1031 lines
40 KiB
Python
1031 lines
40 KiB
Python
#!/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 time
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import torch
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import logging
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from torch.cuda.amp import autocast
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from typing import Union, Dict, List, Tuple, Optional
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from funasr.register import tables
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from funasr.models.ctc.ctc import CTC
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from funasr.utils import postprocess_utils
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from funasr.metrics.compute_acc import th_accuracy
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from funasr.utils.datadir_writer import DatadirWriter
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from funasr.models.paraformer.cif_predictor import mae_loss
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from funasr.train_utils.device_funcs import force_gatherable
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from funasr.losses.label_smoothing_loss import LabelSmoothingLoss
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from funasr.models.transformer.utils.add_sos_eos import add_sos_eos
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from funasr.models.transformer.utils.nets_utils import make_pad_mask, pad_list
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from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank
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from funasr.models.scama.utils import sequence_mask
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@tables.register("model_classes", "UniASR")
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class UniASR(torch.nn.Module):
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"""
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Author: Speech Lab of DAMO Academy, Alibaba Group
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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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encoder2: str = None,
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encoder2_conf: dict = None,
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decoder: str = None,
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decoder_conf: dict = None,
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decoder2: str = None,
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decoder2_conf: dict = None,
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predictor: str = None,
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predictor_conf: dict = None,
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predictor_bias: int = 0,
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predictor_weight: float = 0.0,
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predictor2: str = None,
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predictor2_conf: dict = None,
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predictor2_bias: int = 0,
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predictor2_weight: float = 0.0,
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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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ctc2: str = None,
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ctc2_conf: dict = None,
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ctc2_weight: float = 0.5,
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decoder_attention_chunk_type: str = "chunk",
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decoder_attention_chunk_type2: str = "chunk",
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stride_conv=None,
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stride_conv_conf: dict = None,
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loss_weight_model1: float = 0.5,
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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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share_embedding: bool = False,
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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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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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predictor_class = tables.predictor_classes.get(predictor)
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predictor = predictor_class(**predictor_conf)
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from funasr.models.transformer.utils.subsampling import Conv1dSubsampling
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stride_conv = Conv1dSubsampling(
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**stride_conv_conf,
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idim=input_size + encoder_output_size,
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odim=input_size + encoder_output_size,
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)
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stride_conv_output_size = stride_conv.output_size()
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encoder_class = tables.encoder_classes.get(encoder2)
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encoder2 = encoder_class(input_size=stride_conv_output_size, **encoder2_conf)
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encoder2_output_size = encoder2.output_size()
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decoder_class = tables.decoder_classes.get(decoder2)
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decoder2 = decoder_class(
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vocab_size=vocab_size,
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encoder_output_size=encoder2_output_size,
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**decoder2_conf,
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)
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predictor_class = tables.predictor_classes.get(predictor2)
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predictor2 = predictor_class(**predictor2_conf)
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self.blank_id = blank_id
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self.sos = sos
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self.eos = eos
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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.ctc2_weight = ctc2_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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self.error_calculator = None
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self.decoder = decoder
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self.ctc = None
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self.ctc2 = None
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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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self.predictor = predictor
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self.predictor_weight = predictor_weight
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self.criterion_pre = mae_loss(normalize_length=length_normalized_loss)
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self.encoder1_encoder2_joint_training = kwargs.get("encoder1_encoder2_joint_training", True)
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if self.encoder.overlap_chunk_cls is not None:
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from funasr.models.scama.chunk_utilis import (
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build_scama_mask_for_cross_attention_decoder,
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)
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self.build_scama_mask_for_cross_attention_decoder_fn = (
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build_scama_mask_for_cross_attention_decoder
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)
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self.decoder_attention_chunk_type = decoder_attention_chunk_type
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self.encoder2 = encoder2
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self.decoder2 = decoder2
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self.ctc2_weight = ctc2_weight
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self.predictor2 = predictor2
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self.predictor2_weight = predictor2_weight
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self.decoder_attention_chunk_type2 = decoder_attention_chunk_type2
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self.stride_conv = stride_conv
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self.loss_weight_model1 = loss_weight_model1
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if self.encoder2.overlap_chunk_cls is not None:
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from funasr.models.scama.chunk_utilis import (
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build_scama_mask_for_cross_attention_decoder,
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)
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self.build_scama_mask_for_cross_attention_decoder_fn2 = (
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build_scama_mask_for_cross_attention_decoder
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)
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self.decoder_attention_chunk_type2 = decoder_attention_chunk_type2
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self.length_normalized_loss = length_normalized_loss
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self.enable_maas_finetune = kwargs.get("enable_maas_finetune", False)
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self.freeze_encoder2 = kwargs.get("freeze_encoder2", False)
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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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"""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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"""
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decoding_ind = kwargs.get("decoding_ind", None)
