def __init__(self, microphone):
# type: (AbstractMicrophone) -> VAD
"""
Perform Voice Activity Detection on Microphone Input
Parameters
----------
microphone: AbstractMicrophone
"""
self._microphone = microphone
self._vad = Vad(VAD.MODE)
# Voice Activity Detection Frame Size, Atomic VAD Unit
self._frame_size = VAD.AUDIO_FRAME_MS * self.microphone.rate // 1000
self._frame_size_bytes = self._frame_size * VAD.AUDIO_TYPE_BYTES
self._audio_buffer = np.zeros((VAD.BUFFER_SIZE, self._frame_size),
VAD.AUDIO_TYPE)
self._voice_buffer = np.zeros(VAD.BUFFER_SIZE, np.bool)
self._buffer_index = 0
self._utterance = None
self._utterance_queue = Queue()
self._frame_buffer = bytearray()
self._activation = 0
self.microphone.callbacks += [self._on_audio]
def __init__(self, microphone):
# type: (AbstractMicrophone) -> None
self._microphone = microphone
self._vad = Vad(VAD.MODE)
# Voice Activity Detection Frame Size: VAD works in units of 'frames'
self._frame_size = VAD.AUDIO_FRAME_MS * self.microphone.rate // 1000
self._frame_size_bytes = self._frame_size * VAD.AUDIO_TYPE_BYTES
# Audio & Voice Ring-Buffers
self._audio_buffer = np.zeros((VAD.BUFFER_SIZE, self._frame_size),
VAD.AUDIO_TYPE)
self._voice_buffer = np.zeros(VAD.BUFFER_SIZE, np.bool)
self._buffer_index = 0
self._voice = None
self._voice_queue = Queue()
self._frame_buffer = bytearray()
self._activation = 0
# Subscribe VAD to Microphone on_audio event
self.microphone.callbacks += [self._on_audio]
def __init__(self, samplerate, on_noise=None):
self.samplerate = samplerate
self.speech_timeout = SPEECH_TIMEOUT
self.on_noise = on_noise
self.listening = Lock()
self.vad = Vad()
self.vad.set_mode(3) # very restrictive filtering
def webrtc_split(audio,
rate,
aggressiveness=3,
frame_duration_ms=30,
window_duration_ms=300):
# adapted from https://github.com/wiseman/py-webrtcvad/blob/master/example.py
audio_bytes, audio_rate = to_pcm16(audio, rate)
vad = Vad(aggressiveness)
num_window_frames = int(window_duration_ms / frame_duration_ms)
sliding_window = collections.deque(maxlen=num_window_frames)
triggered = False
voiced_frames = []
for frame in generate_frames(audio_bytes, audio_rate, frame_duration_ms):
is_speech = vad.is_speech(frame.bytes, audio_rate)
sliding_window.append((frame, is_speech))
if not triggered:
num_voiced = len([f for f, speech in sliding_window if speech])
if num_voiced > 0.9 * sliding_window.maxlen:
triggered = True
voiced_frames += [frame for frame, _ in sliding_window]
sliding_window.clear()
else:
voiced_frames.append(frame)
num_unvoiced = len(
[f for f, speech in sliding_window if not speech])
if num_unvoiced > 0.9 * sliding_window.maxlen:
triggered = False
yield voiced_frames, audio_rate
sliding_window.clear()
voiced_frames = []
if voiced_frames:
yield voiced_frames, audio_rate
def __init__(self):
super().__init__()
self._stopPlayingFlag: Optional[AliceEvent] = None
self._playing = False
self._waves: Dict[str, wave.Wave_write] = dict()
if self.ConfigManager.getAliceConfigByName('disableSoundAndMic'):
return
with self.Commons.shutUpAlsaFFS():
self._audio = pyaudio.PyAudio()
self._vad = Vad(2)
try:
self._audioOutput = self._audio.get_default_output_device_info()
except:
self.logFatal('Audio output not found, cannot continue')
return
else:
self.logInfo(
f'Using **{self._audioOutput["name"]}** for audio output')
try:
self._audioInput = self._audio.get_default_input_device_info()
except:
self.logFatal('Audio input not found, cannot continue')
else:
self.logInfo(
f'Using **{self._audioInput["name"]}** for audio input')
def __init__(self, rate=16000, mode=0, duration=1000, on_inactive=None):
super(VAD, self).__init__()
self.rate = rate
self.vad = Vad(mode)
self.on_inactive = on_inactive
self.limit_inactive_cnt = duration / 10 # a frame is 10 ms
self.current_inactive_cnt = 0
def vad_split(
audio_frames,
audio_format=DEFAULT_FORMAT,
num_padding_frames=10,
threshold=0.5,
aggressiveness=3,
):
sample_rate, channels, width = audio_format
if channels != 1:
raise ValueError("VAD-splitting requires mono samples")
if width != 2:
raise ValueError("VAD-splitting requires 16 bit samples")
if sample_rate not in [8000, 16000, 32000, 48000]:
raise ValueError(
"VAD-splitting only supported for sample rates 8000, 16000, 32000, or 48000"
)
if aggressiveness not in [0, 1, 2, 3]:
raise ValueError(
"VAD-splitting aggressiveness mode has to be one of 0, 1, 2, or 3")
ring_buffer = collections.deque(maxlen=num_padding_frames)
triggered = False
vad = Vad(int(aggressiveness))
voiced_frames = []
frame_duration_ms = 0
frame_index = 0
for frame_index, frame in enumerate(audio_frames):
frame_duration_ms = get_pcm_duration(len(frame), audio_format) * 1000
if int(frame_duration_ms) not in [10, 20, 30]:
raise ValueError(
"VAD-splitting only supported for frame durations 10, 20, or 30 ms"
)
is_speech = vad.is_speech(frame, sample_rate)
if not triggered:
ring_buffer.append((frame, is_speech))
num_voiced = len([f for f, speech in ring_buffer if speech])
if num_voiced > threshold * ring_buffer.maxlen:
triggered = True
for f, s in ring_buffer:
voiced_frames.append(f)
ring_buffer.clear()
else:
voiced_frames.append(frame)
ring_buffer.append((frame, is_speech))
num_unvoiced = len([f for f, speech in ring_buffer if not speech])
if num_unvoiced > threshold * ring_buffer.maxlen:
triggered = False
yield b"".join(voiced_frames), frame_duration_ms * max(
0, frame_index -
len(voiced_frames)), frame_duration_ms * frame_index
ring_buffer.clear()
voiced_frames = []
if len(voiced_frames) > 0:
yield b"".join(voiced_frames), frame_duration_ms * (
frame_index -
len(voiced_frames)), frame_duration_ms * (frame_index + 1)
def __init__(self, rate: int = 8000, mode: int = 0):
"""Creates a VAD detector with the given configuration
Args:
rate (int): The audio sample rate, in Hz.
mode (int): Operational mode,
must be [0, 3]
"""
self.__rate = rate
self.__mode = mode
self.__vad = Vad(mode=mode)
def __init__(self):
super().__init__()
self._stopPlayingFlag: Optional[AliceEvent] = None
self._playing = False
self._waves: Dict[str, wave.Wave_write] = dict()
self._audioInputStream = None
if not self.ConfigManager.getAliceConfigByName('disableCapture'):
self._vad = Vad(2)
self._audioInput = None
self._audioOutput = None
def check_for_speech(self, frame_duration_ms=20):
"""Checks for speech.
