189 lines
7.4 KiB
Python
Executable File
189 lines
7.4 KiB
Python
Executable File
# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Stream accuracy recognize commands."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import collections
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import numpy as np
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class RecognizeResult(object):
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"""Save recognition result temporarily.
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Attributes:
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founded_command: A string indicating the word just founded. Default value
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is '_silence_'
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score: An float representing the confidence of founded word. Default
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value is zero.
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is_new_command: A boolean indicating if the founded command is a new one
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against the last one. Default value is False.
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"""
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def __init__(self):
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self._founded_command = "_silence_"
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self._score = 0
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self._is_new_command = False
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@property
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def founded_command(self):
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return self._founded_command
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@founded_command.setter
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def founded_command(self, value):
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self._founded_command = value
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@property
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def score(self):
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return self._score
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@score.setter
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def score(self, value):
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self._score = value
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@property
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def is_new_command(self):
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return self._is_new_command
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@is_new_command.setter
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def is_new_command(self, value):
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self._is_new_command = value
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class RecognizeCommands(object):
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"""Smooth the inference results by using average window.
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Maintain a slide window over the audio stream, which adds new result(a pair of
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the 1.confidences of all classes and 2.the start timestamp of input audio
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clip) directly the inference produces one and removes the most previous one
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and other abnormal values. Then it smooth the results in the window to get
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the most reliable command in this period.
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Attributes:
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_label: A list containing commands at corresponding lines.
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_average_window_duration: The length of average window.
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_detection_threshold: A confidence threshold for filtering out unreliable
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command.
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_suppression_ms: Milliseconds every two reliable founded commands should
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apart.
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_minimum_count: An integer count indicating the minimum results the average
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window should cover.
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_previous_results: A deque to store previous results.
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_label_count: The length of label list.
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_previous_top_label: Last founded command. Initial value is '_silence_'.
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_previous_top_time: The timestamp of _previous results. Default is -np.inf.
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"""
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def __init__(self, labels, average_window_duration_ms, detection_threshold,
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suppression_ms, minimum_count):
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"""Init the RecognizeCommands with parameters used for smoothing."""
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# Configuration
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self._labels = labels
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self._average_window_duration_ms = average_window_duration_ms
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self._detection_threshold = detection_threshold
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self._suppression_ms = suppression_ms
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self._minimum_count = minimum_count
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# Working Variable
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self._previous_results = collections.deque()
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self._label_count = len(labels)
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self._previous_top_label = "_silence_"
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self._previous_top_time = -np.inf
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def process_latest_result(self, latest_results, current_time_ms,
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recognize_element):
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"""Smoothing the results in average window when a new result is added in.
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Receive a new result from inference and put the founded command into
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a RecognizeResult instance after the smoothing procedure.
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Args:
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latest_results: A list containing the confidences of all labels.
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current_time_ms: The start timestamp of the input audio clip.
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recognize_element: An instance of RecognizeResult to store founded
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command, its scores and if it is a new command.
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Raises:
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ValueError: The length of this result from inference doesn't match
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label count.
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ValueError: The timestamp of this result is earlier than the most
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previous one in the average window
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"""
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if latest_results.shape[0] != self._label_count:
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raise ValueError("The results for recognition should contain {} "
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"elements, but there are {} produced".format(
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self._label_count, latest_results.shape[0]))
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if (self._previous_results.__len__() != 0 and
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current_time_ms < self._previous_results[0][0]):
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raise ValueError("Results must be fed in increasing time order, "
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"but receive a timestamp of {}, which was earlier "
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"than the previous one of {}".format(
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current_time_ms, self._previous_results[0][0]))
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# Add the latest result to the head of the deque.
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self._previous_results.append([current_time_ms, latest_results])
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# Prune any earlier results that are too old for the averaging window.
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time_limit = current_time_ms - self._average_window_duration_ms
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while time_limit > self._previous_results[0][0]:
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self._previous_results.popleft()
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# If there are too few results, the result will be unreliable and bail.
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how_many_results = self._previous_results.__len__()
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earliest_time = self._previous_results[0][0]
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sample_duration = current_time_ms - earliest_time
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if (how_many_results < self._minimum_count or
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sample_duration < self._average_window_duration_ms / 4):
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recognize_element.founded_command = self._previous_top_label
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recognize_element.score = 0.0
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recognize_element.is_new_command = False
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return
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# Calculate the average score across all the results in the window.
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average_scores = np.zeros(self._label_count)
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for item in self._previous_results:
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score = item[1]
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for i in range(score.size):
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average_scores[i] += score[i] / how_many_results
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# Sort the averaged results in descending score order.
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sorted_averaged_index_score = []
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for i in range(self._label_count):
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sorted_averaged_index_score.append([i, average_scores[i]])
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sorted_averaged_index_score = sorted(
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sorted_averaged_index_score, key=lambda p: p[1], reverse=True)
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# Use the information of previous result to get current result
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current_top_index = sorted_averaged_index_score[0][0]
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current_top_label = self._labels[current_top_index]
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current_top_score = sorted_averaged_index_score[0][1]
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time_since_last_top = 0
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if (self._previous_top_label == "_silence_" or
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self._previous_top_time == -np.inf):
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time_since_last_top = np.inf
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else:
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time_since_last_top = current_time_ms - self._previous_top_time
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if (current_top_score > self._detection_threshold and
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current_top_label != self._previous_top_label and
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time_since_last_top > self._suppression_ms):
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self._previous_top_label = current_top_label
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self._previous_top_time = current_time_ms
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recognize_element.is_new_command = True
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else:
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recognize_element.is_new_command = False
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recognize_element.founded_command = current_top_label
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recognize_element.score = current_top_score
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