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975 lines
39 KiB
975 lines
39 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 sys
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import time
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from collections import defaultdict
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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 yacs.config import CfgNode
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from deepspeech.decoders.scorers.ctc import CTCPrefixScorer
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from deepspeech.frontend.utility import IGNORE_ID
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from deepspeech.frontend.utility import load_cmvn
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from deepspeech.models.asr_interface import ASRInterface
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from deepspeech.modules.cmvn import GlobalCMVN
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from deepspeech.modules.ctc import CTCDecoder
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from deepspeech.modules.decoder import TransformerDecoder
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from deepspeech.modules.encoder import ConformerEncoder
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from deepspeech.modules.encoder import TransformerEncoder
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from deepspeech.modules.loss import LabelSmoothingLoss
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from deepspeech.modules.mask import make_pad_mask
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from deepspeech.modules.mask import mask_finished_preds
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from deepspeech.modules.mask import mask_finished_scores
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from deepspeech.modules.mask import subsequent_mask
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from deepspeech.utils import checkpoint
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from deepspeech.utils import layer_tools
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from deepspeech.utils.ctc_utils import remove_duplicates_and_blank
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from deepspeech.utils.log import Log
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from deepspeech.utils.tensor_utils import add_sos_eos
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from deepspeech.utils.tensor_utils import pad_sequence
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from deepspeech.utils.tensor_utils import th_accuracy
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from deepspeech.utils.utility import log_add
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from deepspeech.utils.utility import UpdateConfig
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__all__ = ["U2Model", "U2InferModel"]
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logger = Log(__name__).getlog()
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class U2BaseModel(ASRInterface, nn.Layer):
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"""CTC-Attention hybrid Encoder-Decoder model"""
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@classmethod
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def params(cls, config: Optional[CfgNode]=None) -> CfgNode:
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# network architecture
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default = CfgNode()
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# allow add new item when merge_with_file
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default.cmvn_file = ""
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default.cmvn_file_type = "json"
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default.input_dim = 0
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default.output_dim = 0
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# encoder related
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default.encoder = 'transformer'
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default.encoder_conf = CfgNode(
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dict(
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output_size=256, # dimension of attention
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attention_heads=4,
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linear_units=2048, # the number of units of position-wise feed forward
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num_blocks=12, # the number of encoder blocks
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dropout_rate=0.1,
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positional_dropout_rate=0.1,
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attention_dropout_rate=0.0,
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input_layer='conv2d', # encoder input type, you can chose conv2d, conv2d6 and conv2d8
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normalize_before=True,
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# use_cnn_module=True,
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# cnn_module_kernel=15,
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# activation_type='swish',
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# pos_enc_layer_type='rel_pos',
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# selfattention_layer_type='rel_selfattn',
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))
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# decoder related
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default.decoder = 'transformer'
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default.decoder_conf = CfgNode(
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dict(
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attention_heads=4,
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linear_units=2048,
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num_blocks=6,
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dropout_rate=0.1,
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positional_dropout_rate=0.1,
