[tf-signal] Use tf.spectral.dct in mfccs_from_log_mel_spectrograms instead of a private implementation.

PiperOrigin-RevId: 170943986

tensorflow/contrib/signal/python/kernel_tests/mfcc_ops_test.py

@@ -18,75 +18,12 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import importlib
-
-import numpy as np
-
-
 from tensorflow.contrib.signal.python.ops import mfcc_ops
 from tensorflow.python.framework import dtypes
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import random_ops
 from tensorflow.python.ops import spectral_ops_test_util
 from tensorflow.python.platform import test
-from tensorflow.python.platform import tf_logging
-
-
-# TODO(rjryan): Add scipy.fftpack to the TensorFlow build.
-def try_import(name):  # pylint: disable=invalid-name
-  module = None
-  try:
-    module = importlib.import_module(name)
-  except ImportError as e:
-    tf_logging.warning("Could not import %s: %s" % (name, str(e)))
-  return module
-
-
-fftpack = try_import("scipy.fftpack")
-
-
-class DCTTest(test.TestCase):
-
-  def _np_dct2(self, signals, norm=None):
-    """Computes the DCT-II manually with NumPy."""
-    # X_k = sum_{n=0}^{N-1} x_n * cos(\frac{pi}{N} * (n + 0.5) * k)  k=0,...,N-1
-    dct_size = signals.shape[-1]
-    dct = np.zeros_like(signals)
-    for k in range(dct_size):
-      phi = np.cos(np.pi * (np.arange(dct_size) + 0.5) * k / dct_size)
-      dct[..., k] = np.sum(signals * phi, axis=-1)
-    # SciPy's `dct` has a scaling factor of 2.0 which we follow.
-    # https://github.com/scipy/scipy/blob/v0.15.1/scipy/fftpack/src/dct.c.src
-    if norm == "ortho":
-      # The orthogonal scaling includes a factor of 0.5 which we combine with
-      # the overall scaling of 2.0 to cancel.
-      dct[..., 0] *= np.sqrt(1.0 / dct_size)
-      dct[..., 1:] *= np.sqrt(2.0 / dct_size)
-    else:
-      dct *= 2.0
-    return dct
-
-  def test_compare_to_numpy(self):
-    """Compare dct against a manual DCT-II implementation."""
-    with spectral_ops_test_util.fft_kernel_label_map():
-      with self.test_session(use_gpu=True):
-        for size in range(1, 23):
-          signals = np.random.rand(size).astype(np.float32)
-          actual_dct = mfcc_ops._dct2_1d(signals).eval()
-          expected_dct = self._np_dct2(signals)
-          self.assertAllClose(expected_dct, actual_dct, atol=5e-4, rtol=5e-4)
-
-  def test_compare_to_fftpack(self):
-    """Compare dct against scipy.fftpack.dct."""
-    if not fftpack:
-      return
-    with spectral_ops_test_util.fft_kernel_label_map():
-      with self.test_session(use_gpu=True):
-        for size in range(1, 23):
-          signal = np.random.rand(size).astype(np.float32)
-          actual_dct = mfcc_ops._dct2_1d(signal).eval()
-          expected_dct = fftpack.dct(signal, type=2)
-          self.assertAllClose(expected_dct, actual_dct, atol=5e-4, rtol=5e-4)
 
 
 # TODO(rjryan): We have no open source tests for MFCCs at the moment. Internally

tensorflow/contrib/signal/python/ops/mfcc_ops.py

@@ -18,8 +18,6 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import math
-
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
 from tensorflow.python.ops import array_ops
@@ -27,35 +25,6 @@ from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import spectral_ops
 
 
-# TODO(rjryan): Remove once tf.spectral.dct exists.
-def _dct2_1d(signals, name=None):
-  """Computes the type II 1D Discrete Cosine Transform (DCT) of `signals`.
-
-  Args:
-    signals: A `[..., samples]` `float32` `Tensor` containing the signals to
-      take the DCT of.
-    name: An optional name for the operation.
-
-  Returns:
-    A `[..., samples]` `float32` `Tensor` containing the DCT of `signals`.
-
-  """
-  with ops.name_scope(name, 'dct', [signals]):
-    # We use the FFT to compute the DCT and TensorFlow only supports float32 for
-    # FFTs at the moment.
-    signals = ops.convert_to_tensor(signals, dtype=dtypes.float32)
-
-    axis_dim = signals.shape[-1].value or array_ops.shape(signals)[-1]
-    axis_dim_float = math_ops.to_float(axis_dim)
-    scale = 2.0 * math_ops.exp(math_ops.complex(
-        0.0, -math.pi * math_ops.range(axis_dim_float) /
-        (2.0 * axis_dim_float)))
-
-    rfft = spectral_ops.rfft(signals, fft_length=[2 * axis_dim])[..., :axis_dim]
-    dct2 = math_ops.real(rfft * scale)
-    return dct2
-
-
 def mfccs_from_log_mel_spectrograms(log_mel_spectrograms, name=None):
   """Computes [MFCCs][mfcc] of `log_mel_spectrograms`.
 
@@ -134,4 +103,6 @@ def mfccs_from_log_mel_spectrograms(log_mel_spectrograms, name=None):
                          log_mel_spectrograms)
     else:
       num_mel_bins = array_ops.shape(log_mel_spectrograms)[-1]
-    return _dct2_1d(log_mel_spectrograms) * math_ops.rsqrt(num_mel_bins * 2.0)
+
+    dct2 = spectral_ops.dct(log_mel_spectrograms)
+    return dct2 * math_ops.rsqrt(num_mel_bins * 2.0)