def use_me_process(sources_list, output_file_names):
"""The usage process.
:param sources_list: The file names to be used.
:type sources_list: list[str]
:param output_file_names: The output file names to be used.
:type output_file_names: list[list[str]]
"""
print('\n-- Welcome to MaD TwinNet.')
if debug:
print(
'\n-- Cannot proceed in debug mode. Please set debug=False at the settings file.'
)
print('-- Exiting.')
exit(-1)
print(
'-- Now I will extract the voice and the background music from the provided files'
)
# Masker modules
rnn_enc = RNNEnc(hyper_parameters['reduced_dim'],
hyper_parameters['context_length'], debug)
rnn_dec = RNNDec(hyper_parameters['rnn_enc_output_dim'], debug)
fnn = FNNMasker(hyper_parameters['rnn_enc_output_dim'],
hyper_parameters['original_input_dim'],
hyper_parameters['context_length'])
# Denoiser modules
denoiser = FNNDenoiser(hyper_parameters['original_input_dim'])
rnn_enc.load_state_dict(torch.load(output_states_path['rnn_enc']))
rnn_dec.load_state_dict(torch.load(output_states_path['rnn_dec']))
fnn.load_state_dict(torch.load(output_states_path['fnn']))
denoiser.load_state_dict(torch.load(output_states_path['denoiser']))
if not debug and torch.has_cudnn:
rnn_enc = rnn_enc.cuda()
rnn_dec = rnn_dec.cuda()
fnn = fnn.cuda()
denoiser = denoiser.cuda()
testing_it = data_feeder_testing(
window_size=hyper_parameters['window_size'],
fft_size=hyper_parameters['fft_size'],
hop_size=hyper_parameters['hop_size'],
seq_length=hyper_parameters['seq_length'],
context_length=hyper_parameters['context_length'],
batch_size=1,
debug=debug,
sources_list=sources_list)
print('-- Let\'s go!\n')
total_time = 0
for index, data in enumerate(testing_it()):
s_time = time.time()
mix, mix_magnitude, mix_phase, voice_true, bg_true = data
voice_predicted = np.zeros(
(mix_magnitude.shape[0], hyper_parameters['seq_length'] -
hyper_parameters['context_length'] * 2,
hyper_parameters['window_size']),
dtype=np.float32)
for batch in range(
int(mix_magnitude.shape[0] /
training_constants['batch_size'])):
b_start = batch * training_constants['batch_size']
b_end = (batch + 1) * training_constants['batch_size']
v_in = Variable(
torch.from_numpy(mix_magnitude[b_start:b_end, :, :]))
if not debug and torch.has_cudnn:
v_in = v_in.cuda()
tmp_voice_predicted = rnn_enc(v_in)
tmp_voice_predicted = rnn_dec(tmp_voice_predicted)
tmp_voice_predicted = fnn(tmp_voice_predicted, v_in)
tmp_voice_predicted = denoiser(tmp_voice_predicted)
voice_predicted[
b_start:b_end, :, :] = tmp_voice_predicted.data.cpu().numpy()
data_process_results_testing(
index=index,
voice_true=voice_true,
bg_true=bg_true,
voice_predicted=voice_predicted,
window_size=hyper_parameters['window_size'],
mix=mix,
mix_magnitude=mix_magnitude,
mix_phase=mix_phase,
hop=hyper_parameters['hop_size'],
context_length=hyper_parameters['context_length'],
output_file_name=output_file_names[index])
e_time = time.time()
print(
usage_output_string_per_example.format(f=sources_list[index],
t=e_time - s_time))
total_time += e_time - s_time
print('\n-- Testing finished\n')
print(usage_output_string_total.format(t=total_time))
print('-- That\'s all folks!')
def testing_process():
"""The testing process.