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self_attention_dropout_rate=0.0,
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src_attention_dropout_rate=0.0, ))
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# hybrid CTC/attention
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default.model_conf = CfgNode(
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dict(
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ctc_weight=0.3,
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lsm_weight=0.1, # label smoothing option
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length_normalized_loss=False, ))
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if config is not None:
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config.merge_from_other_cfg(default)
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return default
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def __init__(self,
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vocab_size: int,
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encoder: TransformerEncoder,
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decoder: TransformerDecoder,
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ctc: CTCDecoder,
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ctc_weight: float=0.5,
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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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nn.Layer.__init__(self)
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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.encoder = encoder
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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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) -> 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. Attention-decoder branch
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loss_att = None
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if self.ctc_weight != 1.0:
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start = time.time()
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loss_att, acc_att = self._calc_att_loss(encoder_out, encoder_mask,
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text, text_lengths)
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decoder_time = time.time() - start
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#logger.debug(f"decoder time: {decoder_time}")
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# 2b. CTC branch
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loss_ctc = None
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if self.ctc_weight != 0.0:
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start = time.time()
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loss_ctc = self.ctc(encoder_out, encoder_out_lens, text,
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text_lengths)
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ctc_time = time.time() - start
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#logger.debug(f"ctc time: {ctc_time}")
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if loss_ctc is None:
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loss = loss_att
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elif loss_att is None:
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loss = loss_ctc
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else:
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loss = self.ctc_weight * loss_ctc + (1 - self.ctc_weight) * loss_att
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return loss, loss_att, loss_ctc
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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 recognize(
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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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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
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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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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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device = speech.place
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batch_size = speech.shape[0]
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# Let's assume B = batch_size and N = beam_size
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# 1. Encoder
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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 = encoder_out.shape[1]
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encoder_dim = encoder_out.shape[2]
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running_size = batch_size * beam_size
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encoder_out = encoder_out.unsqueeze(1).repeat(1, beam_size, 1, 1).view(
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running_size, maxlen, encoder_dim) # (B*N, maxlen, encoder_dim)
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encoder_mask = encoder_mask.unsqueeze(1).repeat(
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1, beam_size, 1, 1).view(running_size, 1,
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maxlen) # (B*N, 1, max_len)
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hyps = paddle.ones(
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[running_size, 1], dtype=paddle.long).fill_(self.sos) # (B*N, 1)
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# log scale score
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scores = paddle.to_tensor(
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[0.0] + [-float('inf')] * (beam_size - 1), dtype=paddle.float)
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scores = scores.to(device).repeat(batch_size).unsqueeze(1).to(
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device) # (B*N, 1)
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end_flag = paddle.zeros_like(scores, dtype=paddle.bool) # (B*N, 1)
