_hat = specialists[k](X_mag)
s_hat = F.istft(X, mask=M_hat)
te_m_sdr.append(F.calculate_sdr(s, s_hat, offset=actual_sdr).mean().item())
te_m_sisdr.append(F.calculate_sisdr(s, s_hat, offset=actual_sisdr).mean().item())
te_m_mse.append(F.calculate_mse(M, M_hat).item())
te_m_bce.append(F.calculate_bce(M, M_hat).item())
te_m_accuracy.append(float(torch.prod(Y==torch.round(Y_hat), dim=-1).sum().item()/float(len(Y))))
te_m_durations.append(min_length)
# store the weighted average results
results_testing.append([
gender_val,
te_batch_size,
np.average(te_m_sdr, weights=te_m_durations),
np.average(te_m_sisdr, weights=te_m_durations),
np.average(te_m_mse, weights=te_m_durations),
np.average(te_m_bce, weights=te_m_durations),
np.average(te_m_accuracy, weights=te_m_durations),
])
status = (
f'Test Data (Gender={gender_val}) -- ' + \
f'SDR: {results_testing[-1][2]:>6.3f} dB, ' + \
f'\033[33mSISDR: {results_testing[-1][3]:>6.3f} dB\033[39m, ' + \
f'MSE: {results_testing[-1][4]:>6.3f}, ' + \
f'BCE: {results_testing[-1][5]:>6.3f}, ' + \
f'Accuracy: {results_testing[-1][6]:>6.3f}'
)
logging.info(status)
F.write_table(filename=os.path.join(output_directory, f'test_results.txt'),
table_data=results_testing, headers=fields)
return
duration=None)
min_length = min(len(source), len(noise))
(x, s, n) = F.mix_signals(source[:min_length],
noise[:min_length],
snr_db=mixture_snr)
(S, S_mag) = F.stft(s)
(N, N_mag) = F.stft(n)
(X, X_mag) = F.stft(x)
(M) = F.calculate_masking_target(S_mag, N_mag)
X = X.permute(1, 0, 2)
S_mag = S_mag.permute(1, 0)
N_mag = N_mag.permute(1, 0)
X_mag = X_mag.permute(1, 0)
M = M.permute(1, 0)
actual_sdr = float(F.calculate_sdr(s, x).item())
actual_sisdr = float(F.calculate_sisdr(s, x).item())
# inference
M = network(X_mag.unsqueeze(0))
y = F.istft(X.unsqueeze(0), mask=M)
(Y, Y_mag) = F.stft(y)
y = y.squeeze()
output_sdr = float(F.calculate_sdr(s, y).item())
output_sisdr = float(F.calculate_sisdr(s, y).item())
# convert everything back into numpy types
max_amplitude = 1e-30 + 1.15 * float(
max(s.max(), n.max(), x.max(), y.max()))
s = s.detach().cpu().numpy() / max_amplitude
n = n.detach().cpu().numpy() / max_amplitude
x = x.detach().cpu().numpy() / max_amplitude
def evaluation():
with torch.no_grad():
#
# initialize network
#
np.random.seed(0)
torch.manual_seed(0)
network = DenoisingNetwork(args.hidden_size,
args.num_layers).to(device=args.device_id)
network_params = F.count_parameters(network)
network.load_state_dict(torch.load(
args.state_dict_file,
map_location=torch.device(args.device_id),
),
strict=True)
network.eval()
F.write_data(filename=os.path.join(output_directory,
'num_parameters.txt'),
data=network_params)
with torch.cuda.device(args.device_id):
torch.cuda.empty_cache()
te_sisdr = dict()
if args.latent_space in ('gender', 'all'):
np.random.seed(0)
torch.manual_seed(0)
for te_gender in C.gender_all:
logging.info(
f'Now testing model with {te_gender}-gender inputs...')