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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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ind = self.encoder.overlap_chunk_cls.random_choice(self.training, decoding_ind)
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# 1. Encoder
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if self.enable_maas_finetune:
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with torch.no_grad():
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speech_raw, encoder_out, encoder_out_lens = self.encode(
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speech, speech_lengths, ind=ind
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)
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else:
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speech_raw, encoder_out, encoder_out_lens = self.encode(speech, speech_lengths, ind=ind)
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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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loss_pre = None
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loss, loss1, loss2 = 0.0, 0.0, 0.0
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if self.loss_weight_model1 > 0.0:
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## model1
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# 1. CTC branch
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if self.enable_maas_finetune:
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with torch.no_grad():
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loss_att, acc_att, cer_att, wer_att, loss_pre = self._calc_att_predictor_loss(
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encoder_out, encoder_out_lens, text, text_lengths
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)
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loss = loss_att + loss_pre * self.predictor_weight
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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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stats["loss_pre"] = loss_pre.detach().cpu() if loss_pre is not None else None
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else:
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loss_att, acc_att, cer_att, wer_att, loss_pre = self._calc_att_predictor_loss(
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encoder_out, encoder_out_lens, text, text_lengths
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)
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loss = loss_att + loss_pre * self.predictor_weight
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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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stats["loss_pre"] = loss_pre.detach().cpu() if loss_pre is not None else None
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loss1 = loss
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if self.loss_weight_model1 < 1.0:
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## model2
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# encoder2
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if self.freeze_encoder2:
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with torch.no_grad():
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encoder_out, encoder_out_lens = self.encode2(
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encoder_out, encoder_out_lens, speech_raw, speech_lengths, ind=ind
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)
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else:
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encoder_out, encoder_out_lens = self.encode2(
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encoder_out, encoder_out_lens, speech_raw, speech_lengths, ind=ind
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)
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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, loss_pre = self._calc_att_predictor_loss2(
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encoder_out, encoder_out_lens, text, text_lengths
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)
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loss = loss_att + loss_pre * self.predictor2_weight
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# Collect Attn branch stats
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stats["loss_att2"] = loss_att.detach() if loss_att is not None else None
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stats["acc2"] = acc_att
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stats["cer2"] = cer_att
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stats["wer2"] = wer_att
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stats["loss_pre2"] = loss_pre.detach().cpu() if loss_pre is not None else None
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loss2 = loss
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loss = loss1 * self.loss_weight_model1 + loss2 * (1 - self.loss_weight_model1)
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stats["loss1"] = torch.clone(loss1.detach())
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stats["loss2"] = torch.clone(loss2.detach())
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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 collect_feats(
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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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) -> Dict[str, torch.Tensor]:
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if self.extract_feats_in_collect_stats:
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feats, feats_lengths = self._extract_feats(speech, speech_lengths)
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else:
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# Generate dummy stats if extract_feats_in_collect_stats is False
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logging.warning(
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"Generating dummy stats for feats and feats_lengths, "
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"because encoder_conf.extract_feats_in_collect_stats is "
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f"{self.extract_feats_in_collect_stats}"
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)
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feats, feats_lengths = speech, speech_lengths
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return {"feats": feats, "feats_lengths": feats_lengths}
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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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):
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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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"""
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ind = kwargs.get("ind", 0)
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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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speech_raw = speech.clone().to(speech.device)
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# 4. Forward encoder
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encoder_out, encoder_out_lens, _ = self.encoder(speech, speech_lengths, ind=ind)
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if isinstance(encoder_out, tuple):
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encoder_out = encoder_out[0]
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return speech_raw, encoder_out, encoder_out_lens
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def encode2(
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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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speech: torch.Tensor,
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speech_lengths: torch.Tensor,
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**kwargs,
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):
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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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"""
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ind = kwargs.get("ind", 0)
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encoder_out_rm, encoder_out_lens_rm = self.encoder.overlap_chunk_cls.remove_chunk(
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encoder_out,
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encoder_out_lens,
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chunk_outs=None,
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)
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# residual_input
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encoder_out = torch.cat((speech, encoder_out_rm), dim=-1)
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encoder_out_lens = encoder_out_lens_rm
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if self.stride_conv is not None:
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speech, speech_lengths = self.stride_conv(encoder_out, encoder_out_lens)
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if not self.encoder1_encoder2_joint_training:
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speech = speech.detach()
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speech_lengths = speech_lengths.detach()
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# 4. Forward encoder
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# feats: (Batch, Length, Dim)
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# -> encoder_out: (Batch, Length2, Dim2)
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encoder_out, encoder_out_lens, _ = self.encoder2(speech, speech_lengths, ind=ind)
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if isinstance(encoder_out, tuple):
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encoder_out = encoder_out[0]
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return encoder_out, encoder_out_lens
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def nll(
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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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) -> torch.Tensor:
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"""Compute negative log likelihood(nll) from transformer-decoder
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Normally, this function is called in batchify_nll.
