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"""MT Interface module.""" |
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import argparse |
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from espnet.bin.asr_train import get_parser |
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from espnet.utils.fill_missing_args import fill_missing_args |
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class MTInterface: |
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"""MT Interface for ESPnet model implementation.""" |
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@staticmethod |
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def add_arguments(parser): |
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"""Add arguments to parser.""" |
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return parser |
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@classmethod |
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def build(cls, idim: int, odim: 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 an input feature dim. |
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odim (int): The number of output vocab. |
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Returns: |
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ASRinterface: A new instance of ASRInterface. |
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""" |
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def wrap(parser): |
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return get_parser(parser, required=False) |
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args = argparse.Namespace(**kwargs) |
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args = fill_missing_args(args, wrap) |
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args = fill_missing_args(args, cls.add_arguments) |
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return cls(idim, odim, args) |
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def forward(self, xs, ilens, ys): |
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"""Compute loss for training. |
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:param xs: |
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For pytorch, batch of padded source sequences torch.Tensor (B, Tmax, idim) |
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For chainer, list of source sequences chainer.Variable |
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:param ilens: batch of lengths of source sequences (B) |
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For pytorch, torch.Tensor |
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For chainer, list of int |
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:param ys: |
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For pytorch, batch of padded source sequences torch.Tensor (B, Lmax) |
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For chainer, list of source sequences chainer.Variable |
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:return: loss value |
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:rtype: torch.Tensor for pytorch, chainer.Variable for chainer |
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""" |
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raise NotImplementedError("forward method is not implemented") |
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def translate(self, x, trans_args, char_list=None, rnnlm=None): |
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"""Translate x for evaluation. |
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:param ndarray x: input acouctic feature (B, T, D) or (T, D) |
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:param namespace trans_args: argment namespace contraining options |
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:param list char_list: list of characters |
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:param torch.nn.Module rnnlm: language model module |
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:return: N-best decoding results |
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:rtype: list |
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""" |
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raise NotImplementedError("translate method is not implemented") |
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def translate_batch(self, x, trans_args, char_list=None, rnnlm=None): |
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"""Beam search implementation for batch. |
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:param torch.Tensor x: encoder hidden state sequences (B, Tmax, Henc) |
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:param namespace trans_args: argument namespace containing options |
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:param list char_list: list of characters |
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:param torch.nn.Module rnnlm: language model module |
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:return: N-best decoding results |
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:rtype: list |
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""" |
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raise NotImplementedError("Batch decoding is not supported yet.") |
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def calculate_all_attentions(self, xs, ilens, ys): |
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"""Caluculate attention. |
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:param list xs: list of padded input sequences [(T1, idim), (T2, idim), ...] |
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:param ndarray ilens: batch of lengths of input sequences (B) |
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:param list ys: list of character id sequence tensor [(L1), (L2), (L3), ...] |
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:return: attention weights (B, Lmax, Tmax) |
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:rtype: float ndarray |
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""" |
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raise NotImplementedError("calculate_all_attentions method is not implemented") |
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@property |
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def attention_plot_class(self): |
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"""Get attention plot class.""" |
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from espnet.asr.asr_utils import PlotAttentionReport |
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return PlotAttentionReport |
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