def test_gradient_batch(self):
with self.test_session(use_gpu=True) as sess:
signal = array_ops.zeros((2, 10, 10))
frame_hop = 10
reconstruction = reconstruction_ops.overlap_and_add(
signal, frame_hop)
# Multiply the first batch-item's reconstruction by zeros. This will block
# gradient from flowing into the first batch item from the loss. Multiply
# the second batch item by the integers from 0 to 99. Since there is zero
# overlap, the gradient for this batch item will be 0-99 shaped as (10,
# 10).
reconstruction *= array_ops.stack([
array_ops.zeros((100, )),
math_ops.to_float(math_ops.range(100))
])
loss = math_ops.reduce_sum(reconstruction)
# Verify that only the second batch item receives gradient.
gradient = sess.run(gradients_impl.gradients([loss], [signal])[0])
expected_gradient = np.stack([
np.zeros((10, 10)),
np.reshape(np.arange(100).astype(np.float32), (10, 10))
])
self.assertAllEqual(expected_gradient, gradient)
def test_simple(self):
def make_input(frame_length, num_frames=3):
"""Generate a tensor of num_frames frames of frame_length."""
return np.reshape(np.arange(1, num_frames * frame_length + 1),
(-1, frame_length))
# List of (signal, expected_result, frame_hop).
configurations = [
# All hop lengths on a frame length of 2.
(make_input(2), [1, 5, 9, 6], 1),
(make_input(2), [1, 2, 3, 4, 5, 6], 2),
# All hop lengths on a frame length of 3.
(make_input(3), [1, 6, 15, 14, 9], 1),
(make_input(3), [1, 2, 7, 5, 13, 8, 9], 2),
(make_input(3), [1, 2, 3, 4, 5, 6, 7, 8, 9], 3),
# All hop lengths on a frame length of 4.
(make_input(4), [1, 7, 18, 21, 19, 12], 1),
(make_input(4), [1, 2, 8, 10, 16, 18, 11, 12], 2),
(make_input(4), [1, 2, 3, 9, 6, 7, 17, 10, 11, 12], 3),
(make_input(4), [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], 4),
]
with self.test_session(use_gpu=True):
for signal, expected, frame_hop in configurations:
reconstruction = reconstruction_ops.overlap_and_add(
np.array(signal), frame_hop).eval()
expected_output = np.array(expected)
self.assertAllClose(reconstruction, expected_output)
def test_simple(self):
def make_input(frame_length, num_frames=3):
"""Generate a tensor of num_frames frames of frame_length."""
return np.reshape(np.arange(1, num_frames * frame_length + 1),
(-1, frame_length))