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Args:
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encoder_out: (Batch, Length, Dim)
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encoder_out_lens: (Batch,)
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ys_pad: (Batch, Length)
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ys_pad_lens: (Batch,)
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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(
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encoder_out, encoder_out_lens, ys_in_pad, ys_in_lens
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) # [batch, seqlen, dim]
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batch_size = decoder_out.size(0)
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decoder_num_class = decoder_out.size(2)
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# nll: negative log-likelihood
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nll = torch.nn.functional.cross_entropy(
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decoder_out.view(-1, decoder_num_class),
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ys_out_pad.view(-1),
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ignore_index=self.ignore_id,
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reduction="none",
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)
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nll = nll.view(batch_size, -1)
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nll = nll.sum(dim=1)
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assert nll.size(0) == batch_size
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return nll
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def batchify_nll(
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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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batch_size: int = 100,
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):
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"""Compute negative log likelihood(nll) from transformer-decoder
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To avoid OOM, this fuction seperate the input into batches.
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Then call nll for each batch and combine and return results.
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Args:
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encoder_out: (Batch, Length, Dim)
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encoder_out_lens: (Batch,)
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ys_pad: (Batch, Length)
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ys_pad_lens: (Batch,)
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batch_size: int, samples each batch contain when computing nll,
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you may change this to avoid OOM or increase
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GPU memory usage
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"""
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total_num = encoder_out.size(0)
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if total_num <= batch_size:
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nll = self.nll(encoder_out, encoder_out_lens, ys_pad, ys_pad_lens)
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else:
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nll = []
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start_idx = 0
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while True:
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end_idx = min(start_idx + batch_size, total_num)
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batch_encoder_out = encoder_out[start_idx:end_idx, :, :]
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batch_encoder_out_lens = encoder_out_lens[start_idx:end_idx]
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batch_ys_pad = ys_pad[start_idx:end_idx, :]
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batch_ys_pad_lens = ys_pad_lens[start_idx:end_idx]
