def sample_stats(self,
stats_path='data',
sample_size=1000,
train_s_list=None,
train_d_list=None):
"""
Computes statistics for each frequency component of the instantaneous a priori SNR
in dB over a sample of the training set. The statistics are then used to map the
instantaneous a priori SNR in dB between 0 and 1 using its cumulative distribution
function. This forms the mapped a priori SNR (the training target).
Argument/s:
stats_path - path to the saved statistics.
sample_size - number of training examples to compute the statistics from.
train_s_list - train clean speech list.
train_d_list - train noise list.
"""
if os.path.exists(stats_path + '/stats.npz'):
print('Loading sample statistics...')
with np.load(stats_path + '/stats.npz') as stats:
self.mu = stats['mu_hat']
self.sigma = stats['sigma_hat']
elif train_s_list == None:
raise ValueError(
'No stats.npz file exists. data/stats.p is available here: https://github.com/anicolson/DeepXi/blob/master/data/stats.npz.'
)
else:
print('Finding sample statistics...')
s_sample_list = random.sample(self.train_s_list, sample_size)
d_sample_list = random.sample(self.train_d_list, sample_size)
s_sample, d_sample, s_sample_len, d_sample_len, snr_sample = self.wav_batch(
s_sample_list, d_sample_list)
snr_sample = np.array(
random.choices(self.snr_levels, k=sample_size))
# snr_sample = np.random.randint(self.min_snr, self.max_snr + 1, sample_size)
samples = []
for i in tqdm(range(s_sample.shape[0])):
s_STMS, d_STMS, _, _ = self.mix(s_sample[i:i + 1],
d_sample[i:i + 1],
s_sample_len[i:i + 1],
d_sample_len[i:i + 1],
snr_sample[i:i + 1])
xi_db = self.xi_db(s_STMS,
d_STMS) # instantaneous a priori SNR (dB).
samples.append(np.squeeze(xi_db.numpy()))
samples = np.vstack(samples)
if len(samples.shape) != 2:
raise ValueError('Incorrect shape for sample.')
stats = {
'mu_hat': np.mean(samples, axis=0),
'sigma_hat': np.std(samples, axis=0)
}
self.mu, self.sigma = stats['mu_hat'], stats['sigma_hat']
if not os.path.exists(stats_path): os.makedirs(stats_path)
np.savez(stats_path + '/stats.npz',
mu_hat=stats['mu_hat'],
sigma_hat=stats['sigma_hat'])
save_mat(stats_path + '/stats.mat', stats, 'stats')
print('Sample statistics saved.')
def sample(
self,
sample_size,
sample_dir='data',
):
"""
Gathers a sample of the training set. The sample can be used to compute
statistics for mapping functions.
Argument/s:
sample_size - number of training examples included in the sample.
sample_dir - path to the saved sample.
"""
sample_path = sample_dir + '/sample'
if os.path.exists(sample_path + '.npz'):
print('Loading sample...')
with np.load(sample_path + '.npz') as sample:
s_sample = sample['s_sample']
d_sample = sample['d_sample']
x_sample = sample['x_sample']
wav_len = sample['wav_len']
elif self.train_s_list == None:
raise ValueError('No sample.npz file exists.')
else:
if sample_size == None: raise ValueError("sample_size is not set.")
print('Gathering a sample of the training set...')
s_sample_list = random.sample(self.train_s_list, sample_size)
d_sample_list = random.sample(self.train_d_list, sample_size)
s_sample_int, d_sample_int, s_sample_len, d_sample_len, snr_sample = self.wav_batch(
s_sample_list, d_sample_list)
s_sample = np.zeros_like(s_sample_int, np.float32)
d_sample = np.zeros_like(s_sample_int, np.float32)
x_sample = np.zeros_like(s_sample_int, np.float32)
for i in tqdm(range(s_sample.shape[0])):
s, d, x, _ = self.inp_tgt.mix(s_sample_int[i:i + 1],
d_sample_int[i:i + 1],
s_sample_len[i:i + 1],
d_sample_len[i:i + 1],
snr_sample[i:i + 1])
s_sample[i, 0:s_sample_len[i]] = s
d_sample[i, 0:s_sample_len[i]] = d
x_sample[i, 0:s_sample_len[i]] = x
wav_len = s_sample_len
if not os.path.exists(sample_dir): os.makedirs(sample_dir)
np.savez(sample_path + '.npz',
s_sample=s_sample,
d_sample=d_sample,
x_sample=x_sample,
wav_len=wav_len)
sample = {
's_sample': s_sample,
'd_sample': d_sample,
'x_sample': x_sample,
'wav_len': wav_len
}
save_mat(sample_path + '.mat', sample, 'stats')
print('Sample of the training set saved.')
