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678 lines
26 KiB
678 lines
26 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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"""U2 ASR Model
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Unified Streaming and Non-streaming Two-pass End-to-end Model for Speech Recognition
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(https://arxiv.org/pdf/2012.05481.pdf)
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"""
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import time
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from typing import Dict
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from typing import List
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from typing import Optional
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from typing import Tuple
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import paddle
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from paddle import jit
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from paddle import nn
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from paddlespeech.s2t.frontend.utility import IGNORE_ID
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from paddlespeech.s2t.frontend.utility import load_cmvn
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from paddlespeech.s2t.modules.cmvn import GlobalCMVN
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from paddlespeech.s2t.modules.ctc import CTCDecoderBase
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from paddlespeech.s2t.modules.decoder import TransformerDecoder
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from paddlespeech.s2t.modules.encoder import ConformerEncoder
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from paddlespeech.s2t.modules.encoder import TransformerEncoder
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from paddlespeech.s2t.modules.loss import LabelSmoothingLoss
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from paddlespeech.s2t.modules.mask import mask_finished_preds
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from paddlespeech.s2t.modules.mask import mask_finished_scores
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from paddlespeech.s2t.modules.mask import subsequent_mask
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from paddlespeech.s2t.utils import checkpoint
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from paddlespeech.s2t.utils import layer_tools
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from paddlespeech.s2t.utils.log import Log
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from paddlespeech.s2t.utils.tensor_utils import add_sos_eos
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from paddlespeech.s2t.utils.tensor_utils import th_accuracy
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from paddlespeech.s2t.utils.utility import UpdateConfig
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__all__ = ["U2STModel", "U2STInferModel"]
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logger = Log(__name__).getlog()
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class U2STBaseModel(nn.Layer):
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"""CTC-Attention hybrid Encoder-Decoder model"""
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def __init__(self,
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vocab_size: int,
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encoder: TransformerEncoder,
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st_decoder: TransformerDecoder,
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decoder: TransformerDecoder=None,
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ctc: CTCDecoderBase=None,
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ctc_weight: float=0.0,
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asr_weight: float=0.0,
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ignore_id: int=IGNORE_ID,
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lsm_weight: float=0.0,
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length_normalized_loss: bool=False,
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**kwargs):
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assert 0.0 <= ctc_weight <= 1.0, ctc_weight
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super().__init__()
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# note that eos is the same as sos (equivalent ID)
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self.sos = vocab_size - 1
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self.eos = vocab_size - 1
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self.vocab_size = vocab_size
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self.ignore_id = ignore_id
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self.ctc_weight = ctc_weight
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self.asr_weight = asr_weight
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self.encoder = encoder
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self.st_decoder = st_decoder
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self.decoder = decoder
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self.ctc = ctc
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self.criterion_att = LabelSmoothingLoss(
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size=vocab_size,
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padding_idx=ignore_id,
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smoothing=lsm_weight,
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normalize_length=length_normalized_loss, )
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def forward(
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self,
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speech: paddle.Tensor,
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speech_lengths: paddle.Tensor,
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text: paddle.Tensor,
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text_lengths: paddle.Tensor,
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asr_text: paddle.Tensor=None,
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asr_text_lengths: paddle.Tensor=None,
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) -> Tuple[Optional[paddle.Tensor], Optional[paddle.Tensor], Optional[
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paddle.Tensor]]:
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"""Frontend + Encoder + Decoder + Calc loss
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Args:
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speech: (Batch, Length, ...)
