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PaddleSpeech/paddlespeech/server/bin/paddlespeech_server.py

189 lines
6.4 KiB

# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import sys
import warnings
from typing import List
import uvicorn
from fastapi import FastAPI
from prettytable import PrettyTable
from starlette.middleware.cors import CORSMiddleware
from ..executor import BaseExecutor
from ..util import cli_server_register
from ..util import stats_wrapper
from paddlespeech.cli.log import logger
from paddlespeech.resource import CommonTaskResource
from paddlespeech.server.engine.engine_pool import init_engine_pool
from paddlespeech.server.engine.engine_warmup import warm_up
from paddlespeech.server.restful.api import setup_router as setup_http_router
from paddlespeech.server.utils.config import get_config
from paddlespeech.server.ws.api import setup_router as setup_ws_router
warnings.filterwarnings("ignore")
__all__ = ['ServerExecutor', 'ServerStatsExecutor']
app = FastAPI(
title="PaddleSpeech Serving API", description="Api", version="0.0.1")
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"])
@cli_server_register(
name='paddlespeech_server.start', description='Start the service')
class ServerExecutor(BaseExecutor):
def __init__(self):
super(ServerExecutor, self).__init__()
self.parser = argparse.ArgumentParser(
prog='paddlespeech_server.start', add_help=True)
self.parser.add_argument(
"--config_file",
action="store",
help="yaml file of the app",
default=None,
required=True)
self.parser.add_argument(
"--log_file",
action="store",
help="log file",
default="./log/paddlespeech.log")
def init(self, config) -> bool:
"""system initialization
Args:
config (CfgNode): config object
Returns:
bool:
"""
# init api
api_list = list(engine.split("_")[0] for engine in config.engine_list)
if config.protocol == "websocket":
api_router = setup_ws_router(api_list)
elif config.protocol == "http":
api_router = setup_http_router(api_list)
else:
raise Exception("unsupported protocol")
app.include_router(api_router)
logger.info("start to init the engine")
if not init_engine_pool(config):
return False
# warm up
for engine_and_type in config.engine_list:
if not warm_up(engine_and_type):
return False
return True
def execute(self, argv: List[str]) -> bool:
args = self.parser.parse_args(argv)
try:
self(args.config_file, args.log_file)
except Exception as e:
logger.error("Failed to start server.")
logger.error(e)
sys.exit(-1)
@stats_wrapper
def __call__(self,
config_file: str="./conf/application.yaml",
log_file: str="./log/paddlespeech.log"):
"""
Python API to call an executor.
"""
config = get_config(config_file)
if self.init(config):
uvicorn.run(app, host=config.host, port=config.port)
@cli_server_register(
name='paddlespeech_server.stats',
description='Get the models supported by each speech task in the service.')
class ServerStatsExecutor():
def __init__(self):
super(ServerStatsExecutor, self).__init__()
self.parser = argparse.ArgumentParser(
prog='paddlespeech_server.stats', add_help=True)
self.parser.add_argument(
'--task',
type=str,
default=None,
choices=['asr', 'tts', 'cls', 'text', 'vector'],
help='Choose speech task.',
required=True)
self.task_choices = ['asr', 'tts', 'cls', 'text', 'vector']
self.model_name_format = {
'asr': 'Model-Language-Sample Rate',
'tts': 'Model-Language',
'cls': 'Model-Sample Rate',
'text': 'Model-Task-Language',
'vector': 'Model-Sample Rate'
}
def show_support_models(self, pretrained_models: dict):
fields = self.model_name_format[self.task].split("-")
table = PrettyTable(fields)
for key in pretrained_models:
table.add_row(key.split("-"))
print(table)
def execute(self, argv: List[str]) -> bool:
"""
Command line entry.
"""
parser_args = self.parser.parse_args(argv)
self.task = parser_args.task
if self.task not in self.task_choices:
logger.error(
"Please input correct speech task, choices = ['asr', 'tts']")
return False
try:
# Dynamic models
dynamic_pretrained_models = CommonTaskResource(
task=self.task, model_format='dynamic').pretrained_models
if len(dynamic_pretrained_models) > 0:
logger.info(
"Here is the table of {} pretrained models supported in the service.".
format(self.task.upper()))
self.show_support_models(dynamic_pretrained_models)
# Static models
static_pretrained_models = CommonTaskResource(
task=self.task, model_format='static').pretrained_models
if len(static_pretrained_models) > 0:
logger.info(
"Here is the table of {} static pretrained models supported in the service.".
format(self.task.upper()))
self.show_support_models(static_pretrained_models)
return True
except BaseException:
logger.error(
"Failed to get the table of {} pretrained models supported in the service.".
format(self.task.upper()))
return False