Pyaudio：如何在单独的进程多处理中播放wav文件？

如何解决Pyaudio：如何在单独的进程多处理中播放wav文件？

我正在编写一个程序，只要麦克风检测到的振幅超过一定水平，就应该播放wav文件。我想使用pyaudio（并且在某种程度上过去已经成功），因为我需要同时处理2个流。现在问题出在什么时候

我想在与主线程分开的进程中播放wav文件。以下代码仅是概念证明：

import pyaudio,wave
import multiprocessing,time

filename='/home/larfan/Documents/PythonProgramming/overtone_horn/soundsamples/ZOOM0065.WAV'
#open soundfile
wf = wave.open(filename,'rb')


chunk = 1024*4  
p = pyaudio.PyAudio()
class output:
    def __init__(self):
        self.stream = p.open(format = p.get_format_from_width(wf.getsampwidth()),channels = wf.getnchannels(),rate = wf.getframerate(),output = True)
        # Read data in chunks
        self.origdata = wf.readframes(chunk)
    def actualoutput(self):
        data=self.origdata
        # Play the sound by writing the audio data to the stream
        while data:
            self.stream.write(data)
            data = wf.readframes(chunk)
    def testfuntion(self):
        print('hi')
        

out=output()

t1=multiprocessing.Process(target=out.actualoutput)
t1.start()
time.sleep(2)
out.stream.close()

t1.terminate()


p.terminate()

如果运行此代码，我将得到：

Traceback (most recent call last):
  File "/usr/lib/python3.7/multiprocessing/process.py",line 297,in _bootstrap
    self.run()
  File "/usr/lib/python3.7/multiprocessing/process.py",line 99,in run
    self._target(*self._args,**self._kwargs)
  File "/home/larfan/Documents/PythonProgramming/overtone_horn/ultimatetest.py",line 23,in actualoutput
    self.stream.write(data)
  File "/usr/lib/python3/dist-packages/pyaudio.py",line 586,in write
    exception_on_underflow)
OSError: [Errno -9999] Unanticipated host error

当我单独调用out.actualoutput（）时，如果可以正常运行，并且如果我使用run（）而不是start（）进行多处理，那么它也可以运行。但是，我不能使用run（），因为我希望主线程继续运行，并且我想使用上面代码中示例的Terminate（）选项。在实际程序中，我显然想在振幅下降到阈值以下时而不是在固定的秒数后立即调用Terminate（）。

有没有人知道如何进行这项工作。即使只是对解决方案的提示，也将不胜感激。 问候

解决方法

如果您只想与主程序分开播放文件，那么asyncio的import cv2 import numpy as np def convert_to_binary(rgb_image): gray_image = cv2.cvtColor(rgb_image,cv2.COLOR_BGR2GRAY) ret,thresh = cv2.threshold(gray_image,180,255,cv2.THRESH_BINARY_INV) return gray_image,thresh original = cv2.imread('original.jpg') height,width = original.shape[:2] original_gray,original_binary = convert_to_binary(original) # Get binary image cv2.imwrite("binary.jpg",original_binary) closing_original = cv2.morphologyEx(original_binary,cv2.MORPH_DILATE,np.ones((1,2),np.uint8)) # Close small gaps in the binary image cv2.imwrite("closed.jpg",closing_original) contours_original,hierarchy_original = cv2.findContours(closing_original,cv2.RETR_CCOMP,cv2.CHAIN_APPROX_SIMPLE) # Get all the contours bounding_rects_original = [cv2.boundingRect(c) for c in contours_original] # Get all contour bounding boxes orig_boxes = list() all_contour_image = original.copy() for i,(x,y,w,h) in enumerate(bounding_rects_original): if h > height / 2 or w > width / 2: # Eliminate extremely large contours continue if h < w / 2 or w < h / 2: # Eliminate vertical / horuzontal lines continue if w * h < 200: # Eliminate small area contours continue if hierarchy_original[0][i][3] != -1: # Eliminate contours created by heart crosses continue orig_boxes.append((x,h)) cv2.rectangle(all_contour_image,y),(x + w,y + h),(0,0),3) # cv2.imshow("warped",closing_original) cv2.imwrite("all_contours.jpg",all_contour_image) final_image = original.copy() for x,h in orig_boxes: cropped_image = closing_original[y - 2 :y + h + 2,x: x + w] # Get the heart binary image col_pixel_diffs = np.abs(np.diff(cropped_image.T.astype(np.int16))/255) # Obtain all consecutive pixel differences in all the columns column_sums = np.sum(col_pixel_diffs,axis=1) # Get the sum of each column's transitions. This results in an array of size equal # to the number of columns,each element representing the number of black-white and white-black transitions. percent_crosses = np.sum(column_sums >= 6)/ np.sum(column_sums >= 4) # Percentage of columns with 6 transitions among columns with 4 transitions if percent_crosses > 0.4: # Crossed heart criterion cv2.rectangle(final_image,3) cv2.imwrite("crossed_heart.jpg",cropped_image) else: cv2.imwrite("normal_heart.jpg",cropped_image) cv2.imwrite("all_crossed_hearts.jpg",final_image) 可能是最好的选择。当有多个线程并且需要同时启动它们并收集结果时，首选进行多处理。

例如：

run_in_executor()

现在，您可以根据需要运行此函数，它将始终独立于主程序运行而不会进入等待状态：

import pyaudio
import wave
import sys
import asyncio
from datetime import datetime
def background(f):
    def wrapped(*args,**kwargs):
        return asyncio.get_event_loop().run_in_executor(None,f,*args,**kwargs)
    return wrapped

@background
def play_audio(audio_file = 'sample.wav',CHUNK = 1024): #define play function however you like!
    print(f'Audio Play starts at {datetime.now()}')
    wf = wave.open(audio_file,'rb')

    p = pyaudio.PyAudio()

    stream = p.open(format=p.get_format_from_width(wf.getsampwidth()),channels=wf.getnchannels(),rate=wf.getframerate(),output=True)

    data = wf.readframes(CHUNK)

    while len(data)!=0:
        stream.write(data)
        data = wf.readframes(CHUNK)

    stream.stop_stream()
    stream.close()

    p.terminate()
    print(f'Audio play finishes {datetime.now()}')

产生以下输出：

print(f'Main Program Starts at {datetime.now()}')
play_audio('sample.wav',1024)
print(f'Main program finishes at {datetime.now()}')