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batch_nll = self.nll(
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batch_encoder_out,
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batch_encoder_out_lens,
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batch_ys_pad,
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batch_ys_pad_lens,
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)
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nll.append(batch_nll)
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start_idx = end_idx
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if start_idx == total_num:
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break
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nll = torch.cat(nll)
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assert nll.size(0) == total_num
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return nll
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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,
|
|
ys_pad_lens: torch.Tensor,
|
|
):
|
|
ys_in_pad, ys_out_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id)
|
|
ys_in_lens = ys_pad_lens + 1
|
|
|
|
# 1. Forward decoder
|
|
decoder_out, _ = self.decoder(encoder_out, encoder_out_lens, ys_in_pad, ys_in_lens)
|
|
|
|
# 2. Compute attention loss
|
|
loss_att = self.criterion_att(decoder_out, ys_out_pad)
|
|
acc_att = th_accuracy(
|
|
decoder_out.view(-1, self.vocab_size),
|
|
ys_out_pad,
|
|
ignore_label=self.ignore_id,
|
|
)
|
|
|
|
# Compute cer/wer using attention-decoder
|
|
if self.training or self.error_calculator is None:
|
|
cer_att, wer_att = None, None
|
|
else:
|
|
ys_hat = decoder_out.argmax(dim=-1)
|
|
cer_att, wer_att = self.error_calculator(ys_hat.cpu(), ys_pad.cpu())
|
|
|
|
return loss_att, acc_att, cer_att, wer_att
|
|
|
|
def _calc_att_predictor_loss(
|
|
self,
|
|
encoder_out: torch.Tensor,
|
|
encoder_out_lens: torch.Tensor,
|
|
ys_pad: torch.Tensor,
|
|
ys_pad_lens: torch.Tensor,
|
|
):
|
|
ys_in_pad, ys_out_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id)
|
|
ys_in_lens = ys_pad_lens + 1
|
|
|
|
encoder_out_mask = sequence_mask(
|
|
encoder_out_lens,
|
|
maxlen=encoder_out.size(1),
|
|
dtype=encoder_out.dtype,
|
|
device=encoder_out.device,
|
|
)[:, None, :]
|
|
mask_chunk_predictor = None
|
|
if self.encoder.overlap_chunk_cls is not None:
|
|
mask_chunk_predictor = self.encoder.overlap_chunk_cls.get_mask_chunk_predictor(
|
|
None, device=encoder_out.device, batch_size=encoder_out.size(0)
|
|
)
|
|
mask_shfit_chunk = self.encoder.overlap_chunk_cls.get_mask_shfit_chunk(
|
|
None, device=encoder_out.device, batch_size=encoder_out.size(0)
|
|
)
|
|
encoder_out = encoder_out * mask_shfit_chunk
|
|
pre_acoustic_embeds, pre_token_length, pre_alphas, _ = self.predictor(
|
|
encoder_out,
|
|
ys_out_pad,
|
|
encoder_out_mask,
|
|
ignore_id=self.ignore_id,
|
|
mask_chunk_predictor=mask_chunk_predictor,
|
|
target_label_length=ys_in_lens,
|
|
)
|
|
predictor_alignments, predictor_alignments_len = self.predictor.gen_frame_alignments(
|
|
pre_alphas, encoder_out_lens
|
|
)
|
|
|
|
scama_mask = None
|
|
if (
|
|
self.encoder.overlap_chunk_cls is not None
|
|
and self.decoder_attention_chunk_type == "chunk"
|
|
):
|
|
encoder_chunk_size = self.encoder.overlap_chunk_cls.chunk_size_pad_shift_cur
|
|
attention_chunk_center_bias = 0
|
|
attention_chunk_size = encoder_chunk_size
|
|
decoder_att_look_back_factor = (
|
|
self.encoder.overlap_chunk_cls.decoder_att_look_back_factor_cur
|
|
)
|
|
mask_shift_att_chunk_decoder = (
|
|
self.encoder.overlap_chunk_cls.get_mask_shift_att_chunk_decoder(
|
|
None, device=encoder_out.device, batch_size=encoder_out.size(0)
|
|
)
|
|
)
|
|
scama_mask = self.build_scama_mask_for_cross_attention_decoder_fn(
|
|
predictor_alignments=predictor_alignments,
|
|
encoder_sequence_length=encoder_out_lens,
|
|
chunk_size=1,
|
|
encoder_chunk_size=encoder_chunk_size,
|
|
attention_chunk_center_bias=attention_chunk_center_bias,
|
|
attention_chunk_size=attention_chunk_size,
|
|
attention_chunk_type=self.decoder_attention_chunk_type,
|
|
step=None,