:param int frame_duration_ms: Audio frame length in ms.
"""
vad = Vad(self.vad_aggressiveness)
speaking = False # to keep track of if vocalization ongoing
n = int(SAMPLE_RATE * (frame_duration_ms / 1000.) * 2)
# duration = n / SAMPLE_RATE / 2.0
last_timestamp_sent = 0
while not self.done.is_set():
chunk = self.data_queue.get()
offset = 0
framecount = []
while offset + n < len(chunk):
now = time.time(
) * 1000.0 # caveat: this is not the same as PyEPL's clock...
frame = chunk[offset:offset + n]
if vad.is_speech(frame, SAMPLE_RATE):
framecount.append({"timestamp": now})
if len(framecount
) >= self.consecutive_frames and not speaking:
speaking = True
payload = {
"speaking": True,
"timestamp": framecount[0]["timestamp"]
}
self.pipe.send(ipc.message("VOCALIZATION", payload))
self.logger.debug("Started speaking at %f", now)
else:
if speaking:
speaking = False
payload = {"speaking": False, "timestamp": now}
self.pipe.send(ipc.message("VOCALIZATION", payload))
self.logger.debug("Stopped speaking at %f", now)
framecount = []
offset += n
now = time.time() * 1000
if now - last_timestamp_sent >= 1000:
self.pipe.send(ipc.message("TIMESTAMP", dict(timestamp=now)))
last_timestamp_sent = now
def iter_wav_chunks(input_uri,
input_format,
framerate=16000,
vad_duration=0.02,
min_chunk_len=2,
max_chunk_len=10):
vad = Vad(2)
bufferbytes = io.BytesIO()
buffersize = 0
bufferduration = 0
remains = b''
audio_offset = .0
for ok, *payload in \
stream2wav(input_uri, input_format, framerate):
if not ok:
raise RuntimeError(payload[0])
header, body, _, secondsize = payload
chunksize = round(secondsize * 0.02) # 20ms
body = remains + body
if min_chunk_len < 0:
# no limit
bufferbytes.write(body)
buffersize += len(body)
bufferduration = buffersize / secondsize
continue
for offset in range(0, len(body), chunksize):
chunk = body[offset:offset + chunksize]
if len(chunk) < chunksize:
remains = chunk
break
if bufferduration < min_chunk_len or \
(bufferduration < max_chunk_len and
vad.is_speech(chunk, framerate)):
bufferbytes.write(chunk)
buffersize += chunksize
bufferduration += chunksize / secondsize
elif buffersize > 0:
audiodata = bufferbytes.getvalue() + chunk
duration = len(audiodata) / secondsize
yield (header, audiodata, duration, audio_offset)
audio_offset += duration
bufferbytes = io.BytesIO()
buffersize = 0
bufferduration = 0
if buffersize > 0:
audiodata = bufferbytes.getvalue() + remains
duration = len(audiodata) / secondsize
yield (header, audiodata, duration, audio_offset)
def vad_filter(sample_rate: int, vad: webrtcvad.Vad,
frames: List[bytes]) -> bytes:
"""
# Adapted from https://github.com/wiseman/py-webrtcvad/blob/3b39545dbb026d998bf407f1cb86e0ed6192a5a6/example.py#L45
Filters out non-voiced audio frames.
Given a webrtcvad.Vad and a source of audio frames, returns the voiced audio.
Uses a padded, sliding window algorithm over the audio frames.
All frames between [prev(first(speech_frame)), next(last(speech_frame))] will be considered as speech frames.
This is done because we want to remove leading and tailing silence here.
Arguments:
sample_rate - The audio sample rate, in Hz.
vad - An instance of webrtcvad.Vad.
frames - a source of audio frames.
Returns: the speech withing the frames.
"""
voiced_frames_offsets = [
i for i, frame in enumerate(frames)
if vad.is_speech(frame, sample_rate)
]
if len(voiced_frames_offsets) > 0:
return b''.join(frames[max(voiced_frames_offsets[0] -
1, 0):min(voiced_frames_offsets[-1] +
2, len(frames))])
# + 2 because b is not included in list[a:b]
else:
return b''.join(frames)
class VoiceOver(object):
"""
Bag of state for tracking stuff in the stream callback.
"""
def __init__(self):
self.input_buf = np.empty((BLOCK_SIZE, 1), dtype=np.int16)
self.vad = Vad(2)
self.vad_q = deque([False], LAG_TIME)
self.output = cycle(open_wav())
def output_take(self, n_bytes):
return list(islice(self.output, n_bytes))
def input_is_talking(self):
return sum(self.vad_q) > len(self.vad_q) * NECESSARY_FRACTION
def callback(self, in_data, out_data, time_info, status):
self.input_buf = np.concatenate((self.input_buf, in_data))
if len(self.input_buf) > HOP_SIZE: # we can pass data to vad
ten_ms, rest = (self.input_buf[0:HOP_SIZE],
self.input_buf[HOP_SIZE:])
resampled_to_32k = resample(ten_ms, 3 * 320,
axis=0).astype(np.int16).tostring()
self.vad_q.append(self.vad.is_speech(resampled_to_32k, 32000))
self.input_buf = rest
if self.input_is_talking():
out_data[:] = self.output_take(len(out_data))
else:
out_data[:] = np.zeros(out_data.shape)
return continue_flag
class VAD(Element):
def __init__(self, rate=16000, mode=0, duration=1000, on_inactive=None):
super(VAD, self).__init__()
self.rate = rate
self.vad = Vad(mode)
self.on_inactive = on_inactive
self.limit_inactive_cnt = duration / 10 # a frame is 10 ms
self.current_inactive_cnt = 0
def put(self, data):
active = self.vad.is_speech(data, self.rate)
if active:
self.current_inactive_cnt = 0
else:
self.current_inactive_cnt += 1
if self.current_inactive_cnt == self.limit_inactive_cnt:
if callable(self.on_inactive):
self.on_inactive()
self.current_inactive_cnt = 0
super(VAD, self).put(data)
def on_inactive(self, cb):
self.on_inactive = cb
class Listener:
q = Queue()
def __init__(self, samplerate, on_noise=None):
self.samplerate = samplerate
self.speech_timeout = SPEECH_TIMEOUT
self.on_noise = on_noise
self.listening = Lock()
self.vad = Vad()
self.vad.set_mode(3) # very restrictive filtering
@staticmethod
def _device_callback(indata, frames, time, status):
"""
This is called (from a separate thread) for each audio block.
