def __model(self, tf_mix, tf_target, tf_lr):
# define model flow
# stft
stft_module = STFT_Module(
frame_length=self.stft_params["frame_length"],
frame_step=self.stft_params["frame_step"],
fft_length=self.stft_params["fft_length"],
epsilon=self.epsilon,
pad_end=self.stft_params["pad_end"])
mr_stft_module = STFT_Module(
frame_length=self.mr_stft_params["frame_length"],
frame_step=self.mr_stft_params["frame_step"],
fft_length=self.mr_stft_params["fft_length"],
epsilon=self.epsilon,
pad_end=self.mr_stft_params["pad_end"])
# mix data transform
tf_spec_mix = stft_module.STFT(tf_mix)
tf_amp_spec_mix = stft_module.to_amp_spec(tf_spec_mix, normalize=False)
tf_mag_spec_mix = tf.log(tf_amp_spec_mix + self.epsilon)
tf_mag_spec_mix = tf.expand_dims(tf_mag_spec_mix,
-1) # (Batch, Time, Freq, Channel))
tf_amp_spec_mix = tf.expand_dims(tf_amp_spec_mix, -1)
tf_f_512_mag_spec_mix = stft_module.to_F_512(tf_mag_spec_mix)
#mr mix data transform
tf_mr_spec_mix = mr_stft_module.STFT(tf_mix)
tf_mr_spec_mix = tf_mr_spec_mix[:, 1:513, :]
tf_mr_amp_spec_mix = stft_module.to_amp_spec(tf_mr_spec_mix,
normalize=False)
tf_mr_mag_spec_mix = tf.log(tf_mr_amp_spec_mix + self.epsilon)
tf_mr_mag_spec_mix = tf.expand_dims(
tf_mr_mag_spec_mix, -1) # (Batch, Time, Freq, Channel))
tf_mr_f_256_mag_spec_mix = tf_mr_mag_spec_mix[:, :, :256]
# target data transform
tf_spec_target = stft_module.STFT(tf_target)
tf_amp_spec_target = stft_module.to_amp_spec(tf_spec_target,
normalize=False)
tf_amp_spec_target = tf.expand_dims(tf_amp_spec_target, -1)
mr_u_net_ver2 = MRUNet_ver2(
input_shape=(tf_f_512_mag_spec_mix.shape[1:]),
mr_input_shape=(tf_mr_f_256_mag_spec_mix.shape[1:]))
tf_est_masks = mr_u_net_ver2(tf_f_512_mag_spec_mix,
tf_mr_f_256_mag_spec_mix)
#F: 512 → 513
zero_pad = tf.zeros_like(tf_mag_spec_mix)
zero_pad = tf.expand_dims(zero_pad[:, :, 1, :], -1)
tf_est_masks = tf.concat([tf_est_masks, zero_pad], 2)
tf_ora_masks = Masks.iaf(tf_amp_spec_mix, tf_amp_spec_target,
self.epsilon)
tf_loss = 10 * Loss.mean_square_error(tf_est_masks, tf_ora_masks)
tf_train_step = Trainer.Adam(tf_loss, tf_lr)
return tf_train_step, tf_loss, tf_amp_spec_target, tf_mag_spec_mix, tf_spec_mix, tf_est_masks, tf_ora_masks
def __model(self, tf_mix, tf_target, tf_lr):
# define model flow
# stft
stft_module = STFT_Module(
frame_length=self.stft_params["frame_length"],
frame_step=self.stft_params["frame_step"],
fft_length=self.stft_params["fft_length"],
epsilon=self.epsilon,
pad_end=self.stft_params["pad_end"])
mr2_stft_module = STFT_Module(
frame_length=self.mr2_stft_params["frame_length"],
frame_step=self.mr2_stft_params["frame_step"],
fft_length=self.mr2_stft_params["fft_length"],
epsilon=self.epsilon,
pad_end=self.mr2_stft_params["pad_end"])
# mix data transform
tf_spec_mix = stft_module.STFT(tf_mix)
tf_amp_spec_mix = stft_module.to_amp_spec(tf_spec_mix, normalize=False)
tf_mag_spec_mix = tf.log(tf_amp_spec_mix + self.epsilon)
tf_mag_spec_mix = tf.expand_dims(tf_mag_spec_mix,
-1) # (Batch, Time, Freq, Channel))
tf_amp_spec_mix = tf.expand_dims(tf_amp_spec_mix, -1)
tf_f_512_mag_spec_mix = stft_module.to_F_512(tf_mag_spec_mix)
#mr2 mix data transform