te_batch_durations = list()
te_batch_sisdr = list()
files_speech = np.random.choice(F.filter_by_gender(
te_utterances, te_gender),
size=C.te_batch_size)
files_noise = np.random.choice(te_noises, size=C.te_batch_size)
for (i, fs, fn) in zip(range(C.te_batch_size), files_speech,
files_noise):
source = F.load_audio(fs,
duration=None,
random_offset=False,
device=args.device_id)
noise = F.load_audio(fn,
duration=None,
random_offset=False,
device=args.device_id)
min_length = min(len(source), len(noise))
stft_frames = ceil(min_length / C.hop_size)
source = source[:min_length]
noise = noise[:min_length]
(x, s, n) = F.mix_signals(source, noise, snr_db=C.snr_all)
(X, X_mag) = F.stft(x)
X = X.permute(
1, 0,
2)[:stft_frames] # (seq_len, num_features, channel)
X_mag = X_mag.permute(
1, 0)[:stft_frames] # (seq_len, num_features)
X = torch.unsqueeze(X, dim=0)
X_mag = torch.unsqueeze(X_mag, dim=0)
s = torch.unsqueeze(s, dim=0)
x = torch.unsqueeze(x, dim=0)
actual_sisdr = float(F.calculate_sisdr(s, x).item())
# feed-forward
M_hat = network(X_mag)
s_hat = F.istft(X, mask=M_hat)
te_batch_sisdr.append(
(F.calculate_sisdr(s, s_hat,
offset=actual_sisdr).mean().item()))
te_batch_durations.append(min_length)
# store the weighted average results
te_sisdr[str(te_gender)] = np.average(
te_batch_sisdr, weights=te_batch_durations)
te_sisdr['mean_gender'] = np.mean(
[te_sisdr[str(x)] for x in C.gender_all])
if args.latent_space in ('snr', 'all'):
np.random.seed(0)
torch.manual_seed(0)
for te_snr in C.snr_all:
logging.info(
f'Now testing model with {te_snr} dB mixture SDR inputs...'
)
te_batch_durations = list()
te_batch_sisdr = list()
files_speech = np.random.choice(te_utterances,
size=C.te_batch_size)
files_noise = np.random.choice(te_noises, size=C.te_batch_size)
for (i, fs, fn) in zip(range(C.te_batch_size), files_speech,
files_noise):
source = F.load_audio(fs,
duration=None,
random_offset=False,
device=args.device_id)
noise = F.load_audio(fn,
duration=None,
random_offset=False,
device=args.device_id)
min_length = min(len(source), len(noise))
stft_frames = ceil(min_length / C.hop_size)
source = source[:min_length]
noise = noise[:min_length]
(x, s, n) = F.mix_signals(source, noise, snr_db=te_snr)
(X, X_mag) = F.stft(x)
X = X.permute(
1, 0,
2)[:stft_frames] # (seq_len, num_features, channel)
X_mag = X_mag.permute(
1, 0)[:stft_frames] # (seq_len, num_features)
X = torch.unsqueeze(X, dim=0)
X_mag = torch.unsqueeze(X_mag, dim=0)
s = torch.unsqueeze(s, dim=0)
x = torch.unsqueeze(x, dim=0)
actual_sisdr = float(F.calculate_sisdr(s, x).item())
# feed-forward
M_hat = network(X_mag)
s_hat = F.istft(X, mask=M_hat)
te_batch_sisdr.append(
(F.calculate_sisdr(s, s_hat,
offset=actual_sisdr).mean().item()))
te_batch_durations.append(min_length)
# store the weighted average results
te_sisdr[str(te_snr)] = np.average(te_batch_sisdr,
weights=te_batch_durations)
te_sisdr['mean_sisdr'] = np.mean(
[te_sisdr[str(x)] for x in C.snr_all])
logging.info(json.dumps(te_sisdr, sort_keys=True, indent=4))
F.write_data(filename=os.path.join(output_directory,
f'test_results.txt'),
data=te_sisdr)
return
def experiment():
#
# ensure reproducibility
#
np.random.seed(0)
torch.manual_seed(0)
#
# initialize network
#
network = EnsembleNetwork(
filepath_gating=file_gating,
filepaths_denoising=files_specialists,
g_hs=hidden_size_gating,
g_nl=num_layers_gating,
s_hs=hidden_size_specialist,
s_nl=num_layers_specialist,
ct=args.latent_space,
).to(device=args.device_id)
optimizer = torch.optim.Adam(
params=network.parameters(),
lr=args.learning_rate,
)
network_params = F.count_parameters(network.gating) + F.count_parameters(
network.specialists[0])
F.write_data(filename=os.path.join(output_directory, 'num_parameters.txt'),
data=network_params)
F.write_data(filename=os.path.join(output_directory, 'files_gating.txt'),
data=file_gating)
F.write_data(filename=os.path.join(output_directory,
'files_specialist.txt'),
data=files_specialists)
with torch.cuda.device(args.device_id):
torch.cuda.empty_cache()
#
# log experiment configuration
#
os.system('cls' if os.name == 'nt' else 'clear')
logging.info(
f'Training Ensemble network composed of {args.latent_space} specialists...'
