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737 lines
26 KiB
737 lines
26 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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import time
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from collections import OrderedDict
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from typing import Any
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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 numpy as np
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import paddle
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import soundfile as sf
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import yaml
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from yacs.config import CfgNode
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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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from paddlespeech.resource import CommonTaskResource
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from paddlespeech.t2s.exps.syn_utils import get_am_inference
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from paddlespeech.t2s.exps.syn_utils import get_frontend
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from paddlespeech.t2s.exps.syn_utils import get_sess
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from paddlespeech.t2s.exps.syn_utils import get_voc_inference
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from paddlespeech.t2s.exps.syn_utils import run_frontend
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from paddlespeech.t2s.utils import str2bool
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__all__ = ['TTSExecutor']
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ONNX_SUPPORT_SET = {
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'speedyspeech_csmsc',
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'fastspeech2_csmsc',
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'fastspeech2_ljspeech',
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'fastspeech2_aishell3',
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'fastspeech2_vctk',
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'fastspeech2_male',
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'fastspeech2_mix',
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'fastspeech2_canton',
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'pwgan_csmsc',
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'pwgan_ljspeech',
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'pwgan_aishell3',
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'pwgan_vctk',
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'pwgan_male',
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'mb_melgan_csmsc',
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'hifigan_csmsc',
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'hifigan_ljspeech',
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'hifigan_aishell3',
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'hifigan_vctk',
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'hifigan_male',
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}
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class TTSExecutor(BaseExecutor):
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def __init__(self):
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super().__init__('tts')
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self.parser = argparse.ArgumentParser(
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prog='paddlespeech.tts', add_help=True)
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self.parser.add_argument(
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'--input', type=str, default=None, help='Input text to generate.')
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# acoustic model
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self.parser.add_argument(
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'--am',
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type=str,
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default='fastspeech2_csmsc',
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choices=[
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'speedyspeech_csmsc',
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'fastspeech2_csmsc',
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'fastspeech2_ljspeech',
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'fastspeech2_aishell3',
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'fastspeech2_vctk',
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'fastspeech2_mix',
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'tacotron2_csmsc',
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'tacotron2_ljspeech',
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'fastspeech2_male',
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'fastspeech2_canton',
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],
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help='Choose acoustic model type of tts task.')
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self.parser.add_argument(
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'--am_config',
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type=str,
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default=None,
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help='Config of acoustic model. Use deault config when it is None.')
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self.parser.add_argument(
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'--am_ckpt',
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type=str,
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default=None,
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help='Checkpoint file of acoustic model.')
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self.parser.add_argument(
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"--am_stat",
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type=str,
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default=None,
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help="mean and standard deviation used to normalize spectrogram when training acoustic model."
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)
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self.parser.add_argument(
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"--phones_dict",
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type=str,
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default=None,
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help="phone vocabulary file.")
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self.parser.add_argument(
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"--tones_dict",
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type=str,
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default=None,
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help="tone vocabulary file.")
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self.parser.add_argument(
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"--speaker_dict",
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type=str,
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default=None,
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help="speaker id map file.")
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self.parser.add_argument(
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'--spk_id',
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type=int,
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default=0,
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help='spk id for multi speaker acoustic model')
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# vocoder
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self.parser.add_argument(
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'--voc',
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type=str,
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default='hifigan_csmsc',
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choices=[
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'pwgan_csmsc',
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'pwgan_ljspeech',
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'pwgan_aishell3',
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'pwgan_vctk',
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'mb_melgan_csmsc',
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'style_melgan_csmsc',
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'hifigan_csmsc',
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'hifigan_ljspeech',
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'hifigan_aishell3',
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'hifigan_vctk',
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'wavernn_csmsc',
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'pwgan_male',
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'hifigan_male',
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],
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help='Choose vocoder type of tts task.')
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self.parser.add_argument(
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'--voc_config',
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type=str,
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default=None,
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help='Config of voc. Use deault config when it is None.')
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self.parser.add_argument(
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'--voc_ckpt',
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type=str,
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default=None,
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help='Checkpoint file of voc.')
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self.parser.add_argument(
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"--voc_stat",
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type=str,
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default=None,
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help="mean and standard deviation used to normalize spectrogram when training voc."