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speech_lengths: (Batch, )
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text: (Batch, Length)
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text_lengths: (Batch,)
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Returns:
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total_loss, attention_loss, ctc_loss
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"""
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assert text_lengths.dim() == 1, text_lengths.shape
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# Check that batch_size is unified
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assert (speech.shape[0] == speech_lengths.shape[0] == text.shape[0] ==
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text_lengths.shape[0]), (speech.shape, speech_lengths.shape,
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text.shape, text_lengths.shape)
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# 1. Encoder
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start = time.time()
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encoder_out, encoder_mask = self.encoder(speech, speech_lengths)
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encoder_time = time.time() - start
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#logger.debug(f"encoder time: {encoder_time}")
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#TODO(Hui Zhang): sum not support bool type
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#encoder_out_lens = encoder_mask.squeeze(1).sum(1) #[B, 1, T] -> [B]
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encoder_out_lens = encoder_mask.squeeze(1).cast(paddle.int64).sum(
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1) #[B, 1, T] -> [B]
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# 2a. ST-decoder branch
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start = time.time()
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loss_st, acc_st = self._calc_st_loss(encoder_out, encoder_mask, text,
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text_lengths)
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decoder_time = time.time() - start
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loss_asr_att = None
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loss_asr_ctc = None
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# 2b. ASR Attention-decoder branch
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if self.asr_weight > 0.:
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if self.ctc_weight != 1.0:
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start = time.time()
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loss_asr_att, acc_att = self._calc_att_loss(
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encoder_out, encoder_mask, asr_text, asr_text_lengths)
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decoder_time = time.time() - start
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# 2c. CTC branch
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if self.ctc_weight != 0.0:
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start = time.time()
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loss_asr_ctc = self.ctc(encoder_out, encoder_out_lens, asr_text,
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asr_text_lengths)
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ctc_time = time.time() - start
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if loss_asr_ctc is None:
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loss_asr = loss_asr_att
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elif loss_asr_att is None:
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loss_asr = loss_asr_ctc
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else:
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loss_asr = self.ctc_weight * loss_asr_ctc + (1 - self.ctc_weight
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) * loss_asr_att
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loss = self.asr_weight * loss_asr + (1 - self.asr_weight) * loss_st
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else:
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loss = loss_st
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return loss, loss_st, loss_asr_att, loss_asr_ctc
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def _calc_st_loss(
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self,
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encoder_out: paddle.Tensor,
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encoder_mask: paddle.Tensor,
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ys_pad: paddle.Tensor,
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ys_pad_lens: paddle.Tensor, ) -> Tuple[paddle.Tensor, float]:
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"""Calc attention loss.
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Args:
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encoder_out (paddle.Tensor): [B, Tmax, D]
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encoder_mask (paddle.Tensor): [B, 1, Tmax]
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ys_pad (paddle.Tensor): [B, Umax]
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ys_pad_lens (paddle.Tensor): [B]
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Returns:
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Tuple[paddle.Tensor, float]: attention_loss, accuracy rate
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"""
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ys_in_pad, ys_out_pad = add_sos_eos(ys_pad, self.sos, self.eos,
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self.ignore_id)
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ys_in_lens = ys_pad_lens + 1
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# 1. Forward decoder
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decoder_out, _ = self.st_decoder(encoder_out, encoder_mask, ys_in_pad,
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ys_in_lens)
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# 2. Compute attention loss
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loss_att = self.criterion_att(decoder_out, ys_out_pad)
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acc_att = th_accuracy(
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decoder_out.view(-1, self.vocab_size),
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ys_out_pad,
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ignore_label=self.ignore_id, )
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return loss_att, acc_att
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def _calc_att_loss(
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self,
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encoder_out: paddle.Tensor,
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encoder_mask: paddle.Tensor,
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ys_pad: paddle.Tensor,
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ys_pad_lens: paddle.Tensor, ) -> Tuple[paddle.Tensor, float]:
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"""Calc attention loss.
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Args:
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encoder_out (paddle.Tensor): [B, Tmax, D]
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encoder_mask (paddle.Tensor): [B, 1, Tmax]
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ys_pad (paddle.Tensor): [B, Umax]
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ys_pad_lens (paddle.Tensor): [B]
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Returns:
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Tuple[paddle.Tensor, float]: attention_loss, accuracy rate
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"""
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ys_in_pad, ys_out_pad = add_sos_eos(ys_pad, self.sos, self.eos,
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self.ignore_id)
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ys_in_lens = ys_pad_lens + 1
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# 1. Forward decoder
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decoder_out, _ = self.decoder(encoder_out, encoder_mask, ys_in_pad,
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ys_in_lens)
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# 2. Compute attention loss
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loss_att = self.criterion_att(decoder_out, ys_out_pad)
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acc_att = th_accuracy(
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decoder_out.view(-1, self.vocab_size),
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ys_out_pad,
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ignore_label=self.ignore_id, )
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return loss_att, acc_att
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def _forward_encoder(
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self,
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speech: paddle.Tensor,
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speech_lengths: paddle.Tensor,
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decoding_chunk_size: int=-1,
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num_decoding_left_chunks: int=-1,
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simulate_streaming: bool=False,
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) -> Tuple[paddle.Tensor, paddle.Tensor]:
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"""Encoder pass.
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Args:
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speech (paddle.Tensor): [B, Tmax, D]
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speech_lengths (paddle.Tensor): [B]
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decoding_chunk_size (int, optional): chuck size. Defaults to -1.