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cache: Optional[List[paddle.Tensor]] = 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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# Stop if all batch and all beam produce eos
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# TODO(Hui Zhang): if end_flag.sum() == running_size:
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if end_flag.cast(paddle.int64).sum() == running_size:
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break
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# 2.1 Forward decoder step
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hyps_mask = subsequent_mask(i).unsqueeze(0).repeat(
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running_size, 1, 1).to(device) # (B*N, i, i)
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# logp: (B*N, vocab)
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logp, cache = self.decoder.forward_one_step(
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encoder_out, encoder_mask, hyps, hyps_mask, cache)
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# 2.2 First beam prune: select topk best prob at current time
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top_k_logp, top_k_index = logp.topk(beam_size) # (B*N, N)
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top_k_logp = mask_finished_scores(top_k_logp, end_flag)
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top_k_index = mask_finished_preds(top_k_index, end_flag, self.eos)
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# 2.3 Seconde beam prune: select topk score with history
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scores = scores + top_k_logp # (B*N, N), broadcast add
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scores = scores.view(batch_size, beam_size * beam_size) # (B, N*N)
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scores, offset_k_index = scores.topk(k=beam_size) # (B, N)
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scores = scores.view(-1, 1) # (B*N, 1)
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# 2.4. Compute base index in top_k_index,
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# regard top_k_index as (B*N*N),regard offset_k_index as (B*N),
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# then find offset_k_index in top_k_index
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base_k_index = paddle.arange(batch_size).view(-1, 1).repeat(
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1, beam_size) # (B, N)
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base_k_index = base_k_index * beam_size * beam_size
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best_k_index = base_k_index.view(-1) + offset_k_index.view(
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-1) # (B*N)
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# 2.5 Update best hyps
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best_k_pred = paddle.index_select(
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top_k_index.view(-1), index=best_k_index, axis=0) # (B*N)
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best_hyps_index = best_k_index // beam_size
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last_best_k_hyps = paddle.index_select(
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hyps, index=best_hyps_index, axis=0) # (B*N, i)
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hyps = paddle.cat(
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(last_best_k_hyps, best_k_pred.view(-1, 1)),
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dim=1) # (B*N, i+1)
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# 2.6 Update end flag
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end_flag = paddle.eq(hyps[:, -1], self.eos).view(-1, 1)
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# 3. Select best of best
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scores = scores.view(batch_size, beam_size)
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# TODO: length normalization
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best_index = paddle.argmax(scores, axis=-1).long() # (B)
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best_hyps_index = best_index + paddle.arange(
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batch_size, dtype=paddle.long) * beam_size
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best_hyps = paddle.index_select(hyps, index=best_hyps_index, axis=0)
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best_hyps = best_hyps[:, 1:]
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return best_hyps
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def ctc_greedy_search(
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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, ) -> List[List[int]]:
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""" Apply CTC greedy search
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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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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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List[List[int]]: best path result
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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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batch_size = speech.shape[0]
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# Let's assume B = batch_size
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# encoder_out: (B, maxlen, encoder_dim)
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# encoder_mask: (B, 1, Tmax)
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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, simulate_streaming)
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maxlen = encoder_out.shape[1]
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# (TODO Hui Zhang): bool no support reduce_sum
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# encoder_out_lens = encoder_mask.squeeze(1).sum(1)
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encoder_out_lens = encoder_mask.squeeze(1).astype(paddle.int).sum(1)