return s_sample, d_sample, x_sample, wav_len
def sample_old(
self,
sample_size,
sample_dir='data',
):
"""
Gathers a sample of the training set. The sample can be used to compute
statistics for mapping functions.
Argument/s:
sample_size - number of training examples included in the sample.
sample_dir - path to the saved sample.
"""
sample_path = sample_dir + '/sample'
if os.path.exists(sample_path + '.npz'):
print('Loading sample...')
with np.load(sample_path + '.npz') as sample:
samples_s_STMS = sample['samples_s_STMS']
samples_d_STMS = sample['samples_d_STMS']
samples_x_STMS = sample['samples_x_STMS']
elif self.train_s_list == None:
raise ValueError('No sample.npz file exists. data/sample.npz is available here: https://github.com/anicolson/DeepXi/blob/master/data/sample.npz.')
else:
if sample_size == None: raise ValueError("sample_size is not set.")
print('Gathering a sample of the training set...')
s_sample_list = random.sample(self.train_s_list, sample_size)
d_sample_list = random.sample(self.train_d_list, sample_size)
s_sample, d_sample, s_sample_len, d_sample_len, snr_sample = self.wav_batch(s_sample_list, d_sample_list)
snr_sample = np.array(random.choices(self.snr_levels, k=sample_size))
samples_s_STMS = []
samples_d_STMS = []
samples_x_STMS = []
for i in tqdm(range(s_sample.shape[0])):
s, d, x, _ = self.inp_tgt.mix(s_sample[i:i+1], d_sample[i:i+1], s_sample_len[i:i+1],
d_sample_len[i:i+1], snr_sample[i:i+1])
s_STMS, _ = self.inp_tgt.polar_analysis(s)
d_STMS, _ = self.inp_tgt.polar_analysis(d)
x_STMS, _ = self.inp_tgt.polar_analysis(x)
samples_s_STMS.append(np.squeeze(s_STMS.numpy()))
samples_d_STMS.append(np.squeeze(d_STMS.numpy()))
samples_x_STMS.append(np.squeeze(x_STMS.numpy()))
samples_s_STMS = np.vstack(samples_s_STMS)
samples_d_STMS = np.vstack(samples_d_STMS)
samples_x_STMS = np.vstack(samples_x_STMS)
if len(samples_s_STMS.shape) != 2: raise ValueError('Incorrect shape for s_STMS sample.')
if len(samples_d_STMS.shape) != 2: raise ValueError('Incorrect shape for d_STMS sample.')
if len(samples_x_STMS.shape) != 2: raise ValueError('Incorrect shape for x_STMS sample.')
if not os.path.exists(sample_dir): os.makedirs(sample_dir)
np.savez(sample_path + '.npz', samples_s_STMS=samples_s_STMS,
samples_d_STMS=samples_d_STMS, samples_x_STMS=samples_x_STMS)
sample = {'samples_s_STMS': samples_s_STMS,
'samples_d_STMS': samples_d_STMS,
'samples_x_STMS': samples_x_STMS}
save_mat(sample_path + '.mat', sample, 'stats')
print('Sample of the training set saved.')
return samples_s_STMS, samples_d_STMS, samples_x_STMS
def train(
self,
train_s_list,
train_d_list,
model_path='model',
val_s=None,
val_d=None,
val_s_len=None,
val_d_len=None,
val_snr=None,
val_flag=True,
val_save_path=None,
mbatch_size=8,
max_epochs=200,
resume_epoch=0,
stats_path=None,
sample_size=None,
eval_example=False,
save_model=True,
log_iter=False,
):
"""
Deep Xi training.