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num_decoding_left_chunks (int, optional): nums chunks. Defaults to -1.
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simulate_streaming (bool, optional): streaming or not. Defaults to False.
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Returns:
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Tuple[paddle.Tensor, paddle.Tensor]:
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encoder hiddens (B, Tmax, D),
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encoder hiddens mask (B, 1, Tmax).
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"""
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# Let's assume B = batch_size
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# 1. Encoder
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if simulate_streaming and decoding_chunk_size > 0:
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encoder_out, encoder_mask = self.encoder.forward_chunk_by_chunk(
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speech,
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decoding_chunk_size=decoding_chunk_size,
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num_decoding_left_chunks=num_decoding_left_chunks
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) # (B, maxlen, encoder_dim)
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else:
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encoder_out, encoder_mask = self.encoder(
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speech,
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speech_lengths,
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decoding_chunk_size=decoding_chunk_size,
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num_decoding_left_chunks=num_decoding_left_chunks
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) # (B, maxlen, encoder_dim)
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return encoder_out, encoder_mask
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def translate(
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self,
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speech: paddle.Tensor,
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speech_lengths: paddle.Tensor,
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beam_size: int=10,
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word_reward: float=0.0,
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maxlenratio: float=0.5,
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decoding_chunk_size: int=-1,
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num_decoding_left_chunks: int=-1,
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simulate_streaming: bool=False, ) -> paddle.Tensor:
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""" Apply beam search on attention decoder with length penalty
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Args:
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speech (paddle.Tensor): (batch, max_len, feat_dim)
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speech_length (paddle.Tensor): (batch, )
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beam_size (int): beam size for beam search
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word_reward (float): word reward used in beam search
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maxlenratio (float): max length ratio to bound the length of translated text
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decoding_chunk_size (int): decoding chunk for dynamic chunk
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trained model.
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<0: for decoding, use full chunk.
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>0: for decoding, use fixed chunk size as set.
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0: used for training, it's prohibited here
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simulate_streaming (bool): whether do encoder forward in a
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streaming fashion
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Returns:
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paddle.Tensor: decoding result, (batch, max_result_len)
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"""
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assert speech.shape[0] == speech_lengths.shape[0]
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assert decoding_chunk_size != 0
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assert speech.shape[0] == 1
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device = speech.place
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# Let's assume B = batch_size and N = beam_size
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# 1. Encoder and init hypothesis
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encoder_out, encoder_mask = self._forward_encoder(
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speech, speech_lengths, decoding_chunk_size,
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num_decoding_left_chunks,
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simulate_streaming) # (B, maxlen, encoder_dim)
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maxlen = max(int(encoder_out.shape[1] * maxlenratio), 5)
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hyp = {"score": 0.0, "yseq": [self.sos], "cache": None}
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hyps = [hyp]
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ended_hyps = []
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cur_best_score = -float("inf")
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cache = None
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# 2. Decoder forward step by step
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for i in range(1, maxlen + 1):
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ys = paddle.ones((len(hyps), i), dtype=paddle.long)
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if hyps[0]["cache"] is not None:
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cache = [
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paddle.ones(
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(len(hyps), i - 1, hyp_cache.shape[-1]),
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dtype=paddle.float32) for hyp_cache in hyps[0]["cache"]
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]
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for j, hyp in enumerate(hyps):
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ys[j, :] = paddle.to_tensor(hyp["yseq"])
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if hyps[0]["cache"] is not None:
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for k in range(len(cache)):
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cache[k][j] = hyps[j]["cache"][k]
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ys_mask = subsequent_mask(i).unsqueeze(0).to(device)
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logp, cache = self.st_decoder.forward_one_step(
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encoder_out.repeat(len(hyps), 1, 1),
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encoder_mask.repeat(len(hyps), 1, 1), ys, ys_mask, cache)
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hyps_best_kept = []
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for j, hyp in enumerate(hyps):
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top_k_logp, top_k_index = logp[j:j + 1].topk(beam_size)
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for b in range(beam_size):
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new_hyp = {}
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new_hyp["score"] = hyp["score"] + float(top_k_logp[0, b])
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new_hyp["yseq"] = [0] * (1 + len(hyp["yseq"]))
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new_hyp["yseq"][:len(hyp["yseq"])] = hyp["yseq"]
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new_hyp["yseq"][len(hyp["yseq"])] = int(top_k_index[0, b])
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new_hyp["cache"] = [cache_[j] for cache_ in cache]
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# will be (2 x beam) hyps at most
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hyps_best_kept.append(new_hyp)
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hyps_best_kept = sorted(
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hyps_best_kept, key=lambda x: -x["score"])[:beam_size]
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# sort and get nbest
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hyps = hyps_best_kept
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if i == maxlen:
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for hyp in hyps:
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hyp["yseq"].append(self.eos)
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# finalize the ended hypotheses with word reward (by length)