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ctc_probs = self.ctc.log_softmax(encoder_out) # (B, maxlen, vocab_size)
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topk_prob, topk_index = ctc_probs.topk(1, axis=2) # (B, maxlen, 1)
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topk_index = topk_index.view(batch_size, maxlen) # (B, maxlen)
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pad_mask = make_pad_mask(encoder_out_lens) # (B, maxlen)
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topk_index = topk_index.masked_fill_(pad_mask, self.eos) # (B, maxlen)
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hyps = [hyp.tolist() for hyp in topk_index]
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hyps = [remove_duplicates_and_blank(hyp) for hyp in hyps]
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return hyps
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def _ctc_prefix_beam_search(
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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,
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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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blank_id: int=0, ) -> Tuple[List[Tuple[int, float]], paddle.Tensor]:
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""" CTC prefix beam search inner implementation
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Args:
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speech (paddle.Tensor): (batch, max_len, feat_dim)
|
|
speech_length (paddle.Tensor): (batch, )
|
|
beam_size (int): beam size for beam search
|
|
decoding_chunk_size (int): decoding chunk for dynamic chunk
|
|
trained model.
|
|
<0: for decoding, use full chunk.
|
|
>0: for decoding, use fixed chunk size as set.
|
|
0: used for training, it's prohibited here
|
|
simulate_streaming (bool): whether do encoder forward in a
|
|
streaming fashion
|
|
Returns:
|
|
List[Tuple[int, float]]: nbest results, (N,1), (text, likelihood)
|
|
paddle.Tensor: encoder output, (1, max_len, encoder_dim),
|
|
it will be used for rescoring in attention rescoring mode
|
|
"""
|
|
assert speech.shape[0] == speech_lengths.shape[0]
|
|
assert decoding_chunk_size != 0
|
|
batch_size = speech.shape[0]
|
|
# For CTC prefix beam search, we only support batch_size=1
|
|
assert batch_size == 1
|
|
|
|
# Let's assume B = batch_size and N = beam_size
|
|
# 1. Encoder forward and get CTC score
|
|
encoder_out, encoder_mask = self._forward_encoder(
|
|
speech, speech_lengths, decoding_chunk_size,
|
|
num_decoding_left_chunks,
|
|
simulate_streaming) # (B, maxlen, encoder_dim)
|
|
maxlen = encoder_out.shape[1]
|
|
ctc_probs = self.ctc.log_softmax(encoder_out) # (1, maxlen, vocab_size)
|
|
ctc_probs = ctc_probs.squeeze(0)
|
|
|
|
# cur_hyps: (prefix, (blank_ending_score, none_blank_ending_score))
|
|
# blank_ending_score and none_blank_ending_score in ln domain
|
|
cur_hyps = [(tuple(), (0.0, -float('inf')))]
|
|
# 2. CTC beam search step by step
|
|
for t in range(0, maxlen):
|
|
logp = ctc_probs[t] # (vocab_size,)
|
|
# key: prefix, value (pb, pnb), default value(-inf, -inf)
|
|
next_hyps = defaultdict(lambda: (-float('inf'), -float('inf')))
|
|
# 2.1 First beam prune: select topk best
|
|
top_k_logp, top_k_index = logp.topk(beam_size) # (beam_size,)
|
|
for s in top_k_index:
|
|
s = s.item()
|
|
ps = logp[s].item()
|
|
for prefix, (pb, pnb) in cur_hyps:
|
|
last = prefix[-1] if len(prefix) > 0 else None
|
|
if s == blank_id: # blank
|
|
n_pb, n_pnb = next_hyps[prefix]
|
|
n_pb = log_add([n_pb, pb + ps, pnb + ps])
|
|
next_hyps[prefix] = (n_pb, n_pnb)
|
|
elif s == last:
|
|
# Update *ss -> *s;
|
|
n_pb, n_pnb = next_hyps[prefix]
|
|
n_pnb = log_add([n_pnb, pnb + ps])
|
|
next_hyps[prefix] = (n_pb, n_pnb)
|
|
# Update *s-s -> *ss, - is for blank
|
|
n_prefix = prefix + (s, )
|
|
n_pb, n_pnb = next_hyps[n_prefix]
|
|
n_pnb = log_add([n_pnb, pb + ps])
|
|
next_hyps[n_prefix] = (n_pb, n_pnb)
|
|
else:
|
|
n_prefix = prefix + (s, )
|
|
n_pb, n_pnb = next_hyps[n_prefix]
|
|
n_pnb = log_add([n_pnb, pb + ps, pnb + ps])
|
|
next_hyps[n_prefix] = (n_pb, n_pnb)
|
|
|
|
# 2.2 Second beam prune
|
|
next_hyps = sorted(
|
|
next_hyps.items(),
|
|
key=lambda x: log_add(list(x[1])),
|
|
reverse=True)
|
|
cur_hyps = next_hyps[:beam_size]
|
|
|
|
hyps = [(y[0], log_add([y[1][0], y[1][1]])) for y in cur_hyps]
|
|
return hyps, encoder_out
|
|
|
|
def ctc_prefix_beam_search(
|
|
self,
|
|
speech: paddle.Tensor,
|
|
speech_lengths: paddle.Tensor,
|
|
beam_size: int,
|
|
decoding_chunk_size: int=-1,
|
|
num_decoding_left_chunks: int=-1,
|
|
simulate_streaming: bool=False, ) -> List[int]:
|
|
""" Apply CTC prefix beam search
|
|
Args:
|
|
speech (paddle.Tensor): (batch, max_len, feat_dim)
|
|
speech_length (paddle.Tensor): (batch, )
|
|
beam_size (int): beam size for beam search
|
|
decoding_chunk_size (int): decoding chunk for dynamic chunk
|
|
trained model.
|
|
<0: for decoding, use full chunk.
|
|
>0: for decoding, use fixed chunk size as set.
|
|
0: used for training, it's prohibited here
|
|
simulate_streaming (bool): whether do encoder forward in a
|
|
streaming fashion
|
|
Returns:
|
|
List[int]: CTC prefix beam search nbest results
|
|
"""
|
|
hyps, _ = self._ctc_prefix_beam_search(
|
|
speech, speech_lengths, beam_size, decoding_chunk_size,
|
|
num_decoding_left_chunks, simulate_streaming)
|
|
return hyps[0][0]
|
|
|
|
def attention_rescoring(
|
|
self,
|
|
speech: paddle.Tensor,
|
|
speech_lengths: paddle.Tensor,
|
|
beam_size: int,
|
|
decoding_chunk_size: int=-1,
|
|
num_decoding_left_chunks: int=-1,
|
|
ctc_weight: float=0.0,
|
|
simulate_streaming: bool=False, ) -> List[int]:
|
|
""" Apply attention rescoring decoding, CTC prefix beam search
|
|
is applied first to get nbest, then we resoring the nbest on
|
|
attention decoder with corresponding encoder out
|
|
Args:
|
|
speech (paddle.Tensor): (batch, max_len, feat_dim)
|
|
speech_length (paddle.Tensor): (batch, )
|
|
beam_size (int): beam size for beam search
|
|
decoding_chunk_size (int): decoding chunk for dynamic chunk
|
|
trained model.
|
|
<0: for decoding, use full chunk.
|
|
>0: for decoding, use fixed chunk size as set.