# List of (signal, expected_result, frame_hop).
configurations = [
# All hop lengths on a frame length of 2.
(make_input(2), [1, 5, 9, 6], 1),
(make_input(2), [1, 2, 3, 4, 5, 6], 2),
# All hop lengths on a frame length of 3.
(make_input(3), [1, 6, 15, 14, 9], 1),
(make_input(3), [1, 2, 7, 5, 13, 8, 9], 2),
(make_input(3), [1, 2, 3, 4, 5, 6, 7, 8, 9], 3),
# All hop lengths on a frame length of 4.
(make_input(4), [1, 7, 18, 21, 19, 12], 1),
(make_input(4), [1, 2, 8, 10, 16, 18, 11, 12], 2),
(make_input(4), [1, 2, 3, 9, 6, 7, 17, 10, 11, 12], 3),
(make_input(4), [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], 4),
]
with self.session(use_gpu=True):
for signal, expected, frame_hop in configurations:
reconstruction = reconstruction_ops.overlap_and_add(
np.array(signal), frame_hop).eval()
expected_output = np.array(expected)
self.assertAllClose(reconstruction, expected_output)
def test_all_ones(self):
signal = constant_op.constant(np.ones((3, 5)), dtype=dtypes.int64)
reconstruction = reconstruction_ops.overlap_and_add(signal, 2)
with self.test_session(use_gpu=True) as sess:
output = sess.run(reconstruction)
expected_output = np.array([1, 1, 2, 2, 3, 2, 2, 1, 1])
self.assertAllClose(output, expected_output)
def test_gradient_numerical(self):
with self.test_session(use_gpu=True):
shape = (2, 10, 10)
framed_signal = array_ops.zeros(shape)
frame_hop = 10
reconstruction = reconstruction_ops.overlap_and_add(
framed_signal, frame_hop)
error = test.compute_gradient_error(framed_signal, shape,
reconstruction, [2, 100])
self.assertLess(error, 2e-5)
def test_all_ones(self):
signal = constant_op.constant(np.ones((3, 5)), dtype=dtypes.int64)
reconstruction = reconstruction_ops.overlap_and_add(signal, 2)
with self.session(use_gpu=True) as sess:
output = sess.run(reconstruction)
expected_output = np.array([1, 1, 2, 2, 3, 2, 2, 1, 1])
self.assertAllClose(output, expected_output)
def test_powers(self):
signal = constant_op.constant(np.squeeze(self.powers[0, :, :]),
dtype=dtypes.int64)
reconstruction = reconstruction_ops.overlap_and_add(signal, self.frame_hop)
with self.session(use_gpu=True) as sess:
output = sess.run(reconstruction)
string_output = [np.base_repr(x, self.bases[0]) for x in output]
self.assertEqual(string_output, self.expected_string)
def test_gradient_numerical(self):
with self.session(use_gpu=True):
shape = (2, 10, 10)
framed_signal = array_ops.zeros(shape)
frame_hop = 10
reconstruction = reconstruction_ops.overlap_and_add(
framed_signal, frame_hop)
error = test.compute_gradient_error(
framed_signal, shape, reconstruction, [2, 100])
self.assertLess(error, 2e-5)
def test_powers(self):
signal = constant_op.constant(np.squeeze(self.powers[0, :, :]),
dtype=dtypes.int64)
reconstruction = reconstruction_ops.overlap_and_add(
signal, self.frame_hop)
with self.test_session(use_gpu=True) as sess:
output = sess.run(reconstruction)
string_output = [np.base_repr(x, self.bases[0]) for x in output]
self.assertEqual(string_output, self.expected_string)
def inverse_stft(stfts,
frame_length,
frame_step,
fft_length,
window_fn=functools.partial(window_ops.hann_window,
periodic=True),
name=None):
"""Computes the inverse [Short-time Fourier Transform][stft] of `stfts`.
Implemented with GPU-compatible ops and supports gradients.
Args:
stfts: A `complex64` `[..., frames, fft_unique_bins]` `Tensor` of STFT bins
representing a batch of `fft_length`-point STFTs where `fft_unique_bins`
is `fft_length // 2 + 1`
frame_length: An integer scalar `Tensor`. The window length in samples.
frame_step: An integer scalar `Tensor`. The number of samples to step.
fft_length: An integer scalar `Tensor`. The size of the FFT that produced
`stfts`.
window_fn: A callable that takes a window length and a `dtype` keyword
argument and returns a `[window_length]` `Tensor` of samples in the
provided datatype. If set to `None`, no windowing is used.
name: An optional name for the operation.
Returns:
A `[..., samples]` `Tensor` of `float32` signals representing the inverse
STFT for each input STFT in `stfts`.
Raises:
ValueError: If `stfts` is not at least rank 2, `frame_length` is not scalar,
`frame_step` is not scalar, or `fft_length` is not scalar.
[stft]: https://en.wikipedia.org/wiki/Short-time_Fourier_transform
"""
with ops.name_scope(name, 'inverse_stft', [stfts]):
stfts = ops.convert_to_tensor(stfts, name='stfts')
stfts.shape.with_rank_at_least(2)
frame_length = ops.convert_to_tensor(frame_length, name='frame_length')
frame_length.shape.assert_has_rank(0)
frame_step = ops.convert_to_tensor(frame_step, name='frame_step')
frame_step.shape.assert_has_rank(0)
fft_length = ops.convert_to_tensor(fft_length, name='fft_length')
fft_length.shape.assert_has_rank(0)
real_frames = spectral_ops.irfft(stfts,
[fft_length])[..., :frame_length]
# Optionally window and overlap-add the inner 2 dimensions of real_frames
# into a single [samples] dimension.
if window_fn is not None:
window = window_fn(frame_length, dtype=stfts.dtype.real_dtype)
real_frames *= window
return reconstruction_ops.overlap_and_add(real_frames, frame_step)
def test_batch(self):
signal = constant_op.constant(self.powers, dtype=dtypes.int64)
reconstruction = reconstruction_ops.overlap_and_add(signal, self.frame_hop)
with self.session(use_gpu=True) as sess:
output = sess.run(reconstruction)
accumulator = True
for i in range(self.batch_size):
string_output = [np.base_repr(x, self.bases[i]) for x in output[i, :]]
accumulator = accumulator and (string_output == self.expected_string)
self.assertTrue(accumulator)
def inverse_stft(stfts,
frame_length,
frame_step,
fft_length,
window_fn=functools.partial(window_ops.hann_window,
periodic=True),
name=None):
"""Computes the inverse [Short-time Fourier Transform][stft] of `stfts`.
