247 lines
7.9 KiB
Python
247 lines
7.9 KiB
Python
# Python 2 + 3
|
|
# Copyright 2019 Mycroft AI Inc.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
import atexit
|
|
|
|
import time
|
|
from subprocess import PIPE, Popen
|
|
from threading import Thread, Event
|
|
|
|
|
|
class Engine:
|
|
def __init__(self, chunk_size=2048):
|
|
self.chunk_size = chunk_size
|
|
|
|
def start(self):
|
|
pass
|
|
|
|
def stop(self):
|
|
pass
|
|
|
|
def get_prediction(self, chunk):
|
|
raise NotImplementedError
|
|
|
|
|
|
class PreciseEngine(Engine):
|
|
"""
|
|
Wraps a binary precise executable
|
|
|
|
Args:
|
|
exe_file (Union[str, list]): Either filename or list of arguments
|
|
(ie. ['python', 'precise/scripts/engine.py'])
|
|
model_file (str): Location to .pb model file to use (with .pb.params)
|
|
chunk_size (int): Number of *bytes* per prediction. Higher numbers
|
|
decrease CPU usage but increase latency
|
|
"""
|
|
|
|
def __init__(self, exe_file, model_file, chunk_size=2048):
|
|
Engine.__init__(self, chunk_size)
|
|
self.exe_args = exe_file if isinstance(exe_file, list) else [exe_file]
|
|
self.exe_args += [model_file, str(self.chunk_size)]
|
|
self.proc = None
|
|
|
|
def start(self):
|
|
self.proc = Popen(self.exe_args, stdin=PIPE, stdout=PIPE)
|
|
|
|
def stop(self):
|
|
if self.proc:
|
|
self.proc.kill()
|
|
self.proc = None
|
|
|
|
def get_prediction(self, chunk):
|
|
if len(chunk) != self.chunk_size:
|
|
raise ValueError('Invalid chunk size: ' + str(len(chunk)))
|
|
self.proc.stdin.write(chunk)
|
|
self.proc.stdin.flush()
|
|
return float(self.proc.stdout.readline())
|
|
|
|
|
|
class ListenerEngine(Engine):
|
|
def __init__(self, listener, chunk_size=2048):
|
|
Engine.__init__(self, chunk_size)
|
|
self.get_prediction = listener.update
|
|
|
|
|
|
class ReadWriteStream:
|
|
"""
|
|
Class used to support writing binary audio data at any pace,
|
|
optionally chopping when the buffer gets too large
|
|
"""
|
|
def __init__(self, s=b'', chop_samples=-1):
|
|
self.buffer = s
|
|
self.write_event = Event()
|
|
self.chop_samples = chop_samples
|
|
|
|
def __len__(self):
|
|
return len(self.buffer)
|
|
|
|
def read(self, n=-1, timeout=None):
|
|
if n == -1:
|
|
n = len(self.buffer)
|
|
if 0 < self.chop_samples < len(self.buffer):
|
|
samples_left = len(self.buffer) % self.chop_samples
|
|
self.buffer = self.buffer[-samples_left:]
|
|
return_time = 1e10 if timeout is None else (
|
|
timeout + time.time()
|
|
)
|
|
while len(self.buffer) < n:
|
|
self.write_event.clear()
|
|
if not self.write_event.wait(return_time - time.time()):
|
|
return b''
|
|
chunk = self.buffer[:n]
|
|
self.buffer = self.buffer[n:]
|
|
return chunk
|
|
|
|
def write(self, s):
|
|
self.buffer += s
|
|
self.write_event.set()
|
|
|
|
def flush(self):
|
|
"""Makes compatible with sys.stdout"""
|
|
pass
|
|
|
|
|
|
class TriggerDetector:
|
|
"""
|
|
Reads predictions and detects activations
|
|
This prevents multiple close activations from occurring when
|
|
the predictions look like ...!!!..!!...