Argument/s:
train_s_list - clean-speech training list.
train_d_list - noise training list.
model_path - model save path.
val_s - clean-speech validation batch.
val_d - noise validation batch.
val_s_len - clean-speech validation sequence length batch.
val_d_len - noise validation sequence length batch.
val_snr - SNR validation batch.
val_flag - perform validation.
val_save_path - validation batch save path.
mbatch_size - mini-batch size.
max_epochs - maximum number of epochs.
resume_epoch - epoch to resume training from.
stats_path - path to save sample statistics.
sample_size - sample size.
eval_example - evaluate a mini-batch of training examples.
save_model - save architecture, weights, and training configuration.
log_iter - log training loss for each training iteration.
"""
self.train_s_list = train_s_list
self.train_d_list = train_d_list
self.mbatch_size = mbatch_size
self.n_examples = len(self.train_s_list)
self.n_iter = math.ceil(self.n_examples / mbatch_size)
self.sample_stats(stats_path, sample_size, train_s_list, train_d_list)
train_dataset = self.dataset(max_epochs - resume_epoch)
if val_flag:
val_set = self.val_batch(val_save_path, val_s, val_d, val_s_len,
val_d_len, val_snr)
val_steps = len(val_set[0])
else:
val_set, val_steps = None, None
if eval_example:
print("Saving a mini-batch of training examples in .mat files...")
x_STMS_batch, xi_bar_batch, seq_mask_batch = list(
train_dataset.take(1).as_numpy_iterator())[0]
save_mat('./x_STMS_batch.mat', x_STMS_batch, 'x_STMS_batch')
save_mat('./xi_bar_batch.mat', xi_bar_batch, 'xi_bar_batch')
save_mat('./seq_mask_batch.mat', seq_mask_batch, 'seq_mask_batch')
print("Testing if add_noise() works correctly...")
s, d, s_len, d_len, snr_tgt = self.wav_batch(
train_s_list[0:mbatch_size], train_d_list[0:mbatch_size])
(_, s, d) = self.add_noise_batch(self.normalise(s),
self.normalise(d), s_len, d_len,
snr_tgt)
for (i, _) in enumerate(s):
snr_act = self.snr_db(s[i][0:s_len[i]], d[i][0:d_len[i]])
print('SNR target|actual: {:.2f}|{:.2f} (dB).'.format(
snr_tgt[i], snr_act))
if not os.path.exists(model_path): os.makedirs(model_path)
if not os.path.exists("log"): os.makedirs("log")
if not os.path.exists("log/iter"): os.makedirs("log/iter")
callbacks = []
callbacks.append(
CSVLogger("log/" + self.ver + ".csv", separator=',', append=True))
if save_model: callbacks.append(SaveWeights(model_path))
# if log_iter: callbacks.append(CSVLoggerIter("log/iter/" + self.ver + ".csv", separator=',', append=True))
if resume_epoch > 0:
self.model.load_weights(model_path + "/epoch-" +
str(resume_epoch - 1) +
"/variables/variables")
self.model.compile(sample_weight_mode="temporal",
loss="binary_crossentropy",
optimizer=Adam(lr=0.001, clipvalue=1.0))
self.model.fit(x=train_dataset,
initial_epoch=resume_epoch,
epochs=max_epochs,
steps_per_epoch=self.n_iter,
callbacks=callbacks,
validation_data=val_set,
validation_steps=val_steps)
def infer( ## NEED TO ADD DeepMMSE
self,
test_x,
test_x_len,
test_x_base_names,
test_epoch,
model_path='model',
out_type='y',
gain='mmse-lsa',
out_path='out',
stats_path=None,
n_filters=40,
):
"""
Deep Xi inference. The specified 'out_type' is saved.
Argument/s:
test_x - noisy-speech test batch.
test_x_len - noisy-speech test batch lengths.
test_x_base_names - noisy-speech base names.
test_epoch - epoch to test.
model_path - path to model directory.
out_type - output type (see deepxi/args.py).
gain - gain function (see deepxi/args.py).
out_path - path to save output files.
stats_path - path to the saved statistics.
