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220 lines
7.6 KiB
220 lines
7.6 KiB
# Copyright (c) 2021 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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import os
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from collections import OrderedDict
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from typing import List
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from typing import Optional
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from typing import Union
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import paddle
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import yaml
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from paddleaudio.backends import soundfile_load as load_audio
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from paddleaudio.compliance.kaldi import fbank as kaldi_fbank
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from ..executor import BaseExecutor
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from ..log import logger
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from ..utils import stats_wrapper
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__all__ = ['KWSExecutor']
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class KWSExecutor(BaseExecutor):
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def __init__(self):
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super().__init__(task='kws')
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self.parser = argparse.ArgumentParser(
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prog='paddlespeech.kws', add_help=True)
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self.parser.add_argument(
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'--input',
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type=str,
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default=None,
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help='Audio file to keyword spotting.')
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self.parser.add_argument(
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'--threshold',
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type=float,
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default=0.8,
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help='Score threshold for keyword spotting.')
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self.parser.add_argument(
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'--model',
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type=str,
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default='mdtc_heysnips',
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choices=[
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tag[:tag.index('-')]
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for tag in self.task_resource.pretrained_models.keys()
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],
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help='Choose model type of kws task.')
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self.parser.add_argument(
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'--config',
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type=str,
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default=None,
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help='Config of kws task. Use deault config when it is None.')
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self.parser.add_argument(
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'--ckpt_path',
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type=str,
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default=None,
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help='Checkpoint file of model.')
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self.parser.add_argument(
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'--device',
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type=str,
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default=paddle.get_device(),
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help='Choose device to execute model inference.')
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self.parser.add_argument(
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'-d',
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'--job_dump_result',
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action='store_true',
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help='Save job result into file.')
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self.parser.add_argument(
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'-v',
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'--verbose',
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action='store_true',
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help='Increase logger verbosity of current task.')
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def _init_from_path(self,
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model_type: str='mdtc_heysnips',
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cfg_path: Optional[os.PathLike]=None,
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ckpt_path: Optional[os.PathLike]=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 hasattr(self, 'model'):
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logger.debug('Model had been initialized.')
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return
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if ckpt_path is None:
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tag = model_type + '-' + '16k'
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self.task_resource.set_task_model(tag)
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self.cfg_path = os.path.join(
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self.task_resource.res_dir,
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self.task_resource.res_dict['cfg_path'])
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self.ckpt_path = os.path.join(
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self.task_resource.res_dir,
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self.task_resource.res_dict['ckpt_path'] + '.pdparams')
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else:
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self.cfg_path = os.path.abspath(cfg_path)
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self.ckpt_path = os.path.abspath(ckpt_path)
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# config
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with open(self.cfg_path, 'r') as f:
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config = yaml.safe_load(f)
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# model
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backbone_class = self.task_resource.get_model_class(
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model_type.split('_')[0])
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model_class = self.task_resource.get_model_class(
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model_type.split('_')[0] + '_for_kws')
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backbone = backbone_class(
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stack_num=config['stack_num'],
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stack_size=config['stack_size'],
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in_channels=config['in_channels'],
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res_channels=config['res_channels'],
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kernel_size=config['kernel_size'],
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causal=True, )
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self.model = model_class(
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backbone=backbone, num_keywords=config['num_keywords'])
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model_dict = paddle.load(self.ckpt_path)
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self.model.set_state_dict(model_dict)
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self.model.eval()
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self.feature_extractor = lambda x: kaldi_fbank(
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x, sr=config['sample_rate'],
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frame_shift=config['frame_shift'],
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frame_length=config['frame_length'],
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n_mels=config['n_mels']
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)
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def preprocess(self, audio_file: Union[str, os.PathLike]):
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"""
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Input preprocess and return paddle.Tensor stored in self.input.
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Input content can be a text(tts), a file(asr, cls) or a streaming(not supported yet).
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"""
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assert os.path.isfile(audio_file)
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waveform, _ = load_audio(audio_file)
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if isinstance(audio_file, (str, os.PathLike)):
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logger.debug("Preprocessing audio_file:" + audio_file)
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# Feature extraction
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waveform = paddle.to_tensor(waveform).unsqueeze(0)
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self._inputs['feats'] = self.feature_extractor(waveform).unsqueeze(0)
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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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self._outputs['logits'] = self.model(self._inputs['feats'])
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def postprocess(self, threshold: float) -> Union[str, os.PathLike]:
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"""
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Output postprocess and return human-readable results such as texts and audio files.
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"""
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kws_score = max(self._outputs['logits'][0, :, 0]).item()
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return 'Score: {:.3f}, Threshold: {}, Is keyword: {}'.format(
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kws_score, threshold, kws_score > threshold)
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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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model_type = parser_args.model
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cfg_path = parser_args.config
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ckpt_path = parser_args.ckpt_path
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device = parser_args.device
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threshold = parser_args.threshold
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if not parser_args.verbose:
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self.disable_task_loggers()
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task_source = self.get_input_source(parser_args.input)
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task_results = OrderedDict()
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has_exceptions = False
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for id_, input_ in task_source.items():
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try:
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res = self(input_, threshold, model_type, cfg_path, ckpt_path,
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device)
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task_results[id_] = res
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except Exception as e:
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has_exceptions = True
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task_results[id_] = f'{e.__class__.__name__}: {e}'
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self.process_task_results(parser_args.input, task_results,
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parser_args.job_dump_result)
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if has_exceptions:
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return False
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else:
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return True
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@stats_wrapper
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def __call__(self,
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audio_file: os.PathLike,
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threshold: float=0.8,
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model: str='mdtc_heysnips',
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config: Optional[os.PathLike]=None,
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ckpt_path: Optional[os.PathLike]=None,
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device: str=paddle.get_device()):
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"""
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Python API to call an executor.
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"""
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audio_file = os.path.abspath(os.path.expanduser(audio_file))
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paddle.set_device(device)
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self._init_from_path(model, config, ckpt_path)
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self.preprocess(audio_file)
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self.infer()
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res = self.postprocess(threshold)
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return res
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