/
spectral_ops.py
453 lines (389 loc) · 19.7 KB
/
spectral_ops.py
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# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Spectral operations (e.g. Short-time Fourier Transform)."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_util
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops.signal import dct_ops
from tensorflow.python.ops.signal import fft_ops
from tensorflow.python.ops.signal import reconstruction_ops
from tensorflow.python.ops.signal import shape_ops
from tensorflow.python.ops.signal import window_ops
from tensorflow.python.util import dispatch
from tensorflow.python.util.tf_export import tf_export
@tf_export('signal.stft')
@dispatch.add_dispatch_support
def stft(signals, frame_length, frame_step, fft_length=None,
window_fn=window_ops.hann_window,
pad_end=False, name=None):
"""Computes the [Short-time Fourier Transform][stft] of `signals`.
Implemented with TPU/GPU-compatible ops and supports gradients.
Args:
signals: A `[..., samples]` `float32`/`float64` `Tensor` of real-valued
signals.
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 to apply.
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.
pad_end: Whether to pad the end of `signals` with zeros when the provided
frame length and step produces a frame that lies partially past its end.
name: An optional name for the operation.
Returns:
A `[..., frames, fft_unique_bins]` `Tensor` of `complex64`/`complex128`
STFT values where `fft_unique_bins` is `fft_length // 2 + 1` (the unique
components of the FFT).
Raises:
ValueError: If `signals` is not at least rank 1, `frame_length` is
not scalar, or `frame_step` is not scalar.
[stft]: https://en.wikipedia.org/wiki/Short-time_Fourier_transform
"""
with ops.name_scope(name, 'stft', [signals, frame_length,
frame_step]):
signals = ops.convert_to_tensor(signals, name='signals')
signals.shape.with_rank_at_least(1)
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')
framed_signals = shape_ops.frame(
signals, frame_length, frame_step, pad_end=pad_end)
# Optionally window the framed signals.
if window_fn is not None:
window = window_fn(frame_length, dtype=framed_signals.dtype)
framed_signals *= window
# fft_ops.rfft produces the (fft_length/2 + 1) unique components of the
# FFT of the real windowed signals in framed_signals.
return fft_ops.rfft(framed_signals, [fft_length])
@tf_export('signal.inverse_stft_window_fn')
@dispatch.add_dispatch_support
def inverse_stft_window_fn(frame_step,
forward_window_fn=window_ops.hann_window,
name=None):
"""Generates a window function that can be used in `inverse_stft`.
Constructs a window that is equal to the forward window with a further
pointwise amplitude correction. `inverse_stft_window_fn` is equivalent to
`forward_window_fn` in the case where it would produce an exact inverse.
See examples in `inverse_stft` documentation for usage.
Args:
frame_step: An integer scalar `Tensor`. The number of samples to step.
forward_window_fn: window_fn used in the forward transform, `stft`.
name: An optional name for the operation.
Returns:
A callable that takes a window length and a `dtype` keyword argument and
returns a `[window_length]` `Tensor` of samples in the provided datatype.
The returned window is suitable for reconstructing original waveform in
inverse_stft.
"""
def inverse_stft_window_fn_inner(frame_length, dtype):
"""Computes a window that can be used in `inverse_stft`.
Args:
frame_length: An integer scalar `Tensor`. The window length in samples.
dtype: Data type of waveform passed to `stft`.
Returns:
A window suitable for reconstructing original waveform in `inverse_stft`.
Raises:
ValueError: If `frame_length` is not scalar, `forward_window_fn` is not a
callable that takes a window length and a `dtype` keyword argument and
returns a `[window_length]` `Tensor` of samples in the provided datatype
`frame_step` is not scalar, or `frame_step` is not scalar.
"""
with ops.name_scope(name, 'inverse_stft_window_fn', [forward_window_fn]):
frame_step_ = ops.convert_to_tensor(frame_step, name='frame_step')
frame_step_.shape.assert_has_rank(0)
frame_length = ops.convert_to_tensor(frame_length, name='frame_length')
frame_length.shape.assert_has_rank(0)
# Use equation 7 from Griffin + Lim.
forward_window = forward_window_fn(frame_length, dtype=dtype)
denom = math_ops.square(forward_window)
overlaps = -(-frame_length // frame_step_) # Ceiling division. # pylint: disable=invalid-unary-operand-type
denom = array_ops.pad(denom, [(0, overlaps * frame_step_ - frame_length)])
denom = array_ops.reshape(denom, [overlaps, frame_step_])
denom = math_ops.reduce_sum(denom, 0, keepdims=True)
denom = array_ops.tile(denom, [overlaps, 1])
denom = array_ops.reshape(denom, [overlaps * frame_step_])
return forward_window / denom[:frame_length]
return inverse_stft_window_fn_inner
@tf_export('signal.inverse_stft')
@dispatch.add_dispatch_support
def inverse_stft(stfts,
frame_length,
frame_step,
fft_length=None,
window_fn=window_ops.hann_window,
name=None):
"""Computes the inverse [Short-time Fourier Transform][stft] of `stfts`.
