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#!/usr/bin/env python
import collections
import pyaudio
from . import snowboydetect
from robot import utils, logging
import time
import wave
import os
from ctypes import CFUNCTYPE, c_char_p, c_int, cdll
from contextlib import contextmanager
from robot import constants
logger = logging.getLogger("snowboy")
TOP_DIR = os.path.dirname(os.path.abspath(__file__))
RESOURCE_FILE = os.path.join(TOP_DIR, "resources/common.res")
DETECT_DING = os.path.join(TOP_DIR, "resources/ding.wav")
DETECT_DONG = os.path.join(TOP_DIR, "resources/dong.wav")
def py_error_handler(filename, line, function, err, fmt):
pass
ERROR_HANDLER_FUNC = CFUNCTYPE(None, c_char_p, c_int, c_char_p, c_int, c_char_p)
c_error_handler = ERROR_HANDLER_FUNC(py_error_handler)
@contextmanager
def no_alsa_error():
try:
asound = cdll.LoadLibrary('libasound.so')
asound.snd_lib_error_set_handler(c_error_handler)
yield
asound.snd_lib_error_set_handler(None)
except:
yield
pass
class RingBuffer(object):
"""Ring buffer to hold audio from PortAudio"""
def __init__(self, size=4096):
self._buf = collections.deque(maxlen=size)
def extend(self, data):
"""Adds data to the end of buffer"""
self._buf.extend(data)
def get(self):
"""Retrieves data from the beginning of buffer and clears it"""
tmp = bytes(bytearray(self._buf))
self._buf.clear()
return tmp
def play_audio_file(fname=DETECT_DING):
"""Simple callback function to play a wave file. By default it plays
a Ding sound.
:param str fname: wave file name
:return: None
"""
ding_wav = wave.open(fname, 'rb')
ding_data = ding_wav.readframes(ding_wav.getnframes())
with no_alsa_error():
audio = pyaudio.PyAudio()
stream_out = audio.open(
format=audio.get_format_from_width(ding_wav.getsampwidth()),
channels=ding_wav.getnchannels(),
rate=ding_wav.getframerate(), input=False, output=True)
stream_out.start_stream()
stream_out.write(ding_data)
time.sleep(0.2)
stream_out.stop_stream()
stream_out.close()
audio.terminate()
class ActiveListener(object):
""" Active Listening with VAD """
def __init__(self, decoder_model,
resource=RESOURCE_FILE):
logger.debug("activeListen __init__()")
self.recordedData = []
model_str = ",".join(decoder_model)
self.detector = snowboydetect.SnowboyDetect(
resource_filename=resource.encode(), model_str=model_str.encode())
self.ring_buffer = RingBuffer(
self.detector.NumChannels() * self.detector.SampleRate() * 5)
def listen(self, interrupt_check=lambda: False, sleep_time=0.03, silent_count_threshold=15, recording_timeout=100):
"""
:param interrupt_check: a function that returns True if the main loop
needs to stop.
:param silent_count_threshold: indicates how long silence must be heard
to mark the end of a phrase that is
being recorded.
:param float sleep_time: how much time in second every loop waits.
:param recording_timeout: limits the maximum length of a recording.
