paddlespeech_server.py

# 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