LocalAI/backend/python/chatterbox/backend.py

#!/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
import torchaudio as ta
from chatterbox.tts import ChatterboxTTS
from chatterbox.mtl_tts import ChatterboxMultilingualTTS
import grpc

def is_float(s):
    """Check if a string can be converted to float."""
    try:
        float(s)
        return True
    except ValueError:
        return False
def is_int(s):
    """Check if a string can be converted to int."""
    try:
        int(s)
        return True
    except ValueError:
        return False

_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"
        mps_available = hasattr(torch.backends, "mps") and torch.backends.mps.is_available()
        if mps_available:
            device = "mps"
        if not torch.cuda.is_available() and request.CUDA:
            return backend_pb2.Result(success=False, message="CUDA is not available")


        options = request.Options

        # empty dict
        self.options = {}

        # The options are a list of strings in this form optname:optvalue
        # We are storing all the options in a dict so we can use it later when
        # generating the images
        for opt in options:
            if ":" not in opt:
                continue
            key, value = opt.split(":")
            # if value is a number, convert it to the appropriate type
            if is_float(value):
                value = float(value)
            elif is_int(value):
                value = int(value)
            elif value.lower() in ["true", "false"]:
                value = value.lower() == "true"
            self.options[key] = value

        self.AudioPath = None

        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)
            if "multilingual" in self.options:
                # remove key from options
                del self.options["multilingual"]
                self.model = ChatterboxMultilingualTTS.from_pretrained(device=device)
            else:
                self.model = ChatterboxTTS.from_pretrained(device=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:
            kwargs = {}

            if "language" in self.options:
                kwargs["language_id"] = self.options["language"]
            if self.AudioPath is not None:
                kwargs["audio_prompt_path"] = self.AudioPath

            # add options to kwargs
            kwargs.update(self.options)

            # Generate audio using ChatterboxTTS
            wav = self.model.generate(request.text, **kwargs)
            # Save the generated audio
            ta.save(request.dst, wav, self.model.sr)
            
        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),
        options=[
            ('grpc.max_message_length', 50 * 1024 * 1024),  # 50MB
            ('grpc.max_send_message_length', 50 * 1024 * 1024),  # 50MB
            ('grpc.max_receive_message_length', 50 * 1024 * 1024),  # 50MB
        ])
    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)