#!/usr/bin/env python3 """ This is an extra gRPC server of LocalAI for Bark TTS """ from concurrent import futures import time import argparse import signal import sys import os import backend_pb2 import backend_pb2_grpc import torch from TTS.api import TTS import grpc _ONE_DAY_IN_SECONDS = 60 * 60 * 24 # If MAX_WORKERS are specified in the environment use it, otherwise default to 1 MAX_WORKERS = int(os.environ.get('PYTHON_GRPC_MAX_WORKERS', '1')) COQUI_LANGUAGE = os.environ.get('COQUI_LANGUAGE', None) # Implement the BackendServicer class with the service methods class BackendServicer(backend_pb2_grpc.BackendServicer): """ BackendServicer is the class that implements the gRPC service """ def Health(self, request, context): return backend_pb2.Reply(message=bytes("OK", 'utf-8')) def LoadModel(self, request, context): # Get device # device = "cuda" if request.CUDA else "cpu" if torch.cuda.is_available(): print("CUDA is available", file=sys.stderr) device = "cuda" else: print("CUDA is not available", file=sys.stderr) device = "cpu" if not torch.cuda.is_available() and request.CUDA: return backend_pb2.Result(success=False, message="CUDA is not available") self.AudioPath = None # List available 🐸TTS models print(TTS().list_models()) if os.path.isabs(request.AudioPath): self.AudioPath = request.AudioPath elif request.AudioPath and request.ModelFile != "" and not os.path.isabs(request.AudioPath): # get base path of modelFile modelFileBase = os.path.dirname(request.ModelFile) # modify LoraAdapter to be relative to modelFileBase self.AudioPath = os.path.join(modelFileBase, request.AudioPath) try: print("Preparing models, please wait", file=sys.stderr) self.tts = TTS(request.Model).to(device) except Exception as err: return backend_pb2.Result(success=False, message=f"Unexpected {err=}, {type(err)=}") # Implement your logic here for the LoadModel service # Replace this with your desired response return backend_pb2.Result(message="Model loaded successfully", success=True) def TTS(self, request, context): try: # if model is multilangual add language from request or env as fallback lang = request.language or COQUI_LANGUAGE if lang == "": lang = None if self.tts.is_multi_lingual and lang is None: return backend_pb2.Result(success=False, message=f"Model is multi-lingual, but no language was provided") # if model is multi-speaker, use speaker_wav or the speaker_id from request.voice if self.tts.is_multi_speaker and self.AudioPath is None and request.voice is None: return backend_pb2.Result(success=False, message=f"Model is multi-speaker, but no speaker was provided") if self.tts.is_multi_speaker and request.voice is not None: self.tts.tts_to_file(text=request.text, speaker=request.voice, language=lang, file_path=request.dst) else: self.tts.tts_to_file(text=request.text, speaker_wav=self.AudioPath, language=lang, file_path=request.dst) except Exception as err: return backend_pb2.Result(success=False, message=f"Unexpected {err=}, {type(err)=}") return backend_pb2.Result(success=True) def serve(address): server = grpc.server(futures.ThreadPoolExecutor(max_workers=MAX_WORKERS)) backend_pb2_grpc.add_BackendServicer_to_server(BackendServicer(), server) server.add_insecure_port(address) server.start() print("Server started. Listening on: " + address, file=sys.stderr) # Define the signal handler function def signal_handler(sig, frame): print("Received termination signal. Shutting down...") server.stop(0) sys.exit(0) # Set the signal handlers for SIGINT and SIGTERM signal.signal(signal.SIGINT, signal_handler) signal.signal(signal.SIGTERM, signal_handler) try: while True: time.sleep(_ONE_DAY_IN_SECONDS) except KeyboardInterrupt: server.stop(0) if __name__ == "__main__": parser = argparse.ArgumentParser(description="Run the gRPC server.") parser.add_argument( "--addr", default="localhost:50051", help="The address to bind the server to." ) args = parser.parse_args() serve(args.addr)