Coverage for /pythoncovmergedfiles/medio/medio/usr/local/lib/python3.8/site-packages/tensorflow/python/ops/signal/mel_ops.py: 30%
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1# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
2#
3# Licensed under the Apache License, Version 2.0 (the "License");
4# you may not use this file except in compliance with the License.
5# You may obtain a copy of the License at
6#
7# http://www.apache.org/licenses/LICENSE-2.0
8#
9# Unless required by applicable law or agreed to in writing, software
10# distributed under the License is distributed on an "AS IS" BASIS,
11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12# See the License for the specific language governing permissions and
13# limitations under the License.
14# ==============================================================================
15"""mel conversion ops."""
17from tensorflow.python.framework import dtypes
18from tensorflow.python.framework import ops
19from tensorflow.python.framework import tensor_util
20from tensorflow.python.ops import array_ops
21from tensorflow.python.ops import math_ops
22from tensorflow.python.ops.signal import shape_ops
23from tensorflow.python.util import dispatch
24from tensorflow.python.util.tf_export import tf_export
27# mel spectrum constants.
28_MEL_BREAK_FREQUENCY_HERTZ = 700.0
29_MEL_HIGH_FREQUENCY_Q = 1127.0
32def _mel_to_hertz(mel_values, name=None):
33 """Converts frequencies in `mel_values` from the mel scale to linear scale.
35 Args:
36 mel_values: A `Tensor` of frequencies in the mel scale.
37 name: An optional name for the operation.
39 Returns:
40 A `Tensor` of the same shape and type as `mel_values` containing linear
41 scale frequencies in Hertz.
42 """
43 with ops.name_scope(name, 'mel_to_hertz', [mel_values]):
44 mel_values = ops.convert_to_tensor(mel_values)
45 return _MEL_BREAK_FREQUENCY_HERTZ * (
46 math_ops.exp(mel_values / _MEL_HIGH_FREQUENCY_Q) - 1.0
47 )
50def _hertz_to_mel(frequencies_hertz, name=None):
51 """Converts frequencies in `frequencies_hertz` in Hertz to the mel scale.
53 Args:
54 frequencies_hertz: A `Tensor` of frequencies in Hertz.
55 name: An optional name for the operation.
57 Returns:
58 A `Tensor` of the same shape and type of `frequencies_hertz` containing
59 frequencies in the mel scale.
60 """
61 with ops.name_scope(name, 'hertz_to_mel', [frequencies_hertz]):
62 frequencies_hertz = ops.convert_to_tensor(frequencies_hertz)
63 return _MEL_HIGH_FREQUENCY_Q * math_ops.log(
64 1.0 + (frequencies_hertz / _MEL_BREAK_FREQUENCY_HERTZ))
67def _validate_arguments(num_mel_bins, sample_rate,
68 lower_edge_hertz, upper_edge_hertz, dtype):
69 """Checks the inputs to linear_to_mel_weight_matrix."""
70 if num_mel_bins <= 0:
71 raise ValueError('num_mel_bins must be positive. Got: %s' % num_mel_bins)
72 if lower_edge_hertz < 0.0:
73 raise ValueError('lower_edge_hertz must be non-negative. Got: %s' %
74 lower_edge_hertz)
75 if lower_edge_hertz >= upper_edge_hertz:
76 raise ValueError('lower_edge_hertz %.1f >= upper_edge_hertz %.1f' %
77 (lower_edge_hertz, upper_edge_hertz))
78 if not isinstance(sample_rate, ops.Tensor):
79 if sample_rate <= 0.0:
80 raise ValueError('sample_rate must be positive. Got: %s' % sample_rate)
81 if upper_edge_hertz > sample_rate / 2:
82 raise ValueError('upper_edge_hertz must not be larger than the Nyquist '
83 'frequency (sample_rate / 2). Got %s for sample_rate: %s'
84 % (upper_edge_hertz, sample_rate))
85 if not dtype.is_floating:
86 raise ValueError('dtype must be a floating point type. Got: %s' % dtype)
89@tf_export('signal.linear_to_mel_weight_matrix')
90@dispatch.add_dispatch_support
91def linear_to_mel_weight_matrix(num_mel_bins=20,
92 num_spectrogram_bins=129,
93 sample_rate=8000,
94 lower_edge_hertz=125.0,
95 upper_edge_hertz=3800.0,
96 dtype=dtypes.float32,
97 name=None):
98 r"""Returns a matrix to warp linear scale spectrograms to the [mel scale][mel].
100 Returns a weight matrix that can be used to re-weight a `Tensor` containing
101 `num_spectrogram_bins` linearly sampled frequency information from
102 `[0, sample_rate / 2]` into `num_mel_bins` frequency information from
103 `[lower_edge_hertz, upper_edge_hertz]` on the [mel scale][mel].
105 This function follows the [Hidden Markov Model Toolkit
106 (HTK)](http://htk.eng.cam.ac.uk/) convention, defining the mel scale in
107 terms of a frequency in hertz according to the following formula:
109 $$\textrm{mel}(f) = 2595 * \textrm{log}_{10}(1 + \frac{f}{700})$$
111 In the returned matrix, all the triangles (filterbanks) have a peak value
112 of 1.0.