Implemented with GPU-compatible ops and supports gradients.
Args:
stfts: A `complex64` `[..., frames, fft_unique_bins]` `Tensor` of STFT bins
representing a batch of `fft_length`-point STFTs where `fft_unique_bins`
is `fft_length // 2 + 1`
frame_length: An integer scalar `Tensor`. The window length in samples.
frame_step: An integer scalar `Tensor`. The number of samples to step.
fft_length: An integer scalar `Tensor`. The size of the FFT that produced
`stfts`.
window_fn: A callable that takes a window length and a `dtype` keyword
argument and returns a `[window_length]` `Tensor` of samples in the
provided datatype. If set to `None`, no windowing is used.
name: An optional name for the operation.
Returns:
A `[..., samples]` `Tensor` of `float32` signals representing the inverse
STFT for each input STFT in `stfts`.
Raises:
ValueError: If `stfts` is not at least rank 2, `frame_length` is not scalar,
`frame_step` is not scalar, or `fft_length` is not scalar.
[stft]: https://en.wikipedia.org/wiki/Short-time_Fourier_transform
"""
with ops.name_scope(name, 'inverse_stft', [stfts]):
stfts = ops.convert_to_tensor(stfts, name='stfts')
stfts.shape.with_rank_at_least(2)
frame_length = ops.convert_to_tensor(frame_length, name='frame_length')
frame_length.shape.assert_has_rank(0)
frame_step = ops.convert_to_tensor(frame_step, name='frame_step')
frame_step.shape.assert_has_rank(0)
fft_length = ops.convert_to_tensor(fft_length, name='fft_length')
fft_length.shape.assert_has_rank(0)
real_frames = spectral_ops.irfft(stfts, [fft_length])[..., :frame_length]
# Optionally window and overlap-add the inner 2 dimensions of real_frames
# into a single [samples] dimension.
if window_fn is not None:
window = window_fn(frame_length, dtype=stfts.dtype.real_dtype)
real_frames *= window
return reconstruction_ops.overlap_and_add(real_frames, frame_step)
def test_one_element_batch(self):
input_matrix = np.squeeze(self.powers[0, :, :])
input_matrix = input_matrix[np.newaxis, :, :].astype(float)
signal = constant_op.constant(input_matrix, dtype=dtypes.float32)
reconstruction = reconstruction_ops.overlap_and_add(signal, self.frame_hop)
with self.session(use_gpu=True) as sess:
output = sess.run(reconstruction)
string_output = [np.base_repr(int(x), self.bases[0]) for x in
np.squeeze(output)]
self.assertEqual(output.shape, (1, 9))
self.assertEqual(string_output, self.expected_string)
def test_one_element_batch(self):
input_matrix = np.squeeze(self.powers[0, :, :])
input_matrix = input_matrix[np.newaxis, :, :].astype(float)
signal = constant_op.constant(input_matrix, dtype=dtypes.float32)
reconstruction = reconstruction_ops.overlap_and_add(
signal, self.frame_hop)
with self.test_session(use_gpu=True) as sess:
output = sess.run(reconstruction)
string_output = [
np.base_repr(int(x), self.bases[0]) for x in np.squeeze(output)
]
self.assertEqual(output.shape, (1, 9))
self.assertEqual(string_output, self.expected_string)
def test_batch(self):
signal = constant_op.constant(self.powers, dtype=dtypes.int64)
reconstruction = reconstruction_ops.overlap_and_add(
signal, self.frame_hop)
with self.test_session(use_gpu=True) as sess:
output = sess.run(reconstruction)
accumulator = True
for i in range(self.batch_size):
string_output = [
np.base_repr(x, self.bases[i]) for x in output[i, :]
]
accumulator = accumulator and (string_output
== self.expected_string)
self.assertTrue(accumulator)
def test_gradient(self):
configurations = [
((1, 128), 1),
((5, 35), 17),
((10, 128), 128),
((2, 10, 128), 127),
((2, 2, 10, 128), 126),
((2, 2, 2, 10, 128), 125),
]
with self.session(use_gpu=True) as sess:
for shape, frame_hop in configurations:
signal = array_ops.zeros(shape)
reconstruction = reconstruction_ops.overlap_and_add(signal, frame_hop)
loss = math_ops.reduce_sum(reconstruction)
