505 lines
18 KiB
Python
505 lines
18 KiB
Python
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#!/usr/bin/env python3
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# -*- encoding: utf-8 -*-
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# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
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# MIT License (https://opensource.org/licenses/MIT)
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import logging
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from typing import Union, Dict, List, Tuple, Optional
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import time
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import torch
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import torch.nn as nn
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from torch.cuda.amp import autocast
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from funasr.losses.label_smoothing_loss import LabelSmoothingLoss
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from funasr.models.ctc.ctc import CTC
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from funasr.models.transformer.utils.add_sos_eos import add_sos_eos
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from funasr.metrics.compute_acc import th_accuracy
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# from funasr.models.e2e_asr_common import ErrorCalculator
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from funasr.train_utils.device_funcs import force_gatherable
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from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank
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from funasr.utils import postprocess_utils
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from funasr.utils.datadir_writer import DatadirWriter
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from funasr.register import tables
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import pdb
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@tables.register("model_classes", "LCBNet")
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class LCBNet(nn.Module):
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"""
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Author: Speech Lab of DAMO Academy, Alibaba Group
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LCB-NET: LONG-CONTEXT BIASING FOR AUDIO-VISUAL SPEECH RECOGNITION
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https://arxiv.org/abs/2401.06390
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"""
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def __init__(
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self,
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specaug: str = None,
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specaug_conf: dict = None,
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normalize: str = None,
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normalize_conf: dict = None,
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encoder: str = None,
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encoder_conf: dict = None,
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decoder: str = None,
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decoder_conf: dict = None,
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text_encoder: str = None,
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text_encoder_conf: dict = None,
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bias_predictor: str = None,
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bias_predictor_conf: dict = None,
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fusion_encoder: str = None,
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fusion_encoder_conf: dict = None,
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ctc: str = None,
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ctc_conf: dict = None,
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ctc_weight: float = 0.5,
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interctc_weight: float = 0.0,
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select_num: int = 2,
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select_length: int = 3,
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insert_blank: bool = True,
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input_size: int = 80,
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vocab_size: int = -1,
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ignore_id: int = -1,
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blank_id: int = 0,
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sos: int = 1,
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eos: int = 2,
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lsm_weight: float = 0.0,
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length_normalized_loss: bool = False,
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report_cer: bool = True,
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report_wer: bool = True,
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sym_space: str = "<space>",
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sym_blank: str = "<blank>",
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# extract_feats_in_collect_stats: bool = True,
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share_embedding: bool = False,
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# preencoder: Optional[AbsPreEncoder] = None,
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# postencoder: Optional[AbsPostEncoder] = None,
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**kwargs,
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):
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super().__init__()
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if specaug is not None:
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specaug_class = tables.specaug_classes.get(specaug)
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specaug = specaug_class(**specaug_conf)
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if normalize is not None:
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normalize_class = tables.normalize_classes.get(normalize)
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normalize = normalize_class(**normalize_conf)
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encoder_class = tables.encoder_classes.get(encoder)
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encoder = encoder_class(input_size=input_size, **encoder_conf)
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encoder_output_size = encoder.output_size()
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# lcbnet modules: text encoder, fusion encoder and bias predictor
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text_encoder_class = tables.encoder_classes.get(text_encoder)
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text_encoder = text_encoder_class(input_size=vocab_size, **text_encoder_conf)
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fusion_encoder_class = tables.encoder_classes.get(fusion_encoder)
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fusion_encoder = fusion_encoder_class(**fusion_encoder_conf)
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bias_predictor_class = tables.encoder_classes.get(bias_predictor)
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bias_predictor = bias_predictor_class(**bias_predictor_conf)
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if decoder is not None:
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decoder_class = tables.decoder_classes.get(decoder)
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decoder = decoder_class(
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vocab_size=vocab_size,
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encoder_output_size=encoder_output_size,
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**decoder_conf,
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)
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if ctc_weight > 0.0:
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if ctc_conf is None:
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ctc_conf = {}
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ctc = CTC(odim=vocab_size, encoder_output_size=encoder_output_size, **ctc_conf)
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self.blank_id = blank_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.specaug = specaug
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self.normalize = normalize
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self.encoder = encoder
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# lcbnet
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self.text_encoder = text_encoder
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self.fusion_encoder = fusion_encoder
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self.bias_predictor = bias_predictor
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self.select_num = select_num
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self.select_length = select_length
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self.insert_blank = insert_blank
