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238 lines
8.6 KiB
238 lines
8.6 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 os
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from collections import OrderedDict
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from typing import Dict
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from typing import List
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from typing import Optional
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from ..cli.utils import download_and_decompress
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from ..utils.dynamic_import import dynamic_import
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from ..utils.env import MODEL_HOME
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from .model_alias import model_alias
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task_supported = [
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'asr', 'cls', 'st', 'text', 'tts', 'vector', 'kws', 'ssl', 'whisper'
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]
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model_format_supported = ['dynamic', 'static', 'onnx']
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inference_mode_supported = ['online', 'offline']
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class CommonTaskResource:
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def __init__(self, task: str, model_format: str='dynamic', **kwargs):
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assert task in task_supported, 'Arg "task" must be one of {}.'.format(
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task_supported)
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assert model_format in model_format_supported, 'Arg "model_format" must be one of {}.'.format(
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model_format_supported)
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self.task = task
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self.model_format = model_format
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self.pretrained_models = self._get_pretrained_models()
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if 'inference_mode' in kwargs:
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assert kwargs[
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'inference_mode'] in inference_mode_supported, 'Arg "inference_mode" must be one of {}.'.format(
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inference_mode_supported)
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self._inference_mode_filter(kwargs['inference_mode'])
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# Initialize after model and version had been set.
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self.model_tag = None
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self.version = None
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self.res_dict = None
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self.res_dir = None
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if self.task == 'tts':
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# For vocoder
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self.voc_model_tag = None
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self.voc_version = None
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self.voc_res_dict = None
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self.voc_res_dir = None
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def set_task_model(self,
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model_tag: str,
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model_type: int=0,
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skip_download: bool=False,
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version: Optional[str]=None):
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"""Set model tag and version of current task.
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Args:
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model_tag (str): Model tag.
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model_type (int): 0 for acoustic model otherwise vocoder in tts task.
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version (Optional[str], optional): Version of pretrained model. Defaults to None.
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"""
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assert model_tag in self.pretrained_models, \
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"Can't find \"{}\" in resource. Model name must be one of {}".format(model_tag, list(self.pretrained_models.keys()))
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if version is None:
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version = self._get_default_version(model_tag)
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assert version in self.pretrained_models[model_tag], \
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"Can't find version \"{}\" in \"{}\". Model name must be one of {}".format(
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version, model_tag, list(self.pretrained_models[model_tag].keys()))
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if model_type == 0:
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self.model_tag = model_tag
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self.version = version
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self.res_dict = self.pretrained_models[model_tag][version]
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self._format_path(self.res_dict)
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if not skip_download:
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self.res_dir = self._fetch(self.res_dict,
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self._get_model_dir(model_type))
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else:
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assert self.task == 'tts', 'Vocoder will only be used in tts task.'
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self.voc_model_tag = model_tag
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self.voc_version = version
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self.voc_res_dict = self.pretrained_models[model_tag][version]
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self._format_path(self.voc_res_dict)
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if not skip_download:
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self.voc_res_dir = self._fetch(self.voc_res_dict,
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self._get_model_dir(model_type))
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@staticmethod
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def get_model_class(model_name) -> List[object]:
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"""Dynamic import model class.
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Args:
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model_name (str): Model name.
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Returns:
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List[object]: Return a list of model class.
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"""
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assert model_name in model_alias, 'No model classes found for "{}"'.format(
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model_name)
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ret = []
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for import_path in model_alias[model_name]:
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ret.append(dynamic_import(import_path))
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if len(ret) == 1:
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return ret[0]
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else:
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return ret
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def get_versions(self, model_tag: str) -> List[str]:
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"""List all available versions.
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Args:
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model_tag (str): Model tag.
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Returns:
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List[str]: Version list of model.
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"""
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return list(self.pretrained_models[model_tag].keys())
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def _get_default_version(self, model_tag: str) -> str:
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"""Get default version of model.
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Args:
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model_tag (str): Model tag.
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Returns:
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str: Default version.
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"""
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return self.get_versions(model_tag)[-1] # get latest version
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def _get_model_dir(self, model_type: int=0) -> os.PathLike:
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"""Get resource directory.
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Args:
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model_type (int): 0 for acoustic model otherwise vocoder in tts task.
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Returns:
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os.PathLike: Directory of model resource.
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"""
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if model_type == 0:
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model_tag = self.model_tag
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version = self.version
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else:
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model_tag = self.voc_model_tag
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version = self.voc_version
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return os.path.join(MODEL_HOME, model_tag, version)
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def _get_pretrained_models(self) -> Dict[str, str]:
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"""Get all available models for current task.
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Returns:
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Dict[str, str]: A dictionary with model tag and resources info.
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"""
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try:
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import_models = '{}_{}_pretrained_models'.format(self.task,
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self.model_format)
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exec('from .pretrained_models import {}'.format(import_models))
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models = OrderedDict(locals()[import_models])
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except Exception as e:
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models = OrderedDict({}) # no models.
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finally:
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return models
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def _inference_mode_filter(self, inference_mode: Optional[str]):
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"""Filter models dict based on inference_mode.
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Args:
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inference_mode (Optional[str]): 'online', 'offline' or None.
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"""
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if inference_mode is None:
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return
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if self.task == 'asr':
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online_flags = [
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'online' in model_tag
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for model_tag in self.pretrained_models.keys()
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]
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for online_flag, model_tag in zip(
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online_flags, list(self.pretrained_models.keys())):
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if inference_mode == 'online' and online_flag:
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continue
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elif inference_mode == 'offline' and not online_flag:
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continue
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else:
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del self.pretrained_models[model_tag]
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elif self.task == 'tts':
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# Hardcode for tts online models.
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tts_online_models = [
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'fastspeech2_csmsc-zh', 'fastspeech2_cnndecoder_csmsc-zh',
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'mb_melgan_csmsc-zh', 'hifigan_csmsc-zh'
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]
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for model_tag in list(self.pretrained_models.keys()):
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if inference_mode == 'online' and model_tag in tts_online_models:
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continue
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elif inference_mode == 'offline':
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continue
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else:
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del self.pretrained_models[model_tag]
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else:
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raise NotImplementedError('Only supports asr and tts task.')
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@staticmethod
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def _fetch(res_dict: Dict[str, str],
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target_dir: os.PathLike) -> os.PathLike:
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"""Fetch archive from url.
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Args:
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res_dict (Dict[str, str]): Info dict of a resource.
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target_dir (os.PathLike): Directory to save archives.
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Returns:
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os.PathLike: Directory of model resource.
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"""
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return download_and_decompress(res_dict, target_dir)
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@staticmethod
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def _format_path(res_dict: Dict[str, str]):
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for k, v in res_dict.items():
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if isinstance(v, str) and '/' in v:
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if v.startswith('https://') or v.startswith('http://'):
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continue
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else:
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res_dict[k] = os.path.join(*(v.split('/')))
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