|
|
predictor_mask_chunk_hopping=mask_chunk_predictor,
|
|
decoder_att_look_back_factor=decoder_att_look_back_factor,
|
|
mask_shift_att_chunk_decoder=mask_shift_att_chunk_decoder,
|
|
target_length=ys_in_lens,
|
|
is_training=self.training,
|
|
)
|
|
elif self.encoder.overlap_chunk_cls is not None:
|
|
encoder_out, encoder_out_lens = self.encoder.overlap_chunk_cls.remove_chunk(
|
|
encoder_out, encoder_out_lens, chunk_outs=None
|
|
)
|
|
# try:
|
|
# 1. Forward decoder
|
|
decoder_out, _ = self.decoder(
|
|
encoder_out,
|
|
encoder_out_lens,
|
|
ys_in_pad,
|
|
ys_in_lens,
|
|
chunk_mask=scama_mask,
|
|
pre_acoustic_embeds=pre_acoustic_embeds,
|
|
)
|
|
|
|
# 2. Compute attention loss
|
|
loss_att = self.criterion_att(decoder_out, ys_out_pad)
|
|
acc_att = th_accuracy(
|
|
decoder_out.view(-1, self.vocab_size),
|
|
ys_out_pad,
|
|
ignore_label=self.ignore_id,
|
|
)
|
|
# predictor loss
|
|
loss_pre = self.criterion_pre(ys_in_lens.type_as(pre_token_length), pre_token_length)
|
|
# Compute cer/wer using attention-decoder
|
|
if self.training or self.error_calculator is None:
|
|
cer_att, wer_att = None, None
|
|
else:
|
|
ys_hat = decoder_out.argmax(dim=-1)
|
|
cer_att, wer_att = self.error_calculator(ys_hat.cpu(), ys_pad.cpu())
|
|
|
|
return loss_att, acc_att, cer_att, wer_att, loss_pre
|
|
|
|
def _calc_att_predictor_loss2(
|
|
self,
|
|
encoder_out: torch.Tensor,
|
|
encoder_out_lens: torch.Tensor,
|
|
ys_pad: torch.Tensor,
|
|
ys_pad_lens: torch.Tensor,
|
|
):
|
|
ys_in_pad, ys_out_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id)
|
|
ys_in_lens = ys_pad_lens + 1
|
|
|
|
encoder_out_mask = sequence_mask(
|
|
encoder_out_lens,
|
|
maxlen=encoder_out.size(1),
|
|
dtype=encoder_out.dtype,
|
|
device=encoder_out.device,
|
|
)[:, None, :]
|
|
mask_chunk_predictor = None
|
|
if self.encoder2.overlap_chunk_cls is not None:
|
|
mask_chunk_predictor = self.encoder2.overlap_chunk_cls.get_mask_chunk_predictor(
|
|
None, device=encoder_out.device, batch_size=encoder_out.size(0)
|
|
)
|
|
mask_shfit_chunk = self.encoder2.overlap_chunk_cls.get_mask_shfit_chunk(
|
|
None, device=encoder_out.device, batch_size=encoder_out.size(0)
|
|
)
|
|
encoder_out = encoder_out * mask_shfit_chunk
|
|
pre_acoustic_embeds, pre_token_length, pre_alphas, _ = self.predictor2(
|
|
encoder_out,
|
|
ys_out_pad,
|
|
encoder_out_mask,
|
|
ignore_id=self.ignore_id,
|
|
mask_chunk_predictor=mask_chunk_predictor,
|
|
target_label_length=ys_in_lens,
|
|
)
|
|
predictor_alignments, predictor_alignments_len = self.predictor2.gen_frame_alignments(
|
|
pre_alphas, encoder_out_lens
|
|
)
|
|
|
|
scama_mask = None
|
|
if (
|
|
self.encoder2.overlap_chunk_cls is not None
|
|
and self.decoder_attention_chunk_type2 == "chunk"
|
|
):
|
|
encoder_chunk_size = self.encoder2.overlap_chunk_cls.chunk_size_pad_shift_cur
|
|
attention_chunk_center_bias = 0
|
|
attention_chunk_size = encoder_chunk_size
|
|
decoder_att_look_back_factor = (
|
|
self.encoder2.overlap_chunk_cls.decoder_att_look_back_factor_cur
|
|
)
|
|
mask_shift_att_chunk_decoder = (
|
|
self.encoder2.overlap_chunk_cls.get_mask_shift_att_chunk_decoder(
|
|
None, device=encoder_out.device, batch_size=encoder_out.size(0)
|
|
)
|
|
)
|
|
scama_mask = self.build_scama_mask_for_cross_attention_decoder_fn2(
|
|
predictor_alignments=predictor_alignments,
|
|
encoder_sequence_length=encoder_out_lens,
|
|
chunk_size=1,
|
|
encoder_chunk_size=encoder_chunk_size,
|
|
attention_chunk_center_bias=attention_chunk_center_bias,
|
|
attention_chunk_size=attention_chunk_size,
|
|
attention_chunk_type=self.decoder_attention_chunk_type2,
|
|
step=None,
|
|
predictor_mask_chunk_hopping=mask_chunk_predictor,
|
|
decoder_att_look_back_factor=decoder_att_look_back_factor,
|
|
mask_shift_att_chunk_decoder=mask_shift_att_chunk_decoder,
|
|
target_length=ys_in_lens,
|
|
is_training=self.training,
|
|
)
|
|
elif self.encoder2.overlap_chunk_cls is not None:
|
|
encoder_out, encoder_out_lens = self.encoder2.overlap_chunk_cls.remove_chunk(
|
|
encoder_out, encoder_out_lens, chunk_outs=None
|
|
)
|
|
# try:
|
|
# 1. Forward decoder
|
|
decoder_out, _ = self.decoder2(
|
|
encoder_out,