114 For example, the returned matrix `A` can be used to right-multiply a
115 spectrogram `S` of shape `[frames, num_spectrogram_bins]` of linear
116 scale spectrum values (e.g. STFT magnitudes) to generate a "mel spectrogram"
117 `M` of shape `[frames, num_mel_bins]`.
119 # `S` has shape [frames, num_spectrogram_bins]
120 # `M` has shape [frames, num_mel_bins]
121 M = tf.matmul(S, A)
123 The matrix can be used with `tf.tensordot` to convert an arbitrary rank
124 `Tensor` of linear-scale spectral bins into the mel scale.
126 # S has shape [..., num_spectrogram_bins].
127 # M has shape [..., num_mel_bins].
128 M = tf.tensordot(S, A, 1)
130 Args:
131 num_mel_bins: Python int. How many bands in the resulting mel spectrum.
132 num_spectrogram_bins: An integer `Tensor`. How many bins there are in the
133 source spectrogram data, which is understood to be `fft_size // 2 + 1`,
134 i.e. the spectrogram only contains the nonredundant FFT bins.
135 sample_rate: An integer or float `Tensor`. Samples per second of the input
136 signal used to create the spectrogram. Used to figure out the frequencies
137 corresponding to each spectrogram bin, which dictates how they are mapped
138 into the mel scale.
139 lower_edge_hertz: Python float. Lower bound on the frequencies to be
140 included in the mel spectrum. This corresponds to the lower edge of the
141 lowest triangular band.
142 upper_edge_hertz: Python float. The desired top edge of the highest
143 frequency band.
144 dtype: The `DType` of the result matrix. Must be a floating point type.
145 name: An optional name for the operation.
147 Returns:
148 A `Tensor` of shape `[num_spectrogram_bins, num_mel_bins]`.
150 Raises:
151 ValueError: If `num_mel_bins`/`num_spectrogram_bins`/`sample_rate` are not
152 positive, `lower_edge_hertz` is negative, frequency edges are incorrectly
153 ordered, `upper_edge_hertz` is larger than the Nyquist frequency.
155 [mel]: https://en.wikipedia.org/wiki/Mel_scale
156 """
157 with ops.name_scope(name, 'linear_to_mel_weight_matrix') as name:
158 # Convert Tensor `sample_rate` to float, if possible.
159 if isinstance(sample_rate, ops.Tensor):
160 maybe_const_val = tensor_util.constant_value(sample_rate)
161 if maybe_const_val is not None:
162 sample_rate = maybe_const_val
164 # Note: As num_spectrogram_bins is passed to `math_ops.linspace`
165 # and the validation is already done in linspace (both in shape function
166 # and in kernel), there is no need to validate num_spectrogram_bins here.
167 _validate_arguments(num_mel_bins, sample_rate,
168 lower_edge_hertz, upper_edge_hertz, dtype)
170 # This function can be constant folded by graph optimization since there are
171 # no Tensor inputs.
172 sample_rate = math_ops.cast(
173 sample_rate, dtype, name='sample_rate')
174 lower_edge_hertz = ops.convert_to_tensor(
175 lower_edge_hertz, dtype, name='lower_edge_hertz')
176 upper_edge_hertz = ops.convert_to_tensor(
177 upper_edge_hertz, dtype, name='upper_edge_hertz')
178 zero = ops.convert_to_tensor(0.0, dtype)
180 # HTK excludes the spectrogram DC bin.
181 bands_to_zero = 1
182 nyquist_hertz = sample_rate / 2.0
183 linear_frequencies = math_ops.linspace(
184 zero, nyquist_hertz, num_spectrogram_bins)[bands_to_zero:]
185 spectrogram_bins_mel = array_ops.expand_dims(
186 _hertz_to_mel(linear_frequencies), 1)
188 # Compute num_mel_bins triples of (lower_edge, center, upper_edge). The
189 # center of each band is the lower and upper edge of the adjacent bands.
190 # Accordingly, we divide [lower_edge_hertz, upper_edge_hertz] into
191 # num_mel_bins + 2 pieces.
192 band_edges_mel = shape_ops.frame(
193 math_ops.linspace(_hertz_to_mel(lower_edge_hertz),
194 _hertz_to_mel(upper_edge_hertz),
195 num_mel_bins + 2), frame_length=3, frame_step=1)
197 # Split the triples up and reshape them into [1, num_mel_bins] tensors.
198 lower_edge_mel, center_mel, upper_edge_mel = tuple(array_ops.reshape(
199 t, [1, num_mel_bins]) for t in array_ops.split(
200 band_edges_mel, 3, axis=1))
202 # Calculate lower and upper slopes for every spectrogram bin.
203 # Line segments are linear in the mel domain, not Hertz.
204 lower_slopes = (spectrogram_bins_mel - lower_edge_mel) / (
205 center_mel - lower_edge_mel)
206 upper_slopes = (upper_edge_mel - spectrogram_bins_mel) / (
207 upper_edge_mel - center_mel)
209 # Intersect the line segments with each other and zero.
210 mel_weights_matrix = math_ops.maximum(
211 zero, math_ops.minimum(lower_slopes, upper_slopes))
213 # Re-add the zeroed lower bins we sliced out above.
214 return array_ops.pad(
215 mel_weights_matrix, [[bands_to_zero, 0], [0, 0]], name=name)