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)
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# other
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self.parser.add_argument(
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'--lang',
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type=str,
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default='zh',
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help='Choose model language. zh or en or mix')
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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('--cpu_threads', type=int, default=2)
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self.parser.add_argument(
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'--output', type=str, default='output.wav', help='output file name')
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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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self.parser.add_argument(
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"--use_onnx",
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type=str2bool,
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default=False,
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help="whether to usen onnxruntime inference.")
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self.parser.add_argument(
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'--fs',
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type=int,
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default=24000,
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help='sample rate for onnx models when use specified model files.')
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def _init_from_path(
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self,
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am: str='fastspeech2_csmsc',
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am_config: Optional[os.PathLike]=None,
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am_ckpt: Optional[os.PathLike]=None,
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am_stat: Optional[os.PathLike]=None,
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phones_dict: Optional[os.PathLike]=None,
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tones_dict: Optional[os.PathLike]=None,
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speaker_dict: Optional[os.PathLike]=None,
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voc: str='hifigan_csmsc',
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voc_config: Optional[os.PathLike]=None,
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voc_ckpt: Optional[os.PathLike]=None,
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voc_stat: Optional[os.PathLike]=None,
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lang: str='zh', ):
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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, 'am_inference') and hasattr(self, 'voc_inference'):
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logger.debug('Models had been initialized.')
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return
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# am
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if am_ckpt is None or am_config is None or am_stat is None or phones_dict is None:
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use_pretrained_am = True
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else:
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use_pretrained_am = False
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am_tag = am + '-' + lang
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self.task_resource.set_task_model(
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model_tag=am_tag,
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model_type=0, # am
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skip_download=not use_pretrained_am,
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version=None, # default version
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)
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if use_pretrained_am:
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self.am_res_path = self.task_resource.res_dir
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self.am_config = os.path.join(self.am_res_path,
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self.task_resource.res_dict['config'])
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self.am_ckpt = os.path.join(self.am_res_path,
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self.task_resource.res_dict['ckpt'])
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self.am_stat = os.path.join(
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self.am_res_path, self.task_resource.res_dict['speech_stats'])
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# must have phones_dict in acoustic
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self.phones_dict = os.path.join(
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self.am_res_path, self.task_resource.res_dict['phones_dict'])
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logger.debug(self.am_res_path)
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logger.debug(self.am_config)
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logger.debug(self.am_ckpt)
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else:
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self.am_config = os.path.abspath(am_config)
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self.am_ckpt = os.path.abspath(am_ckpt)
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self.am_stat = os.path.abspath(am_stat)
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self.phones_dict = os.path.abspath(phones_dict)
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self.am_res_path = os.path.dirname(self.am_config)
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# for speedyspeech
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self.tones_dict = None
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if 'tones_dict' in self.task_resource.res_dict:
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self.tones_dict = os.path.join(
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self.am_res_path, self.task_resource.res_dict['tones_dict'])
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if tones_dict:
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self.tones_dict = tones_dict
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# for multi speaker fastspeech2
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self.speaker_dict = None
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if 'speaker_dict' in self.task_resource.res_dict:
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self.speaker_dict = os.path.join(
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self.am_res_path, self.task_resource.res_dict['speaker_dict'])
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if speaker_dict:
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self.speaker_dict = speaker_dict
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# voc
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if voc_ckpt is None or voc_config is None or voc_stat is None:
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use_pretrained_voc = True
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else:
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use_pretrained_voc = False
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voc_lang = lang
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# When speaker is 174 (csmsc), use csmsc's vocoder is better than aishell3's
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if lang == 'mix' or lang == 'canton':
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voc_dataset = voc[voc.rindex('_') + 1:]
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if voc_dataset in {"ljspeech", "vctk"}:
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voc_lang = 'en'
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else:
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voc_lang = 'zh'
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voc_tag = voc + '-' + voc_lang
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self.task_resource.set_task_model(
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model_tag=voc_tag,
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model_type=1, # vocoder
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skip_download=not use_pretrained_voc,
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version=None, # default version
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)
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if use_pretrained_voc:
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self.voc_res_path = self.task_resource.voc_res_dir
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self.voc_config = os.path.join(
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self.voc_res_path, self.task_resource.voc_res_dict['config'])
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self.voc_ckpt = os.path.join(
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self.voc_res_path, self.task_resource.voc_res_dict['ckpt'])
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self.voc_stat = os.path.join(
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self.voc_res_path,
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self.task_resource.voc_res_dict['speech_stats'])
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logger.debug(self.voc_res_path)
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logger.debug(self.voc_config)
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logger.debug(self.voc_ckpt)
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else:
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self.voc_config = os.path.abspath(voc_config)
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self.voc_ckpt = os.path.abspath(voc_ckpt)
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self.voc_stat = os.path.abspath(voc_stat)
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self.voc_res_path = os.path.dirname(
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os.path.abspath(self.voc_config))
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# Init body.
