all frame processors are asynchrnous
In this commit we make all frame processors asynchronous, that is, they have an internal queue and they push frames using a task from that queue.
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@@ -86,13 +86,13 @@ async def main():
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),
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)
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llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
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tts = CartesiaHttpTTSService(
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api_key=os.getenv("CARTESIA_API_KEY"),
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voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady
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)
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llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
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imagegen = FalImageGenService(
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params=FalImageGenService.InputParams(image_size="square_hd"),
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aiohttp_session=session,
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@@ -107,8 +107,10 @@ async def main():
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# that, each pipeline runs concurrently and `SyncParallelPipeline` will
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# wait for the input frame to be processed.
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#
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# Note that `SyncParallelPipeline` requires all processors in it to be
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# synchronous (which is the default for most processors).
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# Note that `SyncParallelPipeline` requires the last processor in each
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# of the pipelines to be synchronous. In this case, we use
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# `CartesiaHttpTTSService` and `FalImageGenService` which make HTTP
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# requests and wait for the response.
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pipeline = Pipeline(
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[
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llm, # LLM
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@@ -82,6 +82,7 @@ async def main():
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self.frame = OutputAudioRawFrame(
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bytes(self.audio), frame.sample_rate, frame.num_channels
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)
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await self.push_frame(frame, direction)
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class ImageGrabber(FrameProcessor):
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def __init__(self):
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@@ -93,6 +94,7 @@ async def main():
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if isinstance(frame, URLImageRawFrame):
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self.frame = frame
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await self.push_frame(frame, direction)
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llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
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@@ -121,8 +123,10 @@ async def main():
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# `SyncParallelPipeline` will wait for the input frame to be
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# processed.
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#
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# Note that `SyncParallelPipeline` requires all processors in it to
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# be synchronous (which is the default for most processors).
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# Note that `SyncParallelPipeline` requires the last processor in
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# each of the pipelines to be synchronous. In this case, we use
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# `CartesiaHttpTTSService` and `FalImageGenService` which make HTTP
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# requests and wait for the response.
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pipeline = Pipeline(
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[
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llm, # LLM
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