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if not hasattr(self.encoder, "interctc_use_conditioning"):
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self.encoder.interctc_use_conditioning = False
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if self.encoder.interctc_use_conditioning:
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self.encoder.conditioning_layer = torch.nn.Linear(
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vocab_size, self.encoder.output_size()
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)
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self.interctc_weight = interctc_weight
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# self.error_calculator = None
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if ctc_weight == 1.0:
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self.decoder = None
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else:
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self.decoder = decoder
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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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)
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#
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# if report_cer or report_wer:
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# self.error_calculator = ErrorCalculator(
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# token_list, sym_space, sym_blank, report_cer, report_wer
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# )
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#
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self.error_calculator = None
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if ctc_weight == 0.0:
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self.ctc = None
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else:
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self.ctc = ctc
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self.share_embedding = share_embedding
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if self.share_embedding:
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self.decoder.embed = None
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self.length_normalized_loss = length_normalized_loss
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self.beam_search = None
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def forward(
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self,
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speech: torch.Tensor,
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speech_lengths: torch.Tensor,
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text: torch.Tensor,
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text_lengths: torch.Tensor,
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**kwargs,
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) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]:
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"""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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"""
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if len(text_lengths.size()) > 1:
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text_lengths = text_lengths[:, 0]
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if len(speech_lengths.size()) > 1:
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speech_lengths = speech_lengths[:, 0]
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batch_size = speech.shape[0]
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# 1. Encoder
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encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
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intermediate_outs = None
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if isinstance(encoder_out, tuple):
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intermediate_outs = encoder_out[1]
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encoder_out = encoder_out[0]
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loss_att, acc_att, cer_att, wer_att = None, None, None, None
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loss_ctc, cer_ctc = None, None
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stats = dict()
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# decoder: CTC branch
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if self.ctc_weight != 0.0:
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loss_ctc, cer_ctc = self._calc_ctc_loss(
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encoder_out, encoder_out_lens, text, text_lengths
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)
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# Collect CTC branch stats
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stats["loss_ctc"] = loss_ctc.detach() if loss_ctc is not None else None
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stats["cer_ctc"] = cer_ctc
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# Intermediate CTC (optional)
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loss_interctc = 0.0
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if self.interctc_weight != 0.0 and intermediate_outs is not None:
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for layer_idx, intermediate_out in intermediate_outs:
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# we assume intermediate_out has the same length & padding
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# as those of encoder_out
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loss_ic, cer_ic = self._calc_ctc_loss(
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intermediate_out, encoder_out_lens, text, text_lengths
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)
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loss_interctc = loss_interctc + loss_ic
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# Collect Intermedaite CTC stats
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stats["loss_interctc_layer{}".format(layer_idx)] = (
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loss_ic.detach() if loss_ic is not None else None
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)
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stats["cer_interctc_layer{}".format(layer_idx)] = cer_ic
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loss_interctc = loss_interctc / len(intermediate_outs)
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# calculate whole encoder loss
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loss_ctc = (1 - self.interctc_weight) * loss_ctc + self.interctc_weight * loss_interctc
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# decoder: Attention decoder branch
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loss_att, acc_att, cer_att, wer_att = self._calc_att_loss(
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encoder_out, encoder_out_lens, text, text_lengths
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)
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# 3. CTC-Att loss definition
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if self.ctc_weight == 0.0:
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loss = loss_att
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elif self.ctc_weight == 1.0:
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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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# Collect Attn branch stats
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stats["loss_att"] = loss_att.detach() if loss_att is not None else None
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stats["acc"] = acc_att
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stats["cer"] = cer_att
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stats["wer"] = wer_att
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# Collect total loss stats
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stats["loss"] = torch.clone(loss.detach())
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# force_gatherable: to-device and to-tensor if scalar for DataParallel
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if self.length_normalized_loss:
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batch_size = int((text_lengths + 1).sum())
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loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
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return loss, stats, weight
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def encode(
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self,
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speech: torch.Tensor,
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speech_lengths: torch.Tensor,
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**kwargs,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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"""Frontend + Encoder. Note that this method is used by asr_inference.py
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Args:
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speech: (Batch, Length, ...)