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with open(self.am_config) as f:
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self.am_config = CfgNode(yaml.safe_load(f))
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with open(self.voc_config) as f:
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self.voc_config = CfgNode(yaml.safe_load(f))
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with open(self.phones_dict, 'rt', encoding='utf-8') as f:
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phn_id = [line.strip().split() for line in f.readlines()]
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vocab_size = len(phn_id)
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tone_size = None
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if self.tones_dict:
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with open(self.tones_dict, 'rt', encoding='utf-8') as f:
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tone_id = [line.strip().split() for line in f.readlines()]
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tone_size = len(tone_id)
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spk_num = None
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if self.speaker_dict:
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with open(self.speaker_dict, 'rt', encoding='utf-8') as f:
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spk_id = [line.strip().split() for line in f.readlines()]
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spk_num = len(spk_id)
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# frontend
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self.frontend = get_frontend(
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lang=lang, phones_dict=self.phones_dict, tones_dict=self.tones_dict)
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# acoustic model
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self.am_inference = get_am_inference(
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am=am,
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am_config=self.am_config,
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am_ckpt=self.am_ckpt,
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am_stat=self.am_stat,
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phones_dict=self.phones_dict,
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tones_dict=self.tones_dict,
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speaker_dict=self.speaker_dict)
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# vocoder
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self.voc_inference = get_voc_inference(
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voc=voc,
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voc_config=self.voc_config,
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voc_ckpt=self.voc_ckpt,
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voc_stat=self.voc_stat)
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def _init_from_path_onnx(self,
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am: str='fastspeech2_csmsc',
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am_ckpt: Optional[os.PathLike]=None,
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phones_dict: Optional[os.PathLike]=None,
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tones_dict: Optional[os.PathLike]=None,
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speaker_dict: Optional[os.PathLike]=None,
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voc: str='hifigan_csmsc',
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voc_ckpt: Optional[os.PathLike]=None,
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lang: str='zh',
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device: str='cpu',
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cpu_threads: int=2,
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fs: int=24000):
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if hasattr(self, 'am_sess') and hasattr(self, 'voc_sess'):
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logger.debug('Models had been initialized.')
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return
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# am
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if am_ckpt is None or phones_dict is None:
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use_pretrained_am = True
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else:
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use_pretrained_am = False
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am_tag = am + '_onnx' + '-' + lang
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self.task_resource.set_task_model(
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model_tag=am_tag,
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model_type=0, # am
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skip_download=not use_pretrained_am,
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version=None, # default version
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)
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if use_pretrained_am:
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self.am_res_path = self.task_resource.res_dir
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self.am_ckpt = os.path.join(self.am_res_path,
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self.task_resource.res_dict['ckpt'])
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# must have phones_dict in acoustic
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self.phones_dict = os.path.join(
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self.am_res_path, self.task_resource.res_dict['phones_dict'])
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self.am_fs = self.task_resource.res_dict['sample_rate']
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logger.debug(self.am_res_path)
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logger.debug(self.am_ckpt)
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else:
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self.am_ckpt = os.path.abspath(am_ckpt)
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self.phones_dict = os.path.abspath(phones_dict)
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self.am_res_path = os.path.dirname(self.am_ckpt)
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self.am_fs = fs
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# for speedyspeech
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self.tones_dict = None
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if 'tones_dict' in self.task_resource.res_dict:
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self.tones_dict = os.path.join(
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self.am_res_path, self.task_resource.res_dict['tones_dict'])
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if tones_dict:
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self.tones_dict = tones_dict