"""
Listener.q.put(bytes(indata))
def record(self):
recorded_data = b''
current = time.time()
end = time.time() + self.speech_timeout
# record until no sound is detected or time is over
while current <= end:
data = Listener.q.get()
recorded_data += data
if self.vad.is_speech(data, self.samplerate):
end = time.time() + self.speech_timeout
current = time.time()
#print(end - start)
return recorded_data
def _start(self):
self.listening.acquire()
with sd.RawInputStream(samplerate=self.samplerate, channels=1, callback=Listener._device_callback, dtype='int16', blocksize=int(self.samplerate * 0.03)):
while self.listening.locked():
data = Listener.q.get()
if self.on_noise is not None:
self.on_noise(data)
def start(self):
Thread(target=self._start).start()
def stop(self):
if self.listening.locked():
self.listening.release()
def get_voice_events(filename, frame_dur, aggressiveness):
"""Evaluate the file for voice events.
:param str filename:
:param int frame_dur:
:param int aggressiveness:
"""
assert frame_dur in [10, 20, 30]
assert aggressiveness in range(4)
vad = Vad()
vad.set_mode(args.aggressiveness)
sample_rate = 16000
frame_dur = args.frame_duration
clip = downsample(filename, sample_rate).read()
return [
(frame_dur*n, vad.is_speech(frame.bytes, sample_rate))
for n, frame in enumerate(frame_generator(clip, frame_dur, sample_rate))
]
class VAD(Element):
def __init__(self, rate=16000, mode=0):
super(VAD, self).__init__()
self.rate = rate
self.vad = Vad(mode)
def put(self, data):
voice = '1' if self.vad.is_speech(data, self.rate) else '0'
sys.stdout.write(voice)
sys.stdout.flush()
super(VAD, self).put(data)
def trim_long_silences(wav: np.ndarray,
vad: webrtcvad.Vad = None) -> np.ndarray:
"""
Ensures that segments without voice in the waveform remain no longer than a
threshold determined by the VAD parameters in params.py.
:param wav: the raw waveform as a numpy array of floats
:param vad: an webrtcvad.Vad object. A new one with mode=3 will be created if None.
:return: the same waveform with silences trimmed away (length <= original wav length)
"""
# Compute the voice detection window size
samples_per_window = (vad_window_length * sampling_rate) // 1000
# Trim the end of the audio to have a multiple of the window size
wav = wav[:len(wav) - (len(wav) % samples_per_window)]
# Convert the float waveform to 16-bit mono PCM
pcm_wave = struct.pack(f'{len(wav)}h',
*(np.round(wav * int16_max)).astype(np.int16))
# Perform voice activation detection
voice_flags = []
if vad is None:
vad = webrtcvad.Vad(mode=3)
for window_start in range(0, len(wav), samples_per_window):
window_end = window_start + samples_per_window
voice_flags.append(
vad.is_speech(pcm_wave[window_start * 2:window_end * 2],
sample_rate=sampling_rate))
voice_flags = np.array(voice_flags)
# Smooth the voice detection with a moving average
def moving_average(array, width):
array_padded = np.concatenate((np.zeros(
(width - 1) // 2), array, np.zeros(width // 2)))
ret = np.cumsum(array_padded, dtype=float)
ret[width:] = ret[width:] - ret[:-width]
return ret[width - 1:] / width
audio_mask = moving_average(voice_flags, vad_moving_average_width)
audio_mask = np.round(audio_mask).astype(np.bool)
# Dilate the voiced regions
audio_mask = binary_dilation(audio_mask,
np.ones(vad_max_silence_length + 1))
audio_mask = np.repeat(audio_mask, samples_per_window)
return wav[audio_mask]
def vad_segment_generator(audio_file, aggressiveness):
"""
Generate VAD segments. Filters out non-voiced audio frames.
:param audio_file: Input audio file to run VAD on.
:param aggressiveness: How aggressive filtering out non-speech is (between 0 and 3)
:return: Returns tuple of
segments: a bytearray of multiple smaller audio frames
(The longer audio split into multiple smaller one's)
sample_rate: Sample rate of the input audio file
audio_length: Duration of the input audio file
"""
audio = av.open(audio_file)
sample_rate = 16000
frames = frame_generator(30, audio.decode(audio=0), sample_rate)
vad = Vad(int(aggressiveness))
segments = vad_collector(sample_rate, 30, 300, 0.5, vad, frames)
return segments, sample_rate, audio.duration / av.time_base
def vad_segment_generator(audio_file, aggressiveness):
"""
Generate VAD segments. Filters out non-voiced audio frames.
:param audio_file: Input audio file to run VAD on.
:param aggressiveness: How aggressive filtering out non-speech is (between 0 and 3)
:return: Returns tuple of
segments: a bytearray of multiple smaller audio frames
(The longer audio split into multiple smaller one's)
sample_rate: Sample rate of the input audio file
audio_length: Duration of the input audio file
"""
audio = (AudioSegment.from_file(audio_file)
.set_channels(1)
.set_frame_rate(16000))
vad = Vad(int(aggressiveness))
frames = frame_generator(30, audio.raw_data, audio.frame_rate)
segments = vad_collector(audio.frame_rate, 30, 300, 0.5, vad, frames)
return segments, audio.frame_rate, audio.duration_seconds * 1000
def __init__(self, microphone, callbacks, mode=3):
"""
Detect Utterances of People using Voice Activity Detection
Parameters
----------
microphone: AbstractMicrophone
Microphone to extract Utterances from
callbacks: list of callable
On Utterance Callback
mode: int
Voice Activity Detection (VAD) 'Aggressiveness' (1..3)
"""
self._microphone = microphone
self._microphone.callbacks += [self._on_audio]
self._rate = microphone.rate
self._callbacks = callbacks
self._vad = Vad(mode)
# Number of Elements (np.int16) in Frame
self._frame_size = self.FRAME_MS * self.rate // 1000
self._ringbuffer_index = 0
self._activation = 0
# Initialize Ringbuffers, which will hold Audio data and Vad.is_speech results, respectively
self._audio_ringbuffer = np.zeros((self.BUFFER_SIZE, self._frame_size),
np.int16)
self._vad_ringbuffer = np.zeros(self.BUFFER_SIZE, np.bool)
self._audio_buffer = bytearray(
) # Audio Buffer will be filled with raw Microphone Audio
self._voice_buffer = bytearray(
) # Voice Buffer will be filled with Voiced Audio
self._voice = False # No Voice is present at start
self._log = logger.getChild(self.__class__.__name__)
self._log.debug("Booted")
def __init__(self, rate=16000, mode=0):
super(VAD, self).__init__()
self.rate = rate
self.vad = Vad(mode)
class VAD:
"""This class implements a Voice Activity Detector.
The voice activity detector is a critical component in any speech
processing application. It is able to identify the presence or absence of
human speech in an audio frame.
Generally, It is used to deactivate some processes during non-speech
section of an audio session, saving on computation and on network bandwidth.
Notes:
This algorithm was implemented in the WebRTC project. The algorithm was
originally designed to work with 8KHz, 16 bit PCM, mono audio samples.
The algorithm accepts sampling rates of 8000Hz, 16000Hz, 32000Hz and
48000Hz, but internally all processing will be done 8000 Hz, input data
in higher sample rates will just be down-sampled first.