"""
device = 'cuda' if not debug and torch.cuda.is_available() else 'cpu'
print('\n-- Starting testing process. Debug mode: {}'.format(debug))
print('-- Process on: {}'.format(device), end='\n\n')
print('-- Setting up modules... ', end='')
# Masker modules
rnn_enc = RNNEnc(hyper_parameters['reduced_dim'],
hyper_parameters['context_length'], debug)
rnn_dec = RNNDec(hyper_parameters['rnn_enc_output_dim'], debug)
fnn = FNNMasker(hyper_parameters['rnn_enc_output_dim'],
hyper_parameters['original_input_dim'],
hyper_parameters['context_length'])
# Denoiser modules
denoiser = FNNDenoiser(hyper_parameters['original_input_dim'])
rnn_enc.load_state_dict(torch.load(
output_states_path['rnn_enc'])).to(device)
rnn_dec.load_state_dict(torch.load(
output_states_path['rnn_dec'])).to(device)
fnn.load_state_dict(torch.load(output_states_path['fnn'])).to(device)
denoiser.load_state_dict(torch.load(
output_states_path['denoiser'])).to(device)
print('done.')
testing_it = data_feeder_testing(
window_size=hyper_parameters['window_size'],
fft_size=hyper_parameters['fft_size'],
hop_size=hyper_parameters['hop_size'],
seq_length=hyper_parameters['seq_length'],
context_length=hyper_parameters['context_length'],
batch_size=1,
debug=debug)
print('-- Testing starts\n')
sdr = []
sir = []
total_time = 0
for index, data in enumerate(testing_it()):
s_time = time.time()
mix, mix_magnitude, mix_phase, voice_true, bg_true = data
voice_predicted = np.zeros(
(mix_magnitude.shape[0], hyper_parameters['seq_length'] -
hyper_parameters['context_length'] * 2,
hyper_parameters['window_size']),
dtype=np.float32)
for batch in range(
int(mix_magnitude.shape[0] /
training_constants['batch_size'])):
b_start = batch * training_constants['batch_size']
b_end = (batch + 1) * training_constants['batch_size']
v_in = torch.from_numpy(
mix_magnitude[b_start:b_end, :, :]).to(device)
tmp_voice_predicted = rnn_enc(v_in)
tmp_voice_predicted = rnn_dec(tmp_voice_predicted)
tmp_voice_predicted = fnn(tmp_voice_predicted, v_in)
tmp_voice_predicted = denoiser(tmp_voice_predicted)
voice_predicted[
b_start:b_end, :, :] = tmp_voice_predicted.data.cpu().numpy()
tmp_sdr, tmp_sir = data_process_results_testing(
index=index,
voice_true=voice_true,
bg_true=bg_true,
voice_predicted=voice_predicted,
window_size=hyper_parameters['window_size'],
mix=mix,
mix_magnitude=mix_magnitude,
mix_phase=mix_phase,
hop=hyper_parameters['hop_size'],
context_length=hyper_parameters['context_length'])
e_time = time.time()
print(
testing_output_string_per_example.format(
e=index,
sdr=np.median([i for i in tmp_sdr[0] if not np.isnan(i)]),
sir=np.median([i for i in tmp_sir[0] if not np.isnan(i)]),
t=e_time - s_time))
total_time += e_time - s_time
sdr.append(tmp_sdr)
sir.append(tmp_sir)
print('\n-- Testing finished\n')
print(
testing_output_string_all.format(
sdr=np.median([ii for i in sdr for ii in i[0]
if not np.isnan(ii)]),
sir=np.median([ii for i in sir for ii in i[0]
if not np.isnan(ii)]),
t=total_time))
print('\n-- Saving results... ', end='')
with open(metrics_paths['sdr'], 'wb') as f:
pickle.dump(sdr, f, protocol=2)
with open(metrics_paths['sir'], 'wb') as f:
pickle.dump(sir, f, protocol=2)
print('done!')
print('-- That\'s all folks!')
def testing_process():
"""The testing process.