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# voc
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if voc_ckpt is None:
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use_pretrained_voc = True
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else:
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use_pretrained_voc = False
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voc_lang = lang
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if lang == 'mix' or lang == 'canton':
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voc_dataset = voc[voc.rindex('_') + 1:]
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if voc_dataset in {"ljspeech", "vctk"}:
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voc_lang = 'en'
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else:
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voc_lang = 'zh'
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voc_tag = voc + '_onnx' + '-' + voc_lang
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self.task_resource.set_task_model(
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model_tag=voc_tag,
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model_type=1, # vocoder
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skip_download=not use_pretrained_voc,
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version=None, # default version
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)
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if use_pretrained_voc:
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self.voc_res_path = self.task_resource.voc_res_dir
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self.voc_ckpt = os.path.join(
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self.voc_res_path, self.task_resource.voc_res_dict['ckpt'])
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logger.debug(self.voc_res_path)
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logger.debug(self.voc_ckpt)
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else:
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self.voc_ckpt = os.path.abspath(voc_ckpt)
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self.voc_res_path = os.path.dirname(os.path.abspath(self.voc_ckpt))
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# frontend
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self.frontend = get_frontend(
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lang=lang, phones_dict=self.phones_dict, tones_dict=self.tones_dict)
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self.am_sess = get_sess(
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model_path=self.am_ckpt, device=device, cpu_threads=cpu_threads)
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# vocoder
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self.voc_sess = get_sess(
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model_path=self.voc_ckpt, device=device, cpu_threads=cpu_threads)
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def preprocess(self, input: Any, *args, **kwargs):
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"""
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Input preprocess and return paddle.Tensor stored in self._inputs.
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Input content can be a text(tts), a file(asr, cls), a stream(not supported yet) or anything needed.
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Args:
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input (Any): Input text/file/stream or other content.
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"""
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pass
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@paddle.no_grad()
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def infer(self,
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text: str,
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lang: str='zh',
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am: str='fastspeech2_csmsc',
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spk_id: int=0):
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"""
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Model inference and result stored in self.output.
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"""
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am_name = am[:am.rindex('_')]
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am_dataset = am[am.rindex('_') + 1:]
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merge_sentences = False
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get_tone_ids = False
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if am_name == 'speedyspeech':
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get_tone_ids = True
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frontend_st = time.time()
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frontend_dict = run_frontend(
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frontend=self.frontend,
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text=text,
|
|
merge_sentences=merge_sentences,
|
|
get_tone_ids=get_tone_ids,
|
|
lang=lang)
|
|
self.frontend_time = time.time() - frontend_st
|
|
self.am_time = 0
|
|
self.voc_time = 0
|
|
flags = 0
|
|
phone_ids = frontend_dict['phone_ids']
|
|
for i in range(len(phone_ids)):
|
|
am_st = time.time()
|
|
part_phone_ids = phone_ids[i]
|
|
# am
|
|
if am_name == 'speedyspeech':
|
|
part_tone_ids = frontend_dict['tone_ids'][i]
|
|
mel = self.am_inference(part_phone_ids, part_tone_ids)
|
|
# fastspeech2
|
|
else:
|
|
# multi speaker
|
|
if am_dataset in {'aishell3', 'vctk', 'mix', 'canton'}:
|
|
mel = self.am_inference(
|
|
part_phone_ids, spk_id=paddle.to_tensor([spk_id]))
|
|
else:
|
|
mel = self.am_inference(part_phone_ids)
|
|
self.am_time += (time.time() - am_st)
|
|
# voc
|
|
voc_st = time.time()
|
|
wav = self.voc_inference(mel)
|
|
if flags == 0:
|
|
wav_all = wav
|
|
flags = 1
|
|
else:
|
|
wav_all = paddle.concat([wav_all, wav])
|
|
self.voc_time += (time.time() - voc_st)
|
|
self._outputs['wav'] = wav_all
|
|
|
|
def infer_onnx(self,
|
|
text: str,
|
|
lang: str='zh',
|
|
am: str='fastspeech2_csmsc',
|
|
spk_id: int=0):
|
|
am_name = am[:am.rindex('_')]
|
|
am_dataset = am[am.rindex('_') + 1:]
|
|
merge_sentences = False
|
|
get_tone_ids = False
|
|
if am_name == 'speedyspeech':
|
|
get_tone_ids = True
|
|
am_input_feed = {}
|
|
frontend_st = time.time()
|
|
frontend_dict = run_frontend(
|
|
frontend=self.frontend,
|
|
text=text,
|
|
merge_sentences=merge_sentences,
|
|
get_tone_ids=get_tone_ids,
|
|
lang=lang,
|
|
to_tensor=False, )
|
|
self.frontend_time = time.time() - frontend_st
|
|
phone_ids = frontend_dict['phone_ids']
|
|
self.am_time = 0
|
|
self.voc_time = 0
|
|
flags = 0
|
|
for i in range(len(phone_ids)):
|
|
am_st = time.time()
|
|
part_phone_ids = phone_ids[i]
|
|
if am_name == 'fastspeech2':
|
|
am_input_feed.update({'text': part_phone_ids})
|
|
if am_dataset in {"aishell3", "vctk", "mix", "canton"}:
|
|
# NOTE: 'spk_id' should be List[int] rather than int here!!
|
|
am_input_feed.update({'spk_id': [spk_id]})
|
|
elif am_name == 'speedyspeech':
|
|
part_tone_ids = frontend_dict['tone_ids'][i]
|
|
am_input_feed.update({
|
|
'phones': part_phone_ids,
|
|
'tones': part_tone_ids
|
|
})
|
|
mel = self.am_sess.run(output_names=None, input_feed=am_input_feed)
|
|
mel = mel[0]
|
|
self.am_time += (time.time() - am_st)
|
|
# voc
|
|
voc_st = time.time()
|
|
wav = self.voc_sess.run(
|
|
output_names=None, input_feed={'logmel': mel})
|
|
wav = wav[0]
|
|
if flags == 0:
|
|
wav_all = wav
|
|
flags = 1
|
|
else:
|
|
wav_all = np.concatenate([wav_all, wav])
|
|
self.voc_time += (time.time() - voc_st)
|
|
|
|
self._outputs['wav'] = wav_all
|
|
|
|
def postprocess(self, output: str='output.wav') -> Union[str, os.PathLike]:
|
|
"""
|
|
Output postprocess and return results.
|
|
This method get model output from self._outputs and convert it into human-readable results.
|
|
|
|
Returns:
|
|
Union[str, os.PathLike]: Human-readable results such as texts and audio files.
|
|
"""
|
|
output = os.path.abspath(os.path.expanduser(output))
|
|
sf.write(
|
|
output, self._outputs['wav'].numpy(), samplerate=self.am_config.fs)
|
|
return output
|
|
|
|
def postprocess_onnx(self,
|
|
output: str='output.wav') -> Union[str, os.PathLike]:
|
|
"""
|
|
Output postprocess and return results.
|
|
This method get model output from self._outputs and convert it into human-readable results.
|
|
|
|
Returns:
|
|
Union[str, os.PathLike]: Human-readable results such as texts and audio files.
|
|
"""
|
|
output = os.path.abspath(os.path.expanduser(output))
|
|
sf.write(output, self._outputs['wav'], samplerate=self.am_fs)
|
|
return output
|
|
|
|
# 命令行的入口是这里
|
|
def execute(self, argv: List[str]) -> bool:
|
|
"""
|
|
Command line entry.