)
logging.info(f'\u2022 {architecture_gating} gating architecture')
logging.info(f'\u2022 {architecture_specialist} specialist architecture')
logging.info(
f'\u2022 Softmax annealing strategy = {args.softmax_annealing if args.softmax_annealing else None}'
)
logging.info(f'\u2022 {network_params} learnable parameters')
logging.info(f'\u2022 {args.learning_rate:.3e} learning rate')
logging.info(f'Results will be saved in "{output_directory}".')
logging.info(f'Using GPU device {args.device_id}...')
# softmax_annealing
# experiment loop
#
(iteration, iteration_best) = (0, 0)
sisdr_best = 0
while not C.stopping_criteria(iteration, iteration_best):
network.train()
np.random.seed(iteration)
torch.manual_seed(iteration)
# training
for batch_index in range(100):
# forward propagation
batch = F.generate_batch(
np.random.choice(tr_utterances, size=C.tr_batch_size),
np.random.choice(tr_noises, size=C.tr_batch_size),
device=args.device_id,
)
M_hat = network(batch.X_mag, args.softmax_annealing)
s_hat = F.istft(batch.X, mask=M_hat)
# backward propagation
optimizer.zero_grad()
F.loss_sisdr(batch.s, s_hat).backward()
torch.nn.utils.clip_grad_norm_(network.parameters(), max_norm=1e-4)
optimizer.step()
network.eval()
np.random.seed(0)
torch.manual_seed(0)
# validation
with torch.no_grad():
if args.latent_space == 'gender':
sisdr_batch = {k: 0 for k in C.gender_all}
for vl_gender in C.gender_all:
vl_filtered_files = F.filter_by_gender(
vl_utterances, vl_gender)
batch = F.generate_batch(
np.random.choice(vl_filtered_files,
size=C.vl_batch_size),
np.random.choice(vl_noises, size=C.vl_batch_size),
device=args.device_id,
)
M_hat = network(batch.X_mag)
s_hat = F.istft(batch.X, mask=M_hat)
sisdr_batch[vl_gender] = float(
F.calculate_sisdr(
batch.s, s_hat,
offset=batch.actual_sisdr).mean().item())
else:
sisdr_batch = {k: 0 for k in C.snr_all}
for vl_snr in C.snr_all:
batch = F.generate_batch(
np.random.choice(vl_utterances, size=C.vl_batch_size),
np.random.choice(vl_noises, size=C.vl_batch_size),
mixture_snr=vl_snr,
device=args.device_id,
)
M_hat = network(batch.X_mag)
s_hat = F.istft(batch.X, mask=M_hat)
sisdr_batch[vl_snr] = float(
F.calculate_sisdr(
batch.s, s_hat,
offset=batch.actual_sisdr).mean().item())
sisdr_batch['mean'] = np.mean(list(sisdr_batch.values()))
# print results
if sisdr_batch['mean'] > sisdr_best:
sisdr_best = sisdr_batch['mean']
iteration_best = iteration
F.write_data(filename=os.path.join(output_directory,
'validation_sisdr.txt'),
data=f'{sisdr_best:%}')
torch.save(network.state_dict(),
os.path.join(output_directory, 'model.pt'))
checkmark = ' | \033[32m\u2714\033[39m'
else:
checkmark = ''
status = ''
for (k, v) in sisdr_batch.items():
status += f'\033[33m{k}: {v:>6.3f} dB\033[39m, '
ts_end = int(round(time.time())) - ts_start
status += f'Time Elapsed: {int(ts_end/60)} minutes' + checkmark
logging.info(status)
logging.info(
f'Network # of forwards: {network.num_forwards} \t Alpha: {network.alpha}'
)
iteration += 1
return os.path.join(output_directory, 'model.pt')