"""
def __init__(self, rate: int = 8000, mode: int = 0):
"""Creates a VAD detector with the given configuration
Args:
rate (int): The audio sample rate, in Hz.
mode (int): Operational mode,
must be [0, 3]
"""
self.__rate = rate
self.__mode = mode
self.__vad = Vad(mode=mode)
@property
def mode(self) -> int:
"""Returns an integer representing the operational mode"""
return self.__mode
@property
def sample_rate(self) -> int:
"""Returns the sampling rate in Hz."""
return self.__rate
@mode.setter
def mode(self, mode: int):
"""Set the operational mode of the VAD
A more aggressive (higher mode) VAD is more restrictive in reporting
speech.
Put in other words the probability of being speech when the VAD
returns 1 is increased with increasing mode. As a consequence also the
missed detection rate goes up.
Valid modes are:
- 0 ("quality"):
- 1 ("low bitrate"),
- 2 ("aggressive")
- 3 ("very aggressive").
The default mode is 0.
Args:
mode (int): Operational moder, must be [0, 3]
"""
self.__mode = mode
self.__vad.set_mode(mode)
@profile
def process(self, data: np.ndarray) -> bool:
"""Checks if the given data contains human speech.
Args:
data (np.ndarray): An array containing the data
Returns:
True if the audio data contains speech, false otherwise
Notes:
The input data must be an array of signed 16-bit samples or an array
of floating points storing values in the same range [-32,768,
32,768]
Only mono frames with a length of 10, 20 or 30 ms are supported. For
instance, if the class is using a sampling rate of 8KHz, the
processing function is expecting an numpy.ndarray of shape [80, N],
[160, N] or [240, N] where N is the number of channels in the input
data. The signal may be down-mixed to a single channel before
processing.
"""
mono = np.mean(a=data, axis=0, dtype=np.float32)
mono = Converter.fromFloatToInt16(mono)
mono = Converter.interleave(mono)
result = self.__vad.is_speech(buf=mono,
sample_rate=self.sample_rate,
length=mono.size())
if (result < 0):
raise RuntimeError(
"Invalid frame length. Only frames with a length of 10, 20 or 30 ms are supported."
)
return result
def vad_collector(sample_rate, frame_duration_ms, padding_duration_ms,
aggressiveness, audio):
"""Filters out non-voiced audio frames.
Given a webrtcvad.Vad and a source of audio frames, yields only
the voiced audio.
Uses a padded, sliding window algorithm over the audio frames.
When more than 90% of the frames in the window are voiced (as
reported by the VAD), the collector triggers and begins yielding
audio frames. Then the collector waits until 90% of the frames in
the window are unvoiced to detrigger.
The window is padded at the front and back to provide a small
amount of silence or the beginnings/endings of speech around the
voiced frames.
Arguments:
sample_rate - The audio sample rate, in Hz.
frame_duration_ms - The frame duration in milliseconds.
padding_duration_ms - The amount to pad the window, in milliseconds.
vad - An instance of webrtcvad.Vad.
frames - a source of audio frames (sequence or generator).
Returns: A generator that yields PCM audio data.
"""
vad = Vad(aggressiveness)
num_padding_frames = int(padding_duration_ms / frame_duration_ms)
# We use a deque for our sliding window/ring buffer.
ring_buffer = collections.deque(maxlen=num_padding_frames)
# We have two states: TRIGGERED and NOTTRIGGERED. We start in the
# NOTTRIGGERED state.
triggered = False
voiced_frames = []
for frame in frame_generator(30, audio, sample_rate):
is_speech = vad.is_speech(frame, sample_rate)
if not triggered:
ring_buffer.append((frame, is_speech))
num_voiced = len([f for f, speech in ring_buffer if speech])
# If we're NOTTRIGGERED and more than 90% of the frames in
# the ring buffer are voiced frames, then enter the
# TRIGGERED state.
if num_voiced > 0.9 * ring_buffer.maxlen:
triggered = True
# We want to yield all the audio we see from now until
# we are NOTTRIGGERED, but we have to start with the
# audio that's already in the ring buffer.
for f, s in ring_buffer:
voiced_frames.append(f)
ring_buffer.clear()
else:
# We're in the TRIGGERED state, so collect the audio data
# and add it to the ring buffer.
voiced_frames.append(frame)
ring_buffer.append((frame, is_speech))
num_unvoiced = len([f for f, speech in ring_buffer if not speech])
# If more than 90% of the frames in the ring buffer are
# unvoiced, then enter NOTTRIGGERED and yield whatever
# audio we've collected.
if num_unvoiced > 0.9 * ring_buffer.maxlen:
triggered = False
yield b''.join([f for f in voiced_frames])
ring_buffer.clear()
voiced_frames = []
# If we have any leftover voiced audio when we run out of input,
# yield it.
if voiced_frames:
yield b''.join([f for f in voiced_frames])
class VAD(object):
AUDIO_FRAME_MS = 10
BUFFER_SIZE = 100
AUDIO_TYPE = np.int16
AUDIO_TYPE_BYTES = 2
VOICE_THRESHOLD = 0.6
VOICE_WINDOW = 50
MODE = 3
def __init__(self, microphone):
# type: (AbstractMicrophone) -> VAD
"""
Perform Voice Activity Detection on Microphone Input
Parameters
----------
microphone: AbstractMicrophone
"""
self._microphone = microphone
self._vad = Vad(VAD.MODE)
# Voice Activity Detection Frame Size, Atomic VAD Unit
self._frame_size = VAD.AUDIO_FRAME_MS * self.microphone.rate // 1000
self._frame_size_bytes = self._frame_size * VAD.AUDIO_TYPE_BYTES
self._audio_buffer = np.zeros((VAD.BUFFER_SIZE, self._frame_size),
VAD.AUDIO_TYPE)
self._voice_buffer = np.zeros(VAD.BUFFER_SIZE, np.bool)
self._buffer_index = 0
self._utterance = None
self._utterance_queue = Queue()
self._frame_buffer = bytearray()
self._activation = 0
self.microphone.callbacks += [self._on_audio]
@property
def microphone(self):
"""
VAD Microphone
Returns
-------
microphone: AbstractMicrophone
"""
return self._microphone
@property
def activation(self):
"""
VAD Activation
Returns
-------
activation: float
"""
return self._activation
@property
def utterances(self):
# type: () -> Iterable[Utterance]
"""
Get Utterances from Microphone Stream
Yields
-------
voices: Iterable of Voice
"""
while True:
yield self._utterance_queue.get()
def _on_audio(self, audio):
# type: (np.ndarray) -> None
# Work through Microphone Stream Frame by Frame
self._frame_buffer.extend(audio.tobytes())
while len(self._frame_buffer) >= self._frame_size_bytes:
self._on_frame(
np.frombuffer(self._frame_buffer[:self._frame_size_bytes],
VAD.AUDIO_TYPE))
del self._frame_buffer[:self._frame_size_bytes]
def _on_frame(self, frame):
self._activation = self._calculate_activation(frame)