"""
if out_type == 'xi_hat': out_path = out_path + '/xi_hat'
elif out_type == 'y': out_path = out_path + '/y/' + gain
elif out_type == 'deepmmse': out_path = out_path + '/deepmmse'
elif out_type == 'ibm_hat': out_path = out_path + '/ibm_hat'
elif out_type == 'subband_ibm_hat':
out_path = out_path + '/subband_ibm_hat'
else:
raise ValueError('Invalid output type.')
if not os.path.exists(out_path): os.makedirs(out_path)
if test_epoch < 1:
raise ValueError("test_epoch must be greater than 0.")
# The mel-scale filter bank is to compute an ideal binary mask (IBM)
# estimate for log-spectral subband energies (LSSE).
if out_type == 'subband_ibm_hat':
mel_filter_bank = self.mel_filter_bank(n_filters)
self.sample_stats(stats_path)
self.model.load_weights(model_path + '/epoch-' + str(test_epoch - 1) +
'/variables/variables')
print("Processing observations...")
x_STMS_batch, x_STPS_batch, n_frames = self.observation_batch(
test_x, test_x_len)
print("Performing inference...")
xi_bar_hat_batch = self.model.predict(x_STMS_batch,
batch_size=1,
verbose=1)
print("Performing synthesis...")
batch_size = len(test_x_len)
for i in tqdm(range(batch_size)):
base_name = test_x_base_names[i]
x_STMS = x_STMS_batch[i, :n_frames[i], :]
x_STPS = x_STPS_batch[i, :n_frames[i], :]
xi_bar_hat = xi_bar_hat_batch[i, :n_frames[i], :]
xi_hat = self.xi_hat(xi_bar_hat)
if out_type == 'xi_hat':
save_mat(args.out_path + '/' + base_name + '.mat', xi_hat,
'xi_hat')
elif out_type == 'y':
y_STMS = np.multiply(x_STMS,
gfunc(xi_hat, xi_hat + 1, gtype=gain))
y = self.polar_synthesis(y_STMS, x_STPS).numpy()
save_wav(out_path + '/' + base_name + '.wav', y, self.f_s)
elif out_type == 'deepmmse':
d_PSD_hat = np.multiply(
np.square(x_STMS),
gfunc(xi_hat, xi_hat + 1, gtype='deepmmse'))
save_mat(out_path + '/' + base_name + '.mat', d_PSD_hat,
'd_psd_hat')
elif out_type == 'ibm_hat':
ibm_hat = np.greater(xi_hat, 1.0).astype(bool)
save_mat(out_path + '/' + base_name + '.mat', ibm_hat,
'ibm_hat')
elif out_type == 'subband_ibm_hat':
xi_hat_subband = np.matmul(xi_hat, mel_filter_bank.transpose())
subband_ibm_hat = np.greater(xi_hat_subband, 1.0).astype(bool)
save_mat(out_path + '/' + base_name + '.mat', subband_ibm_hat,
'subband_ibm_hat')
else:
raise ValueError('Invalid output type.')
def infer(
self,
test_x,
test_x_len,
test_x_base_names,
test_epoch,
model_path='model',
out_type='y',
gain='mmse-lsa',
out_path='out',
n_filters=40,
saved_data_path=None,
):
"""
Deep Xi inference. The specified 'out_type' is saved.
Argument/s:
test_x - noisy-speech test batch.
test_x_len - noisy-speech test batch lengths.
test_x_base_names - noisy-speech base names.
test_epoch - epoch to test.
model_path - path to model directory.
out_type - output type (see deepxi/args.py).
gain - gain function (see deepxi/args.py).
out_path - path to save output files.
saved_data_path - path to saved data necessary for enhancement.