#zero pad to fit stft time length 128
mr2_zero_pad = tf.zeros_like(tf_mix)
tf_mr2_mix = tf.concat(
[mr2_zero_pad[:, :384], tf_mix, mr2_zero_pad[:, :384]], axis=1)
tf_mr2_spec_mix = mr2_stft_module.STFT(tf_mr2_mix)
tf_mr2_amp_spec_mix = stft_module.to_amp_spec(tf_mr2_spec_mix,
normalize=False)
tf_mr2_mag_spec_mix = tf.log(tf_mr2_amp_spec_mix + self.epsilon)
tf_mr2_mag_spec_mix = tf.expand_dims(tf_mr2_mag_spec_mix, -1)
tf_mr2_mag_spec_mix = tf_mr2_mag_spec_mix[:, :, :1024, :]
# target data transform
tf_spec_target = stft_module.STFT(tf_target)
tf_amp_spec_target = stft_module.to_amp_spec(tf_spec_target,
normalize=False)
tf_amp_spec_target = tf.expand_dims(tf_amp_spec_target, -1)
mini_u_net_ver4 = mini_UNet_ver4(
input_shape=(tf_f_512_mag_spec_mix.shape[1:]),
mr2_input_shape=(tf_mr2_mag_spec_mix.shape[1:]))
tf_est_masks, _, _, _, _, _ = mini_u_net_ver4(tf_f_512_mag_spec_mix,
tf_mr2_mag_spec_mix)
#F: 512 → 513
zero_pad = tf.zeros_like(tf_mag_spec_mix)
zero_pad = tf.expand_dims(zero_pad[:, :, 1, :], -1)
tf_est_masks = tf.concat([tf_est_masks, zero_pad], 2)
tf_est_spec = tf.math.multiply(tf_est_masks, tf_amp_spec_mix)
tf_loss = 10 * Loss.mean_square_error(tf_est_spec, tf_amp_spec_target)
tf_train_step = Trainer.Adam(tf_loss, tf_lr)
return tf_train_step, tf_loss, tf_amp_spec_target, tf_mag_spec_mix, tf_spec_mix, tf_est_masks, tf_est_spec
def __model(self, tf_mix):
# define model flow
# stft
stft_module = STFT_Module(
frame_length = self.stft_params["frame_length"],
frame_step= self.stft_params["frame_step"],
fft_length = self.stft_params["fft_length"],
epsilon = self.epsilon,
pad_end = self.stft_params["pad_end"]
)
mr_stft_module = STFT_Module(
frame_length = self.mr_stft_params["frame_length"],
frame_step= self.mr_stft_params["frame_step"],
fft_length = self.mr_stft_params["fft_length"],
epsilon = self.epsilon,
pad_end = self.stft_params["pad_end"]
)
# mix data transform
tf_spec_mix = stft_module.STFT(tf_mix)
tf_phase_mix = tf.sign(tf_spec_mix)
tf_phase_mix = self.expand_channel(tf_phase_mix)
tf_amp_spec_mix = stft_module.to_amp_spec(tf_spec_mix, normalize =False)
tf_mag_spec_mix = tf.log(tf_amp_spec_mix + self.epsilon)
tf_mag_spec_mix = tf.expand_dims(tf_mag_spec_mix, -1)# (Batch, Time, Freq, Channel))
tf_amp_spec_mix = tf.expand_dims(tf_amp_spec_mix, -1)
tf_f_512_mag_spec_mix = stft_module.to_F_512(tf_mag_spec_mix)
#mr mix data transform
tf_mr_spec_mix = mr_stft_module.STFT(tf_mix)
tf_mr_spec_mix = tf_mr_spec_mix[:, 1:513, :]
tf_mr_amp_spec_mix = stft_module.to_amp_spec(tf_mr_spec_mix, normalize =False)
tf_mr_mag_spec_mix = tf.log(tf_mr_amp_spec_mix + self.epsilon)
tf_mr_mag_spec_mix = tf.expand_dims(tf_mr_mag_spec_mix, -1)# (Batch, Time, Freq, Channel))
tf_mr_f_256_mag_spec_mix = tf_mr_mag_spec_mix[:, :, :256]
mini_u_net_ver2 = mini_UNet_ver2(
input_shape = (
tf_f_512_mag_spec_mix.shape[1:]
),
mr1_input_shape = (
tf_mr_f_256_mag_spec_mix.shape[1:]
)
)
tf_est_masks,_,_,_,_,_ = mini_u_net_ver2(tf_f_512_mag_spec_mix, tf_mr_f_256_mag_spec_mix)
#F: 512 → 513
zero_pad = tf.zeros_like(tf_mag_spec_mix)
zero_pad = tf.expand_dims(zero_pad[:,:,1,:], -1)
