# 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)
"""ASR Interface module."""
import argparse
from paddlespeech.s2t.utils.dynamic_import import dynamic_import
[docs]class ASRInterface:
"""ASR Interface 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.
"""
args = argparse.Namespace(**kwargs)
return cls(idim, odim, args)
[docs] def forward(self, xs, ilens, ys, olens):
"""Compute loss for training.
:param xs: batch of padded source sequences paddle.Tensor (B, Tmax, idim)
:param ilens: batch of lengths of source sequences (B), paddle.Tensor
:param ys: batch of padded target sequences paddle.Tensor (B, Lmax)
:param olens: batch of lengths of target sequences (B), paddle.Tensor
:return: loss value
:rtype: paddle.Tensor
"""
raise NotImplementedError("forward method is not implemented")
[docs] def recognize(self, x, recog_args, char_list=None, rnnlm=None):
"""Recognize x for evaluation.
:param ndarray x: input acouctic feature (B, T, D) or (T, D)
:param namespace recog_args: argment namespace contraining options
:param list char_list: list of characters
:param paddle.nn.Layer rnnlm: language model module
:return: N-best decoding results
:rtype: list
"""
raise NotImplementedError("recognize method is not implemented")
[docs] def recognize_batch(self, x, recog_args, char_list=None, rnnlm=None):
"""Beam search implementation for batch.
:param paddle.Tensor x: encoder hidden state sequences (B, Tmax, Henc)
:param namespace recog_args: argument namespace containing options
:param list char_list: list of characters
:param paddle.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")
[docs] def calculate_all_ctc_probs(self, xs, ilens, ys):
"""Calculate CTC probability.
:param list xs_pad: 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: CTC probabilities (B, Tmax, vocab)
:rtype: float ndarray
"""
raise NotImplementedError(
"calculate_all_ctc_probs method is not implemented")
@property
def attention_plot_class(self):
"""Get attention plot class."""
from paddlespeech.s2t.training.extensions.plot import PlotAttentionReport
return PlotAttentionReport
@property
def ctc_plot_class(self):
"""Get CTC plot class."""
from paddlespeech.s2t.training.extensions.plot import PlotCTCReport
return PlotCTCReport
[docs] def get_total_subsampling_factor(self):
"""Get total subsampling factor."""
raise NotImplementedError(
"get_total_subsampling_factor method is not implemented")
[docs] def encode(self, feat):
"""Encode feature in `beam_search` (optional).
Args:
x (numpy.ndarray): input feature (T, D)
Returns:
paddle.Tensor: encoded feature (T, D)
"""
raise NotImplementedError("encode method is not implemented")
[docs] def scorers(self):
"""Get scorers for `beam_search` (optional).
Returns:
dict[str, ScorerInterface]: dict of `ScorerInterface` objects
"""
raise NotImplementedError("decoders method is not implemented")
predefined_asr = {
"transformer": "paddlespeech.s2t.models.u2:U2Model",
"conformer": "paddlespeech.s2t.models.u2:U2Model",
}
[docs]def dynamic_import_asr(module):
"""Import ASR models dynamically.
Args:
module (str): asr name. e.g., transformer, conformer
Returns:
type: ASR class
"""
model_class = dynamic_import(module, predefined_asr)
assert issubclass(model_class,
ASRInterface), f"{module} does not implement ASRInterface"
return model_class