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speech_lengths: (Batch, )
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ind: int
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"""
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with autocast(False):
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# Data augmentation
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if self.specaug is not None and self.training:
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speech, speech_lengths = self.specaug(speech, speech_lengths)
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# Normalization for feature: e.g. Global-CMVN, Utterance-CMVN
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if self.normalize is not None:
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speech, speech_lengths = self.normalize(speech, speech_lengths)
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# Forward encoder
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# feats: (Batch, Length, Dim)
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# -> encoder_out: (Batch, Length2, Dim2)
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if self.encoder.interctc_use_conditioning:
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encoder_out, encoder_out_lens, _ = self.encoder(speech, speech_lengths, ctc=self.ctc)
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else:
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encoder_out, encoder_out_lens, _ = self.encoder(speech, speech_lengths)
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intermediate_outs = None
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if isinstance(encoder_out, tuple):
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intermediate_outs = encoder_out[1]
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encoder_out = encoder_out[0]
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if intermediate_outs is not None:
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return (encoder_out, intermediate_outs), encoder_out_lens
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return encoder_out, encoder_out_lens
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def _calc_att_loss(
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self,
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encoder_out: torch.Tensor,
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encoder_out_lens: torch.Tensor,
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ys_pad: torch.Tensor,
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ys_pad_lens: torch.Tensor,
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):
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ys_in_pad, ys_out_pad = add_sos_eos(ys_pad, self.sos, self.eos, 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_out_lens, ys_in_pad, 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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)
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# Compute cer/wer using attention-decoder
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if self.training or self.error_calculator is None:
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cer_att, wer_att = None, None
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else:
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ys_hat = decoder_out.argmax(dim=-1)
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cer_att, wer_att = self.error_calculator(ys_hat.cpu(), ys_pad.cpu())
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return loss_att, acc_att, cer_att, wer_att
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def _calc_ctc_loss(