if not self._utterance:
if self.activation > VAD.VOICE_THRESHOLD:
# Create New Utterance Object
self._utterance = Utterance()
# Add Buffer Contents to Utterance
self._utterance.add_frame(
self._audio_buffer[self._buffer_index:].ravel())
self._utterance.add_frame(
self._audio_buffer[:self._buffer_index].ravel())
# Add Utterance to Utterance Queue
self._utterance_queue.put(self._utterance)
else:
# If Utterance Ongoing: Add Frame to Utterance Object
if self.activation > VAD.VOICE_THRESHOLD:
self._utterance.add_frame(frame)
# Else: Terminate Utterance
else:
self._utterance.add_frame(None)
self._utterance = None
def _calculate_activation(self, frame):
# Update Buffers
self._audio_buffer[self._buffer_index] = frame
self._voice_buffer[self._buffer_index] = self._vad.is_speech(
frame.tobytes(), self.microphone.rate, len(frame))
self._buffer_index = (self._buffer_index + 1) % VAD.BUFFER_SIZE
# Calculate Activation
voice_window = np.arange(self._buffer_index - VAD.VOICE_WINDOW,
self._buffer_index) % VAD.BUFFER_SIZE
return np.mean(self._voice_buffer[voice_window])
def __iter__(self):
return self.utterances
class AudioManager(Manager):
SAMPLERATE = 16000
FRAMES_PER_BUFFER = 320
LAST_USER_SPEECH = 'var/cache/lastUserpeech_{}_{}.wav'
SECOND_LAST_USER_SPEECH = 'var/cache/secondLastUserSpeech_{}_{}.wav'
def __init__(self):
super().__init__()
self._stopPlayingFlag: Optional[AliceEvent] = None
self._playing = False
self._waves: Dict[str, wave.Wave_write] = dict()
self._audioInputStream = None
if not self.ConfigManager.getAliceConfigByName('disableCapture'):
self._vad = Vad(2)
self._audioInput = None
self._audioOutput = None
def onStart(self):
super().onStart()
if not self.ConfigManager.getAliceConfigByName('inputDevice'):
self.logWarning(
'Input device not set in config, trying to find default device'
)
try:
self._audioInput = sd.query_devices(kind='input')['name']
except:
self.logFatal('Audio input not found, cannot continue')
return
self.ConfigManager.updateAliceConfiguration(key='inputDevice',
value=self._audioInput)
else:
self._audioInput = self.ConfigManager.getAliceConfigByName(
'inputDevice')
if not self.ConfigManager.getAliceConfigByName('outputDevice'):
self.logWarning(
'Output device not set in config, trying to find default device'
)
try:
self._audioOutput = sd.query_devices(kind='output')['name']
except:
self.logFatal('Audio output not found, cannot continue')
return
self.ConfigManager.updateAliceConfiguration(
key='outputDevice', value=self._audioOutput)
else:
self._audioOutput = self.ConfigManager.getAliceConfigByName(
'outputDevice')
self.setDefaults()
self._stopPlayingFlag = self.ThreadManager.newEvent('stopPlaying')
self.MqttManager.mqttClient.subscribe(
constants.TOPIC_AUDIO_FRAME.format(
self.ConfigManager.getAliceConfigByName('uuid')))
def onBooted(self):
if not self.ConfigManager.getAliceConfigByName('disableCapture'):
self.ThreadManager.newThread(name='audioPublisher',
target=self.publishAudio)
def setDefaults(self):
self.logInfo(f'Using **{self._audioInput}** for audio input')
self.logInfo(f'Using **{self._audioOutput}** for audio output')
sd.default.device = self._audioInput, self._audioOutput
def onStop(self):
super().onStop()
if self._audioInputStream:
self._audioInputStream.stop(ignore_errors=True)
self._audioInputStream.close(ignore_errors=True)
self.MqttManager.mqttClient.unsubscribe(
constants.TOPIC_AUDIO_FRAME.format(
self.DeviceManager.getMainDevice().uid))
def onStartListening(self, session: DialogSession):
if not self.ConfigManager.getAliceConfigByName(
'recordAudioAfterWakeword'
) and self.WakewordRecorder.state != WakewordRecorderState.RECORDING:
return
path = Path(
self.LAST_USER_SPEECH.format(session.user, session.deviceUid))
if path.exists():
path.rename(
Path(
self.SECOND_LAST_USER_SPEECH.format(
session.user, session.deviceUid)))
waveFile = wave.open(str(path), 'wb')
waveFile.setsampwidth(2)
waveFile.setframerate(self.AudioServer.SAMPLERATE)
waveFile.setnchannels(1)
self._waves[session.deviceUid] = waveFile
def onCaptured(self, session: DialogSession):
wav = self._waves.pop(session.deviceUid, None)
if not wav:
return
wav.close()
def recordFrame(self, deviceUid: str, frame: bytes):
if deviceUid not in self._waves:
return
self._waves[deviceUid].writeframes(frame)
def publishAudio(self) -> None:
"""
captures the audio and broadcasts it via publishAudioFrames to the topic 'hermes/audioServer/{}/audioFrame'
furthermore it will publish VAD_UP and VAD_DOWN when detected
:return:
"""
self.logInfo('Starting audio publisher')
self._audioInputStream = sd.RawInputStream(
dtype='int16',
channels=1,
samplerate=self.SAMPLERATE,
blocksize=self.FRAMES_PER_BUFFER,
)
self._audioInputStream.start()
speech = False
silence = self.SAMPLERATE / self.FRAMES_PER_BUFFER
speechFrames = 0
minSpeechFrames = round(silence / 3)
while True:
if self.ProjectAlice.shuttingDown:
break
try:
frames = self._audioInputStream.read(
frames=self.FRAMES_PER_BUFFER)[0]
if self._vad.is_speech(frames, self.SAMPLERATE):
if not speech and speechFrames < minSpeechFrames:
speechFrames += 1
elif speechFrames >= minSpeechFrames:
speech = True
self.MqttManager.publish(
topic=constants.TOPIC_VAD_UP.format(
self.DeviceManager.getMainDevice().uid),
payload={
'siteId':
self.DeviceManager.getMainDevice().uid
})
silence = self.SAMPLERATE / self.FRAMES_PER_BUFFER
speechFrames = 0
else:
if speech:
if silence > 0:
silence -= 1
else:
speech = False
self.MqttManager.publish(
topic=constants.TOPIC_VAD_DOWN.format(
self.DeviceManager.getMainDevice().uid),
payload={
'siteId':
self.DeviceManager.getMainDevice().uid
})
else:
speechFrames = 0
self.publishAudioFrames(frames)
except Exception as e:
self.logDebug(f'Error publishing frame: {e}')
def publishAudioFrames(self, frames: bytes) -> None:
"""
receives some audio frames, adds them to the buffer and publishes them to MQTT
:param frames:
:return:
"""
with io.BytesIO() as buffer:
with wave.open(buffer, 'wb') as wav:
wav.setnchannels(1)
wav.setsampwidth(2)
wav.setframerate(self.SAMPLERATE)
wav.writeframes(frames)
audioFrames = buffer.getvalue()
self.MqttManager.publish(topic=constants.TOPIC_AUDIO_FRAME.format(
self.DeviceManager.getMainDevice().uid),
payload=bytearray(audioFrames))
def onPlayBytes(self,
payload: bytearray,
deviceUid: str,
sessionId: str = None,
requestId: str = None):
"""
Handles the playing of arbitrary bytes, be it sound, voice or even music.
