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/* 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/examples/speech_commands/recognize_commands.h"
namespace tensorflow {
RecognizeCommands::RecognizeCommands(const std::vector<string>& labels,
int32 average_window_duration_ms,
float detection_threshold,
int32 suppression_ms, int32 minimum_count)
: labels_(labels),
average_window_duration_ms_(average_window_duration_ms),
detection_threshold_(detection_threshold),
suppression_ms_(suppression_ms),
minimum_count_(minimum_count) {
labels_count_ = labels.size();
previous_top_label_ = "_silence_";
previous_top_label_time_ = std::numeric_limits<int64>::min();
}
Status RecognizeCommands::ProcessLatestResults(const Tensor& latest_results,
const int64 current_time_ms,
string* found_command,
float* score,
bool* is_new_command) {
if (latest_results.NumElements() != labels_count_) {
return errors::InvalidArgument(
"The results for recognition should contain ", labels_count_,
" elements, but there are ", latest_results.NumElements());
}
if ((!previous_results_.empty()) &&
(current_time_ms < previous_results_.front().first)) {
return errors::InvalidArgument(
"Results must be fed in increasing time order, but received a "
"timestamp of ",
current_time_ms, " that was earlier than the previous one of ",
previous_results_.front().first);
}
// Add the latest results to the head of the queue.
previous_results_.push_back({current_time_ms, latest_results});
// Prune any earlier results that are too old for the averaging window.
const int64 time_limit = current_time_ms - average_window_duration_ms_;
while (previous_results_.front().first < time_limit) {
previous_results_.pop_front();
}
// If there are too few results, assume the result will be unreliable and
// bail.
const int64 how_many_results = previous_results_.size();
const int64 earliest_time = previous_results_.front().first;
const int64 samples_duration = current_time_ms - earliest_time;
if ((how_many_results < minimum_count_) ||
(samples_duration < (average_window_duration_ms_ / 4))) {
*found_command = previous_top_label_;
*score = 0.0f;
*is_new_command = false;
return Status::OK();
}
// Calculate the average score across all the results in the window.
std::vector<float> average_scores(labels_count_);
for (const auto& previous_result : previous_results_) {
const Tensor& scores_tensor = previous_result.second;
auto scores_flat = scores_tensor.flat<float>();
for (int i = 0; i < scores_flat.size(); ++i) {
average_scores[i] += scores_flat(i) / how_many_results;
}
}
// Sort the averaged results in descending score order.
std::vector<std::pair<int, float>> sorted_average_scores;
sorted_average_scores.reserve(labels_count_);
for (int i = 0; i < labels_count_; ++i) {
sorted_average_scores.push_back(
std::pair<int, float>({i, average_scores[i]}));
}
std::sort(sorted_average_scores.begin(), sorted_average_scores.end(),
[](const std::pair<int, float>& left,
const std::pair<int, float>& right) {
return left.second > right.second;
});
// See if the latest top score is enough to trigger a detection.
const int current_top_index = sorted_average_scores[0].first;
const string current_top_label = labels_[current_top_index];
const float current_top_score = sorted_average_scores[0].second;
// If we've recently had another label trigger, assume one that occurs too
// soon afterwards is a bad result.
int64 time_since_last_top;
if ((previous_top_label_ == "_silence_") ||
(previous_top_label_time_ == std::numeric_limits<int64>::min())) {
time_since_last_top = std::numeric_limits<int64>::max();
} else {
time_since_last_top = current_time_ms - previous_top_label_time_;
}
if ((current_top_score > detection_threshold_) &&
(current_top_label != previous_top_label_) &&
(time_since_last_top > suppression_ms_)) {
previous_top_label_ = current_top_label;
previous_top_label_time_ = current_time_ms;
*is_new_command = true;
} else {
*is_new_command = false;
}
*found_command = current_top_label;
*score = current_top_score;
return Status::OK();
}
} // namespace tensorflow
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