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self,
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encoder_out: torch.Tensor,
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encoder_out_lens: torch.Tensor,
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ys_pad: torch.Tensor,
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ys_pad_lens: torch.Tensor,
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):
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# Calc CTC loss
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loss_ctc = self.ctc(encoder_out, encoder_out_lens, ys_pad, ys_pad_lens)
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# Calc CER using CTC
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cer_ctc = None
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if not self.training and self.error_calculator is not None:
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ys_hat = self.ctc.argmax(encoder_out).data
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cer_ctc = self.error_calculator(ys_hat.cpu(), ys_pad.cpu(), is_ctc=True)
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return loss_ctc, cer_ctc
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def init_beam_search(
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self,
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**kwargs,
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):
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from funasr.models.transformer.search import BeamSearch
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from funasr.models.transformer.scorers.ctc import CTCPrefixScorer
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from funasr.models.transformer.scorers.length_bonus import LengthBonus
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# 1. Build ASR model
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scorers = {}
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if self.ctc != None:
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||
|
|
ctc = CTCPrefixScorer(ctc=self.ctc, eos=self.eos)
|
||
|
|
scorers.update(ctc=ctc)
|
||
|
|
token_list = kwargs.get("token_list")
|
||
|
|
scorers.update(
|
||
|
|
decoder=self.decoder,
|
||
|
|
length_bonus=LengthBonus(len(token_list)),
|
||
|
|
)
|
||
|
|
|
||
|
|
# 3. Build ngram model
|
||
|
|
# ngram is not supported now
|
||
|
|
ngram = None
|
||
|
|
scorers["ngram"] = ngram
|
||
|
|
|
||
|
|
weights = dict(
|
||
|
|
decoder=1.0 - kwargs.get("decoding_ctc_weight", 0.3),
|
||
|
|
ctc=kwargs.get("decoding_ctc_weight", 0.3),
|
||
|
|
lm=kwargs.get("lm_weight", 0.0),
|
||
|
|
ngram=kwargs.get("ngram_weight", 0.0),
|
||
|
|
length_bonus=kwargs.get("penalty", 0.0),
|
||
|
|
)
|
||
|
|
beam_search = BeamSearch(
|
||
|
|
beam_size=kwargs.get("beam_size", 20),
|
||
|
|
weights=weights,
|
||
|
|
scorers=scorers,
|
||
|
|
sos=self.sos,
|
||
|
|
eos=self.eos,
|
||
|
|
vocab_size=len(token_list),
|
||
|
|
token_list=token_list,
|
||
|
|
pre_beam_score_key=None if self.ctc_weight == 1.0 else "full",
|
||
|
|
)
|
||
|
|
|
||
|
|
self.beam_search = beam_search
|
||
|
|
|
||
|
|
def inference(
|
||
|
|
self,
|
||
|
|
data_in,
|
||
|
|
data_lengths=None,
|
||
|
|
key: list = None,
|
||
|
|
tokenizer=None,
|
||
|
|
frontend=None,
|
||
|
|
**kwargs,
|
||
|
|
):
|
||
|
|
|
||
|
|
if kwargs.get("batch_size", 1) > 1:
|
||
|
|
raise NotImplementedError("batch decoding is not implemented")
|
||
|
|
|
||
|
|
# init beamsearch
|
||
|
|
if self.beam_search is None:
|
||
|
|
logging.info("enable beam_search")
|
||
|
|
self.init_beam_search(**kwargs)
|
||
|
|
self.nbest = kwargs.get("nbest", 1)
|
||
|
|
|
||
|
|
meta_data = {}
|
||
|
|
if (
|
||
|
|
