# Copyright (c) 2021 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 argparse
import os
import re
from collections import OrderedDict
from typing import List
from typing import Optional
from typing import Union
import paddle
from ..executor import BaseExecutor
from ..log import logger
from ..utils import stats_wrapper
__all__ = ['TextExecutor']
class TextExecutor(BaseExecutor):
def __init__(self):
super().__init__(task='text')
self.parser = argparse.ArgumentParser(
prog='paddlespeech.text', add_help=True)
self.parser.add_argument(
'--input', type=str, default=None, help='Input text.')
self.parser.add_argument(
'--task',
type=str,
default='punc',
choices=['punc'],
help='Choose text task.')
self.parser.add_argument(
'--model',
type=str,
default='ernie_linear_p7_wudao',
choices=[
tag[:tag.index('-')]
for tag in self.task_resource.pretrained_models.keys()
],
help='Choose model type of text task.')
self.parser.add_argument(
'--lang',
type=str,
default='zh',
choices=['zh', 'en'],
help='Choose model language.')
self.parser.add_argument(
'--config',
type=str,
default=None,
help='Config of cls task. Use deault config when it is None.')
self.parser.add_argument(
'--ckpt_path',
type=str,
default=None,
help='Checkpoint file of model.')
self.parser.add_argument(
'--punc_vocab',
type=str,
default=None,
help='Vocabulary file of punctuation restoration task.')
self.parser.add_argument(
'--device',
type=str,
default=paddle.get_device(),
help='Choose device to execute model inference.')
self.parser.add_argument(
'-d',
'--job_dump_result',
action='store_true',
help='Save job result into file.')
self.parser.add_argument(
'-v',
'--verbose',
action='store_true',
help='Increase logger verbosity of current task.')
def _init_from_path(self,
task: str='punc',
model_type: str='ernie_linear_p7_wudao',
lang: str='zh',
cfg_path: Optional[os.PathLike]=None,
ckpt_path: Optional[os.PathLike]=None,
vocab_file: Optional[os.PathLike]=None):
"""
Init model and other resources from a specific path.
"""
if hasattr(self, 'model'):
logger.info('Model had been initialized.')
return
self.task = task
if cfg_path is None or ckpt_path is None or vocab_file is None:
tag = '-'.join([model_type, task, lang])
self.task_resource.set_task_model(tag, version=None)
self.cfg_path = os.path.join(
self.task_resource.res_dir,
self.task_resource.res_dict['cfg_path'])
self.ckpt_path = os.path.join(
self.task_resource.res_dir,
self.task_resource.res_dict['ckpt_path'])
self.vocab_file = os.path.join(
self.task_resource.res_dir,
self.task_resource.res_dict['vocab_file'])
else:
self.cfg_path = os.path.abspath(cfg_path)
self.ckpt_path = os.path.abspath(ckpt_path)
self.vocab_file = os.path.abspath(vocab_file)
model_name = model_type[:model_type.rindex('_')]
if self.task == 'punc':
# punc list
self._punc_list = []
with open(self.vocab_file, 'r') as f:
for line in f:
self._punc_list.append(line.strip())
# model
model_class, tokenizer_class = self.task_resource.get_model_class(
model_name)
self.model = model_class(
cfg_path=self.cfg_path, ckpt_path=self.ckpt_path)
self.tokenizer = tokenizer_class.from_pretrained('ernie-1.0')
else:
raise NotImplementedError
self.model.eval()
def _clean_text(self, text):
text = text.lower()
text = re.sub('[^A-Za-z0-9\u4e00-\u9fa5]', '', text)
text = re.sub(f'[{"".join([p for p in self._punc_list][1:])}]', '',
text)
return text
def preprocess(self, text: Union[str, os.PathLike]):
"""
Input preprocess and return paddle.Tensor stored in self.input.
Input content can be a text(tts), a file(asr, cls) or a streaming(not supported yet).
"""
if self.task == 'punc':
clean_text = self._clean_text(text)
assert len(clean_text) > 0, f'Invalid input string: {text}'
tokenized_input = self.tokenizer(
list(clean_text), return_length=True, is_split_into_words=True)
self._inputs['input_ids'] = tokenized_input['input_ids']
self._inputs['seg_ids'] = tokenized_input['token_type_ids']
self._inputs['seq_len'] = tokenized_input['seq_len']
else:
raise NotImplementedError
@paddle.no_grad()
def infer(self):
"""
Model inference and result stored in self.output.
"""
if self.task == 'punc':
input_ids = paddle.to_tensor(self._inputs['input_ids']).unsqueeze(0)
seg_ids = paddle.to_tensor(self._inputs['seg_ids']).unsqueeze(0)
logits, _ = self.model(input_ids, seg_ids)
preds = paddle.argmax(logits, axis=-1).squeeze(0)
self._outputs['preds'] = preds
else:
raise NotImplementedError
def postprocess(self) -> Union[str, os.PathLike]:
"""
Output postprocess and return human-readable results such as texts and audio files.
"""
if self.task == 'punc':
input_ids = self._inputs['input_ids']
seq_len = self._inputs['seq_len']
preds = self._outputs['preds']
tokens = self.tokenizer.convert_ids_to_tokens(
input_ids[1:seq_len - 1])
labels = preds[1:seq_len - 1].tolist()
assert len(tokens) == len(labels)
text = ''
for t, l in zip(tokens, labels):
text += t
if l != 0: # Non punc.
text += self._punc_list[l]
return text
else:
raise NotImplementedError
def execute(self, argv: List[str]) -> bool:
"""
Command line entry.
"""
parser_args = self.parser.parse_args(argv)
task = parser_args.task
model_type = parser_args.model
lang = parser_args.lang
cfg_path = parser_args.config
ckpt_path = parser_args.ckpt_path
punc_vocab = parser_args.punc_vocab
device = parser_args.device
if not parser_args.verbose:
self.disable_task_loggers()
task_source = self.get_input_source(parser_args.input)
task_results = OrderedDict()
has_exceptions = False
for id_, input_ in task_source.items():
try:
res = self(input_, task, model_type, lang, cfg_path, ckpt_path,
punc_vocab, device)
task_results[id_] = res
except Exception as e:
has_exceptions = True
task_results[id_] = f'{e.__class__.__name__}: {e}'
self.process_task_results(parser_args.input, task_results,
parser_args.job_dump_result)
if has_exceptions:
return False
else:
return True
@stats_wrapper
def __call__(
self,
text: str,
task: str='punc',
model: str='ernie_linear_p7_wudao',
lang: str='zh',
config: Optional[os.PathLike]=None,
ckpt_path: Optional[os.PathLike]=None,
punc_vocab: Optional[os.PathLike]=None,
device: str=paddle.get_device(), ):
"""
Python API to call an executor.
"""
paddle.set_device(device)
self._init_from_path(task, model, lang, config, ckpt_path, punc_vocab)
self.preprocess(text)
self.infer()
res = self.postprocess() # Retrieve result of text task.
return res