def _pad_time_signal(input_placeholder, frame_size):
if frame_size > self._reference_frame_size:
return tf.concat(
[input_signal, tf.ones(
[get_shape(input_signal)[0], frame_size - self._reference_frame_size,
get_shape(input_signal)[2]]) * 1e-7], axis=1)
else:
return input_placeholder
def _crop_stft_output_to_reference_frame_size_length(
channel_concatenated_stft, crop_size):
return tf.slice(channel_concatenated_stft, [0, 0, 0], [
get_shape(channel_concatenated_stft)[0], crop_size,
get_shape(channel_concatenated_stft)[2]
])
def _apply_stft_to_input(self):
from returnn.tf.util.basic import get_shape
# noinspection PyShadowingNames
def _crop_stft_output_to_reference_frame_size_length(
channel_concatenated_stft, crop_size):
return tf.slice(channel_concatenated_stft, [0, 0, 0], [
get_shape(channel_concatenated_stft)[0], crop_size,
get_shape(channel_concatenated_stft)[2]
])
input_placeholder = self.input_data.get_placeholder_as_batch_major()
channel_wise_stft_res_list = list()
for fft_size, frame_size in zip(self._fft_sizes, self._frame_sizes):
def _get_window(window_length, dtype):
if self._window == "hanning":
try:
# noinspection PyPackageRequirements
from tensorflow.signal import hann_window
except ImportError:
# noinspection PyPackageRequirements,PyUnresolvedReferences
from tensorflow.contrib.signal import hann_window
window = hann_window(window_length, dtype=dtype)
elif self._window == "blackman":
# noinspection PyPackageRequirements
import scipy.signal
window = tf.constant(
scipy.signal.windows.blackman(frame_size),
dtype=tf.float32)
elif self._window == "None" or self._window == "ones":
window = tf.ones((window_length, ), dtype=dtype)
else:
assert False, "Window was not parsed correctly: {}".format(
self._window)
return window
# noinspection PyShadowingNames
def _pad_time_signal(input_placeholder, frame_size):
if frame_size > self._reference_frame_size:
return tf.concat([
input_signal,
tf.ones([
get_shape(input_signal)[0],
frame_size - self._reference_frame_size,
get_shape(input_signal)[2]
]) * 1e-7
],
axis=1)
else:
return input_placeholder
input_signal = _pad_time_signal(input_placeholder, frame_size)
if self._use_rfft:
try:
# noinspection PyPackageRequirements
from tensorflow.signal import stft
except ImportError:
# noinspection PyPackageRequirements,PyUnresolvedReferences
from tensorflow.contrib.signal import stft
channel_wise_stft = stft(signals=tf.transpose(
input_signal, [0, 2, 1]),
frame_length=frame_size,
frame_step=self._frame_shift,
fft_length=fft_size,
window_fn=_get_window,
pad_end=self._pad_last_frame)
channel_wise_stft = tf.transpose(channel_wise_stft,
[0, 2, 1, 3])
batch_dim = tf.shape(channel_wise_stft)[0]
time_dim = tf.shape(channel_wise_stft)[1]
concat_feature_dim = channel_wise_stft.shape[
2] * channel_wise_stft.shape[3]
channel_concatenated_stft = tf.reshape(
channel_wise_stft,
(batch_dim, time_dim, concat_feature_dim))
if channel_wise_stft_res_list:
channel_concatenated_stft = (
_crop_stft_output_to_reference_frame_size_length(
channel_concatenated_stft,
get_shape(channel_wise_stft_res_list[0])[1]))
channel_wise_stft_res_list.append(channel_concatenated_stft)
output_placeholder = tf.concat(channel_wise_stft_res_list, axis=2)
return output_placeholder
def _cropStftOutputToReferenceFrameSizeLength(
channel_concatenated_stft, crop_size):
return tf.slice(channel_concatenated_stft, [0, 0, 0], [
get_shape(channel_concatenated_stft)[0], crop_size,
get_shape(channel_concatenated_stft)[2]
])
def _apply_stft_to_input(self):
from returnn.tf.util.basic import get_shape
def _cropStftOutputToReferenceFrameSizeLength(
channel_concatenated_stft, crop_size):
return tf.slice(channel_concatenated_stft, [0, 0, 0], [
get_shape(channel_concatenated_stft)[0], crop_size,
get_shape(channel_concatenated_stft)[2]
])
input_placeholder = self.input_data.get_placeholder_as_batch_major()
channel_wise_stft_res_list = list()
for fft_size, frame_size in zip(self._fft_sizes, self._frame_sizes):
def _get_window(window_length, dtype):
if self._window == "hanning":
window = tf.contrib.signal.hann_window(window_length,
dtype=dtype)
if self._window == "blackman":
tf_compat.v1.assert_equal(frame_size, window_length)
import scipy.signal
window = tf.constant(scipy.signal.blackman(frame_size),
dtype=tf.float32)
if self._window == "None" or self._window == "ones":
window = tf.ones((window_length, ), dtype=dtype)
return window
def _padTimeSignal(input_placeholder, frame_size):
if frame_size > self._reference_frame_size:
return tf.concat([
input_signal,
tf.ones([
get_shape(input_signal)[0],
frame_size - self._reference_frame_size,
get_shape(input_signal)[2]
]) * 1e-7
],
axis=1)
else:
return input_placeholder
input_signal = _padTimeSignal(input_placeholder, frame_size)
if self._use_rfft:
channel_wise_stft = tf.contrib.signal.stft(
signals=tf.transpose(input_signal, [0, 2, 1]),
frame_length=frame_size,
frame_step=self._frame_shift,
fft_length=fft_size,
window_fn=_get_window,
pad_end=self._pad_last_frame)
channel_wise_stft = tf.transpose(channel_wise_stft,
[0, 2, 1, 3])
batch_dim = tf.shape(channel_wise_stft)[0]
time_dim = tf.shape(channel_wise_stft)[1]
concat_feature_dim = channel_wise_stft.shape[
2] * channel_wise_stft.shape[3]
channel_concatenated_stft = tf.reshape(
channel_wise_stft,
(batch_dim, time_dim, concat_feature_dim))
if channel_wise_stft_res_list:
channel_concatenated_stft = _cropStftOutputToReferenceFrameSizeLength(
channel_concatenated_stft,
get_shape(channel_wise_stft_res_list[0])[1])
channel_wise_stft_res_list.append(channel_concatenated_stft)
output_placeholder = tf.concat(channel_wise_stft_res_list, axis=2)
return output_placeholder