|
|
0: used for training, it's prohibited here
|
|
simulate_streaming (bool): whether do encoder forward in a
|
|
streaming fashion
|
|
Returns:
|
|
List[int]: Attention rescoring result
|
|
"""
|
|
assert speech.shape[0] == speech_lengths.shape[0]
|
|
assert decoding_chunk_size != 0
|
|
device = speech.place
|
|
batch_size = speech.shape[0]
|
|
# For attention rescoring we only support batch_size=1
|
|
assert batch_size == 1
|
|
|
|
# len(hyps) = beam_size, encoder_out: (1, maxlen, encoder_dim)
|
|
hyps, encoder_out = self._ctc_prefix_beam_search(
|
|
speech, speech_lengths, beam_size, decoding_chunk_size,
|
|
num_decoding_left_chunks, simulate_streaming)
|
|
assert len(hyps) == beam_size
|
|
|
|
hyps_pad = pad_sequence([
|
|
paddle.to_tensor(hyp[0], place=device, dtype=paddle.long)
|
|
for hyp in hyps
|
|
], True, self.ignore_id) # (beam_size, max_hyps_len)
|
|
hyps_lens = paddle.to_tensor(
|
|
[len(hyp[0]) for hyp in hyps], place=device,
|
|
dtype=paddle.long) # (beam_size,)
|
|
hyps_pad, _ = add_sos_eos(hyps_pad, self.sos, self.eos, self.ignore_id)
|
|
hyps_lens = hyps_lens + 1 # Add <sos> at begining
|
|
|
|
encoder_out = encoder_out.repeat(beam_size, 1, 1)
|
|
encoder_mask = paddle.ones(
|
|
(beam_size, 1, encoder_out.shape[1]), dtype=paddle.bool)
|
|
decoder_out, _ = self.decoder(
|
|
encoder_out, encoder_mask, hyps_pad,
|
|
hyps_lens) # (beam_size, max_hyps_len, vocab_size)
|
|
# ctc score in ln domain
|
|
decoder_out = paddle.nn.functional.log_softmax(decoder_out, axis=-1)
|
|
decoder_out = decoder_out.numpy()
|
|
|
|
# Only use decoder score for rescoring
|
|
best_score = -float('inf')
|
|
best_index = 0
|
|
# hyps is List[(Text=List[int], Score=float)], len(hyps)=beam_size
|
|
for i, hyp in enumerate(hyps):
|
|
score = 0.0
|
|
for j, w in enumerate(hyp[0]):
|
|
score += decoder_out[i][j][w]
|
|
# last decoder output token is `eos`, for laste decoder input token.
|
|
score += decoder_out[i][len(hyp[0])][self.eos]
|
|
# add ctc score (which in ln domain)
|
|
score += hyp[1] * ctc_weight
|
|
if score > best_score:
|
|
best_score = score
|
|
best_index = i
|
|
return hyps[best_index][0]
|
|
|
|
#@jit.to_static
|
|
def subsampling_rate(self) -> int:
|
|
""" Export interface for c++ call, return subsampling_rate of the
|
|
model
|
|
"""
|
|
return self.encoder.embed.subsampling_rate
|
|
|
|
#@jit.to_static
|
|
def right_context(self) -> int:
|
|
""" Export interface for c++ call, return right_context of the model
|
|
"""
|
|
return self.encoder.embed.right_context
|
|
|
|
#@jit.to_static
|
|
def sos_symbol(self) -> int:
|
|
""" Export interface for c++ call, return sos symbol id of the model
|
|
"""
|
|
return self.sos
|
|
|
|
#@jit.to_static
|
|
def eos_symbol(self) -> int:
|
|
""" Export interface for c++ call, return eos symbol id of the model
|
|
"""
|
|
return self.eos
|
|
|
|
@jit.to_static
|
|
def forward_encoder_chunk(
|
|
self,
|
|
xs: paddle.Tensor,
|
|
offset: int,
|
|
required_cache_size: int,
|
|
subsampling_cache: Optional[paddle.Tensor]=None,
|
|
elayers_output_cache: Optional[List[paddle.Tensor]]=None,
|
|
conformer_cnn_cache: Optional[List[paddle.Tensor]]=None,
|
|
) -> Tuple[paddle.Tensor, paddle.Tensor, List[paddle.Tensor], List[
|
|
paddle.Tensor]]:
|
|
""" Export interface for c++ call, give input chunk xs, and return
|
|
output from time 0 to current chunk.
|
|
Args:
|
|
xs (paddle.Tensor): chunk input
|
|
subsampling_cache (Optional[paddle.Tensor]): subsampling cache
|
|
elayers_output_cache (Optional[List[paddle.Tensor]]):
|
|
transformer/conformer encoder layers output cache
|
|
conformer_cnn_cache (Optional[List[paddle.Tensor]]): conformer
|
|
cnn cache
|
|
Returns:
|
|
paddle.Tensor: output, it ranges from time 0 to current chunk.