Triggered via MQTT onPlayBytes topic.
Ignoring any request when sound is disabled via config
:param payload:
:param deviceUid:
:param sessionId:
:param requestId:
:return:
"""
if deviceUid != self.DeviceManager.getMainDevice(
).uid or self.ConfigManager.getAliceConfigByName(
'disableSound') or self.DeviceManager.getDevice(
uid=deviceUid).getParam('soundMuted'):
return
requestId = requestId or sessionId or str(uuid.uuid4())
if self.ConfigManager.getAliceConfigByName('debug'):
with Path('/tmp/onPlayBytes.wav').open('wb') as file:
file.write(payload)
self._playing = True
with io.BytesIO(payload) as buffer:
try:
with wave.open(buffer, 'rb') as wav:
channels = wav.getnchannels()
framerate = wav.getframerate()
def streamCallback(outData: buffer, frames: int,
_time: CData,
_status: sd.CallbackFlags):
data = wav.readframes(frames)
if len(data) < len(outData):
outData[:len(data)] = data
outData[len(data):] = b'\x00' * (len(outData) -
len(data))
raise sd.CallbackStop
else:
outData[:] = data
stream = sd.RawOutputStream(dtype='int16',
channels=channels,
samplerate=framerate,
callback=streamCallback)
self.logDebug(
f'Playing wav stream using **{self._audioOutput}** audio output from device **{self.DeviceManager.getDevice(uid=deviceUid).displayName}** (channels: {channels}, rate: {framerate})'
)
stream.start()
while stream.active:
if self._stopPlayingFlag.is_set():
if not sessionId:
raise PlayBytesStopped
session = self.DialogManager.getSession(
sessionId=sessionId)
if session.lastWasSoundPlayOnly:
raise PlayBytesStopped
self.MqttManager.publish(
topic=constants.TOPIC_TTS_FINISHED,
payload={
'id': requestId,
'sessionId': sessionId,
'siteId': deviceUid
})
self.DialogManager.onEndSession(session)
time.sleep(0.1)
except PlayBytesStopped:
self.logDebug('Playing bytes stopped')
except Exception as e:
self.logError(f'Playing wav failed with error: {e}')
finally:
self.logDebug('Playing bytes finished')
stream.stop()
stream.close()
self._stopPlayingFlag.clear()
self._playing = False
# Session id support is not Hermes protocol official
self.MqttManager.publish(
topic=constants.TOPIC_PLAY_BYTES_FINISHED.format(deviceUid),
payload={
'id': requestId,
'sessionId': sessionId
})
def stopPlaying(self):
self._stopPlayingFlag.set()
def updateAudioDevices(self):
self._audioInput = self.ConfigManager.getAliceConfigByName(
'inputDevice')
self._audioOutput = self.ConfigManager.getAliceConfigByName(
'outputDevice')
self.setDefaults()
@property
def isPlaying(self) -> bool:
return self._playing
class AudioManager(Manager):
SAMPLERATE = 16000
FRAMES_PER_BUFFER = 320
LAST_USER_SPEECH = 'var/cache/lastUserpeech_{}_{}.wav'
SECOND_LAST_USER_SPEECH = 'var/cache/secondLastUserSpeech_{}_{}.wav'
# Inspired by https://github.com/koenvervloesem/hermes-audio-server
def __init__(self):
super().__init__()
self._stopPlayingFlag: Optional[AliceEvent] = None
self._playing = False
self._waves: Dict[str, wave.Wave_write] = dict()
if self.ConfigManager.getAliceConfigByName('disableSoundAndMic'):
return
with self.Commons.shutUpAlsaFFS():
self._audio = pyaudio.PyAudio()
self._vad = Vad(2)
try:
self._audioOutput = self._audio.get_default_output_device_info()
except:
self.logFatal('Audio output not found, cannot continue')
return
else:
self.logInfo(
f'Using **{self._audioOutput["name"]}** for audio output')
try:
self._audioInput = self._audio.get_default_input_device_info()
except:
self.logFatal('Audio input not found, cannot continue')
else:
self.logInfo(
f'Using **{self._audioInput["name"]}** for audio input')
def onStart(self):
super().onStart()
self._stopPlayingFlag = self.ThreadManager.newEvent('stopPlaying')
self.MqttManager.mqttClient.subscribe(
constants.TOPIC_AUDIO_FRAME.format(
self.ConfigManager.getAliceConfigByName('uuid')))
if not self.ConfigManager.getAliceConfigByName('disableSoundAndMic'):
self.ThreadManager.newThread(name='audioPublisher',
target=self.publishAudio)
def onStop(self):
super().onStop()
self.MqttManager.mqttClient.unsubscribe(
constants.TOPIC_AUDIO_FRAME.format(
self.ConfigManager.getAliceConfigByName('uuid')))
if not self.ConfigManager.getAliceConfigByName('disableSoundAndMic'):
self._audio.terminate()
def onStartListening(self, session: DialogSession):
if not self.ConfigManager.getAliceConfigByName(
'recordAudioAfterWakeword'):
return
path = Path(self.LAST_USER_SPEECH.format(session.user, session.siteId))
if path.exists():
path.rename(
Path(
self.SECOND_LAST_USER_SPEECH.format(
session.user, session.siteId)))
waveFile = wave.open(str(path), 'wb')
waveFile.setsampwidth(2)
waveFile.setframerate(self.AudioServer.SAMPLERATE)
waveFile.setnchannels(1)
self._waves[session.siteId] = waveFile
def onCaptured(self, session: DialogSession):
wav = self._waves.pop(session.siteId, None)
if not wav:
return
wav.close()
def recordFrame(self, siteId: str, frame: bytes):
if siteId not in self._waves:
return
self._waves[siteId].writeframes(frame)
def publishAudio(self):
self.logInfo('Starting audio publisher')
audioStream = self._audio.open(
format=pyaudio.paInt16,
channels=1,
rate=self.SAMPLERATE,
frames_per_buffer=self.FRAMES_PER_BUFFER,
input=True)
speech = False
silence = self.SAMPLERATE / self.FRAMES_PER_BUFFER
speechFrames = 0
minSpeechFrames = round(silence / 3)
while True:
if self.ProjectAlice.shuttingDown:
break
try:
frames = audioStream.read(num_frames=self.FRAMES_PER_BUFFER,
exception_on_overflow=False)
if self._vad.is_speech(frames, self.SAMPLERATE):
if not speech and speechFrames < minSpeechFrames:
speechFrames += 1
elif speechFrames >= minSpeechFrames:
speech = True
self.MqttManager.publish(
topic=constants.TOPIC_VAD_UP.format(
self.ConfigManager.getAliceConfigByName(
'uuid')),
payload={
'siteId':
self.ConfigManager.getAliceConfigByName('uuid')
})
silence = self.SAMPLERATE / self.FRAMES_PER_BUFFER
speechFrames = 0
else:
if speech:
if silence > 0:
silence -= 1
else:
speech = False
self.MqttManager.publish(
topic=constants.TOPIC_VAD_DOWN.format(