"""
# Check what device we'll be using
device = 'cuda' if not debug and cuda.is_available() else 'cpu'
# Inform about the device and time and date
printing.print_intro_messages(device)
printing.print_msg('Starting training process. '
'Debug mode: {}'.format(debug))
# Set up MaD TwinNet
with printing.InformAboutProcess('Setting up MaD TwinNet'):
mad = MaD(rnn_enc_input_dim=hyper_parameters['reduced_dim'],
rnn_dec_input_dim=hyper_parameters['rnn_enc_output_dim'],
original_input_dim=hyper_parameters['original_input_dim'],
context_length=hyper_parameters['context_length'])
with printing.InformAboutProcess('Loading states'):
mad.load_state_dict(load(output_states_path['mad']))
mad = mad.to(device).eval()
with printing.InformAboutProcess('Initializing data feeder'):
testing_it = data_feeder.data_feeder_testing(
window_size=hyper_parameters['window_size'],
fft_size=hyper_parameters['fft_size'],
hop_size=hyper_parameters['hop_size'],
seq_length=hyper_parameters['seq_length'],
context_length=hyper_parameters['context_length'],
batch_size=1,
debug=debug)
p_testing = partial(_testing_process,
mad=mad,
device=device,
seq_length=hyper_parameters['seq_length'],
context_length=hyper_parameters['context_length'],
window_size=hyper_parameters['window_size'],
batch_size=training_constants['batch_size'],
hop_size=hyper_parameters['hop_size'])
printing.print_msg('Testing starts', end='\n\n')
sdr, sir, total_time = [
e for e in zip(*[
i for index, data in enumerate(testing_it())
for i in [p_testing(data, index)]
])
]
total_time = sum(total_time)
printing.print_msg('Testing finished', start='\n-- ', end='\n\n')
printing.print_msg(testing_output_string_all.format(
sdr=np.median([ii for i in sdr for ii in i[0] if not np.isnan(ii)]),
sir=np.median([ii for i in sir for ii in i[0] if not np.isnan(ii)]),
t=total_time),
end='\n\n')
with printing.InformAboutProcess('Saving results... '):
with open(metrics_paths['sdr'], 'wb') as f:
pickle.dump(sdr, f, protocol=2)
with open(metrics_paths['sir'], 'wb') as f:
pickle.dump(sir, f, protocol=2)
printing.print_msg('That\'s all folks!')
def testing_process():
"""The testing process.
"""
# Check what device we'll be using
device = 'cuda' if not debug and cuda.is_available() else 'cpu'
torch.backends.cudnn.benchmark = True
# Inform about the device and time and date
printing.print_intro_messages(device)
printing.print_msg('Starting training process. '
'Debug mode: {}'.format(debug))
latent_n =args.layers
lats = args.layers
do = args.do
channels = args.channels
print("Using " + str(lats) + " latent layers between encoder and decoder.", flush=True)
print("Using " + str(args.channels) + " cnn channels.", flush=True)
print("Using " + str(args.do/100) + " dropout.", flush=True)
print(("Using residual connections" if args.residual else "Not using residual connections"))
outputPath = str(lats)+str(args.channels)+str(args.do)+("res" if args.residual else "")+ _dataset_parent_dir[7:]
print("Output path: " + outputPath)
pt_path = ("./outputs/states/" + str(lats) + str(args.channels)+str(int(args.do))+("res" if args.residual else "")+".pt"+ _dataset_parent_dir[7:])
pt_path = "./outputs/states/mad.pt" + str(lats) + "latents" + str(args.channels) +"features"+str(args.do/100) + \
"dropout" + ("res" if args.residual else "") + str(90) + "epochs"
# print("USING " + str(latent_n) + " conv layers between enc/dec")
print("Weights path: " + pt_path, flush=True)
# Set up MaD TwinNet
with printing.InformAboutProcess('Setting up MaD TwinNet'):
mad = MaDConv(
cnn_channels=args.channels,
inner_kernel_size=1,
inner_padding=0,
cnn_dropout=do/100,
original_input_dim=hyper_parameters['original_input_dim'],
context_length=hyper_parameters['context_length'],
latent_n=latent_n,
residual=args.residual
)
with printing.InformAboutProcess('Loading states'):
mad.load_state_dict(load(pt_path))
mad = mad.to(device).eval()
with printing.InformAboutProcess('Initializing data feeder'):
testing_it = data_feeder.data_feeder_testing(
window_size=hyper_parameters['window_size'],
fft_size=hyper_parameters['fft_size'],
hop_size=hyper_parameters['hop_size'],
seq_length=hyper_parameters['seq_length'],
context_length=hyper_parameters['context_length'],
batch_size=1, debug=debug)
p_testing = partial(
_testing_process, mad=mad, device=device,
seq_length=hyper_parameters['seq_length'],
context_length=hyper_parameters['context_length'],
window_size=hyper_parameters['window_size'],
batch_size=4,#training_constants['batch_size'],
hop_size=hyper_parameters['hop_size'], outputPath=outputPath)
printing.print_msg('Testing starts', end='\n\n')
sdr, sir, sar, total_time = [e for e in zip(*[
i for index, data in enumerate(testing_it())
for i in [p_testing(data, index)]])]
total_time = sum(total_time)
printing.print_msg('Testing finished', start='\n-- ', end='\n\n')
printing.print_msg(testing_output_string_all.format(
sdr=np.median([ii for i in sdr for ii in i[0] if not np.isnan(ii)]),
sir=np.median([ii for i in sir for ii in i[0] if not np.isnan(ii)]),
sar=np.median([ii for i in sar for ii in i[0] if not np.isnan(ii)]),
t=total_time), end='\n\n')
with printing.InformAboutProcess('Saving results... '):
with open(metrics_paths['sdr'], 'wb') as f:
pickle.dump(sdr, f, protocol=2)
with open(metrics_paths['sir'], 'wb') as f:
pickle.dump(sir, f, protocol=2)
with open(metrics_paths['sar'], 'wb') as f:
pickle.dump(sar, f, protocol=2)
print("Using " + str(lats) + " latent layers between encoder and decoder.", flush=True)
print("Using " + str(args.channels) + " cnn channels.", flush=True)
print("Using " + str(args.do / 100) + " dropout.", flush=True)
printing.print_msg('That\'s all folks!')
