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audio_tools.py
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audio_tools.py
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import os, logging, contextlib, sys
import collections, queue, os, os.path
import numpy as np
import pyaudio
import wave
import webrtcvad
#from halo import Halo
from scipy import signal
#
# Suppress ALSA errrors; thanks to Louie on StackOverflow
# https://stackoverflow.com/questions/7088672/pyaudio-working-but-spits-out-error-messages-each-time
#
@contextlib.contextmanager
def ignoreStderr():
devnull = os.open(os.devnull, os.O_WRONLY)
old_stderr = os.dup(2)
sys.stderr.flush()
os.dup2(devnull, 2)
os.close(devnull)
try:
yield
finally:
os.dup2(old_stderr, 2)
os.close(old_stderr)
class Audio(object):
"""Streams raw audio from microphone. Data is received in a separate thread, and stored in a buffer, to be read from."""
FORMAT = pyaudio.paInt16
# Network/VAD rate-space
RATE_PROCESS = 16000
CHANNELS = 1
BLOCKS_PER_SECOND = 50
def __init__(self, callback=None, device=None, input_rate=RATE_PROCESS, file=None):
def proxy_callback(in_data, frame_count, time_info, status):
#pylint: disable=unused-argument
if self.chunk is not None:
in_data = self.wf.readframes(self.chunk)
callback(in_data)
return (None, pyaudio.paContinue)
if callback is None: callback = lambda in_data: self.buffer_queue.put(in_data)
self.buffer_queue = queue.Queue()
self.device = device
self.input_rate = input_rate
self.sample_rate = self.RATE_PROCESS
self.block_size = int(self.RATE_PROCESS / float(self.BLOCKS_PER_SECOND))
self.block_size_input = int(self.input_rate / float(self.BLOCKS_PER_SECOND))
with ignoreStderr():
self.pa = pyaudio.PyAudio()
kwargs = {
'format': self.FORMAT,
'channels': self.CHANNELS,
'rate': self.input_rate,
'input': True,
'frames_per_buffer': self.block_size_input,
'stream_callback': proxy_callback,
}
self.chunk = None
# if not default device
if self.device:
kwargs['input_device_index'] = self.device
elif file is not None:
self.chunk = 320
self.wf = wave.open(file, 'rb')
self.stream = self.pa.open(**kwargs)
self.stream.start_stream()
def resample(self, data, input_rate):
"""
Microphone may not support our native processing sampling rate, so
resample from input_rate to RATE_PROCESS here for webrtcvad and
deepspeech
Args:
data (binary): Input audio stream
input_rate (int): Input audio rate to resample from
"""
data16 = np.fromstring(string=data, dtype=np.int16)
resample_size = int(len(data16) / self.input_rate * self.RATE_PROCESS)
resample = signal.resample(data16, resample_size)
resample16 = np.array(resample, dtype=np.int16)
return resample16.tostring()
def read_resampled(self):
"""Return a block of audio data resampled to 16000hz, blocking if necessary."""
return self.resample(data=self.buffer_queue.get(),
input_rate=self.input_rate)
def read(self):
"""Return a block of audio data, blocking if necessary."""
return self.buffer_queue.get()
def destroy(self):
self.stream.stop_stream()
self.stream.close()
self.pa.terminate()
frame_duration_ms = property(lambda self: 1000 * self.block_size // self.sample_rate)
def write_wav(self, filename, data):
logging.info("write wav %s", filename)
wf = wave.open(filename, 'wb')
wf.setnchannels(self.CHANNELS)
# wf.setsampwidth(self.pa.get_sample_size(FORMAT))
assert self.FORMAT == pyaudio.paInt16
wf.setsampwidth(2)
wf.setframerate(self.sample_rate)
wf.writeframes(data)
wf.close()
class VADAudio(Audio):
"""Filter & segment audio with voice activity detection."""
def __init__(self, aggressiveness=3, device=None, input_rate=None, file=None):
super().__init__(device=device, input_rate=input_rate, file=file)
self.vad = webrtcvad.Vad(aggressiveness)
def frame_generator(self):
"""Generator that yields all audio frames from microphone."""
if self.input_rate == self.RATE_PROCESS:
while True:
yield self.read()
else:
while True:
yield self.read_resampled()
def vad_collector(self, padding_ms=300, ratio=0.75, frames=None):
"""Generator that yields series of consecutive audio frames comprising each utterence, separated by yielding a single None.
Determines voice activity by ratio of frames in padding_ms. Uses a buffer to include padding_ms prior to being triggered.
Example: (frame, ..., frame, None, frame, ..., frame, None, ...)
|---utterence---| |---utterence---|
"""
if frames is None: frames = self.frame_generator()
num_padding_frames = padding_ms // self.frame_duration_ms
ring_buffer = collections.deque(maxlen=num_padding_frames)
triggered = False
for frame in frames:
if len(frame) < 640:
return
is_speech = self.vad.is_speech(frame, self.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 > ratio * ring_buffer.maxlen:
triggered = True
for f, s in ring_buffer:
yield f
ring_buffer.clear()
else:
yield frame
ring_buffer.append((frame, is_speech))
num_unvoiced = len([f for f, speech in ring_buffer if not speech])
if num_unvoiced > ratio * ring_buffer.maxlen:
triggered = False
yield None
ring_buffer.clear()