|
|
paddle.Tensor: subsampling cache
|
|
List[paddle.Tensor]: attention cache
|
|
List[paddle.Tensor]: conformer cnn cache
|
|
"""
|
|
return self.encoder.forward_chunk(
|
|
xs, offset, required_cache_size, subsampling_cache,
|
|
elayers_output_cache, conformer_cnn_cache)
|
|
|
|
# @jit.to_static
|
|
def ctc_activation(self, xs: paddle.Tensor) -> paddle.Tensor:
|
|
""" Export interface for c++ call, apply linear transform and log
|
|
softmax before ctc
|
|
Args:
|
|
xs (paddle.Tensor): encoder output, (B, T, D)
|
|
Returns:
|
|
paddle.Tensor: activation before ctc
|
|
"""
|
|
return self.ctc.log_softmax(xs)
|
|
|
|
@jit.to_static
|
|
def forward_attention_decoder(
|
|
self,
|
|
hyps: paddle.Tensor,
|
|
hyps_lens: paddle.Tensor,
|
|
encoder_out: paddle.Tensor, ) -> paddle.Tensor:
|
|
""" Export interface for c++ call, forward decoder with multiple
|
|
hypothesis from ctc prefix beam search and one encoder output
|
|
Args:
|
|
hyps (paddle.Tensor): hyps from ctc prefix beam search, already
|
|
pad sos at the begining, (B, T)
|
|
hyps_lens (paddle.Tensor): length of each hyp in hyps, (B)
|
|
encoder_out (paddle.Tensor): corresponding encoder output, (B=1, T, D)
|
|
Returns:
|
|
paddle.Tensor: decoder output, (B, L)
|
|
"""
|
|
assert encoder_out.shape[0] == 1
|
|
num_hyps = hyps.shape[0]
|
|
assert hyps_lens.shape[0] == num_hyps
|
|
encoder_out = encoder_out.repeat(num_hyps, 1, 1)
|
|
# (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,
|
|
lang_model_path: str,
|
|
beam_alpha: float,
|
|
beam_beta: float,
|
|
beam_size: int,
|
|
cutoff_prob: float,
|
|
cutoff_top_n: int,
|
|
num_processes: int,
|
|
ctc_weight: float=0.0,
|
|
decoding_chunk_size: int=-1,
|
|
num_decoding_left_chunks: int=-1,
|
|
simulate_streaming: bool=False):
|
|
"""u2 decoding.
|
|
|
|
Args:
|
|
feats (Tenosr): audio features, (B, T, D)
|
|
feats_lengths (Tenosr): (B)
|
|
text_feature (TextFeaturizer): text feature object.
|
|
decoding_method (str): decoding mode, e.g.
|
|
'attention', 'ctc_greedy_search',
|
|
'ctc_prefix_beam_search', 'attention_rescoring'
|
|
lang_model_path (str): lm path.
|
|
beam_alpha (float): lm weight.
|
|
beam_beta (float): length penalty.
|
|
beam_size (int): beam size for search
|
|
cutoff_prob (float): for prune.
|
|
cutoff_top_n (int): for prune.
|
|
num_processes (int):
|
|
ctc_weight (float, optional): ctc weight for attention rescoring decode mode. Defaults to 0.0.