isinstance(data_in, torch.Tensor) and kwargs.get("data_type", "sound") == "fbank"
|
||
|
|
): # fbank
|
||
|
|
speech, speech_lengths = data_in, data_lengths
|
||
|
|
if len(speech.shape) < 3:
|
||
|
|
speech = speech[None, :, :]
|
||
|
|
if speech_lengths is None:
|
||
|
|
speech_lengths = speech.shape[1]
|
||
|
|
else:
|
||
|
|
# extract fbank feats
|
||
|
|
time1 = time.perf_counter()
|
||
|
|
sample_list = load_audio_text_image_video(
|
||
|
|
data_in,
|
||
|
|
fs=frontend.fs,
|
||
|
|
audio_fs=kwargs.get("fs", 16000),
|
||
|
|
data_type=kwargs.get("data_type", "sound"),
|
||
|
|
tokenizer=tokenizer,
|
||
|
|
)
|
||
|
|
time2 = time.perf_counter()
|
||
|
|
meta_data["load_data"] = f"{time2 - time1:0.3f}"
|
||
|
|
audio_sample_list = sample_list[0]
|
||
|
|
if len(sample_list) > 1:
|
||
|
|
ocr_sample_list = sample_list[1]
|
||
|
|
else:
|
||
|
|
ocr_sample_list = [[294, 0]]
|
||
|
|
speech, speech_lengths = extract_fbank(
|
||
|
|
audio_sample_list, data_type=kwargs.get("data_type", "sound"), frontend=frontend
|
||
|
|
)
|
||
|
|
time3 = time.perf_counter()
|
||
|
|
meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
|
||
|
|
frame_shift = 10
|
||
|
|
meta_data["batch_data_time"] = speech_lengths.sum().item() * frame_shift / 1000
|
||
|
|
|
||
|
|
speech = speech.to(device=kwargs["device"])
|
||
|
|
speech_lengths = speech_lengths.to(device=kwargs["device"])
|
||
|
|
# Encoder
|
||
|
|
encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
|
||
|
|
if isinstance(encoder_out, tuple):
|
||
|
|
encoder_out = encoder_out[0]
|
||
|
|
|
||
|
|
ocr_list_new = [[x + 1 if x != 0 else x for x in sublist] for sublist in ocr_sample_list]
|
||
|
|
ocr = torch.tensor(ocr_list_new).to(device=kwargs["device"])
|
||
|
|
ocr_lengths = ocr.new_full([1], dtype=torch.long, fill_value=ocr.size(1)).to(
|
||
|
|
device=kwargs["device"]
|
||
|
|
)
|
||
|
|
ocr, ocr_lens, _ = self.text_encoder(ocr, ocr_lengths)
|
||
|
|
fusion_out, _, _, _ = self.fusion_encoder(encoder_out, None, ocr, None)
|
||
|
|
encoder_out = encoder_out + fusion_out
|
||
|
|
# c. Passed the encoder result and the beam search
|
||
|
|
nbest_hyps = self.beam_search(
|
||
|
|
x=encoder_out[0],
|
||
|
|
maxlenratio=kwargs.get("maxlenratio", 0.0),
|
||
|
|
minlenratio=kwargs.get("minlenratio", 0.0),
|
||
|
|
)
|
||
|
|
|
||
|
|
nbest_hyps = nbest_hyps[: self.nbest]
|
||
|
|
|
||
|
|
results = []
|
||
|
|
b, n, d = encoder_out.size()
|
||
|
|
for i in range(b):
|
||
|
|
|
||
|
|
for nbest_idx, hyp in enumerate(nbest_hyps):
|
||
|
|
ibest_writer = None
|
||
|
|
if kwargs.get("output_dir") is not None:
|
||
|
|
if not hasattr(self, "writer"):
|
||
|
|
self.writer = DatadirWriter(kwargs.get("output_dir"))
|
||
|
|
ibest_writer = self.writer[f"{nbest_idx + 1}best_recog"]
|
||
|
|
|
||
|
|
# remove sos/eos and get results
|
||
|
|
last_pos = -1
|
||
|
|
if isinstance(hyp.yseq, list):
|
||
|
|
token_int = hyp.yseq[1:last_pos]
|
||
|
|
else:
|
||
|
|
token_int = hyp.yseq[1:last_pos].tolist()
|
||
|
|
|
||
|
|
# remove blank symbol id, which is assumed to be 0
|
||
|
|
token_int = list(
|
||
|
|
filter(
|
||
|
|
lambda x: x != self.eos and x != self.sos and x != self.blank_id, token_int
|
||
|
|
)
|
||
|
|
)
|
||
|
|
|
||
|
|
# Change integer-ids to tokens
|
||
|
|
token = tokenizer.ids2tokens(token_int)
|
||
|
|
text = tokenizer.tokens2text(token)
|
||
|
|
|
||
|
|
text_postprocessed, _ = postprocess_utils.sentence_postprocess(token)
|
||
|
|
result_i = {"key": key[i], "token": token, "text": text_postprocessed}
|
||
|
|
results.append(result_i)
|
||
|
|
|
||
|
|
if ibest_writer is not None:
|
||
|
|
ibest_writer["token"][key[i]] = " ".join(token)
|
||
|
|
ibest_writer["text"][key[i]] = text_postprocessed
|
||
|
|
|
||
|
|
return results, meta_data
|