1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
|
/* 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.
==============================================================================*/
#include "tensorflow/core/framework/common_shape_fns.h"
#include "tensorflow/core/framework/op.h"
#include "tensorflow/core/framework/shape_inference.h"
#include "tensorflow/core/lib/core/bits.h"
namespace tensorflow {
namespace {
using shape_inference::DimensionHandle;
using shape_inference::InferenceContext;
using shape_inference::ShapeHandle;
Status DecodeWavShapeFn(InferenceContext* c) {
ShapeHandle unused;
TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &unused));
DimensionHandle channels_dim;
int32 desired_channels;
TF_RETURN_IF_ERROR(c->GetAttr("desired_channels", &desired_channels));
if (desired_channels == -1) {
channels_dim = c->UnknownDim();
} else {
if (desired_channels < 0) {
return errors::InvalidArgument("channels must be non-negative, got ",
desired_channels);
}
channels_dim = c->MakeDim(desired_channels);
}
DimensionHandle samples_dim;
int32 desired_samples;
TF_RETURN_IF_ERROR(c->GetAttr("desired_samples", &desired_samples));
if (desired_samples == -1) {
samples_dim = c->UnknownDim();
} else {
if (desired_samples < 0) {
return errors::InvalidArgument("samples must be non-negative, got ",
desired_samples);
}
samples_dim = c->MakeDim(desired_samples);
}
c->set_output(0, c->MakeShape({samples_dim, channels_dim}));
c->set_output(1, c->Scalar());
return Status::OK();
}
Status EncodeWavShapeFn(InferenceContext* c) {
ShapeHandle unused;
TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 2, &unused));
TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &unused));
c->set_output(0, c->Scalar());
return Status::OK();
}
Status SpectrogramShapeFn(InferenceContext* c) {
ShapeHandle input;
TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 2, &input));
int32 window_size;
TF_RETURN_IF_ERROR(c->GetAttr("window_size", &window_size));
int32 stride;
TF_RETURN_IF_ERROR(c->GetAttr("stride", &stride));
DimensionHandle input_length = c->Dim(input, 0);
DimensionHandle input_channels = c->Dim(input, 1);
DimensionHandle output_length;
if (!c->ValueKnown(input_length)) {
output_length = c->UnknownDim();
} else {
const int64 input_length_value = c->Value(input_length);
const int64 length_minus_window = (input_length_value - window_size);
int64 output_length_value;
if (length_minus_window < 0) {
output_length_value = 0;
} else {
output_length_value = 1 + (length_minus_window / stride);
}
output_length = c->MakeDim(output_length_value);
}
DimensionHandle output_channels =
c->MakeDim(1 + NextPowerOfTwo(window_size) / 2);
c->set_output(0,
c->MakeShape({input_channels, output_length, output_channels}));
return Status::OK();
}
Status MfccShapeFn(InferenceContext* c) {
ShapeHandle spectrogram;
TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 3, &spectrogram));
ShapeHandle unused;
TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &unused));
int32 dct_coefficient_count;
TF_RETURN_IF_ERROR(
c->GetAttr("dct_coefficient_count", &dct_coefficient_count));
DimensionHandle spectrogram_channels = c->Dim(spectrogram, 0);
DimensionHandle spectrogram_length = c->Dim(spectrogram, 1);
DimensionHandle output_channels = c->MakeDim(dct_coefficient_count);
c->set_output(0, c->MakeShape({spectrogram_channels, spectrogram_length,
output_channels}));
return Status::OK();
}
} // namespace
REGISTER_OP("DecodeWav")
.Input("contents: string")
.Attr("desired_channels: int = -1")
.Attr("desired_samples: int = -1")
.Output("audio: float")
.Output("sample_rate: int32")
.SetShapeFn(DecodeWavShapeFn);
REGISTER_OP("EncodeWav")
.Input("audio: float")
.Input("sample_rate: int32")
.Output("contents: string")
.SetShapeFn(EncodeWavShapeFn);
REGISTER_OP("AudioSpectrogram")
.Input("input: float")
.Attr("window_size: int")
.Attr("stride: int")
.Attr("magnitude_squared: bool = false")
.Output("spectrogram: float")
.SetShapeFn(SpectrogramShapeFn);
REGISTER_OP("Mfcc")
.Input("spectrogram: float")
.Input("sample_rate: int32")
.Attr("upper_frequency_limit: float = 4000")
.Attr("lower_frequency_limit: float = 20")
.Attr("filterbank_channel_count: int = 40")
.Attr("dct_coefficient_count: int = 13")
.Output("output: float")
.SetShapeFn(MfccShapeFn);
} // namespace tensorflow
|
