def feat_extr(s, d, s_len, d_len, Q, Nw, Ns, NFFT, fs, P, nconst, mu, sigma):
'''
Extracts input features and targets from given clean speech and noise.
Inputs:
s - clean waveform (dtype=tf.int32).
d - noisy waveform (dtype=tf.int32).
s_len - clean waveform length without padding (samples).
d_len - noise waveform length without padding (samples).
Q - SNR level.
Nw - window length (samples).
Ns - window shift (samples).
NFFT - DFT components.
fs - sampling frequency (Hz).
P - padded waveform length (samples).
nconst - normalization constant.
mu - mean of a priori SNR in dB.
sigma - standard deviation of a priori SNR in dB.
Outputs:
x_MS - padded noisy single-sided magnitude spectrum.
xi_mapped - mapped a priori SNR.
seq_len - length of each sequence without padding.
'''
(s, x, d) = tf.map_fn(
lambda z: feat.addnoisepad(z[0], z[1], z[2], z[3], z[4], P, nconst),
(s, d, s_len, d_len, Q),
dtype=(tf.float32, tf.float32, tf.float32)) # padded waveforms.
seq_len = feat.nframes(s_len, Ns) # length of each sequence.
s_MS = feat.stms(s, Nw, Ns, NFFT) # clean speech magnitude spectrum.
d_MS = feat.stms(d, Nw, Ns, NFFT) # noise magnitude spectrum.
x_MS = feat.stms(x, Nw, Ns, NFFT) # noisy speech magnitude spectrum.
xi = tf.div(tf.square(tf.maximum(s_MS, 1e-12)),
tf.square(tf.maximum(d_MS, 1e-12))) # a priori SNR.
xi_dB = tf.multiply(10.0, log10(xi)) # a priori SNR dB.
xi_mapped = tf.multiply(
0.5,
tf.add(
1.0,
tf.erf(
tf.div(tf.subtract(xi_dB, mu), tf.multiply(
sigma, tf.sqrt(2.0)))))) # cdf of a priori SNR in dB.
xi_mapped = tf.boolean_mask(xi_mapped,
tf.sequence_mask(seq_len)) # convert to 2D.
return (x_MS, xi_mapped, seq_len)
shape=[None, args.input_dim],
name='G_ph') # gain function placeholder.
## ANALYSIS
x = tf.div(
tf.cast(tf.slice(tf.squeeze(x_ph), [0], [tf.squeeze(x_len_ph)]),
tf.float32), args.nconst) # remove padding and normalise.
x_DFT = feat.stft(
x, args.Nw, args.Ns,
args.NFFT) # noisy speech single-sided short-time Fourier transform.
x_MS_3D = tf.expand_dims(
tf.abs(x_DFT),
0) # noisy speech single-sided magnitude spectrum (in 3D form).
x_MS = tf.abs(x_DFT) # noisy speech single-sided magnitude spectrum.
x_PS = tf.angle(x_DFT) # noisy speech single-sided phase spectrum.
x_seq_len = feat.nframes(x_len_ph, args.Ns) # length of each sequence.
## ENHANCEMENT
if args.gain == 'ibm':
G = tf.cast(tf.greater(xi_hat_ph, 1), tf.float32) # IBM gain function.
if args.gain == 'wf':
G = tf.div(xi_hat_ph, tf.add(xi_hat_ph, 1.0)) # WF gain function.
if args.gain == 'srwf':
G = tf.sqrt(tf.div(xi_hat_ph, tf.add(xi_hat_ph,
1.0))) # SRWF gain function.
if args.gain == 'irm':
G = tf.sqrt(tf.div(xi_hat_ph, tf.add(xi_hat_ph,
1.0))) # IRM gain function.
if args.gain == 'cwf':
G = tf.sqrt(xi_hat_ph)
G = tf.div(G, tf.add(G, 1.0)) # SPP gain function.
def __init__(self, args):
## PLACEHOLDERS
self.s_ph = tf.placeholder(tf.int16, shape=[None, None],
name='s_ph') # clean speech placeholder.
self.d_ph = tf.placeholder(tf.int16, shape=[None, None],
name='d_ph') # noise placeholder.
self.x_ph = tf.placeholder(tf.int16, shape=[None, None],
name='x_ph') # noisy speech placeholder.
self.s_len_ph = tf.placeholder(
tf.int32, shape=[None],
name='s_len_ph') # clean speech sequence length placeholder.
self.d_len_ph = tf.placeholder(
tf.int32, shape=[None],
name='d_len_ph') # noise sequence length placeholder.
self.x_len_ph = tf.placeholder(
tf.int32, shape=[None],
name='x_len_ph') # noisy speech sequence length placeholder.
self.snr_ph = tf.placeholder(tf.float32, shape=[None],
name='snr_ph') # SNR placeholder.
self.x_MS_ph = tf.placeholder(
tf.float32, shape=[None, None, args.input_dim],
name='x_MS_ph') # noisy speech MS placeholder.
self.x_MS_len_ph = tf.placeholder(
tf.int32, shape=[None],
name='x_MS_len_ph') # noisy speech MS sequence length placeholder.
self.target_ph = tf.placeholder(
tf.float32, shape=[None, args.input_dim],
name='target_phh') # training target placeholder.
self.keep_prob_ph = tf.placeholder(
tf.float32, name='keep_prob_ph') # keep probability placeholder.
self.training_ph = tf.placeholder(
tf.bool, name='training_ph') # training placeholder.
