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import ray
from ray.util.queue import Queue
from ray.actor import ActorHandle
import torch
import numpy as np
@ray.remote
class WebRtcAVQueueActor:
def __init__(self):
self.in_audio_queue = Queue(maxsize=100) # Adjust the size as needed
self.in_video_queue = Queue(maxsize=100) # Adjust the size as needed
self.out_audio_queue = Queue(maxsize=100) # Adjust the size as needed
def enqueue_in_video_frame(self, shared_tensor_ref):
if self.in_video_queue.full():
evicted_item = self.in_video_queue.get()
del evicted_item
self.in_video_queue.put(shared_tensor_ref)
def enqueue_in_audio_frame(self, shared_buffer_ref):
if self.in_audio_queue.full():
evicted_item = self.in_audio_queue.get()
del evicted_item
self.in_audio_queue.put(shared_buffer_ref)
def get_in_audio_frames(self):
audio_frames = []
if self.in_audio_queue.empty():
return audio_frames
while not self.in_audio_queue.empty():
shared_tensor_ref = self.in_audio_queue.get()
audio_frames.append(shared_tensor_ref)
return audio_frames
def get_in_video_frames(self):
video_frames = []
if self.in_video_queue.empty():
return video_frames
while not self.in_video_queue.empty():
shared_tensor_ref = self.in_video_queue.get()
video_frames.append(shared_tensor_ref)
return video_frames
def get_out_audio_queue(self):
return self.out_audio_queue
async def get_out_audio_frame(self):
if self.out_audio_queue.empty():
return None
audio_frame = await self.out_audio_queue.get_async()
return audio_frame
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