:return: recorded file path
"""
logger.debug("activeListen listen()")
self._running = True
def audio_callback(in_data, frame_count, time_info, status):
self.ring_buffer.extend(in_data)
play_data = chr(0) * len(in_data)
return play_data, pyaudio.paContinue
with no_alsa_error():
self.audio = pyaudio.PyAudio()
logger.debug('opening audio stream')
try:
self.stream_in = self.audio.open(
input=True, output=False,
format=self.audio.get_format_from_width(
self.detector.BitsPerSample() / 8),
channels=self.detector.NumChannels(),
rate=self.detector.SampleRate(),
frames_per_buffer=2048,
stream_callback=audio_callback)
except Exception as e:
logger.critical(e)
return
logger.debug('audio stream opened')
if interrupt_check():
logger.debug("detect voice return")
return
silentCount = 0
recordingCount = 0
logger.debug("begin activeListen loop")
while self._running is True:
if interrupt_check():
logger.debug("detect voice break")
break
data = self.ring_buffer.get()
if len(data) == 0:
time.sleep(sleep_time)
continue
status = self.detector.RunDetection(data)
if status == -1:
logger.warning("Error initializing streams or reading audio data")
stopRecording = False
if recordingCount > recording_timeout:
stopRecording = True
elif status == -2: #silence found
if silentCount > silent_count_threshold:
stopRecording = True
else:
silentCount = silentCount + 1
elif status == 0: #voice found
silentCount = 0
if stopRecording == True:
return self.saveMessage()
recordingCount = recordingCount + 1
self.recordedData.append(data)
logger.debug("finished.")
def saveMessage(self):
"""
Save the message stored in self.recordedData to a timestamped file.
"""
filename = os.path.join(constants.TEMP_PATH, 'output' + str(int(time.time())) + '.wav')
data = b''.join(self.recordedData)
#use wave to save data
wf = wave.open(filename, 'wb')
wf.setnchannels(self.detector.NumChannels())
wf.setsampwidth(self.audio.get_sample_size(
self.audio.get_format_from_width(self.detector.BitsPerSample() / 8)))
wf.setframerate(self.detector.SampleRate())
wf.writeframes(data)
wf.close()
logger.debug("finished saving: " + filename)
self.stream_in.stop_stream()
self.stream_in.close()
self.audio.terminate()
return filename
class HotwordDetector(object):
"""
Snowboy decoder to detect whether a keyword specified by `decoder_model`
exists in a microphone input stream.
:param decoder_model: decoder model file path, a string or a list of strings
:param resource: resource file path.
:param sensitivity: decoder sensitivity, a float of a list of floats.
The bigger the value, the more senstive the
decoder. If an empty list is provided, then the
default sensitivity in the model will be used.
:param audio_gain: multiply input volume by this factor.
:param apply_frontend: applies the frontend processing algorithm if True.
"""
def __init__(self, decoder_model,
resource=RESOURCE_FILE,
sensitivity=[],
audio_gain=1,
apply_frontend=False):
self._running = False
tm = type(decoder_model)
ts = type(sensitivity)
if tm is not list:
decoder_model = [decoder_model]
if ts is not list:
sensitivity = [sensitivity]
model_str = ",".join(decoder_model)
self.detector = snowboydetect.SnowboyDetect(
resource_filename=resource.encode(), model_str=model_str.encode())
self.detector.SetAudioGain(audio_gain)
self.detector.ApplyFrontend(apply_frontend)
self.num_hotwords = self.detector.NumHotwords()
if len(decoder_model) > 1 and len(sensitivity) == 1:
sensitivity = sensitivity * self.num_hotwords
if len(sensitivity) != 0:
assert self.num_hotwords == len(sensitivity), \
"number of hotwords in decoder_model (%d) and sensitivity " \
"(%d) does not match" % (self.num_hotwords, len(sensitivity))
sensitivity_str = ",".join([str(t) for t in sensitivity])
if len(sensitivity) != 0:
self.detector.SetSensitivity(sensitivity_str.encode())
self.ring_buffer = RingBuffer(
self.detector.NumChannels() * self.detector.SampleRate() * 5)
def start(self, detected_callback=play_audio_file,
interrupt_check=lambda: False,
sleep_time=0.03,
audio_recorder_callback=None,
silent_count_threshold=15,
recording_timeout=100):
"""
Start the voice detector. For every `sleep_time` second it checks the
audio buffer for triggering keywords. If detected, then call
corresponding function in `detected_callback`, which can be a single
function (single model) or a list of callback functions (multiple
models). Every loop it also calls `interrupt_check` -- if it returns
True, then breaks from the loop and return.