"""
out_path_base = out_path
if not isinstance(test_epoch, list): test_epoch = [test_epoch]
if not isinstance(gain, list): gain = [gain]
# The mel-scale filter bank is to compute an ideal binary mask (IBM)
# estimate for log-spectral subband energies (LSSE).
if out_type == 'subband_ibm_hat':
mel_filter_bank = self.mel_filter_bank(n_filters)
for e in test_epoch:
if e < 1: raise ValueError("test_epoch must be greater than 0.")
for g in gain:
out_path = out_path_base + '/' + self.ver + '/' + 'e' + str(
e) # output path.
if out_type == 'xi_hat': out_path = out_path + '/xi_hat'
elif out_type == 'gamma_hat':
out_path = out_path + '/gamma_hat'
elif out_type == 's_STPS_hat':
out_path = out_path + '/s_STPS_hat'
elif out_type == 'y':
if self.inp_tgt_type == 'MagIRM':
out_path = out_path + '/y'
else:
out_path = out_path + '/y/' + g
elif out_type == 'deepmmse':
out_path = out_path + '/deepmmse'
elif out_type == 'ibm_hat':
out_path = out_path + '/ibm_hat'
elif out_type == 'subband_ibm_hat':
out_path = out_path + '/subband_ibm_hat'
elif out_type == 'cd_hat':
out_path = out_path + '/cd_hat'
else:
raise ValueError('Invalid output type.')
if not os.path.exists(out_path): os.makedirs(out_path)
self.model.load_weights(model_path + '/epoch-' + str(e - 1) +
'/variables/variables')
print("Processing observations...")
inp_batch, supplementary_batch, n_frames = self.observation_batch(
test_x, test_x_len)
print("Performing inference...")
tgt_hat_batch = self.model.predict(inp_batch,
batch_size=1,
verbose=1)
print("Saving outputs...")
batch_size = len(test_x_len)
for i in tqdm(range(batch_size)):
base_name = test_x_base_names[i]
inp = inp_batch[i, :n_frames[i], :]
tgt_hat = tgt_hat_batch[i, :n_frames[i], :]
# if tf.is_tensor(supplementary_batch):
supplementary = supplementary_batch[i, :n_frames[i], :]
if saved_data_path is not None:
saved_data = read_mat(saved_data_path + '/' +
base_name + '.mat')
supplementary = (supplementary, saved_data)
if out_type == 'xi_hat':
xi_hat = self.inp_tgt.xi_hat(tgt_hat)
save_mat(out_path + '/' + base_name + '.mat', xi_hat,
'xi_hat')
elif out_type == 'gamma_hat':
gamma_hat = self.inp_tgt.gamma_hat(tgt_hat)
save_mat(out_path + '/' + base_name + '.mat',
gamma_hat, 'gamma_hat')
elif out_type == 's_STPS_hat':
s_STPS_hat = self.inp_tgt.s_stps_hat(tgt_hat)
save_mat(out_path + '/' + base_name + '.mat',
s_STPS_hat, 's_STPS_hat')
elif out_type == 'y':
y = self.inp_tgt.enhanced_speech(
inp, supplementary, tgt_hat, g).numpy()
save_wav(out_path + '/' + base_name + '.wav', y,
self.inp_tgt.f_s)
elif out_type == 'deepmmse':
xi_hat = self.inp_tgt.xi_hat(tgt_hat)
d_PSD_hat = np.multiply(
np.square(inp),
gfunc(xi_hat, xi_hat + 1.0, gtype='deepmmse'))
save_mat(out_path + '/' + base_name + '.mat',
d_PSD_hat, 'd_psd_hat')
elif out_type == 'ibm_hat':
xi_hat = self.inp_tgt.xi_hat(tgt_hat)
ibm_hat = np.greater(xi_hat, 1.0).astype(bool)
save_mat(out_path + '/' + base_name + '.mat', ibm_hat,
'ibm_hat')
elif out_type == 'subband_ibm_hat':
xi_hat = self.inp_tgt.xi_hat(tgt_hat)
xi_hat_subband = np.matmul(xi_hat,
mel_filter_bank.transpose())
subband_ibm_hat = np.greater(xi_hat_subband,
1.0).astype(bool)
save_mat(out_path + '/' + base_name + '.mat',
subband_ibm_hat, 'subband_ibm_hat')
elif out_type == 'cd_hat':
cd_hat = self.inp_tgt.cd_hat(tgt_hat)
save_mat(out_path + '/' + base_name + '.mat', cd_hat,
'cd_hat')
else:
raise ValueError('Invalid output type.')
def train(
self,
train_s_list,
train_d_list,
mbatch_size,
max_epochs,
loss_fnc,
model_path='model',
val_s=None,
val_d=None,
val_s_len=None,
val_d_len=None,
val_snr=None,
val_flag=True,
val_save_path=None,
resume_epoch=0,
eval_example=False,
save_model=True,
log_iter=False,
):
"""
Deep Xi training.
