Source code for dragonfly.grammar.recobs

#
# This file is part of Dragonfly.
# (c) Copyright 2007, 2008 by Christo Butcher
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#
#   Dragonfly is free software: you can redistribute it and/or modify it
#   under the terms of the GNU Lesser General Public License as published
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#   Lesser General Public License for more details.
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"""
Recognition observer classes
============================================================================

"""

import logging
import time

from six                        import integer_types

from dragonfly.actions.actions  import Playback
from dragonfly.engines          import get_engine


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[docs] class RecognitionObserver(object): """ Recognition observer base class. Sub-classes should override one or more of the event methods. """ _log = logging.getLogger("grammar") def __init__(self): pass def __del__(self): try: self.unregister() except Exception: pass
[docs] def register(self): """ Register the observer for recognition state events. """ engine = get_engine() engine.register_recognition_observer(self)
[docs] def unregister(self): """ Unregister the observer for recognition state events. """ engine = get_engine() engine.unregister_recognition_observer(self)
[docs] def on_begin(self): """ Method called when the observer is registered and speech start is detected. """
[docs] def on_recognition(self, words, results=None): """ Method called when speech successfully decoded to a grammar rule or to dictation. This is called *before* grammar rule processing, i.e., before ``Rule.process_recognition()``. :param words: recognized words :type words: tuple :param results: *optional* engine recognition results object :type results: :ref:`engine-specific type<RefGrammarCallbackResultsTypes>` """
[docs] def on_failure(self, results=None): """ Method called when speech failed to decode to a grammar rule or to dictation. :param results: *optional* engine recognition results object :type results: :ref:`engine-specific type<RefGrammarCallbackResultsTypes>` """
[docs] def on_end(self, results=None): """ Method called when speech ends, either with a successful recognition (after ``on_recognition``) or in failure (after ``on_failure``). :param results: *optional* engine recognition results object :type results: :ref:`engine-specific type<RefGrammarCallbackResultsTypes>` """
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[docs] class RecognitionHistory(list, RecognitionObserver): """ Storage class for recent recognitions. Instances of this class monitor recognitions and store them internally. This class derives from the built in *list* type and can be accessed as if it were a normal *list* containing recent recognitions. Note that an instance's contents are updated automatically as recognitions are received. """ def __init__(self, length=10): list.__init__(self) RecognitionObserver.__init__(self) self._complete = True if (length is None or (isinstance(length, integer_types) and length >= 1)): self._length = length else: raise ValueError("length must be a positive int or None," " received %r." % length) @property def complete(self): """ *False* if phrase-start detected but no recognition yet. """ return self._complete def on_begin(self): """""" self._complete = False def on_recognition(self, words, results=None): """""" self._complete = True self.append(self._recognition_to_item(words)) if self._length: while len(self) > self._length: self.pop(0) def _recognition_to_item(self, words): """""" # pylint: disable=no-self-use return words
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[docs] class PlaybackHistory(RecognitionHistory): """ Storage class for playing back recent recognitions via the :class:`Playback` action. Instances of this class monitor recognitions and store them internally. This class derives from the built in *list* type and can be accessed as if it were a normal *list* containing :class:`Playback` actions for recent recognitions. Note that an instance's contents are updated automatically as recognitions are received. """ def __init__(self, length=10): RecognitionHistory.__init__(self, length) def _recognition_to_item(self, words): return (words, time.time()) def __getitem__(self, key): result = RecognitionHistory.__getitem__(self, key) if len(result) == 1: return Playback([(result[0][0], 0)]) elif len(result) == 2 and isinstance(result[1], float): return Playback([(result[0], 0)]) elif len(result) == 0: return Playback(()) else: pairs = [(w1, t2 - t1) for ((w1, t1), (w2, t2)) in zip(result[:-1], result[1:])] pairs.append((result[-1][0], 0)) return Playback(pairs) def __getslice__(self, i, j): return self.__getitem__(slice(i, j))