Source code for espnet.nets.mt_interface

"""MT Interface module."""

import argparse

from espnet.bin.asr_train import get_parser
from espnet.utils.fill_missing_args import fill_missing_args


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