为什么我会流血记忆？

Question

这是一个用于检测和记录声音的脚本，我已经研究了一段时间了。它工作得很好，除了每次成功检测后它的内存使用量都会增加。我解决了长时间沉默期间发生的类似问题 (if num_listening > 4096:...)，但这个问题让我很困惑。


from sys import byteorder
from array import array
from struct import pack
from datetime import datetime

import pyaudio
import wave
import os
import time

THRESHOLD = 6348
MAX_SILENCE = 500
CHUNK_SIZE = 1024
FORMAT = pyaudio.paInt16
RATE = 44100
MAX_LENGTH = 1024

def is_silent(snd_data):
    "Returns 'True' if below the 'silent' threshold"
    return max(snd_data) < THRESHOLD

def normalize(snd_data):
    "Average the volume out"
    MAXIMUM = 16384
    times = float(MAXIMUM)/max(abs(i) for i in snd_data)

    r = array('h')
    for i in snd_data:
        r.append(int(i*times))
    return r

def trim(snd_data):
    "Trim the blank spots at the start and end"
    def _trim(snd_data):
        snd_started = False
        r = array('h')

        for i in snd_data:
            if not snd_started and abs(i) > THRESHOLD:
                snd_started = True
                r.append(i)

            elif snd_started:
                r.append(i)
        return r

    # Trim to the left
    snd_data = _trim(snd_data)

    # Trim to the right
    snd_data.reverse()
    snd_data = _trim(snd_data)
    snd_data.reverse()
    return snd_data

def add_silence(snd_data, seconds):
    "Add silence to the start and end of 'snd_data' of length 'seconds' (float)"
    silence = [0] * int(seconds * RATE)
    r = array('h', silence)
    r.extend(snd_data)
    r.extend(silence)
    return r

def record():
    """
    Record a word or words from the microphone and 
    return the data as an array of signed shorts.

    Normalizes the audio, trims silence from the 
    start and end, and pads with 0.5 seconds of 
    blank sound to make sure VLC et al can play 
    it without getting chopped off.
    """
    p = pyaudio.PyAudio()
    stream = p.open(format=FORMAT, channels=1, rate=RATE,
        input=True, output=True,
        frames_per_buffer=CHUNK_SIZE)

    num_silent = 0
    num_snd = 0
    num_listening = 0
    snd_started = False

    r = array('h')

    while num_snd < MAX_LENGTH:
        # little endian, signed short
        snd_data = array('h', stream.read(CHUNK_SIZE, exception_on_overflow = False))
        if byteorder == 'big':
            snd_data.byteswap()
        r.extend(snd_data)

        silent = is_silent(snd_data)

        if not silent and not snd_started:
            snd_started = True

        if snd_started:
            num_snd += 1
            if num_silent > MAX_SILENCE:
                break

        if silent:
            if snd_started:
                num_silent += 1
            if not snd_started:
                num_listening += 1
                if num_listening > 4096:
                    del r[:]
                    num_listening = 0

    sample_width = p.get_sample_size(FORMAT)
    stream.stop_stream()
    stream.close()
    p.terminate()

    del r[0:8000]

    r = normalize(r)
    r = trim(r)
    r = add_silence(r, 0.5)
    return sample_width, r

def record_to_file(path):
    "Records from the microphone and outputs the resulting data to 'path'"
    sample_width, data = record()
    data = pack('<' + ('h'*len(data)), *data)

    wf = wave.open(path, 'wb')
    wf.setnchannels(1)
    wf.setsampwidth(sample_width)
    wf.setframerate(RATE)
    wf.writeframes(data)
    wf.close()

if __name__ == '__main__':
    while True:
        print("Ready!")
        recorded = datetime.now()
        recorded = "testpi1_" + recorded.strftime("%Y-%m-%d--%H-%M-%S") + ".wav"
        record_to_file("/motion/" + recorded)
        os.system("./convert-audio.py " + recorded)

Answer 1

用 multiprocessing.Process 分叉 record_to_file 函数解决了这个问题。

添加

import multiprocessing

调整

if __name__ == '__main__':
    while True:
        print("Ready!")
        recorded = datetime.now()
        recorded = "testpi1_" + recorded.strftime("%Y-%m-%d--%H-%M-%S") + ".wav"
        record_to_file("/motion/" + recorded)
        os.system("./convert-audio.py " + recorded)

到

if __name__ == '__main__':
    while True:
        print("Ready!")
        recorded = datetime.now()
        recorded = "testpi1_" + recorded.strftime("%Y-%m-%d--%H-%M-%S") + ".wav"
        p1 = multiprocessing.Process(target=record_to_file,args=("/motion/" + recorded,))
        p1.start()
        p1.join()
        os.system("./convert-audio.py " + recorded)

为什么我会流血记忆？

Why am I bleeding memory?

python

memory

audio

detection

recording