# Increasing any sample in the input frames by one will increase the sum
# of all the samples in the reconstruction by 1, so the gradient should
# be all ones, no matter the shape or hop.
gradient = sess.run(gradients_impl.gradients([loss], [signal])[0])
self.assertTrue((gradient == 1.0).all())
def test_gradient_batch(self):
with self.session(use_gpu=True) as sess:
signal = array_ops.zeros((2, 10, 10))
frame_hop = 10
reconstruction = reconstruction_ops.overlap_and_add(signal, frame_hop)
# Multiply the first batch-item's reconstruction by zeros. This will block
# gradient from flowing into the first batch item from the loss. Multiply
# the second batch item by the integers from 0 to 99. Since there is zero
# overlap, the gradient for this batch item will be 0-99 shaped as (10,
# 10).
reconstruction *= array_ops.stack(
[array_ops.zeros((100,)), math_ops.to_float(math_ops.range(100))])
loss = math_ops.reduce_sum(reconstruction)
# Verify that only the second batch item receives gradient.
gradient = sess.run(gradients_impl.gradients([loss], [signal])[0])
expected_gradient = np.stack([
np.zeros((10, 10)),
np.reshape(np.arange(100).astype(np.float32), (10, 10))])
self.assertAllEqual(expected_gradient, gradient)
def test_gradient(self):
configurations = [
((1, 128), 1),
((5, 35), 17),
((10, 128), 128),
((2, 10, 128), 127),
((2, 2, 10, 128), 126),
((2, 2, 2, 10, 128), 125),
]
for shape, frame_hop in configurations:
with self.test_session(use_gpu=True) as sess:
signal = array_ops.zeros(shape)
reconstruction = reconstruction_ops.overlap_and_add(
signal, frame_hop)
loss = math_ops.reduce_sum(reconstruction)
# Increasing any sample in the input frames by one will increase the sum
# of all the samples in the reconstruction by 1, so the gradient should
# be all ones, no matter the shape or hop.
gradient = sess.run(
gradients_impl.gradients([loss], [signal])[0])
self.assertTrue((gradient == 1.0).all())
def inverse_stft(stfts,
frame_length,
frame_step,
fft_length=None,
window_fn=functools.partial(window_ops.hann_window,
periodic=True),
name=None):
"""Computes the inverse [Short-time Fourier Transform][stft] of `stfts`.
To reconstruct an original waveform, a complimentary window function should
be used in inverse_stft. Such a window function can be constructed with
tf.contrib.signal.inverse_stft_window_fn.
Example:
```python
frame_length = 400
frame_step = 160
waveform = tf.placeholder(dtype=tf.float32, shape=[1000])
stft = tf.contrib.signal.stft(waveform, frame_length, frame_step)
inverse_stft = tf.contrib.signal.inverse_stft(
stft, frame_length, frame_step,
window_fn=tf.contrib.signal.inverse_stft_window_fn(frame_step))
```
if a custom window_fn is used in stft, it must be passed to
inverse_stft_window_fn:
```python
frame_length = 400
frame_step = 160
window_fn = functools.partial(window_ops.hamming_window, periodic=True),
waveform = tf.placeholder(dtype=tf.float32, shape=[1000])
stft = tf.contrib.signal.stft(
waveform, frame_length, frame_step, window_fn=window_fn)
inverse_stft = tf.contrib.signal.inverse_stft(
stft, frame_length, frame_step,
window_fn=tf.contrib.signal.inverse_stft_window_fn(
frame_step, forward_window_fn=window_fn))
```
Implemented with GPU-compatible ops and supports gradients.
Args:
stfts: A `complex64` `[..., frames, fft_unique_bins]` `Tensor` of STFT bins
representing a batch of `fft_length`-point STFTs where `fft_unique_bins`
is `fft_length // 2 + 1`
frame_length: An integer scalar `Tensor`. The window length in samples.
frame_step: An integer scalar `Tensor`. The number of samples to step.
fft_length: An integer scalar `Tensor`. The size of the FFT that produced
`stfts`. If not provided, uses the smallest power of 2 enclosing
`frame_length`.
window_fn: A callable that takes a window length and a `dtype` keyword
argument and returns a `[window_length]` `Tensor` of samples in the
provided datatype. If set to `None`, no windowing is used.
name: An optional name for the operation.