|
|
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 in ['ctc_prefix_beam_search',
|
|
'attention_rescoring'] and batch_size > 1:
|
|
logger.fatal(
|
|
f'decoding mode {decoding_method} must be running with batch_size == 1'
|
|
)
|
|
sys.exit(1)
|
|
|
|
if decoding_method == 'attention':
|
|
hyps = self.recognize(
|
|
feats,
|
|
feats_lengths,
|
|
beam_size=beam_size,
|
|
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]
|
|
elif decoding_method == 'ctc_greedy_search':
|
|
hyps = self.ctc_greedy_search(
|
|
feats,
|
|
feats_lengths,
|
|
decoding_chunk_size=decoding_chunk_size,
|
|
num_decoding_left_chunks=num_decoding_left_chunks,
|
|
simulate_streaming=simulate_streaming)
|
|
# ctc_prefix_beam_search and attention_rescoring only return one
|
|
# result in List[int], change it to List[List[int]] for compatible
|
|
# with other batch decoding mode
|
|
elif decoding_method == 'ctc_prefix_beam_search':
|
|
assert feats.shape[0] == 1
|
|
hyp = self.ctc_prefix_beam_search(
|
|
feats,
|
|
feats_lengths,
|
|
beam_size,
|
|
decoding_chunk_size=decoding_chunk_size,
|
|
num_decoding_left_chunks=num_decoding_left_chunks,
|
|
simulate_streaming=simulate_streaming)
|
|
hyps = [hyp]
|
|
elif decoding_method == 'attention_rescoring':
|
|
assert feats.shape[0] == 1
|
|
hyp = self.attention_rescoring(
|
|
feats,
|
|
feats_lengths,
|
|
beam_size,
|
|
decoding_chunk_size=decoding_chunk_size,
|
|
num_decoding_left_chunks=num_decoding_left_chunks,
|
|
ctc_weight=ctc_weight,
|
|
simulate_streaming=simulate_streaming)
|
|
hyps = [hyp]
|
|
else:
|
|
raise ValueError(f"Not support decoding method: {decoding_method}")
|
|
|
|
res = [text_feature.defeaturize(hyp) for hyp in hyps]
|
|
res_tokenids = [hyp for hyp in hyps]
|
|
return res, res_tokenids
|
|
|
|
|
|
class U2DecodeModel(U2BaseModel):
|
|
def scorers(self):
|
|
"""Scorers."""
|
|
return dict(
|
|
decoder=self.decoder, ctc=CTCPrefixScorer(self.ctc, self.eos))
|
|
|
|
def encode(self, x):
|
|
"""Encode acoustic features.
|
|
|
|
:param ndarray x: source acoustic feature (T, D)
|
|
:return: encoder outputs
|
|
:rtype: paddle.Tensor
|
|
"""
|
|
self.eval()
|
|
x = paddle.to_tensor(x).unsqueeze(0)
|
|
ilen = x.size(1)
|
|
enc_output, _ = self._forward_encoder(x, ilen)
|
|
return enc_output.squeeze(0)
|
|
|
|
|
|
class U2Model(U2DecodeModel):
|
|
def __init__(self, configs: dict):
|
|
vocab_size, encoder, decoder, ctc = U2Model._init_from_config(configs)
|
|
|
|
super().__init__(
|
|
vocab_size=vocab_size,
|
|
encoder=encoder,
|
|
decoder=decoder,
|
|
ctc=ctc,
|
|
**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
|
|
"""
|
|
# cmvn
|
|
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 & output dim
|
|
input_dim = configs['input_dim']
|
|
vocab_size = configs['output_dim']
|
|
assert input_dim != 0, input_dim
|
|
assert vocab_size != 0, vocab_size
|
|
|
|
# encoder
|
|
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}")
|
|
|
|
# decoder
|
|
decoder = TransformerDecoder(vocab_size,
|
|
encoder.output_size(),
|
|
**configs['decoder_conf'])
|
|
|
|
# ctc decoder and ctc loss
|
|
model_conf = configs['model_conf']
|
|
ctc = CTCDecoder(
|
|
odim=vocab_size,
|
|
enc_n_units=encoder.output_size(),
|
|
blank_id=0,
|
|
dropout_rate=model_conf['ctc_dropoutrate'],
|
|
reduction=True, # sum
|
|
batch_average=True, # sum / batch_size
|
|
grad_norm_type=model_conf['ctc_grad_norm_type'])
|
|
|
|
return vocab_size, encoder, decoder, ctc
|
|
|
|
@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: U2Model
|
|
"""
|
|
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.Checkpoint().load_parameters(
|
|
model, checkpoint_path=checkpoint_path)
|
|
logger.info(f"checkpoint info: {infos}")
|
|
layer_tools.summary(model)
|
|
return model
|
|
|
|
|
|
class U2InferModel(U2Model):
|
|
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.ctc_greedy_search(
|
|
feats,
|
|
feats_lengths,
|
|
decoding_chunk_size=decoding_chunk_size,
|
|
num_decoding_left_chunks=num_decoding_left_chunks,
|
|
simulate_streaming=simulate_streaming)
|