To reconstruct an original waveform, a complementary window function should
be used with `inverse_stft`. Such a window function can be constructed with
`tf.signal.inverse_stft_window_fn`.
Example:
```python
frame_length = 400
frame_step = 160
waveform = tf.random.normal(dtype=tf.float32, shape=[1000])
stft = tf.signal.stft(waveform, frame_length, frame_step)
inverse_stft = tf.signal.inverse_stft(
stft, frame_length, frame_step,
window_fn=tf.signal.inverse_stft_window_fn(frame_step))
```
If a custom `window_fn` is used with `tf.signal.stft`, it must be passed to
`tf.signal.inverse_stft_window_fn`:
```python
frame_length = 400
frame_step = 160
window_fn = tf.signal.hamming_window
waveform = tf.random.normal(dtype=tf.float32, shape=[1000])
stft = tf.signal.stft(
waveform, frame_length, frame_step, window_fn=window_fn)
inverse_stft = tf.signal.inverse_stft(
stft, frame_length, frame_step,
window_fn=tf.signal.inverse_stft_window_fn(
frame_step, forward_window_fn=window_fn))
```
Implemented with TPU/GPU-compatible ops and supports gradients.
Args:
stfts: A `complex64`/`complex128` `[..., 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`/`float64` 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 = fft_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.as_list()[-1] 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.as_list()[-1] > frame_length_static:
real_frames = real_frames[..., :frame_length_static]
elif real_frames.shape.as_list()[-1] < frame_length_static:
pad_amount = frame_length_static - real_frames.shape.as_list()[-1]
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 _enclosing_power_of_two(value):
"""Return 2**N for integer N such that 2**N >= value."""
value_static = tensor_util.constant_value(value)
if value_static is not None:
return constant_op.constant(
int(2**np.ceil(np.log(value_static) / np.log(2.0))), value.dtype)
return math_ops.cast(
math_ops.pow(
2.0,
math_ops.ceil(
math_ops.log(math_ops.cast(value, dtypes.float32)) /
math_ops.log(2.0))), value.dtype)
@tf_export('signal.mdct')
@dispatch.add_dispatch_support
def mdct(signals, frame_length, window_fn=window_ops.vorbis_window,
pad_end=False, norm=None, name=None):
"""Computes the [Modified Discrete Cosine Transform][mdct] of `signals`.
Implemented with TPU/GPU-compatible ops and supports gradients.
Args:
signals: A `[..., samples]` `float32`/`float64` `Tensor` of real-valued
signals.
frame_length: An integer scalar `Tensor`. The window length in samples
which must be divisible by 4.
window_fn: A callable that takes a frame_length and a `dtype` keyword
argument and returns a `[frame_length]` `Tensor` of samples in the
provided datatype. If set to `None`, a rectangular window with a scale of
1/sqrt(2) is used. For perfect reconstruction of a signal from `mdct`
followed by `inverse_mdct`, please use `tf.signal.vorbis_window`,
`tf.signal.kaiser_bessel_derived_window` or `None`. If using another
window function, make sure that w[n]^2 + w[n + frame_length // 2]^2 = 1
and w[n] = w[frame_length - n - 1] for n = 0,...,frame_length // 2 - 1 to
achieve perfect reconstruction.
pad_end: Whether to pad the end of `signals` with zeros when the provided
frame length and step produces a frame that lies partially past its end.
norm: If it is None, unnormalized dct4 is used, if it is "ortho"
orthonormal dct4 is used.
name: An optional name for the operation.
Returns:
A `[..., frames, frame_length // 2]` `Tensor` of `float32`/`float64`
MDCT values where `frames` is roughly `samples // (frame_length // 2)`
when `pad_end=False`.
Raises:
ValueError: If `signals` is not at least rank 1, `frame_length` is
not scalar, or `frame_length` is not a multiple of `4`.
