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"""MT Interface module."""
import argparse
from espnet.bin.asr_train import get_parser
from espnet.utils.fill_missing_args import fill_missing_args
class MTInterface:
"""MT Interface for ESPnet model implementation."""
@staticmethod
def add_arguments(parser):
"""Add arguments to parser."""
return parser
@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)
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")
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")
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.")
def calculate_all_attentions(self, xs, ilens, ys):
"""Caluculate 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
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