self.ConfigManager.getAliceConfigByName(
'uuid')),
payload={
'siteId':
self.ConfigManager.getAliceConfigByName(
'uuid')
})
else:
speechFrames = 0
self.publishAudioFrames(frames)
except Exception as e:
self.logDebug(f'Error publishing frame: {e}')
def publishAudioFrames(self, frames: bytes):
with io.BytesIO() as buffer:
with wave.open(buffer, 'wb') as wav:
wav.setnchannels(1)
wav.setsampwidth(2)
wav.setframerate(self.SAMPLERATE)
wav.writeframes(frames)
audioFrames = buffer.getvalue()
self.MqttManager.publish(topic=constants.TOPIC_AUDIO_FRAME.format(
self.ConfigManager.getAliceConfigByName('uuid')),
payload=bytearray(audioFrames))
def onPlayBytes(self,
requestId: str,
payload: bytearray,
siteId: str,
sessionId: str = None):
if siteId != self.ConfigManager.getAliceConfigByName(
'uuid') or self.ConfigManager.getAliceConfigByName(
'disableSoundAndMic'):
return
self._playing = True
with io.BytesIO(payload) as buffer:
try:
with wave.open(buffer, 'rb') as wav:
sampleWidth = wav.getsampwidth()
nFormat = self._audio.get_format_from_width(sampleWidth)
channels = wav.getnchannels()
framerate = wav.getframerate()
def streamCallback(_inData, frameCount, _timeInfo,
_status) -> tuple:
data = wav.readframes(frameCount)
return data, pyaudio.paContinue
audioStream = self._audio.open(
format=nFormat,
channels=channels,
rate=framerate,
output=True,
output_device_index=self._audioOutput['index'],
stream_callback=streamCallback)
self.logDebug(
f'Playing wav stream using **{self._audioOutput["name"]}** audio output from site id **{self.DeviceManager.siteIdToDeviceName(siteId)}** (Format: {nFormat}, channels: {channels}, rate: {framerate})'
)
audioStream.start_stream()
while audioStream.is_active():
if self._stopPlayingFlag.is_set():
audioStream.stop_stream()
audioStream.close()
if sessionId:
self.MqttManager.publish(
topic=constants.TOPIC_TTS_FINISHED,
payload={
'id': requestId,
'sessionId': sessionId,
'siteId': siteId
})
self.DialogManager.onEndSession(
self.DialogManager.getSession(sessionId))
raise PlayBytesStopped
time.sleep(0.1)
audioStream.stop_stream()
audioStream.close()
except PlayBytesStopped:
self.logDebug('Playing bytes stopped')
except Exception as e:
self.logError(f'Playing wav failed with error: {e}')
finally:
self._stopPlayingFlag.clear()
self._playing = False
# Session id support is not Hermes protocol official
self.MqttManager.publish(
topic=constants.TOPIC_PLAY_BYTES_FINISHED.format(siteId),
payload={
'id': requestId,
'sessionId': sessionId
})
def stopPlaying(self):
self._stopPlayingFlag.set()
@property
def isPlaying(self) -> bool:
return self._playing
def vad_split(audio_frames, audio_format=DEFAULT_FORMAT, num_padding_frames=10, threshold=0.5, aggressiveness=3):
"""
Credit: https://github.com/mozilla/DSAlign
Splits audio into segments using Voice Activity Detection.
Parameters
----------
audio_frames : list
List of audio frames
audio_format : tuple
Tuple containing the audio sample rate, channels & width
num_padding_frames : int
Number of frames to pad
threshold : float
Minimum threshold
aggressiveness : int
Aggressivess of VAD split
Yields
-------
Audio segments (tuples containing number of frames, start time & end time))
"""
sample_rate, channels, width = audio_format
if channels != 1:
raise ValueError("VAD-splitting requires mono samples")
if width != 2:
raise ValueError("VAD-splitting requires 16 bit samples")
if sample_rate not in [8000, 16000, 32000, 48000]:
raise ValueError("VAD-splitting only supported for sample rates 8000, 16000, 32000, or 48000")
if aggressiveness not in [0, 1, 2, 3]:
raise ValueError("VAD-splitting aggressiveness mode has to be one of 0, 1, 2, or 3")
ring_buffer = collections.deque(maxlen=num_padding_frames)
triggered = False
vad = Vad(int(aggressiveness))
voiced_frames = []
frame_duration_ms = 0
frame_index = 0
for frame_index, frame in enumerate(audio_frames):
frame_duration_ms = get_pcm_duration(len(frame), audio_format) * 1000
if int(frame_duration_ms) not in [10, 20, 30]:
raise ValueError("VAD-splitting only supported for frame durations 10, 20, or 30 ms")
is_speech = vad.is_speech(frame, sample_rate)
if not triggered:
ring_buffer.append((frame, is_speech))
num_voiced = len([f for f, speech in ring_buffer if speech])
if num_voiced > threshold * ring_buffer.maxlen:
triggered = True
for f, s in ring_buffer:
voiced_frames.append(f)
ring_buffer.clear()
else:
voiced_frames.append(frame)
ring_buffer.append((frame, is_speech))
num_unvoiced = len([f for f, speech in ring_buffer if not speech])
if num_unvoiced > threshold * ring_buffer.maxlen:
triggered = False
yield b"".join(voiced_frames), frame_duration_ms * max(
0, frame_index - len(voiced_frames)
), frame_duration_ms * frame_index
ring_buffer.clear()
voiced_frames = []
if len(voiced_frames) > 0:
yield b"".join(voiced_frames), frame_duration_ms * (frame_index - len(voiced_frames)), frame_duration_ms * (
frame_index + 1
)
def __init__(self):
self.input_buf = np.empty((BLOCK_SIZE, 1), dtype=np.int16)
self.vad = Vad(2)
self.vad_q = deque([False], LAG_TIME)
self.output = cycle(open_wav())
class AudioManager(Manager):
SAMPLERATE = 16000
FRAMES_PER_BUFFER = 320
LAST_USER_SPEECH = 'var/cache/lastUserpeech_{}_{}.wav'
SECOND_LAST_USER_SPEECH = 'var/cache/secondLastUserSpeech_{}_{}.wav'
def __init__(self):
super().__init__()
self._stopPlayingFlag: Optional[AliceEvent] = None
self._playing = False
self._waves: Dict[str, wave.Wave_write] = dict()
self._audioInputStream = None
if self.ConfigManager.getAliceConfigByName('disableSoundAndMic'):
return
self._vad = Vad(2)
self._audioInput = None
self._audioOutput = None
def onStart(self):
super().onStart()
if not self.ConfigManager.getAliceConfigByName('inputDevice'):
self.logWarning(
'Input device not set in config, trying to find default device'
)
try:
self._audioInput = sd.query_devices(kind='input')['name']
except:
self.logFatal('Audio input not found, cannot continue')