[mdct]: https://en.wikipedia.org/wiki/Modified_discrete_cosine_transform
"""
with ops.name_scope(name, 'mdct', [signals, frame_length]):
signals = ops.convert_to_tensor(signals, name='signals')
signals.shape.with_rank_at_least(1)
frame_length = ops.convert_to_tensor(frame_length, name='frame_length')
frame_length.shape.assert_has_rank(0)
# Assert that frame_length is divisible by 4.
frame_length_static = tensor_util.constant_value(frame_length)
if frame_length_static is not None:
if frame_length_static % 4 != 0:
raise ValueError('The frame length must be a multiple of 4.')
frame_step = ops.convert_to_tensor(frame_length_static // 2,
dtype=frame_length.dtype)
else:
frame_step = frame_length // 2
framed_signals = shape_ops.frame(
signals, frame_length, frame_step, pad_end=pad_end)
# Optionally window the framed signals.
if window_fn is not None:
window = window_fn(frame_length, dtype=framed_signals.dtype)
framed_signals *= window
else:
framed_signals *= 1.0 / np.sqrt(2)
split_frames = array_ops.split(framed_signals, 4, axis=-1)
frame_firsthalf = -array_ops.reverse(split_frames[2],
[-1]) - split_frames[3]
frame_secondhalf = split_frames[0] - array_ops.reverse(split_frames[1],
[-1])
frames_rearranged = array_ops.concat((frame_firsthalf, frame_secondhalf),
axis=-1)
# Below call produces the (frame_length // 2) unique components of the
# type 4 orthonormal DCT of the real windowed signals in frames_rearranged.
return dct_ops.dct(frames_rearranged, type=4, norm=norm)
@tf_export('signal.inverse_mdct')
@dispatch.add_dispatch_support
def inverse_mdct(mdcts,
window_fn=window_ops.vorbis_window,
norm=None,
name=None):
"""Computes the inverse modified DCT of `mdcts`.
To reconstruct an original waveform, the same window function should
be used with `mdct` and `inverse_mdct`.
Example usage:
>>> @tf.function
... def compare_round_trip():
... samples = 1000
... frame_length = 400
... halflen = frame_length // 2
... waveform = tf.random.normal(dtype=tf.float32, shape=[samples])
... waveform_pad = tf.pad(waveform, [[halflen, 0],])
... mdct = tf.signal.mdct(waveform_pad, frame_length, pad_end=True,
... window_fn=tf.signal.vorbis_window)
... inverse_mdct = tf.signal.inverse_mdct(mdct,
... window_fn=tf.signal.vorbis_window)
... inverse_mdct = inverse_mdct[halflen: halflen + samples]
... return waveform, inverse_mdct
>>> waveform, inverse_mdct = compare_round_trip()
>>> np.allclose(waveform.numpy(), inverse_mdct.numpy(), rtol=1e-3, atol=1e-4)
True
Implemented with TPU/GPU-compatible ops and supports gradients.
Args:
mdcts: A `float32`/`float64` `[..., frames, frame_length // 2]`
`Tensor` of MDCT bins representing a batch of `frame_length // 2`-point
MDCTs.
window_fn: A callable that takes a frame_length and a `dtype` keyword
argument and returns a `[frame_length]` `Tensor` of samples in the
provided datatype. If set to `None`, a rectangular window with a scale of
1/sqrt(2) is used. For perfect reconstruction of a signal from `mdct`
followed by `inverse_mdct`, please use `tf.signal.vorbis_window`,
`tf.signal.kaiser_bessel_derived_window` or `None`. If using another
window function, make sure that w[n]^2 + w[n + frame_length // 2]^2 = 1
and w[n] = w[frame_length - n - 1] for n = 0,...,frame_length // 2 - 1 to
achieve perfect reconstruction.
norm: If "ortho", orthonormal inverse DCT4 is performed, if it is None,
a regular dct4 followed by scaling of `1/frame_length` is performed.
name: An optional name for the operation.
Returns:
A `[..., samples]` `Tensor` of `float32`/`float64` signals representing
the inverse MDCT for each input MDCT in `mdcts` where `samples` is
`(frames - 1) * (frame_length // 2) + frame_length`.
Raises:
ValueError: If `mdcts` is not at least rank 2.
[mdct]: https://en.wikipedia.org/wiki/Modified_discrete_cosine_transform
"""
with ops.name_scope(name, 'inverse_mdct', [mdcts]):
mdcts = ops.convert_to_tensor(mdcts, name='mdcts')
mdcts.shape.with_rank_at_least(2)
half_len = math_ops.cast(mdcts.shape[-1], dtype=dtypes.int32)
if norm is None:
half_len_float = math_ops.cast(half_len, dtype=mdcts.dtype)
result_idct4 = (0.5 / half_len_float) * dct_ops.dct(mdcts, type=4)
elif norm == 'ortho':
result_idct4 = dct_ops.dct(mdcts, type=4, norm='ortho')
split_result = array_ops.split(result_idct4, 2, axis=-1)
real_frames = array_ops.concat((split_result[1],
-array_ops.reverse(split_result[1], [-1]),
-array_ops.reverse(split_result[0], [-1]),
-split_result[0]), axis=-1)
# 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(2 * half_len, dtype=mdcts.dtype)
real_frames *= window
else:
real_frames *= 1.0 / np.sqrt(2)
return reconstruction_ops.overlap_and_add(real_frames, half_len)