|
|
"""
|
|
|
|
args = self.parser.parse_args(argv)
|
|
|
|
am = args.am
|
|
am_config = args.am_config
|
|
am_ckpt = args.am_ckpt
|
|
am_stat = args.am_stat
|
|
phones_dict = args.phones_dict
|
|
tones_dict = args.tones_dict
|
|
speaker_dict = args.speaker_dict
|
|
voc = args.voc
|
|
voc_config = args.voc_config
|
|
voc_ckpt = args.voc_ckpt
|
|
voc_stat = args.voc_stat
|
|
lang = args.lang
|
|
device = args.device
|
|
spk_id = args.spk_id
|
|
use_onnx = args.use_onnx
|
|
cpu_threads = args.cpu_threads
|
|
fs = args.fs
|
|
|
|
if not args.verbose:
|
|
self.disable_task_loggers()
|
|
|
|
task_source = self.get_input_source(args.input)
|
|
task_results = OrderedDict()
|
|
has_exceptions = False
|
|
|
|
for id_, input_ in task_source.items():
|
|
if len(task_source) > 1:
|
|
assert isinstance(args.output,
|
|
str) and args.output.endswith('.wav')
|
|
output = args.output.replace('.wav', f'_{id_}.wav')
|
|
else:
|
|
output = args.output
|
|
|
|
try:
|
|
res = self(
|
|
text=input_,
|
|
# acoustic model related
|
|
am=am,
|
|
am_config=am_config,
|
|
am_ckpt=am_ckpt,
|
|
am_stat=am_stat,
|
|
phones_dict=phones_dict,
|
|
tones_dict=tones_dict,
|
|
speaker_dict=speaker_dict,
|
|
spk_id=spk_id,
|
|
# vocoder related
|
|
voc=voc,
|
|
voc_config=voc_config,
|
|
voc_ckpt=voc_ckpt,
|
|
voc_stat=voc_stat,
|
|
# other
|
|
lang=lang,
|
|
device=device,
|
|
output=output,
|
|
use_onnx=use_onnx,
|
|
cpu_threads=cpu_threads,
|
|
fs=fs)
|
|
task_results[id_] = res
|
|
except Exception as e:
|
|
has_exceptions = True
|
|
task_results[id_] = f'{e.__class__.__name__}: {e}'
|
|
|
|
self.process_task_results(args.input, task_results,
|
|
args.job_dump_result)
|
|
|
|
if has_exceptions:
|
|
return False
|
|
else:
|
|
return True
|
|
|
|
# pyton api 的入口是这里
|
|
@stats_wrapper
|
|
def __call__(self,
|
|
text: str,
|
|
am: str='fastspeech2_csmsc',
|
|
am_config: Optional[os.PathLike]=None,
|
|
am_ckpt: Optional[os.PathLike]=None,
|
|
am_stat: Optional[os.PathLike]=None,
|
|
spk_id: int=0,
|
|
phones_dict: Optional[os.PathLike]=None,
|
|
tones_dict: Optional[os.PathLike]=None,
|
|
speaker_dict: Optional[os.PathLike]=None,
|
|
voc: str='hifigan_csmsc',
|
|
voc_config: Optional[os.PathLike]=None,
|
|
voc_ckpt: Optional[os.PathLike]=None,
|
|
voc_stat: Optional[os.PathLike]=None,
|
|
lang: str='zh',
|
|
device: str=paddle.get_device(),
|
|
output: str='output.wav',
|
|
use_onnx: bool=False,
|
|
cpu_threads: int=2,
|
|
fs: int=24000):
|
|
"""
|
|
Python API to call an executor.
|
|
"""
|
|
if not use_onnx:
|
|
paddle.set_device(device)
|
|
self._init_from_path(
|
|
am=am,
|
|
am_config=am_config,
|
|
am_ckpt=am_ckpt,
|
|
am_stat=am_stat,
|
|
phones_dict=phones_dict,
|
|
tones_dict=tones_dict,
|
|
speaker_dict=speaker_dict,
|
|
voc=voc,
|
|
voc_config=voc_config,
|
|
voc_ckpt=voc_ckpt,
|
|
voc_stat=voc_stat,
|
|
lang=lang)
|
|
|
|
self.infer(text=text, lang=lang, am=am, spk_id=spk_id)
|
|
res = self.postprocess(output=output)
|
|
return res
|
|
else:
|
|
# use onnx
|
|
# we use `cpu` for onnxruntime by default
|
|
# please see description in https://github.com/PaddlePaddle/PaddleSpeech/pull/2220
|
|
self.task_resource = CommonTaskResource(
|
|
task='tts', model_format='onnx')
|
|
assert (
|
|
am in ONNX_SUPPORT_SET and voc in ONNX_SUPPORT_SET
|
|
), f'the am and voc you choose, they should be in {ONNX_SUPPORT_SET}'
|
|
self._init_from_path_onnx(
|
|
am=am,
|
|
am_ckpt=am_ckpt,
|
|
phones_dict=phones_dict,
|
|
tones_dict=tones_dict,
|
|
speaker_dict=speaker_dict,
|
|
voc=voc,
|
|
voc_ckpt=voc_ckpt,
|
|
lang=lang,
|
|
device=device,
|
|
cpu_threads=cpu_threads,
|
|
fs=fs)
|
|
self.infer_onnx(text=text, lang=lang, am=am, spk_id=spk_id)
|
|
res = self.postprocess_onnx(output=output)
|
|
return res
|