mirror of
https://github.com/mudler/LocalAI.git
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117 lines
4.1 KiB
Python
117 lines
4.1 KiB
Python
#!/usr/bin/env python3
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"""
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This is an extra gRPC server of LocalAI for Bark TTS
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"""
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from concurrent import futures
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import time
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import argparse
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import signal
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import sys
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import os
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import backend_pb2
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import backend_pb2_grpc
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import torch
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import torchaudio as ta
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from chatterbox.tts import ChatterboxTTS
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import grpc
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_ONE_DAY_IN_SECONDS = 60 * 60 * 24
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# If MAX_WORKERS are specified in the environment use it, otherwise default to 1
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MAX_WORKERS = int(os.environ.get('PYTHON_GRPC_MAX_WORKERS', '1'))
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COQUI_LANGUAGE = os.environ.get('COQUI_LANGUAGE', None)
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# Implement the BackendServicer class with the service methods
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class BackendServicer(backend_pb2_grpc.BackendServicer):
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"""
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BackendServicer is the class that implements the gRPC service
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"""
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def Health(self, request, context):
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return backend_pb2.Reply(message=bytes("OK", 'utf-8'))
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def LoadModel(self, request, context):
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# Get device
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# device = "cuda" if request.CUDA else "cpu"
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if torch.cuda.is_available():
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print("CUDA is available", file=sys.stderr)
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device = "cuda"
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else:
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print("CUDA is not available", file=sys.stderr)
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device = "cpu"
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if not torch.cuda.is_available() and request.CUDA:
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return backend_pb2.Result(success=False, message="CUDA is not available")
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self.AudioPath = None
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if os.path.isabs(request.AudioPath):
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self.AudioPath = request.AudioPath
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elif request.AudioPath and request.ModelFile != "" and not os.path.isabs(request.AudioPath):
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# get base path of modelFile
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modelFileBase = os.path.dirname(request.ModelFile)
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# modify LoraAdapter to be relative to modelFileBase
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self.AudioPath = os.path.join(modelFileBase, request.AudioPath)
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try:
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print("Preparing models, please wait", file=sys.stderr)
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self.model = ChatterboxTTS.from_pretrained(device=device)
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except Exception as err:
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return backend_pb2.Result(success=False, message=f"Unexpected {err=}, {type(err)=}")
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# Implement your logic here for the LoadModel service
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# Replace this with your desired response
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return backend_pb2.Result(message="Model loaded successfully", success=True)
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def TTS(self, request, context):
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try:
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# Generate audio using ChatterboxTTS
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if self.AudioPath is not None:
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wav = self.model.generate(request.text, audio_prompt_path=self.AudioPath)
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else:
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wav = self.model.generate(request.text)
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# Save the generated audio
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ta.save(request.dst, wav, self.model.sr)
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except Exception as err:
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return backend_pb2.Result(success=False, message=f"Unexpected {err=}, {type(err)=}")
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return backend_pb2.Result(success=True)
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def serve(address):
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server = grpc.server(futures.ThreadPoolExecutor(max_workers=MAX_WORKERS),
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options=[
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('grpc.max_message_length', 50 * 1024 * 1024), # 50MB
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('grpc.max_send_message_length', 50 * 1024 * 1024), # 50MB
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('grpc.max_receive_message_length', 50 * 1024 * 1024), # 50MB
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])
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backend_pb2_grpc.add_BackendServicer_to_server(BackendServicer(), server)
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server.add_insecure_port(address)
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server.start()
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print("Server started. Listening on: " + address, file=sys.stderr)
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# Define the signal handler function
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def signal_handler(sig, frame):
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print("Received termination signal. Shutting down...")
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server.stop(0)
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sys.exit(0)
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# Set the signal handlers for SIGINT and SIGTERM
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signal.signal(signal.SIGINT, signal_handler)
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signal.signal(signal.SIGTERM, signal_handler)
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try:
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while True:
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time.sleep(_ONE_DAY_IN_SECONDS)
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except KeyboardInterrupt:
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server.stop(0)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="Run the gRPC server.")
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parser.add_argument(
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"--addr", default="localhost:50051", help="The address to bind the server to."
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)
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args = parser.parse_args()
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serve(args.addr)
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