Returns:
A `[..., samples]` `Tensor` of `float32` signals representing the inverse
STFT for each input STFT in `stfts`.
Raises:
ValueError: If `stfts` is not at least rank 2, `frame_length` is not scalar,
`frame_step` is not scalar, or `fft_length` is not scalar.
[stft]: https://en.wikipedia.org/wiki/Short-time_Fourier_transform
"""
with ops.name_scope(name, 'inverse_stft', [stfts]):
stfts = ops.convert_to_tensor(stfts, name='stfts')
stfts.shape.with_rank_at_least(2)
frame_length = ops.convert_to_tensor(frame_length, name='frame_length')
frame_length.shape.assert_has_rank(0)
frame_step = ops.convert_to_tensor(frame_step, name='frame_step')
frame_step.shape.assert_has_rank(0)
if fft_length is None:
fft_length = _enclosing_power_of_two(frame_length)
else:
fft_length = ops.convert_to_tensor(fft_length, name='fft_length')
fft_length.shape.assert_has_rank(0)
real_frames = spectral_ops.irfft(stfts, [fft_length])
# frame_length may be larger or smaller than fft_length, so we pad or
# truncate real_frames to frame_length.
frame_length_static = tensor_util.constant_value(frame_length)
# If we don't know the shape of real_frames's inner dimension, pad and
# truncate to frame_length.
if (frame_length_static is None or
real_frames.shape.ndims is None or
real_frames.shape[-1].value is None):
real_frames = real_frames[..., :frame_length]
real_frames_rank = array_ops.rank(real_frames)
real_frames_shape = array_ops.shape(real_frames)
paddings = array_ops.concat(
[array_ops.zeros([real_frames_rank - 1, 2],
dtype=frame_length.dtype),
[[0, math_ops.maximum(0, frame_length - real_frames_shape[-1])]]], 0)
real_frames = array_ops.pad(real_frames, paddings)
# We know real_frames's last dimension and frame_length statically. If they
# are different, then pad or truncate real_frames to frame_length.
elif real_frames.shape[-1].value > frame_length_static:
real_frames = real_frames[..., :frame_length_static]
elif real_frames.shape[-1].value < frame_length_static:
pad_amount = frame_length_static - real_frames.shape[-1].value
real_frames = array_ops.pad(real_frames,
[[0, 0]] * (real_frames.shape.ndims - 1) +
[[0, pad_amount]])
# The above code pads the inner dimension of real_frames to frame_length,
# but it does so in a way that may not be shape-inference friendly.
# Restore shape information if we are able to.
if frame_length_static is not None and real_frames.shape.ndims is not None:
real_frames.set_shape([None] * (real_frames.shape.ndims - 1) +
[frame_length_static])
# Optionally window and overlap-add the inner 2 dimensions of real_frames
# into a single [samples] dimension.
if window_fn is not None:
window = window_fn(frame_length, dtype=stfts.dtype.real_dtype)
real_frames *= window
return reconstruction_ops.overlap_and_add(real_frames, frame_step)
def inverse_stft(stfts,
frame_length,
frame_step,
fft_length=None,
window_fn=functools.partial(window_ops.hann_window,
periodic=True),
name=None):
"""Computes the inverse [Short-time Fourier Transform][stft] of `stfts`.
To reconstruct an original waveform, a complimentary window function should
be used in inverse_stft. Such a window function can be constructed with
tf.contrib.signal.inverse_stft_window_fn.
Example:
```python
frame_length = 400
frame_step = 160
waveform = tf.placeholder(dtype=tf.float32, shape=[1000])
stft = tf.contrib.signal.stft(waveform, frame_length, frame_step)
inverse_stft = tf.contrib.signal.inverse_stft(
stft, frame_length, frame_step,
window_fn=tf.contrib.signal.inverse_stft_window_fn(frame_step))
```
if a custom window_fn is used in stft, it must be passed to
inverse_stft_window_fn:
```python
frame_length = 400
frame_step = 160
window_fn = functools.partial(window_ops.hamming_window, periodic=True),
waveform = tf.placeholder(dtype=tf.float32, shape=[1000])
stft = tf.contrib.signal.stft(
waveform, frame_length, frame_step, window_fn=window_fn)
inverse_stft = tf.contrib.signal.inverse_stft(
stft, frame_length, frame_step,
window_fn=tf.contrib.signal.inverse_stft_window_fn(
frame_step, forward_window_fn=window_fn))
```
Implemented with GPU-compatible ops and supports gradients.