|
|
encoder_out_lens,
|
|
ys_in_pad,
|
|
ys_in_lens,
|
|
chunk_mask=scama_mask,
|
|
pre_acoustic_embeds=pre_acoustic_embeds,
|
|
)
|
|
|
|
# 2. Compute attention loss
|
|
loss_att = self.criterion_att(decoder_out, ys_out_pad)
|
|
acc_att = th_accuracy(
|
|
decoder_out.view(-1, self.vocab_size),
|
|
ys_out_pad,
|
|
ignore_label=self.ignore_id,
|
|
)
|
|
# predictor loss
|
|
loss_pre = self.criterion_pre(ys_in_lens.type_as(pre_token_length), pre_token_length)
|
|
# Compute cer/wer using attention-decoder
|
|
if self.training or self.error_calculator is None:
|
|
cer_att, wer_att = None, None
|
|
else:
|
|
ys_hat = decoder_out.argmax(dim=-1)
|
|
cer_att, wer_att = self.error_calculator(ys_hat.cpu(), ys_pad.cpu())
|
|
|
|
return loss_att, acc_att, cer_att, wer_att, loss_pre
|
|
|
|
def calc_predictor_mask(
|
|
self,
|
|
encoder_out: torch.Tensor,
|
|
encoder_out_lens: torch.Tensor,
|
|
ys_pad: torch.Tensor = None,
|
|
ys_pad_lens: torch.Tensor = None,
|
|
):
|
|
# ys_in_pad, ys_out_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id)
|
|
# ys_in_lens = ys_pad_lens + 1
|
|
ys_out_pad, ys_in_lens = None, None
|
|
|
|
encoder_out_mask = sequence_mask(
|
|
encoder_out_lens,
|
|
maxlen=encoder_out.size(1),
|
|
dtype=encoder_out.dtype,
|
|
device=encoder_out.device,
|
|
)[:, None, :]
|
|
mask_chunk_predictor = None
|
|
if self.encoder.overlap_chunk_cls is not None:
|
|
mask_chunk_predictor = self.encoder.overlap_chunk_cls.get_mask_chunk_predictor(
|
|
None, device=encoder_out.device, batch_size=encoder_out.size(0)
|
|
)
|
|
mask_shfit_chunk = self.encoder.overlap_chunk_cls.get_mask_shfit_chunk(
|
|
None, device=encoder_out.device, batch_size=encoder_out.size(0)
|
|
)
|
|
encoder_out = encoder_out * mask_shfit_chunk
|
|
pre_acoustic_embeds, pre_token_length, pre_alphas, _ = self.predictor(
|
|
encoder_out,
|
|
ys_out_pad,
|
|
encoder_out_mask,
|
|
ignore_id=self.ignore_id,
|
|
mask_chunk_predictor=mask_chunk_predictor,
|
|
target_label_length=ys_in_lens,
|
|
)
|
|
predictor_alignments, predictor_alignments_len = self.predictor.gen_frame_alignments(
|
|
pre_alphas, encoder_out_lens
|
|
)
|
|
|
|
scama_mask = None
|
|
if (
|
|
self.encoder.overlap_chunk_cls is not None
|
|
and self.decoder_attention_chunk_type == "chunk"
|
|
):
|
|
encoder_chunk_size = self.encoder.overlap_chunk_cls.chunk_size_pad_shift_cur
|
|
attention_chunk_center_bias = 0
|
|
attention_chunk_size = encoder_chunk_size
|
|
decoder_att_look_back_factor = (
|
|
self.encoder.overlap_chunk_cls.decoder_att_look_back_factor_cur
|
|
)
|
|
mask_shift_att_chunk_decoder = (
|
|
self.encoder.overlap_chunk_cls.get_mask_shift_att_chunk_decoder(
|
|
None, device=encoder_out.device, batch_size=encoder_out.size(0)
|
|
)
|
|
)
|
|
scama_mask = self.build_scama_mask_for_cross_attention_decoder_fn(
|
|
predictor_alignments=predictor_alignments,
|
|
encoder_sequence_length=encoder_out_lens,
|
|
chunk_size=1,
|
|
encoder_chunk_size=encoder_chunk_size,
|
|
attention_chunk_center_bias=attention_chunk_center_bias,
|
|
attention_chunk_size=attention_chunk_size,
|
|
attention_chunk_type=self.decoder_attention_chunk_type,
|
|
step=None,
|
|
predictor_mask_chunk_hopping=mask_chunk_predictor,
|
|
decoder_att_look_back_factor=decoder_att_look_back_factor,
|
|
mask_shift_att_chunk_decoder=mask_shift_att_chunk_decoder,
|
|
target_length=ys_in_lens,
|
|
is_training=self.training,
|
|
)
|
|
elif self.encoder.overlap_chunk_cls is not None:
|
|
encoder_out, encoder_out_lens = self.encoder.overlap_chunk_cls.remove_chunk(
|
|
encoder_out, encoder_out_lens, chunk_outs=None
|
|
)
|
|
|
|
return (
|
|
pre_acoustic_embeds,
|
|
pre_token_length,
|
|
predictor_alignments,
|
|
predictor_alignments_len,
|
|
scama_mask,
|
|
)
|
|
|
|
def calc_predictor_mask2(
|
|
self,
|
|
encoder_out: torch.Tensor,
|
|
encoder_out_lens: torch.Tensor,
|
|
ys_pad: torch.Tensor = None,
|
|
ys_pad_lens: torch.Tensor = None,
|
|
):
|
|
# ys_in_pad, ys_out_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id)
|
|
# ys_in_lens = ys_pad_lens + 1
|
|
ys_out_pad, ys_in_lens = None, None
|
|
|
|
encoder_out_mask = sequence_mask(
|
|
encoder_out_lens,
|
|
maxlen=encoder_out.size(1),