tf_est_masks = tf.concat( [tf_est_masks, zero_pad], 2)
tf_est_spec = tf.math.multiply(tf_est_masks, tf_amp_spec_mix)
tf_est_source_spec = tf.math.multiply(tf.complex(tf_est_spec, 0.), tf_phase_mix)
tf_est_source_spec = tf.squeeze(tf_est_source_spec, axis=-1)
est_source = stft_module.ISTFT(tf_est_source_spec)
return est_source
def __model(self, tf_mix):
# define model flow
# stft
stft_module = STFT_Module(
frame_length=self.stft_params["frame_length"],
frame_step=self.stft_params["frame_step"],
fft_length=self.stft_params["fft_length"],
pad_end=self.stft_params["pad_end"],
epsilon=self.epsilon)
# mix data transform
tf_spec_mix = stft_module.STFT(tf_mix)
print("spec mix", tf_spec_mix.dtype)
tf_spec_mix = stft_module.to_T_256(
tf_spec_mix) # cut time dimension to 256 for u-net architecture
tf_phase_mix = tf.sign(tf_spec_mix)
tf_phase_mix = self.expand_channel(tf_phase_mix)
# tf_mag_spec_mix = stft_module.to_magnitude_spec(tf_spec_mix, normalize=False)
tf_amp_spec_mix = stft_module.to_amp_spec(tf_spec_mix, normalize=False)
tf_mag_spec_mix = tf.log(tf_amp_spec_mix + self.epsilon)
tf_mag_spec_mix = tf.expand_dims(tf_mag_spec_mix,
-1) # (Batch, Time, Freq, Channel))
tf_amp_spec_mix = tf.expand_dims(tf_amp_spec_mix, -1)
tf_f_512_mag_spec_mix = stft_module.to_F_512(tf_mag_spec_mix)
# target data transform
# tf_spec_target = stft_module.STFT(tf_target)
# tf_spec_target = stft_module.to_T_256(tf_spec_target) # cut time dimensiton to 256 for u-net architecture
# tf_amp_spec_target = stft_module.to_amp_spec(tf_spec_target, normalize=False)
# tf_amp_spec_target = tf.expand_dims(tf_amp_spec_target, -1)
conv_ffn = Conv_FFN(
input_shape=(tf_f_512_mag_spec_mix.shape[1:]),
out_dim=512,
h_dim=512,
)
tf_est_masks = conv_ffn(tf_f_512_mag_spec_mix)
#F: 512 → 513
zero_pad = tf.zeros_like(tf_mag_spec_mix)
zero_pad = tf.expand_dims(zero_pad[:, :, 1, :], -1)
tf_est_masks = tf.concat([tf_est_masks, zero_pad], 2)
tf_est_spec = tf.math.multiply(tf_est_masks, tf_amp_spec_mix)
tf_est_source_spec = tf.math.multiply(tf.complex(tf_est_spec, 0.),
tf_phase_mix)
tf_est_source_spec = tf.squeeze(tf_est_source_spec, axis=-1)
est_source = stft_module.ISTFT(tf_est_source_spec)
return est_source
def __model(self, tf_mix, tf_target, tf_lr):
# define model flow
# stft
stft_module = STFT_Module(
frame_length=self.stft_params["frame_length"],
frame_step=self.stft_params["frame_step"],
fft_length=self.stft_params["fft_length"],
epsilon=self.epsilon,
pad_end=self.stft_params["pad_end"])
# mix data transform
tf_spec_mix = stft_module.STFT(tf_mix)
# tf_mag_spec_mix = stft_module.to_magnitude_spec(tf_spec_mix, normalize=False)
tf_amp_spec_mix = stft_module.to_amp_spec(tf_spec_mix, normalize=False)
tf_mag_spec_mix = tf.log(tf_amp_spec_mix + self.epsilon)
tf_mag_spec_mix = tf.expand_dims(tf_mag_spec_mix,
-1) # (Batch, Time, Freq, Channel))
tf_amp_spec_mix = tf.expand_dims(tf_amp_spec_mix, -1)
tf_f_512_mag_spec_mix = stft_module.to_F_512(tf_mag_spec_mix)
# target data transform
tf_spec_target = stft_module.STFT(tf_target)
tf_amp_spec_target = stft_module.to_amp_spec(tf_spec_target,
normalize=False)
tf_amp_spec_target = tf.expand_dims(tf_amp_spec_target, -1)