|
|
"""
|
|
def __init__(self, chunk_size, sensitivity=0.5, trigger_level=3):
|
|
self.chunk_size = chunk_size
|
|
self.sensitivity = sensitivity
|
|
self.trigger_level = trigger_level
|
|
self.activation = 0
|
|
|
|
def update(self, prob):
|
|
# type: (float) -> bool
|
|
"""Returns whether the new prediction caused an activation"""
|
|
chunk_activated = prob > 1.0 - self.sensitivity
|
|
|
|
if chunk_activated or self.activation < 0:
|
|
self.activation += 1
|
|
has_activated = self.activation > self.trigger_level
|
|
if has_activated or chunk_activated and self.activation < 0:
|
|
self.activation = -(8 * 2048) // self.chunk_size
|
|
|
|
if has_activated:
|
|
return True
|
|
elif self.activation > 0:
|
|
self.activation -= 1
|
|
return False
|
|
|
|
|
|
class PreciseRunner:
|
|
"""
|
|
Wrapper to use Precise. Example:
|
|
>>> def on_act():
|
|
... print('Activation!')
|
|
...
|
|
>>> p = PreciseRunner(PreciseEngine('./precise-engine'), on_activation=on_act)
|
|
>>> p.start()
|
|
>>> from time import sleep; sleep(10)
|
|
>>> p.stop()
|
|
|
|
Args:
|
|
engine (Engine): Object containing info on the binary engine
|
|
trigger_level (int): Number of chunk activations needed to trigger on_activation
|
|
Higher values add latency but reduce false positives
|
|
sensitivity (float): From 0.0 to 1.0, how sensitive the network should be
|
|
stream (BinaryIO): Binary audio stream to read 16000 Hz 1 channel int16
|
|
audio from. If not given, the microphone is used
|
|
on_prediction (Callable): callback for every new prediction
|
|
on_activation (Callable): callback for when the wake word is heard
|
|
"""
|
|
|
|
def __init__(self, engine, trigger_level=3, sensitivity=0.5, stream=None,
|
|
on_prediction=lambda x: None, on_activation=lambda: None):
|
|
self.engine = engine
|
|
self.trigger_level = trigger_level
|
|
self.stream = stream
|
|
self.on_prediction = on_prediction
|
|
self.on_activation = on_activation
|
|
self.chunk_size = engine.chunk_size
|
|
|
|
self.pa = None
|
|
self.thread = None
|
|
self.running = False
|
|
self.is_paused = False
|
|
self.detector = TriggerDetector(self.chunk_size, sensitivity, trigger_level)
|
|
atexit.register(self.stop)
|
|
|
|
def _wrap_stream_read(self, stream):
|
|
"""
|
|
pyaudio.Stream.read takes samples as n, not bytes
|
|
so read(n) should be read(n // sample_depth)
|
|
"""
|
|
import pyaudio
|
|
if getattr(stream.read, '__func__', None) is pyaudio.Stream.read:
|
|
stream.read = lambda x: pyaudio.Stream.read(stream, x // 2, False)
|
|
|
|
def start(self):
|
|
"""Start listening from stream"""
|
|
if self.stream is None:
|
|
from pyaudio import PyAudio, paInt16
|
|
self.pa = PyAudio()
|
|
self.stream = self.pa.open(
|
|
16000, 1, paInt16, True, frames_per_buffer=self.chunk_size
|
|
)
|
|
|
|
self._wrap_stream_read(self.stream)
|
|
|
|
self.engine.start()
|
|
self.running = True
|
|
self.is_paused = False
|
|
self.thread = Thread(target=self._handle_predictions, daemon=True)
|
|
self.thread.daemon = True
|
|
self.thread.start()
|
|
|
|
def stop(self):
|
|
"""Stop listening and close stream"""
|
|
if self.thread:
|
|
self.running = False
|
|
if isinstance(self.stream, ReadWriteStream):
|
|
self.stream.write(b'\0' * self.chunk_size)
|
|
self.thread.join()
|
|
self.thread = None
|
|
|
|
self.engine.stop()
|
|
|
|
if self.pa:
|
|
self.pa.terminate()
|
|
self.stream.stop_stream()
|
|
self.stream = self.pa = None
|
|
|
|
def pause(self):
|
|
self.is_paused = True
|
|
|
|
def play(self):
|
|
self.is_paused = False
|
|
|
|
def _handle_predictions(self):
|
|
"""Continuously check Precise process output"""
|
|
while self.running:
|
|
#t0 = time.time()
|
|
chunk = self.stream.read(self.chunk_size)
|
|
|
|
if self.is_paused:
|
|
continue
|
|
|
|
prob = self.engine.get_prediction(chunk)
|
|
self.on_prediction(prob)
|
|
if self.detector.update(prob):
|
|
self.on_activation()
|
|
#t1 = time.time()
|
|
#print("Prediction time: %.4f" % ((t1-t0)))
|