Python struct.pack 导致内存泄漏,即使在删除对象之后
Python struct.pack causing a memory leak, even after deleting object
我正在使用 PyAudio 检测音频源的 activity。我正在从经常死的流上的每个音频事件创建 WAV 文件。使用 memory_profiler 我注意到 record_to_file() 方法中的 pack 方法使用的内存量通常是我通过删除数据对象重新获得的内存量的 4 倍。此代码取自 Detect & Record Audio in Python
from sys import byteorder
import sys
from array import array
import struct
import gc
import pyaudio
import wave
import subprocess
import objgraph
from memory_profiler import profile
from guppy import hpy
THRESHOLD = 5000
CHUNK_SIZE = 1024
FORMAT = pyaudio.paInt16
RATE = 44100
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)"
r = array('h', [0 for i in xrange(int(seconds*RATE))])
r.extend(snd_data)
r.extend([0 for i in xrange(int(seconds*RATE))])
return r
def record():
"""g
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
snd_started = False
r = array('h')
while 1:
# little endian, signed short
snd_data = array('h', stream.read(CHUNK_SIZE))
if byteorder == 'big':
snd_data.byteswap()
r.extend(snd_data)
silent = is_silent(snd_data)
if silent and snd_started:
num_silent += 1
elif not silent and not snd_started:
snd_started = True
if snd_started and num_silent > 400:
break
sample_width = p.get_sample_size(FORMAT)
stream.stop_stream()
stream.close()
p.terminate()
#r = normalize(r)
r = trim(r)
r = add_silence(r, 0.5)
return sample_width, r
@profile
def record_to_file(path):
"Records from the microphone and outputs the resulting data to 'path'"
sample_width, data = record()
data = struct.pack('<' + ('h'*len(data)), *data)
print(sys.getsizeof(data))
wf = wave.open(path, 'wb')
wf.setnchannels(1)
wf.setsampwidth(sample_width)
wf.setframerate(RATE)
wf.writeframes(data)
wf.close()
del data
gc.collect()
if __name__ == '__main__':
count = 1
h = hpy()
f = open('heap.txt','w')
objgraph.show_growth(limit=3)
while(1):
filename = 'demo' + str(count) + '.wav'
print("please speak a word into the microphone")
record_to_file(filename)
print("done - result written to {0}".format(filename))
cmd = 'cd "C:\Users\user\Desktop\Raudio" & ffmpeg\bin\ffmpeg -i {0} -acodec libmp3lame {1}.mp3'.format(filename, filename)
"subprocess.call(cmd, shell=True)"
count += 1
objgraph.show_growth()
print h.heap()
这是内存分析器模块输出的一次迭代:
Line # Mem usage Increment Line Contents
================================================
117 19.6 MiB 0.0 MiB @profile
118 def record_to_file(path):
119 "Records from the microphone and outputs the resulting data to 'path'"
120 22.4 MiB 2.8 MiB sample_width, data = record()
125 31.3 MiB 8.9 MiB data = struct.pack('<' + ('h'*len(data)), *data)
126 31.3 MiB 0.0 MiB print(sys.getsizeof(data))
127 31.3 MiB 0.0 MiB wf = wave.open(path, 'wb')
128 31.3 MiB 0.0 MiB wf.setnchannels(1)
129 31.3 MiB 0.0 MiB wf.setsampwidth(sample_width)
130 31.4 MiB 0.0 MiB wf.setframerate(RATE)
131 31.4 MiB 0.0 MiB wf.writeframes(data)
132 31.4 MiB 0.0 MiB wf.close()
133 30.4 MiB -0.9 MiB del data
134 27.9 MiB -2.5 MiB gc.collect()
使用 VS 调试进程时,我在系统内存中看到大量 'h' 个字符,这可能是发生泄漏的原因。任何帮助将不胜感激
我正在使用 PyAudio 检测音频源的 activity。我正在从经常死的流上的每个音频事件创建 WAV 文件。使用 memory_profiler 我注意到 record_to_file() 方法中的 pack 方法使用的内存量通常是我通过删除数据对象重新获得的内存量的 4 倍。此代码取自 Detect & Record Audio in Python
from sys import byteorder
import sys
from array import array
import struct
import gc
import pyaudio
import wave
import subprocess
import objgraph
from memory_profiler import profile
from guppy import hpy
THRESHOLD = 5000
CHUNK_SIZE = 1024
FORMAT = pyaudio.paInt16
RATE = 44100
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)"
r = array('h', [0 for i in xrange(int(seconds*RATE))])
r.extend(snd_data)
r.extend([0 for i in xrange(int(seconds*RATE))])
return r
def record():
"""g
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
snd_started = False
r = array('h')
while 1:
# little endian, signed short
snd_data = array('h', stream.read(CHUNK_SIZE))
if byteorder == 'big':
snd_data.byteswap()
r.extend(snd_data)
silent = is_silent(snd_data)
if silent and snd_started:
num_silent += 1
elif not silent and not snd_started:
snd_started = True
if snd_started and num_silent > 400:
break
sample_width = p.get_sample_size(FORMAT)
stream.stop_stream()
stream.close()
p.terminate()
#r = normalize(r)
r = trim(r)
r = add_silence(r, 0.5)
return sample_width, r
@profile
def record_to_file(path):
"Records from the microphone and outputs the resulting data to 'path'"
sample_width, data = record()
data = struct.pack('<' + ('h'*len(data)), *data)
print(sys.getsizeof(data))
wf = wave.open(path, 'wb')
wf.setnchannels(1)
wf.setsampwidth(sample_width)
wf.setframerate(RATE)
wf.writeframes(data)
wf.close()
del data
gc.collect()
if __name__ == '__main__':
count = 1
h = hpy()
f = open('heap.txt','w')
objgraph.show_growth(limit=3)
while(1):
filename = 'demo' + str(count) + '.wav'
print("please speak a word into the microphone")
record_to_file(filename)
print("done - result written to {0}".format(filename))
cmd = 'cd "C:\Users\user\Desktop\Raudio" & ffmpeg\bin\ffmpeg -i {0} -acodec libmp3lame {1}.mp3'.format(filename, filename)
"subprocess.call(cmd, shell=True)"
count += 1
objgraph.show_growth()
print h.heap()
这是内存分析器模块输出的一次迭代:
Line # Mem usage Increment Line Contents
================================================
117 19.6 MiB 0.0 MiB @profile
118 def record_to_file(path):
119 "Records from the microphone and outputs the resulting data to 'path'"
120 22.4 MiB 2.8 MiB sample_width, data = record()
125 31.3 MiB 8.9 MiB data = struct.pack('<' + ('h'*len(data)), *data)
126 31.3 MiB 0.0 MiB print(sys.getsizeof(data))
127 31.3 MiB 0.0 MiB wf = wave.open(path, 'wb')
128 31.3 MiB 0.0 MiB wf.setnchannels(1)
129 31.3 MiB 0.0 MiB wf.setsampwidth(sample_width)
130 31.4 MiB 0.0 MiB wf.setframerate(RATE)
131 31.4 MiB 0.0 MiB wf.writeframes(data)
132 31.4 MiB 0.0 MiB wf.close()
133 30.4 MiB -0.9 MiB del data
134 27.9 MiB -2.5 MiB gc.collect()
使用 VS 调试进程时,我在系统内存中看到大量 'h' 个字符,这可能是发生泄漏的原因。任何帮助将不胜感激