conv_ffn = Conv_FFN(
input_shape=(tf_f_512_mag_spec_mix.shape[1:]),
out_dim=512,
h_dim=512,
)
tf_est_masks = conv_ffn(tf_f_512_mag_spec_mix)
#F: 512 → 513
zero_pad = tf.zeros_like(tf_mag_spec_mix)
zero_pad = tf.expand_dims(zero_pad[:, :, 1, :], -1)
tf_est_masks = tf.concat([tf_est_masks, zero_pad], 2)
tf_est_spec = tf.math.multiply(tf_est_masks, tf_amp_spec_mix)
tf_loss = 10 * Loss.mean_square_error(tf_est_spec, tf_amp_spec_target)
tf_train_step = Trainer.Adam(tf_loss, tf_lr)
return tf_train_step, tf_loss, tf_amp_spec_target, tf_mag_spec_mix, tf_spec_mix, tf_est_masks, tf_est_spec
def __model(self, tf_mix):
# define model flow
# stft
stft_module = STFT_Module(
frame_length=self.stft_params["frame_length"],
frame_step=self.stft_params["frame_step"],
fft_length=self.stft_params["fft_length"],
epsilon=self.epsilon,
pad_end=self.stft_params["pad_end"])
mr1_stft_module = STFT_Module(
frame_length=self.mr1_stft_params["frame_length"],
frame_step=self.mr1_stft_params["frame_step"],
fft_length=self.mr1_stft_params["fft_length"],
epsilon=self.epsilon,
pad_end=self.mr1_stft_params["pad_end"])
mr2_stft_module = STFT_Module(
frame_length=self.mr2_stft_params["frame_length"],
frame_step=self.mr2_stft_params["frame_step"],
fft_length=self.mr2_stft_params["fft_length"],
epsilon=self.epsilon,
pad_end=self.mr2_stft_params["pad_end"])
# mix data transform
tf_spec_mix = stft_module.STFT(tf_mix)
tf_phase_mix = tf.sign(tf_spec_mix)
# tf_phase_mix = self.expand_channel(tf_phase_mix)
tf_amp_spec_mix = stft_module.to_amp_spec(tf_spec_mix, normalize=False)
tf_mag_spec_mix = tf.log(tf_amp_spec_mix + self.epsilon)
# tf_mag_spec_mix = tf.expand_dims(tf_mag_spec_mix, -1)# (Batch, Time, Freq, Channel))
# tf_amp_spec_mix = tf.expand_dims(tf_amp_spec_mix, -1)
tf_f_512_mag_spec_mix = stft_module.to_F_512(tf_mag_spec_mix)
#mr 1mix data transform
tf_mr1_spec_mix = mr1_stft_module.STFT(tf_mix)
tf_mr1_spec_mix = tf_mr1_spec_mix[:, 1:513, :]
tf_mr1_amp_spec_mix = stft_module.to_amp_spec(tf_mr1_spec_mix,
normalize=False)
tf_mr1_mag_spec_mix = tf.log(tf_mr1_amp_spec_mix + self.epsilon)
# tf_mr1_mag_spec_mix = tf.expand_dims(tf_mr1_mag_spec_mix, -1)# (Batch, Time, Freq, Channel))
tf_mr1_f_256_mag_spec_mix = tf_mr1_mag_spec_mix[:, :, :256]
#mr2 mix data transform
#zero pad to fit stft time length 128
mr2_zero_pad = tf.zeros_like(tf_mix)
tf_mr2_mix = tf.concat(
[mr2_zero_pad[:, :384], tf_mix, mr2_zero_pad[:, :384]], axis=1)
tf_mr2_spec_mix = mr2_stft_module.STFT(tf_mr2_mix)
tf_mr2_amp_spec_mix = stft_module.to_amp_spec(tf_mr2_spec_mix,
normalize=False)
tf_mr2_mag_spec_mix = tf.log(tf_mr2_amp_spec_mix + self.epsilon)
# tf_mr2_mag_spec_mix = tf.expand_dims(tf_mr2_mag_spec_mix, -1)
tf_mr2_mag_spec_mix = tf_mr2_mag_spec_mix[:, :, :1024]
ffn_ver2 = FFN_ver2(
out_dim=512,
h_dim=512,
)
tf_est_masks = ffn_ver2(tf_f_512_mag_spec_mix,
tf_mr1_f_256_mag_spec_mix, tf_mr2_mag_spec_mix)
#F: 512 → 513
zero_pad = tf.zeros_like(tf_mag_spec_mix)
zero_pad = tf.expand_dims(zero_pad[:, :, 1], -1)
tf_est_masks = tf.concat([tf_est_masks, zero_pad], 2)
tf_est_spec = tf.math.multiply(tf_est_masks, tf_amp_spec_mix)