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remained_hyps = []
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for hyp in hyps:
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if hyp["yseq"][-1] == self.eos:
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hyp["score"] += (i - 1) * word_reward
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cur_best_score = max(cur_best_score, hyp["score"])
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ended_hyps.append(hyp)
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else:
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# stop while guarantee the optimality
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if hyp["score"] + maxlen * word_reward > cur_best_score:
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remained_hyps.append(hyp)
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# stop predition when there is no unended hypothesis
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if not remained_hyps:
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break
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hyps = remained_hyps
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# 3. Select best of best
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best_hyp = max(ended_hyps, key=lambda x: x["score"])
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return paddle.to_tensor([best_hyp["yseq"][1:]])
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# @jit.to_static
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def subsampling_rate(self) -> int:
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""" Export interface for c++ call, return subsampling_rate of the
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model
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"""
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return self.encoder.embed.subsampling_rate
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# @jit.to_static
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def right_context(self) -> int:
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""" Export interface for c++ call, return right_context of the model
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"""
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return self.encoder.embed.right_context
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# @jit.to_static
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def sos_symbol(self) -> int:
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""" Export interface for c++ call, return sos symbol id of the model
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"""
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return self.sos
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# @jit.to_static
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def eos_symbol(self) -> int:
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""" Export interface for c++ call, return eos symbol id of the model
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"""
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return self.eos
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@jit.to_static
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def forward_encoder_chunk(
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self,
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xs: paddle.Tensor,
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offset: int,
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required_cache_size: int,
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subsampling_cache: Optional[paddle.Tensor]=None,
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elayers_output_cache: Optional[List[paddle.Tensor]]=None,
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conformer_cnn_cache: Optional[List[paddle.Tensor]]=None,
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) -> Tuple[paddle.Tensor, paddle.Tensor, List[paddle.Tensor], List[
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paddle.Tensor]]:
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""" Export interface for c++ call, give input chunk xs, and return
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output from time 0 to current chunk.
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Args:
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xs (paddle.Tensor): chunk input
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subsampling_cache (Optional[paddle.Tensor]): subsampling cache
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elayers_output_cache (Optional[List[paddle.Tensor]]):
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transformer/conformer encoder layers output cache
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conformer_cnn_cache (Optional[List[paddle.Tensor]]): conformer
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cnn cache
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Returns:
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paddle.Tensor: output, it ranges from time 0 to current chunk.
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paddle.Tensor: subsampling cache
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List[paddle.Tensor]: attention cache
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List[paddle.Tensor]: conformer cnn cache
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"""
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return self.encoder.forward_chunk(
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xs, offset, required_cache_size, subsampling_cache,
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elayers_output_cache, conformer_cnn_cache)
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# @jit.to_static
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def ctc_activation(self, xs: paddle.Tensor) -> paddle.Tensor:
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""" Export interface for c++ call, apply linear transform and log
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softmax before ctc
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Args:
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xs (paddle.Tensor): encoder output
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Returns:
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paddle.Tensor: activation before ctc
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"""
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return self.ctc.log_softmax(xs)
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@jit.to_static
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def forward_attention_decoder(
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self,
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hyps: paddle.Tensor,
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hyps_lens: paddle.Tensor,
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encoder_out: paddle.Tensor, ) -> paddle.Tensor:
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""" Export interface for c++ call, forward decoder with multiple
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hypothesis from ctc prefix beam search and one encoder output
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Args:
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hyps (paddle.Tensor): hyps from ctc prefix beam search, already
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pad sos at the begining, (B, T)
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hyps_lens (paddle.Tensor): length of each hyp in hyps, (B)
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encoder_out (paddle.Tensor): corresponding encoder output, (B=1, T, D)
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Returns:
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paddle.Tensor: decoder output, (B, L)
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"""
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assert encoder_out.shape[0] == 1
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num_hyps = hyps.shape[0]
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assert hyps_lens.shape[0] == num_hyps
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encoder_out = encoder_out.repeat(num_hyps, 1, 1)
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# (B, 1, T)
|
|
encoder_mask = paddle.ones(
|
|
[num_hyps, 1, encoder_out.shape[1]], dtype=paddle.bool)
|
|
# (num_hyps, max_hyps_len, vocab_size)
|
|
decoder_out, _ = self.decoder(encoder_out, encoder_mask, hyps,
|
|
hyps_lens)
|
|
decoder_out = paddle.nn.functional.log_softmax(decoder_out, dim=-1)
|
|
return decoder_out
|
|
|
|
@paddle.no_grad()
|
|
def decode(self,
|
|
feats: paddle.Tensor,
|
|
feats_lengths: paddle.Tensor,
|
|
text_feature: Dict[str, int],
|
|
decoding_method: str,
|
|
beam_size: int,
|
|
word_reward: float=0.0,
|
|
maxlenratio: float=0.5,
|
|
decoding_chunk_size: int=-1,
|
|
num_decoding_left_chunks: int=-1,
|
|
simulate_streaming: bool=False):
|
|
"""u2 decoding.
