mycroft-core/mycroft/client/speech/local_recognizer.py

77 lines
2.7 KiB
Python

# Copyright 2016 Mycroft AI, Inc.
#
# This file is part of Mycroft Core.
#
# Mycroft Core is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# Mycroft Core is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with Mycroft Core. If not, see <http://www.gnu.org/licenses/>.
import os
import tempfile
import time
from os.path import join, dirname, abspath
from pocketsphinx import Decoder
__author__ = 'seanfitz, jdorleans'
BASEDIR = dirname(abspath(__file__))
class LocalRecognizer(object):
def __init__(self, key_phrase, phonemes, threshold, sample_rate=16000,
lang="en-us"):
self.lang = str(lang)
self.key_phrase = str(key_phrase)
self.sample_rate = sample_rate
self.threshold = threshold
self.phonemes = phonemes
dict_name = self.create_dict(key_phrase, phonemes)
self.decoder = Decoder(self.create_config(dict_name))
def create_dict(self, key_phrase, phonemes):
(fd, file_name) = tempfile.mkstemp()
words = key_phrase.split()
phoneme_groups = phonemes.split('.')
with os.fdopen(fd, 'w') as f:
for word, phoneme in zip(words, phoneme_groups):
f.write(word + ' ' + phoneme + '\n')
return file_name
def create_config(self, dict_name):
config = Decoder.default_config()
config.set_string('-hmm', join(BASEDIR, 'model', self.lang, 'hmm'))
config.set_string('-dict', dict_name)
config.set_string('-keyphrase', self.key_phrase)
config.set_float('-kws_threshold', self.threshold)
config.set_float('-samprate', self.sample_rate)
config.set_int('-nfft', 2048)
config.set_string('-logfn', '/dev/null')
return config
def transcribe(self, byte_data, metrics=None):
start = time.time()
self.decoder.start_utt()
self.decoder.process_raw(byte_data, False, False)
self.decoder.end_utt()
if metrics:
metrics.timer("mycroft.stt.local.time_s", time.time() - start)
return self.decoder.hyp()
def is_recognized(self, byte_data, metrics):
hyp = self.transcribe(byte_data, metrics)
return hyp and self.key_phrase in hyp.hypstr.lower()
def found_wake_word(self, hypothesis):
return hypothesis and self.key_phrase in hypothesis.hypstr.lower()