Argument/s:
train_s_list - clean-speech training list.
train_d_list - noise training list.
model_path - model save path.
val_s - clean-speech validation batch.
val_d - noise validation batch.
val_s_len - clean-speech validation sequence length batch.
val_d_len - noise validation sequence length batch.
val_snr - SNR validation batch.
val_flag - perform validation.
val_save_path - validation batch save path.
mbatch_size - mini-batch size.
max_epochs - maximum number of epochs.
resume_epoch - epoch to resume training from.
eval_example - evaluate a mini-batch of training examples.
save_model - save architecture, weights, and training configuration.
log_iter - log training loss for each training iteration.
loss_fnc - loss function.
"""
self.train_s_list = train_s_list
self.train_d_list = train_d_list
self.mbatch_size = mbatch_size
self.n_examples = len(self.train_s_list)
self.n_iter = math.ceil(self.n_examples / mbatch_size)
train_dataset = self.dataset(max_epochs - resume_epoch)
if val_flag:
val_set = self.val_batch(val_save_path, val_s, val_d, val_s_len,
val_d_len, val_snr)
val_steps = len(val_set[0])
else:
val_set, val_steps = None, None
if not os.path.exists(model_path): os.makedirs(model_path)
if not os.path.exists("log/loss"): os.makedirs("log/loss")
callbacks = []
callbacks.append(
CSVLogger("log/loss/" + self.ver + ".csv",
separator=',',
append=True))
if save_model: callbacks.append(SaveWeights(model_path))
# if log_iter: callbacks.append(CSVLoggerIter("log/iter/" + self.ver + ".csv", separator=',', append=True))
if resume_epoch > 0:
self.model.load_weights(model_path + "/epoch-" +
str(resume_epoch - 1) +
"/variables/variables")
if eval_example:
print("Saving a mini-batch of training examples in .mat files...")
inp_batch, tgt_batch, seq_mask_batch = list(
train_dataset.take(1).as_numpy_iterator())[0]
save_mat('./inp_batch.mat', inp_batch, 'inp_batch')
save_mat('./tgt_batch.mat', tgt_batch, 'tgt_batch')
save_mat('./seq_mask_batch.mat', seq_mask_batch, 'seq_mask_batch')
print("Testing if add_noise() works correctly...")
s, d, s_len, d_len, snr_tgt = self.wav_batch(
self.train_s_list[0:mbatch_size],
self.train_d_list[0:mbatch_size])
(_, s, d) = self.inp_tgt.add_noise_batch(self.inp_tgt.normalise(s),
self.inp_tgt.normalise(d),
s_len, d_len, snr_tgt)
for (i, _) in enumerate(s):
snr_act = self.inp_tgt.snr_db(s[i][0:s_len[i]],
d[i][0:d_len[i]])
print('SNR target|actual: {:.2f}|{:.2f} (dB).'.format(
snr_tgt[i], snr_act))
if self.network_type == "MHANet":
lr_schedular = TransformerSchedular(self.network.d_model,
self.network.warmup_steps)
opt = Adam(learning_rate=lr_schedular,
clipvalue=1.0,
beta_1=0.9,
beta_2=0.98,
epsilon=1e-9)
else:
opt = Adam(learning_rate=0.001, clipvalue=1.0)
if loss_fnc == "BinaryCrossentropy": loss = BinaryCrossentropy()
elif loss_fnc == "MeanSquaredError": loss = MeanSquaredError()
else: raise ValueError("Invalid loss function")
self.model.compile(sample_weight_mode="temporal",
loss=loss,
optimizer=opt)
print("SNR levels used for training:")
print(self.snr_levels)
self.model.fit(x=train_dataset,
initial_epoch=resume_epoch,
epochs=max_epochs,
steps_per_epoch=self.n_iter,
callbacks=callbacks,
validation_data=val_set,
validation_steps=val_steps)