tf_est_source_spec = tf.math.multiply(tf.complex(tf_est_spec, 0.),
tf_phase_mix)
tf_est_source_spec = tf.squeeze(tf_est_source_spec, axis=-1)
est_source = stft_module.ISTFT(tf_est_source_spec)
return est_source
def __model(self, tf_mix, tf_target, tf_lr):
stft_module = STFT_Module(
frame_length=self.stft_params["frame_length"],
frame_step=self.stft_params["frame_step"],
fft_length=self.stft_params["fft_length"],
epsilon=self.epsilon,
pad_end=self.stft_params["pad_end"])
mr1_stft_module = STFT_Module(
frame_length=self.mr1_stft_params["frame_length"],
frame_step=self.mr1_stft_params["frame_step"],
fft_length=self.mr1_stft_params["fft_length"],
epsilon=self.epsilon,
pad_end=self.mr1_stft_params["pad_end"])
mr2_stft_module = STFT_Module(
frame_length=self.mr2_stft_params["frame_length"],
frame_step=self.mr2_stft_params["frame_step"],
fft_length=self.mr2_stft_params["fft_length"],
epsilon=self.epsilon,
pad_end=self.mr2_stft_params["pad_end"])
# mix data transform
tf_spec_mix = stft_module.STFT(tf_mix)
tf_amp_spec_mix = stft_module.to_amp_spec(tf_spec_mix, normalize=False)
tf_mag_spec_mix = tf.log(tf_amp_spec_mix + self.epsilon)
# tf_mag_spec_mix = tf.expand_dims(tf_mag_spec_mix, -1)# (Batch, Time, Freq, Channel))
# tf_amp_spec_mix = tf.expand_dims(tf_amp_spec_mix, -1)
tf_f_512_mag_spec_mix = stft_module.to_F_512(tf_mag_spec_mix)
#mr1 mix data transform
tf_mr1_spec_mix = mr1_stft_module.STFT(tf_mix)
tf_mr1_spec_mix = tf_mr1_spec_mix[:, 1:513, :]
tf_mr1_amp_spec_mix = stft_module.to_amp_spec(tf_mr1_spec_mix,
normalize=False)
tf_mr1_mag_spec_mix = tf.log(tf_mr1_amp_spec_mix + self.epsilon)
# tf_mr1_mag_spec_mix = tf.expand_dims(tf_mr1_mag_spec_mix, -1)# (Batch, Time, Freq, Channel))
tf_mr1_f_256_mag_spec_mix = tf_mr1_mag_spec_mix[:, :, :256]
#mr2 mix data transform
#zero pad to fit stft time length 128
mr2_zero_pad = tf.zeros_like(tf_mix)
tf_mr2_mix = tf.concat(
[mr2_zero_pad[:, :384], tf_mix, mr2_zero_pad[:, :384]], axis=1)
tf_mr2_spec_mix = mr2_stft_module.STFT(tf_mr2_mix)
tf_mr2_amp_spec_mix = stft_module.to_amp_spec(tf_mr2_spec_mix,
normalize=False)
tf_mr2_mag_spec_mix = tf.log(tf_mr2_amp_spec_mix + self.epsilon)
# tf_mr2_mag_spec_mix = tf.expand_dims(tf_mr2_mag_spec_mix, -1)
tf_mr2_mag_spec_mix = tf_mr2_mag_spec_mix[:, :, :1024]
# target data transform
tf_spec_target = stft_module.STFT(tf_target)
tf_amp_spec_target = stft_module.to_amp_spec(tf_spec_target,
normalize=False)
# tf_amp_spec_target = tf.expand_dims(tf_amp_spec_target, -1)
ffn_ver2 = FFN_ver2(
out_dim=512,
h_dim=512,
)
tf_est_masks = ffn_ver2(tf_f_512_mag_spec_mix,
tf_mr1_f_256_mag_spec_mix, tf_mr2_mag_spec_mix)
#F: 512 → 513
zero_pad = tf.zeros_like(tf_mag_spec_mix)
zero_pad = tf.expand_dims(zero_pad[:, :, 1], -1)
tf_est_masks = tf.concat([tf_est_masks, zero_pad], 2)
print("est_mask", tf_est_masks.shape)
print("amp_spec_mix", tf_amp_spec_mix.shape)
tf_est_spec = tf.math.multiply(tf_est_masks, tf_amp_spec_mix)
tf_loss = 10 * Loss.mean_square_error(tf_est_spec, tf_amp_spec_target)
tf_train_step = Trainer.Adam(tf_loss, tf_lr)
return tf_train_step, tf_loss, tf_amp_spec_target, tf_mag_spec_mix, tf_spec_mix, tf_est_masks, tf_est_spec