def use_me_process(sources_list, output_file_names):
"""The usage process.
:param sources_list: The file names to be used.
:type sources_list: list[pathlib.Path]
:param output_file_names: The output file names to be used.
:type output_file_names: list[list[str]]
"""
printing.print_msg('Welcome to MaD TwinNet.', end='\n\n')
if debug:
printing.print_msg('Cannot proceed in debug mode. '
'Please set `debug=False` at the settings '
'file.')
printing.print_msg('Exiting.')
exit(-1)
printing.print_msg('Now I will extract the voice and the '
'background music from the provided files')
device = 'cuda' if not debug and torch.cuda.is_available() else 'cpu'
# MaD setting up
mad = MaD(rnn_enc_input_dim=hyper_parameters['reduced_dim'],
rnn_dec_input_dim=hyper_parameters['rnn_enc_output_dim'],
original_input_dim=hyper_parameters['original_input_dim'],
context_length=hyper_parameters['context_length'])
mad.load_state_dict(torch.load(output_states_path['mad']))
mad = mad.to(device).eval()
testing_it = data_feeder.data_feeder_testing(
window_size=hyper_parameters['window_size'],
fft_size=hyper_parameters['fft_size'],
hop_size=hyper_parameters['hop_size'],
seq_length=hyper_parameters['seq_length'],
context_length=hyper_parameters['context_length'],
batch_size=1,
debug=debug,
sources_list=sources_list)
printing.print_msg('Let\'s go!', end='\n\n')
total_time = 0
for index, data in enumerate(testing_it()):
s_time = time.time()
mix, mix_magnitude, mix_phase, voice_true, bg_true = data
voice_predicted = np.zeros(
(mix_magnitude.shape[0], hyper_parameters['seq_length'] -
hyper_parameters['context_length'] * 2,
hyper_parameters['window_size']),
dtype=np.float32)
for batch in range(
int(mix_magnitude.shape[0] /
training_constants['batch_size'])):
b_start = batch * training_constants['batch_size']
b_end = (batch + 1) * training_constants['batch_size']
v_in = torch.from_numpy(
mix_magnitude[b_start:b_end, :, :]).to(device)
voice_predicted[b_start:b_end, :, :] = mad(
v_in).v_j_filt.cpu().numpy()
data_feeder.data_process_results_testing(
index=index,
voice_true=voice_true,
bg_true=bg_true,
voice_predicted=voice_predicted,
window_size=hyper_parameters['window_size'],
mix=mix,
mix_magnitude=mix_magnitude,
mix_phase=mix_phase,
hop=hyper_parameters['hop_size'],
context_length=hyper_parameters['context_length'],
output_file_name=output_file_names[index])
e_time = time.time()
printing.print_msg(
usage_output_string_per_example.format(f=sources_list[index],
t=e_time - s_time))
total_time += e_time - s_time
printing.print_msg('MaDTwinNet finished')
printing.print_msg(usage_output_string_total.format(t=total_time))
printing.print_msg('That\'s all folks!')