|
|
dtype=encoder_out.dtype,
|
|
device=encoder_out.device,
|
|
)[:, None, :]
|
|
mask_chunk_predictor = None
|
|
if self.encoder2.overlap_chunk_cls is not None:
|
|
mask_chunk_predictor = self.encoder2.overlap_chunk_cls.get_mask_chunk_predictor(
|
|
None, device=encoder_out.device, batch_size=encoder_out.size(0)
|
|
)
|
|
mask_shfit_chunk = self.encoder2.overlap_chunk_cls.get_mask_shfit_chunk(
|
|
None, device=encoder_out.device, batch_size=encoder_out.size(0)
|
|
)
|
|
encoder_out = encoder_out * mask_shfit_chunk
|
|
pre_acoustic_embeds, pre_token_length, pre_alphas, _ = self.predictor2(
|
|
encoder_out,
|
|
ys_out_pad,
|
|
encoder_out_mask,
|
|
ignore_id=self.ignore_id,
|
|
mask_chunk_predictor=mask_chunk_predictor,
|
|
target_label_length=ys_in_lens,
|
|
)
|
|
predictor_alignments, predictor_alignments_len = self.predictor2.gen_frame_alignments(
|
|
pre_alphas, encoder_out_lens
|
|
)
|
|
|
|
scama_mask = None
|
|
if (
|
|
self.encoder2.overlap_chunk_cls is not None
|
|
and self.decoder_attention_chunk_type2 == "chunk"
|
|
):
|
|
encoder_chunk_size = self.encoder2.overlap_chunk_cls.chunk_size_pad_shift_cur
|
|
attention_chunk_center_bias = 0
|
|
attention_chunk_size = encoder_chunk_size
|
|
decoder_att_look_back_factor = (
|
|
self.encoder2.overlap_chunk_cls.decoder_att_look_back_factor_cur
|
|
)
|
|
mask_shift_att_chunk_decoder = (
|
|
self.encoder2.overlap_chunk_cls.get_mask_shift_att_chunk_decoder(
|
|
None, device=encoder_out.device, batch_size=encoder_out.size(0)
|
|
)
|
|
)
|
|
scama_mask = self.build_scama_mask_for_cross_attention_decoder_fn2(
|
|
predictor_alignments=predictor_alignments,
|
|
encoder_sequence_length=encoder_out_lens,
|
|
chunk_size=1,
|
|
encoder_chunk_size=encoder_chunk_size,
|
|
attention_chunk_center_bias=attention_chunk_center_bias,
|
|
attention_chunk_size=attention_chunk_size,
|
|
attention_chunk_type=self.decoder_attention_chunk_type2,
|
|
step=None,
|
|
predictor_mask_chunk_hopping=mask_chunk_predictor,
|
|
decoder_att_look_back_factor=decoder_att_look_back_factor,
|
|
mask_shift_att_chunk_decoder=mask_shift_att_chunk_decoder,
|
|
target_length=ys_in_lens,
|
|
is_training=self.training,
|
|
)
|
|
elif self.encoder2.overlap_chunk_cls is not None:
|
|
encoder_out, encoder_out_lens = self.encoder2.overlap_chunk_cls.remove_chunk(
|
|
encoder_out, encoder_out_lens, chunk_outs=None
|
|
)
|
|
|
|
return (
|
|
pre_acoustic_embeds,
|
|
pre_token_length,
|
|
predictor_alignments,
|
|
predictor_alignments_len,
|
|
scama_mask,
|
|
)
|
|
|
|
def init_beam_search(
|
|
self,
|
|
**kwargs,
|
|
):
|
|
from funasr.models.uniasr.beam_search import BeamSearchScama
|
|
from funasr.models.transformer.scorers.ctc import CTCPrefixScorer
|
|
from funasr.models.transformer.scorers.length_bonus import LengthBonus
|
|
|
|
decoding_mode = kwargs.get("decoding_mode", "model1")
|
|
if decoding_mode == "model1":
|
|
decoder = self.decoder
|
|
else:
|
|
decoder = self.decoder2
|
|
# 1. Build ASR model
|
|
scorers = {}
|
|
|
|
if self.ctc != None:
|
|
ctc = CTCPrefixScorer(ctc=self.ctc, eos=self.eos)
|
|
scorers.update(ctc=ctc)
|
|
token_list = kwargs.get("token_list")
|
|
scorers.update(
|
|
decoder=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", 0.0),
|
|
ctc=kwargs.get("decoding_ctc_weight", 0.0),
|
|
lm=kwargs.get("lm_weight", 0.0),
|
|
ngram=kwargs.get("ngram_weight", 0.0),
|
|
length_bonus=kwargs.get("penalty", 0.0),
|
|
)
|
|
beam_search = BeamSearchScama(
|
|
beam_size=kwargs.get("beam_size", 5),
|
|
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(
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self,
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data_in,
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data_lengths=None,
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key: list = None,
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tokenizer=None,
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frontend=None,
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**kwargs,
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):
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|