Args:
stfts: A `complex64` `[..., frames, fft_unique_bins]` `Tensor` of STFT bins
representing a batch of `fft_length`-point STFTs where `fft_unique_bins`
is `fft_length // 2 + 1`
frame_length: An integer scalar `Tensor`. The window length in samples.
frame_step: An integer scalar `Tensor`. The number of samples to step.
fft_length: An integer scalar `Tensor`. The size of the FFT that produced
`stfts`. If not provided, uses the smallest power of 2 enclosing
`frame_length`.
window_fn: A callable that takes a window length and a `dtype` keyword
argument and returns a `[window_length]` `Tensor` of samples in the
provided datatype. If set to `None`, no windowing is used.
name: An optional name for the operation.
Returns:
A `[..., samples]` `Tensor` of `float32` signals representing the inverse
STFT for each input STFT in `stfts`.
Raises:
ValueError: If `stfts` is not at least rank 2, `frame_length` is not scalar,
`frame_step` is not scalar, or `fft_length` is not scalar.
[stft]: https://en.wikipedia.org/wiki/Short-time_Fourier_transform
"""
with ops.name_scope(name, 'inverse_stft', [stfts]):
stfts = ops.convert_to_tensor(stfts, name='stfts')
stfts.shape.with_rank_at_least(2)
frame_length = ops.convert_to_tensor(frame_length, name='frame_length')
frame_length.shape.assert_has_rank(0)
frame_step = ops.convert_to_tensor(frame_step, name='frame_step')
frame_step.shape.assert_has_rank(0)
if fft_length is None:
fft_length = _enclosing_power_of_two(frame_length)
else:
fft_length = ops.convert_to_tensor(fft_length, name='fft_length')
fft_length.shape.assert_has_rank(0)
real_frames = spectral_ops.irfft(stfts, [fft_length])
# frame_length may be larger or smaller than fft_length, so we pad or
# truncate real_frames to frame_length.
frame_length_static = tensor_util.constant_value(frame_length)
# If we don't know the shape of real_frames's inner dimension, pad and
# truncate to frame_length.
if (frame_length_static is None or real_frames.shape.ndims is None
or real_frames.shape[-1].value is None):
real_frames = real_frames[..., :frame_length]
real_frames_rank = array_ops.rank(real_frames)
real_frames_shape = array_ops.shape(real_frames)
paddings = array_ops.concat([
array_ops.zeros([real_frames_rank - 1, 2],
dtype=frame_length.dtype),
[[
0,
math_ops.maximum(0, frame_length - real_frames_shape[-1])
]]
], 0)
real_frames = array_ops.pad(real_frames, paddings)
# We know real_frames's last dimension and frame_length statically. If they
# are different, then pad or truncate real_frames to frame_length.
elif real_frames.shape[-1].value > frame_length_static:
real_frames = real_frames[..., :frame_length_static]
elif real_frames.shape[-1].value < frame_length_static:
pad_amount = frame_length_static - real_frames.shape[-1].value
real_frames = array_ops.pad(
real_frames,
[[0, 0]] * (real_frames.shape.ndims - 1) + [[0, pad_amount]])
# The above code pads the inner dimension of real_frames to frame_length,
# but it does so in a way that may not be shape-inference friendly.
# Restore shape information if we are able to.
if frame_length_static is not None and real_frames.shape.ndims is not None:
real_frames.set_shape([None] * (real_frames.shape.ndims - 1) +
[frame_length_static])
# Optionally window and overlap-add the inner 2 dimensions of real_frames
# into a single [samples] dimension.
if window_fn is not None:
window = window_fn(frame_length, dtype=stfts.dtype.real_dtype)
real_frames *= window
return reconstruction_ops.overlap_and_add(real_frames, frame_step)
def overlap(tup):
framed_signals, frame_step_out = tup
new_wav = reconstruction_ops.overlap_and_add(framed_signals,
frame_step_out)
return tf_get_word(new_wav)