# 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.
# Modified from espnet(https://github.com/espnet/espnet)
"""Language model interface."""
import argparse
from paddlespeech.s2t.decoders.scorers.scorer_interface import ScorerInterface
from paddlespeech.s2t.utils.dynamic_import import dynamic_import
[docs]class LMInterface(ScorerInterface):
"""LM Interface model implementation."""
[docs] @staticmethod
def add_arguments(parser):
"""Add arguments to command line argument parser."""
return parser
[docs] @classmethod
def build(cls, n_vocab: int, **kwargs):
"""Initialize this class with python-level args.
Args:
idim (int): The number of vocabulary.
Returns:
LMinterface: A new instance of LMInterface.
"""
args = argparse.Namespace(**kwargs)
return cls(n_vocab, args)
[docs] def forward(self, x, t):
"""Compute LM loss value from buffer sequences.
Args:
x (torch.Tensor): Input ids. (batch, len)
t (torch.Tensor): Target ids. (batch, len)
Returns:
tuple[torch.Tensor, torch.Tensor, torch.Tensor]: Tuple of
loss to backward (scalar),
negative log-likelihood of t: -log p(t) (scalar) and
the number of elements in x (scalar)
Notes:
The last two return values are used
in perplexity: p(t)^{-n} = exp(-log p(t) / n)
"""
raise NotImplementedError("forward method is not implemented")
predefined_lms = {
"transformer": "paddlespeech.s2t.models.lm.transformer:TransformerLM",
}
[docs]def dynamic_import_lm(module):
"""Import LM class dynamically.
Args:
module (str): module_name:class_name or alias in `predefined_lms`
Returns:
type: LM class
"""
model_class = dynamic_import(module, predefined_lms)
assert issubclass(model_class,
LMInterface), f"{module} does not implement LMInterface"
return model_class