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