return
self.ConfigManager.updateAliceConfiguration(key='inputDevice',
value=self._audioInput)
else:
self._audioInput = self.ConfigManager.getAliceConfigByName(
'inputDevice')
if not self.ConfigManager.getAliceConfigByName('outputDevice'):
self.logWarning(
'Output device not set in config, trying to find default device'
)
try:
self._audioOutput = sd.query_devices(kind='output')['name']
except:
self.logFatal('Audio output not found, cannot continue')
return
self.ConfigManager.updateAliceConfiguration(
key='outputDevice', value=self._audioOutput)
else:
self._audioOutput = self.ConfigManager.getAliceConfigByName(
'outputDevice')
self.setDefaults()
self._stopPlayingFlag = self.ThreadManager.newEvent('stopPlaying')
self.MqttManager.mqttClient.subscribe(
constants.TOPIC_AUDIO_FRAME.format(
self.ConfigManager.getAliceConfigByName('uuid')))
if not self.ConfigManager.getAliceConfigByName('disableSoundAndMic'):
self.ThreadManager.newThread(name='audioPublisher',
target=self.publishAudio)
def setDefaults(self):
self.logInfo(f'Using **{self._audioInput}** for audio input')
self.logInfo(f'Using **{self._audioOutput}** for audio output')
sd.default.device = self._audioInput, self._audioOutput
def onStop(self):
super().onStop()
self._audioInputStream.stop(ignore_errors=True)
self._audioInputStream.close(ignore_errors=True)
self.MqttManager.mqttClient.unsubscribe(
constants.TOPIC_AUDIO_FRAME.format(
self.ConfigManager.getAliceConfigByName('uuid')))
def onStartListening(self, session: DialogSession):
if not self.ConfigManager.getAliceConfigByName(
'recordAudioAfterWakeword'):
return
path = Path(self.LAST_USER_SPEECH.format(session.user, session.siteId))
if path.exists():
path.rename(
Path(
self.SECOND_LAST_USER_SPEECH.format(
session.user, session.siteId)))
waveFile = wave.open(str(path), 'wb')
waveFile.setsampwidth(2)
waveFile.setframerate(self.AudioServer.SAMPLERATE)
waveFile.setnchannels(1)
self._waves[session.siteId] = waveFile
def onCaptured(self, session: DialogSession):
wav = self._waves.pop(session.siteId, None)
if not wav:
return
wav.close()
def recordFrame(self, siteId: str, frame: bytes):
if siteId not in self._waves:
return
self._waves[siteId].writeframes(frame)
def publishAudio(self):
self.logInfo('Starting audio publisher')
self._audioInputStream = sd.RawInputStream(
dtype='int16',
channels=1,
samplerate=self.SAMPLERATE,
blocksize=self.FRAMES_PER_BUFFER,
)
self._audioInputStream.start()
speech = False
silence = self.SAMPLERATE / self.FRAMES_PER_BUFFER
speechFrames = 0
minSpeechFrames = round(silence / 3)
while True:
if self.ProjectAlice.shuttingDown:
break
try:
frames = self._audioInputStream.read(
frames=self.FRAMES_PER_BUFFER)[0]
if self._vad.is_speech(frames, self.SAMPLERATE):
if not speech and speechFrames < minSpeechFrames:
speechFrames += 1
elif speechFrames >= minSpeechFrames:
speech = True
self.MqttManager.publish(
topic=constants.TOPIC_VAD_UP.format(
self.ConfigManager.getAliceConfigByName(
'uuid')),
payload={
'siteId':
self.ConfigManager.getAliceConfigByName('uuid')
})
silence = self.SAMPLERATE / self.FRAMES_PER_BUFFER
speechFrames = 0
else:
if speech:
if silence > 0:
silence -= 1
else:
speech = False
self.MqttManager.publish(
topic=constants.TOPIC_VAD_DOWN.format(
self.ConfigManager.getAliceConfigByName(
'uuid')),
payload={
'siteId':
self.ConfigManager.getAliceConfigByName(
'uuid')
})
else:
speechFrames = 0
self.publishAudioFrames(frames)
except Exception as e:
self.logDebug(f'Error publishing frame: {e}')
def publishAudioFrames(self, frames: bytes):
with io.BytesIO() as buffer:
with wave.open(buffer, 'wb') as wav:
wav.setnchannels(1)
wav.setsampwidth(2)
wav.setframerate(self.SAMPLERATE)
wav.writeframes(frames)
audioFrames = buffer.getvalue()
self.MqttManager.publish(topic=constants.TOPIC_AUDIO_FRAME.format(
self.ConfigManager.getAliceConfigByName('uuid')),
payload=bytearray(audioFrames))
def onPlayBytes(self,
requestId: str,
payload: bytearray,
siteId: str,
sessionId: str = None):
if siteId != self.ConfigManager.getAliceConfigByName(
'uuid') or self.ConfigManager.getAliceConfigByName(
'disableSoundAndMic'):
return
self._playing = True
with io.BytesIO(payload) as buffer:
try:
with wave.open(buffer, 'rb') as wav:
channels = wav.getnchannels()
framerate = wav.getframerate()
def streamCallback(outdata, frameCount, _timeInfo,
_status):
data = wav.readframes(frameCount)
if len(data) < len(outdata):
outdata[:len(data)] = data
outdata[len(data):] = b'\x00' * (len(outdata) -
len(data))
raise sd.CallbackStop
else:
outdata[:] = data
stream = sd.RawOutputStream(dtype='int16',
channels=channels,
samplerate=framerate,
callback=streamCallback)
self.logDebug(
f'Playing wav stream using **{self._audioOutput}** audio output from site id **{self.DeviceManager.siteIdToDeviceName(siteId)}** (channels: {channels}, rate: {framerate})'
)
stream.start()
while stream.active:
if self._stopPlayingFlag.is_set():
stream.stop()
stream.close()
if sessionId:
self.MqttManager.publish(
topic=constants.TOPIC_TTS_FINISHED,
payload={
'id': requestId,
'sessionId': sessionId,
'siteId': siteId
})
self.DialogManager.onEndSession(
self.DialogManager.getSession(sessionId))
raise PlayBytesStopped
time.sleep(0.1)
stream.stop()
stream.close()
except PlayBytesStopped:
self.logDebug('Playing bytes stopped')
except Exception as e:
self.logError(f'Playing wav failed with error: {e}')
finally:
self._stopPlayingFlag.clear()
self._playing = False
# Session id support is not Hermes protocol official
self.MqttManager.publish(
topic=constants.TOPIC_PLAY_BYTES_FINISHED.format(siteId),
payload={
'id': requestId,
'sessionId': sessionId
})
def stopPlaying(self):
self._stopPlayingFlag.set()
def updateAudioDevices(self):
self._audioInput = self.ConfigManager.getAliceConfigByName(
'inputDevice')
self._audioOutput = self.ConfigManager.getAliceConfigByName(
'outputDevice')
self.setDefaults()
@property
def isPlaying(self) -> bool:
return self._playing
