# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from collections import OrderedDict
from typing import Dict
from typing import List
from typing import Optional
from ..cli.utils import download_and_decompress
from ..utils.dynamic_import import dynamic_import
from ..utils.env import MODEL_HOME
from .model_alias import model_alias
task_supported = [
'asr', 'cls', 'st', 'text', 'tts', 'vector', 'kws', 'ssl', 'whisper'
]
model_format_supported = ['dynamic', 'static', 'onnx']
inference_mode_supported = ['online', 'offline']
class CommonTaskResource:
def __init__(self, task: str, model_format: str='dynamic', **kwargs):
assert task in task_supported, 'Arg "task" must be one of {}.'.format(
task_supported)
assert model_format in model_format_supported, 'Arg "model_format" must be one of {}.'.format(
model_format_supported)
self.task = task
self.model_format = model_format
self.pretrained_models = self._get_pretrained_models()
if 'inference_mode' in kwargs:
assert kwargs[
'inference_mode'] in inference_mode_supported, 'Arg "inference_mode" must be one of {}.'.format(
inference_mode_supported)
self._inference_mode_filter(kwargs['inference_mode'])
# Initialize after model and version had been set.
self.model_tag = None
self.version = None
self.res_dict = None
self.res_dir = None
if self.task == 'tts':
# For vocoder
self.voc_model_tag = None
self.voc_version = None
self.voc_res_dict = None
self.voc_res_dir = None
def set_task_model(self,
model_tag: str,
model_type: int=0,
skip_download: bool=False,
version: Optional[str]=None):
"""Set model tag and version of current task.
Args:
model_tag (str): Model tag.
model_type (int): 0 for acoustic model otherwise vocoder in tts task.
version (Optional[str], optional): Version of pretrained model. Defaults to None.
"""
assert model_tag in self.pretrained_models, \
"Can't find \"{}\" in resource. Model name must be one of {}".format(model_tag, list(self.pretrained_models.keys()))
if version is None:
version = self._get_default_version(model_tag)
assert version in self.pretrained_models[model_tag], \
"Can't find version \"{}\" in \"{}\". Model name must be one of {}".format(
version, model_tag, list(self.pretrained_models[model_tag].keys()))
if model_type == 0:
self.model_tag = model_tag
self.version = version
self.res_dict = self.pretrained_models[model_tag][version]
self._format_path(self.res_dict)
if not skip_download:
self.res_dir = self._fetch(self.res_dict,
self._get_model_dir(model_type))
else:
assert self.task == 'tts', 'Vocoder will only be used in tts task.'
self.voc_model_tag = model_tag
self.voc_version = version
self.voc_res_dict = self.pretrained_models[model_tag][version]
self._format_path(self.voc_res_dict)
if not skip_download:
self.voc_res_dir = self._fetch(self.voc_res_dict,
self._get_model_dir(model_type))
@staticmethod
def get_model_class(model_name) -> List[object]:
"""Dynamic import model class.
Args:
model_name (str): Model name.
Returns:
List[object]: Return a list of model class.
"""
assert model_name in model_alias, 'No model classes found for "{}"'.format(
model_name)
ret = []
for import_path in model_alias[model_name]:
ret.append(dynamic_import(import_path))
if len(ret) == 1:
return ret[0]
else:
return ret
def get_versions(self, model_tag: str) -> List[str]:
"""List all available versions.
Args:
model_tag (str): Model tag.
Returns:
List[str]: Version list of model.
"""
return list(self.pretrained_models[model_tag].keys())
def _get_default_version(self, model_tag: str) -> str:
"""Get default version of model.
Args:
model_tag (str): Model tag.
Returns:
str: Default version.
"""
return self.get_versions(model_tag)[-1] # get latest version
def _get_model_dir(self, model_type: int=0) -> os.PathLike:
"""Get resource directory.
Args:
model_type (int): 0 for acoustic model otherwise vocoder in tts task.
Returns:
os.PathLike: Directory of model resource.
"""
if model_type == 0:
model_tag = self.model_tag
version = self.version
else:
model_tag = self.voc_model_tag
version = self.voc_version
return os.path.join(MODEL_HOME, model_tag, version)
def _get_pretrained_models(self) -> Dict[str, str]:
"""Get all available models for current task.
Returns:
Dict[str, str]: A dictionary with model tag and resources info.
"""
try:
import_models = '{}_{}_pretrained_models'.format(self.task,
self.model_format)
exec('from .pretrained_models import {}'.format(import_models))
models = OrderedDict(locals()[import_models])
except Exception as e:
models = OrderedDict({}) # no models.
finally:
return models
def _inference_mode_filter(self, inference_mode: Optional[str]):
"""Filter models dict based on inference_mode.
Args:
inference_mode (Optional[str]): 'online', 'offline' or None.
"""
if inference_mode is None:
return
if self.task == 'asr':
online_flags = [
'online' in model_tag
for model_tag in self.pretrained_models.keys()
]
for online_flag, model_tag in zip(
online_flags, list(self.pretrained_models.keys())):
if inference_mode == 'online' and online_flag:
continue
elif inference_mode == 'offline' and not online_flag:
continue
else:
del self.pretrained_models[model_tag]
elif self.task == 'tts':
# Hardcode for tts online models.
tts_online_models = [
'fastspeech2_csmsc-zh', 'fastspeech2_cnndecoder_csmsc-zh',
'mb_melgan_csmsc-zh', 'hifigan_csmsc-zh'
]
for model_tag in list(self.pretrained_models.keys()):
if inference_mode == 'online' and model_tag in tts_online_models:
continue
elif inference_mode == 'offline':
continue
else:
del self.pretrained_models[model_tag]
else:
raise NotImplementedError('Only supports asr and tts task.')
@staticmethod
def _fetch(res_dict: Dict[str, str],
target_dir: os.PathLike) -> os.PathLike:
"""Fetch archive from url.
Args:
res_dict (Dict[str, str]): Info dict of a resource.
target_dir (os.PathLike): Directory to save archives.
Returns:
os.PathLike: Directory of model resource.
"""
return download_and_decompress(res_dict, target_dir)
@staticmethod
def _format_path(res_dict: Dict[str, str]):
for k, v in res_dict.items():
if isinstance(v, str) and '/' in v:
if v.startswith('https://') or v.startswith('http://'):
continue
else:
res_dict[k] = os.path.join(*(v.split('/')))