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225 lines
7.3 KiB
225 lines
7.3 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 io
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import os
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import time
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from typing import Optional
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import numpy as np
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import paddle
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import yaml
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from paddlespeech.cli.cls.infer import CLSExecutor
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from paddlespeech.cli.log import logger
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from paddlespeech.cli.utils import download_and_decompress
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from paddlespeech.cli.utils import MODEL_HOME
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from paddlespeech.server.engine.base_engine import BaseEngine
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from paddlespeech.server.utils.paddle_predictor import init_predictor
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from paddlespeech.server.utils.paddle_predictor import run_model
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__all__ = ['CLSEngine']
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pretrained_models = {
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"panns_cnn6-32k": {
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'url':
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'https://paddlespeech.bj.bcebos.com/cls/inference_model/panns_cnn6_static.tar.gz',
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'md5':
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'da087c31046d23281d8ec5188c1967da',
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'cfg_path':
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'panns.yaml',
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'model_path':
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'inference.pdmodel',
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'params_path':
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'inference.pdiparams',
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'label_file':
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'audioset_labels.txt',
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},
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"panns_cnn10-32k": {
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'url':
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'https://paddlespeech.bj.bcebos.com/cls/inference_model/panns_cnn10_static.tar.gz',
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'md5':
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'5460cc6eafbfaf0f261cc75b90284ae1',
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'cfg_path':
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'panns.yaml',
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'model_path':
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'inference.pdmodel',
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'params_path':
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'inference.pdiparams',
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'label_file':
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'audioset_labels.txt',
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},
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"panns_cnn14-32k": {
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'url':
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'https://paddlespeech.bj.bcebos.com/cls/inference_model/panns_cnn14_static.tar.gz',
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'md5':
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'ccc80b194821274da79466862b2ab00f',
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'cfg_path':
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'panns.yaml',
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'model_path':
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'inference.pdmodel',
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'params_path':
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'inference.pdiparams',
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'label_file':
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'audioset_labels.txt',
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},
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}
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class CLSServerExecutor(CLSExecutor):
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def __init__(self):
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super().__init__()
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pass
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def _get_pretrained_path(self, tag: str) -> os.PathLike:
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"""
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Download and returns pretrained resources path of current task.
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"""
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support_models = list(pretrained_models.keys())
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assert tag in pretrained_models, 'The model "{}" you want to use has not been supported, please choose other models.\nThe support models includes:\n\t\t{}\n'.format(
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tag, '\n\t\t'.join(support_models))
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res_path = os.path.join(MODEL_HOME, tag)
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decompressed_path = download_and_decompress(pretrained_models[tag],
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res_path)
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decompressed_path = os.path.abspath(decompressed_path)
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logger.info(
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'Use pretrained model stored in: {}'.format(decompressed_path))
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return decompressed_path
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def _init_from_path(
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self,
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model_type: str='panns_cnn14',
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cfg_path: Optional[os.PathLike]=None,
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model_path: Optional[os.PathLike]=None,
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params_path: Optional[os.PathLike]=None,
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label_file: Optional[os.PathLike]=None,
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predictor_conf: dict=None, ):
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"""
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Init model and other resources from a specific path.
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"""
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if cfg_path is None or model_path is None or params_path is None or label_file is None:
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tag = model_type + '-' + '32k'
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self.res_path = self._get_pretrained_path(tag)
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self.cfg_path = os.path.join(self.res_path,
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pretrained_models[tag]['cfg_path'])
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self.model_path = os.path.join(self.res_path,
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pretrained_models[tag]['model_path'])
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self.params_path = os.path.join(
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self.res_path, pretrained_models[tag]['params_path'])
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self.label_file = os.path.join(self.res_path,
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pretrained_models[tag]['label_file'])
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else:
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self.cfg_path = os.path.abspath(cfg_path)
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self.model_path = os.path.abspath(model_path)
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self.params_path = os.path.abspath(params_path)
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self.label_file = os.path.abspath(label_file)
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logger.info(self.cfg_path)
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logger.info(self.model_path)
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logger.info(self.params_path)
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logger.info(self.label_file)
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# config
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with open(self.cfg_path, 'r') as f:
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self._conf = yaml.safe_load(f)
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logger.info("Read cfg file successfully.")
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# labels
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self._label_list = []
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with open(self.label_file, 'r') as f:
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for line in f:
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self._label_list.append(line.strip())
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logger.info("Read label file successfully.")
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# Create predictor
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self.predictor_conf = predictor_conf
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self.predictor = init_predictor(
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model_file=self.model_path,
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params_file=self.params_path,
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predictor_conf=self.predictor_conf)
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logger.info("Create predictor successfully.")
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@paddle.no_grad()
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def infer(self):
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"""
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Model inference and result stored in self.output.
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"""
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output = run_model(self.predictor, [self._inputs['feats'].numpy()])
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self._outputs['logits'] = output[0]
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class CLSEngine(BaseEngine):
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"""CLS server engine
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Args:
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metaclass: Defaults to Singleton.
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"""
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def __init__(self):
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super(CLSEngine, self).__init__()
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def init(self, config: dict) -> bool:
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"""init engine resource
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Args:
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config_file (str): config file
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Returns:
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bool: init failed or success
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"""
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self.executor = CLSServerExecutor()
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self.config = config
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self.executor._init_from_path(
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self.config.model_type, self.config.cfg_path,
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self.config.model_path, self.config.params_path,
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self.config.label_file, self.config.predictor_conf)
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logger.info("Initialize CLS server engine successfully.")
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return True
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def run(self, audio_data):
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"""engine run
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Args:
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audio_data (bytes): base64.b64decode
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"""
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self.executor.preprocess(io.BytesIO(audio_data))
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st = time.time()
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self.executor.infer()
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infer_time = time.time() - st
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logger.info("inference time: {}".format(infer_time))
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logger.info("cls engine type: inference")
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def postprocess(self, topk: int):
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"""postprocess
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"""
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assert topk <= len(self.executor._label_list
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), 'Value of topk is larger than number of labels.'
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result = np.squeeze(self.executor._outputs['logits'], axis=0)
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topk_idx = (-result).argsort()[:topk]
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topk_results = []
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for idx in topk_idx:
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res = {}
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label, score = self.executor._label_list[idx], result[idx]
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res['class_name'] = label
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res['prob'] = score
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topk_results.append(res)
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return topk_results
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