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#!/usr/bin/env python3
"""
This is an extra gRPC server of LocalAI for Kokoro 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 kokoro import KPipeline
import soundfile as sf
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'))
KOKORO_LANG_CODE = os.environ.get('KOKORO_LANG_CODE', 'a')
# 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):
try:
print("Preparing Kokoro TTS pipeline, please wait", file=sys.stderr)
# empty dict
self.options = {}
options = request.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(":")
self.options[key] = value
# Initialize Kokoro pipeline with language code
lang_code = self.options.get("lang_code", KOKORO_LANG_CODE)
self.pipeline = KPipeline(lang_code=lang_code)
print(f"Kokoro TTS pipeline loaded with language code: {lang_code}", file=sys.stderr)
except Exception as err:
return backend_pb2.Result(success=False, message=f"Unexpected {err=}, {type(err)=}")
return backend_pb2.Result(message="Kokoro TTS pipeline loaded successfully", success=True)
def TTS(self, request, context):
try:
# Get voice from request, default to 'af_heart' if not specified
voice = request.voice if request.voice else 'af_heart'
# Generate audio using Kokoro pipeline
generator = self.pipeline(request.text, voice=voice)
speechs = []
# Get all the audio segment
for i, (gs, ps, audio) in enumerate(generator):
speechs.append(audio)
print(f"Generated audio segment {i}: gs={gs}, ps={ps}", file=sys.stderr)
# Merges the audio segments and writes them to the destination
speech = torch.cat(speechs, dim=0)
sf.write(request.dst, speech, 24000)
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)
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