|
|
|
|
Args:
|
|
feats (Tensor): audio features, (B, T, D)
|
|
feats_lengths (Tensor): (B)
|
|
text_feature (TextFeaturizer): text feature object.
|
|
decoding_method (str): decoding mode, e.g.
|
|
'fullsentence',
|
|
'simultaneous'
|
|
beam_size (int): beam size for search
|
|
decoding_chunk_size (int, optional): decoding chunk size. Defaults to -1.
|
|
<0: for decoding, use full chunk.
|
|
>0: for decoding, use fixed chunk size as set.
|
|
0: used for training, it's prohibited here.
|
|
num_decoding_left_chunks (int, optional):
|
|
number of left chunks for decoding. Defaults to -1.
|
|
simulate_streaming (bool, optional): simulate streaming inference. Defaults to False.
|
|
|
|
Raises:
|
|
ValueError: when not support decoding_method.
|
|
|
|
Returns:
|
|
List[List[int]]: transcripts.
|
|
"""
|
|
batch_size = feats.shape[0]
|
|
|
|
if decoding_method == 'fullsentence':
|
|
hyps = self.translate(
|
|
feats,
|
|
feats_lengths,
|
|
beam_size=beam_size,
|
|
word_reward=word_reward,
|
|
maxlenratio=maxlenratio,
|
|
decoding_chunk_size=decoding_chunk_size,
|
|
num_decoding_left_chunks=num_decoding_left_chunks,
|
|
simulate_streaming=simulate_streaming)
|
|
hyps = [hyp.tolist() for hyp in hyps]
|
|
else:
|
|
raise ValueError(f"Not support decoding method: {decoding_method}")
|
|
|
|
res = [text_feature.defeaturize(hyp) for hyp in hyps]
|
|
return res
|
|
|
|
|
|
class U2STModel(U2STBaseModel):
|
|
def __init__(self, configs: dict):
|
|
vocab_size, encoder, decoder = U2STModel._init_from_config(configs)
|
|
|
|
if isinstance(decoder, Tuple):
|
|
st_decoder, asr_decoder, ctc = decoder
|
|
super().__init__(
|
|
vocab_size=vocab_size,
|
|
encoder=encoder,
|
|
st_decoder=st_decoder,
|
|
decoder=asr_decoder,
|
|
ctc=ctc,
|
|
**configs['model_conf'])
|
|
else:
|
|
super().__init__(
|
|
vocab_size=vocab_size,
|
|
encoder=encoder,
|
|
st_decoder=decoder,
|
|
**configs['model_conf'])
|
|
|
|
@classmethod
|
|
def _init_from_config(cls, configs: dict):
|
|
"""init sub module for model.
|
|
|
|
Args:
|
|
configs (dict): config dict.
|
|
|
|
Raises:
|
|
ValueError: raise when using not support encoder type.
