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