658867fce4
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
99 lines
4.0 KiB
Python
99 lines
4.0 KiB
Python
# Copyright (C) 2024 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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import asyncio
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import base64
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import os
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from comps import AudioQnAGateway, MicroService, ServiceOrchestrator, ServiceType
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MEGA_SERVICE_HOST_IP = os.getenv("MEGA_SERVICE_HOST_IP", "0.0.0.0")
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MEGA_SERVICE_PORT = int(os.getenv("MEGA_SERVICE_PORT", 8888))
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WHISPER_SERVER_HOST_IP = os.getenv("WHISPER_SERVER_HOST_IP", "0.0.0.0")
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WHISPER_SERVER_PORT = int(os.getenv("WHISPER_SERVER_PORT", 7066))
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GPT_SOVITS_SERVER_HOST_IP = os.getenv("GPT_SOVITS_SERVER_HOST_IP", "0.0.0.0")
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GPT_SOVITS_SERVER_PORT = int(os.getenv("GPT_SOVITS_SERVER_PORT", 9088))
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LLM_SERVER_HOST_IP = os.getenv("LLM_SERVER_HOST_IP", "0.0.0.0")
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LLM_SERVER_PORT = int(os.getenv("LLM_SERVER_PORT", 8888))
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def align_inputs(self, inputs, cur_node, runtime_graph, llm_parameters_dict, **kwargs):
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print(inputs)
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if self.services[cur_node].service_type == ServiceType.ASR:
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# {'byte_str': 'UklGRigAAABXQVZFZm10IBIAAAABAAEARKwAAIhYAQACABAAAABkYXRhAgAAAAEA'}
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inputs["audio"] = inputs["byte_str"]
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del inputs["byte_str"]
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elif self.services[cur_node].service_type == ServiceType.LLM:
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# convert TGI/vLLM to unified OpenAI /v1/chat/completions format
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next_inputs = {}
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next_inputs["model"] = "tgi" # specifically clarify the fake model to make the format unified
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next_inputs["messages"] = [{"role": "user", "content": inputs["asr_result"]}]
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next_inputs["max_tokens"] = llm_parameters_dict["max_tokens"]
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next_inputs["top_p"] = llm_parameters_dict["top_p"]
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next_inputs["stream"] = inputs["streaming"] # False as default
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next_inputs["frequency_penalty"] = inputs["frequency_penalty"]
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# next_inputs["presence_penalty"] = inputs["presence_penalty"]
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# next_inputs["repetition_penalty"] = inputs["repetition_penalty"]
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next_inputs["temperature"] = inputs["temperature"]
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inputs = next_inputs
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elif self.services[cur_node].service_type == ServiceType.TTS:
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next_inputs = {}
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next_inputs["text"] = inputs["choices"][0]["message"]["content"]
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next_inputs["text_language"] = kwargs["tts_text_language"] if "tts_text_language" in kwargs else "zh"
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inputs = next_inputs
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return inputs
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def align_outputs(self, data, cur_node, inputs, runtime_graph, llm_parameters_dict, **kwargs):
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if self.services[cur_node].service_type == ServiceType.TTS:
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audio_base64 = base64.b64encode(data).decode("utf-8")
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return {"byte_str": audio_base64}
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return data
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class AudioQnAService:
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def __init__(self, host="0.0.0.0", port=8000):
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self.host = host
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self.port = port
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ServiceOrchestrator.align_inputs = align_inputs
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ServiceOrchestrator.align_outputs = align_outputs
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self.megaservice = ServiceOrchestrator()
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def add_remote_service(self):
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asr = MicroService(
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name="asr",
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host=WHISPER_SERVER_HOST_IP,
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port=WHISPER_SERVER_PORT,
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# endpoint="/v1/audio/transcriptions",
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endpoint="/v1/asr",
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use_remote_service=True,
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service_type=ServiceType.ASR,
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)
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llm = MicroService(
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name="llm",
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host=LLM_SERVER_HOST_IP,
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port=LLM_SERVER_PORT,
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endpoint="/v1/chat/completions",
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use_remote_service=True,
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service_type=ServiceType.LLM,
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)
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tts = MicroService(
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name="tts",
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host=GPT_SOVITS_SERVER_HOST_IP,
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port=GPT_SOVITS_SERVER_PORT,
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# endpoint="/v1/audio/speech",
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endpoint="/",
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use_remote_service=True,
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service_type=ServiceType.TTS,
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)
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self.megaservice.add(asr).add(llm).add(tts)
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self.megaservice.flow_to(asr, llm)
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self.megaservice.flow_to(llm, tts)
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self.gateway = AudioQnAGateway(megaservice=self.megaservice, host="0.0.0.0", port=self.port)
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if __name__ == "__main__":
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audioqna = AudioQnAService(host=MEGA_SERVICE_HOST_IP, port=MEGA_SERVICE_PORT)
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audioqna.add_remote_service()
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