|
|
|
|
Returns:
|
|
int, nn.Layer, nn.Layer, nn.Layer: vocab size, encoder, decoder, ctc
|
|
"""
|
|
if configs['cmvn_file'] is not None:
|
|
mean, istd = load_cmvn(configs['cmvn_file'],
|
|
configs['cmvn_file_type'])
|
|
global_cmvn = GlobalCMVN(
|
|
paddle.to_tensor(mean, dtype=paddle.float),
|
|
paddle.to_tensor(istd, dtype=paddle.float))
|
|
else:
|
|
global_cmvn = None
|
|
|
|
input_dim = configs['input_dim']
|
|
vocab_size = configs['output_dim']
|
|
assert input_dim != 0, input_dim
|
|
assert vocab_size != 0, vocab_size
|
|
|
|
encoder_type = configs.get('encoder', 'transformer')
|
|
logger.info(f"U2 Encoder type: {encoder_type}")
|
|
if encoder_type == 'transformer':
|
|
encoder = TransformerEncoder(
|
|
input_dim, global_cmvn=global_cmvn, **configs['encoder_conf'])
|
|
elif encoder_type == 'conformer':
|
|
encoder = ConformerEncoder(
|
|
input_dim, global_cmvn=global_cmvn, **configs['encoder_conf'])
|
|
else:
|
|
raise ValueError(f"not support encoder type:{encoder_type}")
|
|
|
|
st_decoder = TransformerDecoder(vocab_size,
|
|
encoder.output_size(),
|
|
**configs['decoder_conf'])
|
|
|
|
asr_weight = configs['model_conf']['asr_weight']
|
|
logger.info(f"ASR Joint Training Weight: {asr_weight}")
|
|
|
|
if asr_weight > 0.:
|
|
decoder = TransformerDecoder(vocab_size,
|
|
encoder.output_size(),
|
|
**configs['decoder_conf'])
|
|
# ctc decoder and ctc loss
|
|
model_conf = configs['model_conf']
|
|
dropout_rate = model_conf.get('ctc_dropout_rate', 0.0)
|
|
grad_norm_type = model_conf.get('ctc_grad_norm_type', None)
|
|
ctc = CTCDecoderBase(
|
|
odim=vocab_size,
|
|
enc_n_units=encoder.output_size(),
|
|
blank_id=0,
|
|
dropout_rate=dropout_rate,
|
|
reduction=True, # sum
|
|
batch_average=True, # sum / batch_size
|
|
grad_norm_type=grad_norm_type)
|
|
|
|
return vocab_size, encoder, (st_decoder, decoder, ctc)
|
|
else:
|
|
return vocab_size, encoder, st_decoder
|
|
|
|
@classmethod
|
|
def from_config(cls, configs: dict):
|
|
"""init model.
|
|
|
|
Args:
|
|
configs (dict): config dict.
|
|
|
|
Raises:
|
|
ValueError: raise when using not support encoder type.
|
|
|
|
Returns:
|
|
nn.Layer: U2STModel
|
|
"""
|
|
model = cls(configs)
|
|
return model
|
|
|
|
@classmethod
|
|
def from_pretrained(cls, dataloader, config, checkpoint_path):
|
|
"""Build a DeepSpeech2Model model from a pretrained model.
|
|
|
|
Args:
|
|
dataloader (paddle.io.DataLoader): not used.
|
|
config (yacs.config.CfgNode): model configs
|
|
checkpoint_path (Path or str): the path of pretrained model checkpoint, without extension name
|
|
|
|
Returns:
|
|
DeepSpeech2Model: The model built from pretrained result.
|
|
"""
|
|
with UpdateConfig(config):
|
|
config.input_dim = dataloader.collate_fn.feature_size
|
|
config.output_dim = dataloader.collate_fn.vocab_size
|
|
|
|
model = cls.from_config(config)
|
|
|
|
if checkpoint_path:
|
|
infos = checkpoint.load_parameters(
|
|
model, checkpoint_path=checkpoint_path)
|
|
logger.info(f"checkpoint info: {infos}")
|
|
layer_tools.summary(model)
|
|
return model
|
|
|
|
|
|
class U2STInferModel(U2STModel):
|
|
def __init__(self, configs: dict):
|
|
super().__init__(configs)
|
|
|
|
def forward(self,
|
|
feats,
|
|
feats_lengths,
|
|
decoding_chunk_size=-1,
|
|
num_decoding_left_chunks=-1,
|
|
simulate_streaming=False):
|
|
"""export model function
|
|
|
|
Args:
|
|
feats (Tensor): [B, T, D]
|
|
feats_lengths (Tensor): [B]
|
|
|
|
Returns:
|
|
List[List[int]]: best path result
|
|
"""
|
|
return self.translate(
|
|
feats,
|
|
feats_lengths,
|
|
decoding_chunk_size=decoding_chunk_size,
|
|
num_decoding_left_chunks=num_decoding_left_chunks,
|
|
simulate_streaming=simulate_streaming)
|