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decoding_model = kwargs.get("decoding_model", "normal")
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token_num_relax = kwargs.get("token_num_relax", 5)
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if decoding_model == "fast":
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|
decoding_ind = 0
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|
decoding_mode = "model1"
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elif decoding_model == "offline":
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|
decoding_ind = 1
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|
decoding_mode = "model2"
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else:
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|
decoding_ind = 0
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|
decoding_mode = "model2"
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|
# init beamsearch
|
|
|
|
if self.beam_search is None:
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|
logging.info("enable beam_search")
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|
self.init_beam_search(decoding_mode=decoding_mode, **kwargs)
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|
self.nbest = kwargs.get("nbest", 1)
|
|
|
|
meta_data = {}
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|
if (
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|
isinstance(data_in, torch.Tensor) and kwargs.get("data_type", "sound") == "fbank"
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|
): # fbank
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|
speech, speech_lengths = data_in, data_lengths
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|
if len(speech.shape) < 3:
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|
speech = speech[None, :, :]
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|
if speech_lengths is None:
|
|
speech_lengths = speech.shape[1]
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|
else:
|
|
# extract fbank feats
|
|
time1 = time.perf_counter()
|
|
audio_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()
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|
meta_data["load_data"] = f"{time2 - time1:0.3f}"
|
|
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}"
|
|
meta_data["batch_data_time"] = (
|
|
speech_lengths.sum().item() * frontend.frame_shift * frontend.lfr_n / 1000
|
|
)
|
|
|
|
speech = speech.to(device=kwargs["device"])
|
|
speech_lengths = speech_lengths.to(device=kwargs["device"])
|
|
speech_raw = speech.clone().to(device=kwargs["device"])
|
|
# Encoder
|
|
_, encoder_out, encoder_out_lens = self.encode(speech, speech_lengths, ind=decoding_ind)
|
|
if decoding_mode == "model1":
|
|
predictor_outs = self.calc_predictor_mask(encoder_out, encoder_out_lens)
|
|
else:
|
|
encoder_out, encoder_out_lens = self.encode2(
|
|
encoder_out, encoder_out_lens, speech_raw, speech_lengths, ind=decoding_ind
|
|
)
|
|
predictor_outs = self.calc_predictor_mask2(encoder_out, encoder_out_lens)
|
|
|
|
scama_mask = predictor_outs[4]
|
|
pre_token_length = predictor_outs[1]
|
|
pre_acoustic_embeds = predictor_outs[0]
|
|
maxlen = pre_token_length.sum().item() + token_num_relax
|
|
minlen = max(0, pre_token_length.sum().item() - token_num_relax)
|
|
# c. Passed the encoder result and the beam search
|
|
nbest_hyps = self.beam_search(
|
|
x=encoder_out[0],
|
|
scama_mask=scama_mask,
|
|
pre_acoustic_embeds=pre_acoustic_embeds,
|
|
maxlenratio=0.0,
|
|
minlenratio=0.0,
|
|
maxlen=int(maxlen),
|
|
minlen=int(minlen),
|
|
)
|
|
|
|
nbest_hyps = nbest_hyps[: self.nbest]
|
|
|
|
results = []
|
|
for hyp in nbest_hyps:
|
|
|
|
# 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 != 0, token_int))
|
|
|
|
# Change integer-ids to tokens
|
|
token = tokenizer.ids2tokens(token_int)
|
|
text_postprocessed = tokenizer.tokens2text(token)
|
|
if not hasattr(tokenizer, "bpemodel"):
|
|
text_postprocessed, _ = postprocess_utils.sentence_postprocess(token)
|
|
|
|
result_i = {"key": key[0], "text": text_postprocessed}
|
|
results.append(result_i)
|
|
|
|
return results, meta_data
|