:param detected_callback: a function or list of functions. The number of
items must match the number of models in
`decoder_model`.
:param interrupt_check: a function that returns True if the main loop
needs to stop.
:param float sleep_time: how much time in second every loop waits.
:param audio_recorder_callback: if specified, this will be called after
a keyword has been spoken and after the
phrase immediately after the keyword has
been recorded. The function will be
passed the name of the file where the
phrase was recorded.
:param silent_count_threshold: indicates how long silence must be heard
to mark the end of a phrase that is
being recorded.
:param recording_timeout: limits the maximum length of a recording.
:return: None
"""
self._running = True
def audio_callback(in_data, frame_count, time_info, status):
self.ring_buffer.extend(in_data)
play_data = chr(0) * len(in_data)
return play_data, pyaudio.paContinue
with no_alsa_error():
self.audio = pyaudio.PyAudio()
self.stream_in = self.audio.open(
input=True, output=False,
format=self.audio.get_format_from_width(
self.detector.BitsPerSample() / 8),
channels=self.detector.NumChannels(),
rate=self.detector.SampleRate(),
frames_per_buffer=2048,
stream_callback=audio_callback)
if interrupt_check():
logger.debug("detect voice return")
return
tc = type(detected_callback)
if tc is not list:
detected_callback = [detected_callback]
if len(detected_callback) == 1 and self.num_hotwords > 1:
detected_callback *= self.num_hotwords
assert self.num_hotwords == len(detected_callback), \
"Error: hotwords in your models (%d) do not match the number of " \
"callbacks (%d)" % (self.num_hotwords, len(detected_callback))
logger.debug("detecting...")
state = "PASSIVE"
while self._running is True:
if interrupt_check():
logger.debug("detect voice break")
break
data = self.ring_buffer.get()
if len(data) == 0:
time.sleep(sleep_time)
continue
status = self.detector.RunDetection(data)
if status == -1:
logger.warning("Error initializing streams or reading audio data")
#small state machine to handle recording of phrase after keyword
if state == "PASSIVE":
if status > 0: #key word found
self.recordedData = []
self.recordedData.append(data)
silentCount = 0
recordingCount = 0
message = "Keyword " + str(status) + " detected at time: "
message += time.strftime("%Y-%m-%d %H:%M:%S",
time.localtime(time.time()))
logger.info(message)
callback = detected_callback[status-1]
if callback is not None:
callback()
if audio_recorder_callback is not None and status == 1 and utils.is_proper_time():
state = "ACTIVE"
continue
elif state == "ACTIVE":
stopRecording = False
if recordingCount > recording_timeout:
stopRecording = True
elif status == -2: #silence found
if silentCount > silent_count_threshold:
stopRecording = True
else:
silentCount = silentCount + 1
elif status == 0: #voice found
silentCount = 0
if stopRecording == True:
fname = self.saveMessage()
audio_recorder_callback(fname)
state = "PASSIVE"
continue
recordingCount = recordingCount + 1
self.recordedData.append(data)
logger.debug("finished.")
def saveMessage(self):
"""
Save the message stored in self.recordedData to a timestamped file.
"""
filename = os.path.join(constants.TEMP_PATH, 'output' + str(int(time.time())) + '.wav')
data = b''.join(self.recordedData)
#use wave to save data
wf = wave.open(filename, 'wb')
wf.setnchannels(self.detector.NumChannels())
wf.setsampwidth(self.audio.get_sample_size(
self.audio.get_format_from_width(
self.detector.BitsPerSample() / 8)))
wf.setframerate(self.detector.SampleRate())
wf.writeframes(data)
wf.close()
logger.debug("finished saving: " + filename)
return filename
def terminate(self):
"""
Terminate audio stream. Users can call start() again to detect.
:return: None
"""
if self._running:
self.stream_in.stop_stream()
self.stream_in.close()
self.audio.terminate()
self._running = False
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