## A PRIORI SNR IN DB STATISTICS
self.mu = tf.constant(args.mu_mat['mu'], dtype=tf.float32)
self.sigma = tf.constant(args.sigma_mat['sigma'], dtype=tf.float32)
## FEATURE GRAPH
print('Preparing graph...')
self.P = tf.reduce_max(self.s_len_ph) # padded waveform length.
self.feature = feat.xi_mapped(self.s_ph, self.d_ph, self.s_len_ph,
self.d_len_ph, self.snr_ph, args.Nw,
args.Ns, args.NFFT, args.fs, self.P,
args.nconst, self.mu,
self.sigma) # feature graph.
## RESNET
self.output = residual.Residual(self.x_MS_ph, self.x_MS_len_ph,
self.keep_prob_ph, self.training_ph,
args.num_outputs, args)
## LOSS & OPTIMIZER
self.loss = residual.loss(self.target_ph, self.output,
'sigmoid_cross_entropy')
self.total_loss = tf.reduce_mean(self.loss, axis=0)
self.trainer, _ = residual.optimizer(self.total_loss,
optimizer='adam',
grad_clip=True)
## SAVE VARIABLES
self.saver = tf.train.Saver(max_to_keep=256)
## NUMBER OF PARAMETERS
if args.verbose:
print("No. of trainable parameters: %g." % (np.sum([
np.prod(v.get_shape().as_list())
for v in tf.trainable_variables()
])))
## INFERENCE GRAPH
if args.infer:
## PLACEHOLDERS
self.output_ph = tf.placeholder(
tf.float32, shape=[None, args.input_dim],
name='output_ph') # network output placeholder.
self.x_MS_2D_ph = tf.placeholder(
tf.float32, shape=[None, args.input_dim],
name='x_MS_2D_ph') # noisy speech MS placeholder (in 2D form).
self.x_PS_ph = tf.placeholder(
tf.float32, shape=[None, args.input_dim],
name='x_PS_ph') # noisy speech PS placeholder.
self.xi_hat_ph = tf.placeholder(
tf.float32, shape=[None, args.input_dim],
name='xi_hat_ph') # a priori SNR estimate placeholder.
self.G_ph = tf.placeholder(
tf.float32, shape=[None, args.input_dim],
name='G_ph') # gain function placeholder.
## ANALYSIS
self.x = tf.truediv(
tf.cast(
tf.slice(tf.squeeze(self.x_ph), [0],
[tf.squeeze(self.x_len_ph)]), tf.float32),
args.nconst) # remove padding and normalise.
self.x_DFT = feat.stft(
self.x, args.Nw, args.Ns, args.NFFT
) # noisy speech single-sided short-time Fourier transform.
self.x_MS_3D = tf.expand_dims(
tf.abs(self.x_DFT), 0
) # noisy speech single-sided magnitude spectrum (in 3D form).
self.x_MS = tf.abs(
self.x_DFT) # noisy speech single-sided magnitude spectrum.
self.x_PS = tf.angle(
self.x_DFT) # noisy speech single-sided phase spectrum.
self.x_seq_len = feat.nframes(self.x_len_ph,
args.Ns) # length of each sequence.
## MODIFICATION (SPEECH ENHANCEMENT)
if args.gain == 'ibm':
self.G = tf.cast(tf.greater(self.xi_hat_ph, 1),
tf.float32) # IBM gain function.
if args.gain == 'wf':
self.G = tf.truediv(self.xi_hat_ph,
tf.add(self.xi_hat_ph,
1.0)) # WF gain function.
if args.gain == 'srwf':
self.G = tf.sqrt(
tf.truediv(self.xi_hat_ph,
tf.add(self.xi_hat_ph,
1.0))) # SRWF gain function.
if args.gain == 'irm':
self.G = tf.sqrt(
tf.truediv(self.xi_hat_ph,
tf.add(self.xi_hat_ph,
1.0))) # IRM gain function.
if args.gain == 'cwf':
self.G = tf.sqrt(self.xi_hat_ph)
self.G = tf.truediv(self.G, tf.add(self.G,
1.0)) # cWF gain function.
self.s_hat_MS = tf.multiply(
self.x_MS_2D_ph,
self.G_ph) # enhanced speech single-sided magnitude spectrum.
## SYNTHESIS GRAPH
self.y_DFT = tf.cast(self.s_hat_MS, tf.complex64) * tf.exp(
1j * tf.cast(self.x_PS_ph, tf.complex64)
) # enhanced speech single-sided short-time Fourier transform.
self.y = tf.contrib.signal.inverse_stft(
self.y_DFT, args.Nw, args.Ns, args.NFFT,
tf.contrib.signal.inverse_stft_window_fn(
args.Ns,
forward_window_fn=tf.contrib.signal.hamming_window)
) # synthesis.
