--- /dev/null
+/* 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.
+==============================================================================*/
+// Unit test for speech EndPointer model using TFLite Ops.
+
+#include <string.h>
+
+#include <memory>
+#include <string>
+
+#include "base/logging.h"
+#include "testing/base/public/googletest.h"
+#include <gtest/gtest.h>
+#include "absl/strings/str_cat.h"
+#include "tensorflow/contrib/lite/context.h"
+#include "tensorflow/contrib/lite/interpreter.h"
+#include "tensorflow/contrib/lite/kernels/register.h"
+#include "tensorflow/contrib/lite/model.h"
+#include "tensorflow/contrib/lite/models/test_utils.h"
+
+namespace tflite {
+namespace models {
+
+constexpr int kModelInputTensor = 0;
+constexpr int kLstmLayer1OutputStateTensor = 28;
+constexpr int kLstmLayer1CellStateTensor = 29;
+constexpr int kLstmLayer2OutputStateTensor = 49;
+constexpr int kLstmLayer2CellStateTensor = 50;
+constexpr int kModelOutputTensor = 58;
+
+TEST(SpeechEndpointer, EndpointerTest) {
+ // Read the model.
+ string tflite_file_path =
+ StrCat(TestDataPath(), "/", "speech_endpointer_model.tflite");
+ auto model = FlatBufferModel::BuildFromFile(tflite_file_path.c_str());
+ CHECK(model) << "Failed to read model from file " << tflite_file_path;
+
+ // Initialize the interpreter.
+ ops::builtin::BuiltinOpResolver builtins;
+ std::unique_ptr<Interpreter> interpreter;
+ InterpreterBuilder(*model, builtins)(&interpreter);
+ CHECK(interpreter != nullptr);
+ interpreter->AllocateTensors();
+
+ // Load the input frames.
+ Frames input_frames;
+ const string input_file_path =
+ StrCat(TestDataPath(), "/", "speech_endpointer_model_in.csv");
+ ReadFrames(input_file_path, &input_frames);
+
+ // Load the golden output results.
+ Frames output_frames;
+ const string output_file_path =
+ StrCat(TestDataPath(), "/", "speech_endpointer_model_out.csv");
+ ReadFrames(output_file_path, &output_frames);
+
+ const int speech_batch_size =
+ interpreter->tensor(kModelInputTensor)->dims->data[0];
+ const int speech_input_size =
+ interpreter->tensor(kModelInputTensor)->dims->data[1];
+ const int speech_output_size =
+ interpreter->tensor(kModelOutputTensor)->dims->data[1];
+
+ float* input_ptr = interpreter->tensor(kModelInputTensor)->data.f;
+ float* output_ptr = interpreter->tensor(kModelOutputTensor)->data.f;
+
+ // Clear the LSTM state for layers.
+ memset(interpreter->tensor(kLstmLayer1OutputStateTensor)->data.raw, 0,
+ interpreter->tensor(kLstmLayer1OutputStateTensor)->bytes);
+ memset(interpreter->tensor(kLstmLayer1CellStateTensor)->data.raw, 0,
+ interpreter->tensor(kLstmLayer1CellStateTensor)->bytes);
+ memset(interpreter->tensor(kLstmLayer2OutputStateTensor)->data.raw, 0,
+ interpreter->tensor(kLstmLayer2OutputStateTensor)->bytes);
+ memset(interpreter->tensor(kLstmLayer2CellStateTensor)->data.raw, 0,
+ interpreter->tensor(kLstmLayer2CellStateTensor)->bytes);
+
+ for (int i = 0; i < input_frames.size(); i++) {
+ // Feed the input to model.
+ int frame_ptr = 0;
+ for (int k = 0; k < speech_input_size * speech_batch_size; k++) {
+ input_ptr[k] = input_frames[i][frame_ptr++];
+ }
+ // Run the model.
+ interpreter->Invoke();
+ // Validate the output.
+ for (int k = 0; k < speech_output_size; k++) {
+ ASSERT_NEAR(output_ptr[k], output_frames[i][k], 1e-5);
+ }
+ }
+}
+
+} // namespace models
+} // namespace tflite
+### Endpointer Model
+
+The endpointer model is the neural network model for predicting end of speech
+in an utterance. More precisely, it generates posterior probabilities of various
+events that allow detection of speech start and end events.
+It has an input size of 40 (float) which are speech frontend features
+(log-mel filterbanks), and an output size of four corresponding to:
+speech, intermediate non-speech, initial non-speech, and final non-speech.
+The model consists of a convolutional layer, followed by a fully-connected
+layer, two LSTM layers, and two additional fully-connected layers.
+The corresponding parameters as shown in the figure.
+
+
+
## Speech models test input/output generation
As mentioned above the input to models are generated from a pre-processing
[ASR AM model
test](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/models/speech_terse_am_model_test.cc)
+[Endpointer model
+test](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/models/speech_endpointer_model_test.cc)
+
## Android Support
The models have been tested on Android phones, using the following tests:
projects / platform / upstream / tensorflow.git / commitdiff
