| /external/tensorflow/tensorflow/core/kernels/ |
| mfcc_op.cc | 28 // Create a speech fingerpring from spectrogram data.
43 const Tensor& spectrogram = context->input(0); variable
44 OP_REQUIRES(context, spectrogram.dims() == 3,
45 errors::InvalidArgument("spectrogram must be 3-dimensional",
46 spectrogram.shape().DebugString()));
54 const int spectrogram_channels = spectrogram.dim_size(2);
55 const int spectrogram_samples = spectrogram.dim_size(1);
56 const int audio_channels = spectrogram.dim_size(0);
76 const float* spectrogram_flat = spectrogram.flat<float>().data();
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| spectrogram_op.cc | 23 #include "tensorflow/core/kernels/spectrogram.h"
28 // Create a spectrogram frequency visualization from audio data.
44 Spectrogram spectrogram; variable
45 OP_REQUIRES(context, spectrogram.Initialize(window_size_, stride_),
47 "Spectrogram initialization failed for window size ",
55 const int64 output_width = spectrogram.output_frequency_channels();
82 spectrogram.ComputeSquaredMagnitudeSpectrogram(
84 errors::InvalidArgument("Spectrogram compute failed"));
87 "Spectrogram size calculation failed: Expected height "
[all...] |
| spectrogram_op_test.cc | 56 {spectrogram_op.spectrogram}, &outputs));
90 {spectrogram_op.spectrogram}, &outputs));
|
| spectrogram_test.cc | 18 #include "tensorflow/core/kernels/spectrogram.h"
48 Spectrogram sgram;
60 Spectrogram sgram;
72 Spectrogram sgram;
84 Spectrogram sgram;
101 Spectrogram sgram;
126 Spectrogram sgram;
145 Spectrogram sgram;
174 double GetMaximumAbsolute(const std::vector<std::vector<Sample>>& spectrogram) {
176 for (int i = 0; i < spectrogram.size(); ++i)
[all...] |
| /external/tensorflow/tensorflow/lite/kernels/ |
| audio_spectrogram.cc | 20 #include "tensorflow/lite/kernels/internal/spectrogram.h"
44 internal::Spectrogram* spectrogram; member in struct:tflite::ops::custom::audio_spectrogram::__anon45995
57 data->spectrogram = new internal::Spectrogram;
64 delete params->spectrogram;
83 TF_LITE_ENSURE(context, params->spectrogram->Initialize(params->window_size,
95 output_size->data[2] = params->spectrogram->output_frequency_channels();
108 TF_LITE_ENSURE(context, params->spectrogram->Initialize(params->window_size,
116 const int64_t output_width = params->spectrogram->output_frequency_channels()
[all...] |
| /external/tensorflow/tensorflow/core/ops/ |
| audio_ops.cc | 104 ShapeHandle spectrogram; local
105 TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 3, &spectrogram));
113 DimensionHandle spectrogram_channels = c->Dim(spectrogram, 0);
114 DimensionHandle spectrogram_length = c->Dim(spectrogram, 1);
144 .Output("spectrogram: float")
148 .Input("spectrogram: float")
|
| /external/tensorflow/tensorflow/examples/wav_to_spectrogram/ |
| wav_to_spectrogram.cc | 51 // - Creates a float spectrogram from those samples.
52 // - Scales, clamps, and converts that spectrogram to 0 to 255 uint8's.
59 Output spectrogram = AudioSpectrogram(root.WithOpName("spectrogram"), local
64 Output mul = Mul(root.WithOpName("mul"), spectrogram, brightness_placeholder);
|
| /external/tensorflow/tensorflow/examples/speech_commands/ |
| input_data_test.py | 267 spectrogram = results[0]
268 self.assertEqual(1, spectrogram.shape[0])
269 self.assertEqual(16, spectrogram.shape[1])
270 self.assertEqual(11, spectrogram.shape[2])
271 self.assertNear(0, spectrogram[0, 0, 0], 0.1)
272 self.assertNear(200, spectrogram[0, 0, 5], 0.1)
|
| freeze.py | 80 preprocess: How the spectrogram is processed to produce features, for
99 spectrogram = contrib_audio.audio_spectrogram(
107 tf.expand_dims(spectrogram, -1),
114 spectrogram,
200 help='How long each spectrogram timeslice is',)
205 help='How long the stride is between spectrogram timeslices',)
238 help='Spectrogram processing mode. Can be "mfcc" or "average"')
|
| input_data.py | 171 # of spectrogram and MFCC inputs. If the preprocessing pipeline changes,
370 shifts it in time, adds in background noise, calculates a spectrogram, and
425 # Run the spectrogram and MFCC ops to get a 2D 'fingerprint' of the audio.
426 spectrogram = contrib_audio.audio_spectrogram(
432 'spectrogram', tf.expand_dims(spectrogram, -1), max_outputs=1)
433 # The number of buckets in each FFT row in the spectrogram will depend on
443 tf.expand_dims(spectrogram, -1),
451 spectrogram,
611 Runs the feature generation process (generally producing a spectrogram fro
[all...] |
| /external/tensorflow/tensorflow/python/kernel_tests/signal/ |
| mel_ops_test.py | 59 """Return a matrix that can post-multiply spectrogram rows to make mel.
65 spectrogram values (STFT magnitudes) arranged as frames x bins to generate a
66 "mel spectrogram" M of frames x num_mel_bins. M = S A.
81 num_spectrogram_bins: How many bins there are in the source spectrogram
82 data, which is understood to be fft_size/2 + 1, i.e. the spectrogram
85 spectrogram. We need this to figure out the actual frequencies for
86 each spectrogram bin, which dictates how they are mapped into mel.
111 # of spectrogram values.
115 # Calculate lower and upper slopes for every spectrogram bin.
124 # HTK excludes the spectrogram DC bin; make sure it always gets a zer
[all...] |
| /external/tensorflow/tensorflow/python/ops/signal/ |
| mel_ops.py | 106 spectrogram `S` of shape `[frames, num_spectrogram_bins]` of linear
107 scale spectrum values (e.g. STFT magnitudes) to generate a "mel spectrogram"
126 source spectrogram data, which is understood to be `fft_size // 2 + 1`,
127 i.e. the spectrogram only contains the nonredundant FFT bins.
129 create the spectrogram. We need this to figure out the actual frequencies
130 for each spectrogram bin, which dictates how they are mapped into the mel
167 # HTK excludes the spectrogram DC bin.
189 # Calculate lower and upper slopes for every spectrogram bin.
|
| /external/tensorflow/tensorflow/go/op/ |
| wrappers.go | [all...] |
