740 lines
26 KiB
Python
740 lines
26 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 torch.nn as nn
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import torch.functional as F
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import logging
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from typing import Dict, Tuple
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from contextlib import contextmanager
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from distutils.version import LooseVersion
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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.model import Paraformer
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from funasr.models.paraformer.search import Hypothesis
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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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if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"):
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from torch.cuda.amp import autocast
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else:
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# Nothing to do if torch<1.6.0
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@contextmanager
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def autocast(enabled=True):
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yield
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@tables.register("model_classes", "SCAMA")
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class SCAMA(nn.Module):
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"""
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Author: Shiliang Zhang, Zhifu Gao, Haoneng Luo, Ming Lei, Jie Gao, Zhijie Yan, Lei Xie
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SCAMA: Streaming chunk-aware multihead attention for online end-to-end speech recognition
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https://arxiv.org/abs/2006.01712
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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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ctc: str = None,
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ctc_conf: dict = None,
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ctc_weight: float = 0.5,
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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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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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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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predictor_class = tables.predictor_classes.get(predictor)
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predictor = predictor_class(**predictor_conf)
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# note that eos is the same as sos (equivalent ID)
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self.blank_id = blank_id
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self.sos = sos if sos is not None else vocab_size - 1
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self.eos = eos if eos is not None else 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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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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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.predictor = predictor
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self.predictor_weight = predictor_weight
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self.predictor_bias = predictor_bias
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self.criterion_pre = mae_loss(normalize_length=length_normalized_loss)
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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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self.error_calculator = None
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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 = kwargs.get("decoder_attention_chunk_type", "chunk")
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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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decoding_ind = kwargs.get("decoding_ind")
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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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# Encoder
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ind = self.encoder.overlap_chunk_cls.random_choice(self.training, decoding_ind)
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encoder_out, encoder_out_lens = self.encode(speech, speech_lengths, ind=ind)
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loss_ctc, cer_ctc = None, None
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loss_pre = 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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encoder_out_ctc, encoder_out_lens_ctc = self.encoder.overlap_chunk_cls.remove_chunk(
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encoder_out, encoder_out_lens, chunk_outs=None
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)
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loss_ctc, cer_ctc = self._calc_ctc_loss(
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encoder_out_ctc, encoder_out_lens_ctc, 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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# decoder: Attention decoder branch
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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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# 3. CTC-Att loss definition
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if self.ctc_weight == 0.0:
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loss = loss_att + loss_pre * self.predictor_weight
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else:
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loss = (
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self.ctc_weight * loss_ctc
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+ (1 - self.ctc_weight) * loss_att
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+ loss_pre * self.predictor_weight
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)
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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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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 = (text_lengths + self.predictor_bias).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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"""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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encoder_out, encoder_out_lens, _ = self.encoder(speech, speech_lengths)
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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 encode_chunk(
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self,
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speech: torch.Tensor,
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speech_lengths: torch.Tensor,
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cache: dict = None,
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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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encoder_out, encoder_out_lens, _ = self.encoder.forward_chunk(
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speech, speech_lengths, cache=cache["encoder"]
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)
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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, torch.tensor([encoder_out.size(1)])
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def calc_predictor_chunk(self, encoder_out, encoder_out_lens, cache=None, **kwargs):
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is_final = kwargs.get("is_final", False)
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return self.predictor.forward_chunk(encoder_out, cache["encoder"], is_final=is_final)
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def _calc_att_predictor_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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encoder_out_mask = sequence_mask(
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encoder_out_lens,
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maxlen=encoder_out.size(1),
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dtype=encoder_out.dtype,
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device=encoder_out.device,
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)[:, None, :]
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mask_chunk_predictor = None
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if self.encoder.overlap_chunk_cls is not None:
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mask_chunk_predictor = self.encoder.overlap_chunk_cls.get_mask_chunk_predictor(
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None, device=encoder_out.device, batch_size=encoder_out.size(0)
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)
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mask_shfit_chunk = self.encoder.overlap_chunk_cls.get_mask_shfit_chunk(
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None, device=encoder_out.device, batch_size=encoder_out.size(0)
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)
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encoder_out = encoder_out * mask_shfit_chunk
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pre_acoustic_embeds, pre_token_length, pre_alphas, _ = self.predictor(
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encoder_out,
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ys_out_pad,
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encoder_out_mask,
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ignore_id=self.ignore_id,
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mask_chunk_predictor=mask_chunk_predictor,
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target_label_length=ys_in_lens,
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)
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predictor_alignments, predictor_alignments_len = self.predictor.gen_frame_alignments(
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pre_alphas, encoder_out_lens
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)
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encoder_chunk_size = self.encoder.overlap_chunk_cls.chunk_size_pad_shift_cur
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attention_chunk_center_bias = 0
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attention_chunk_size = encoder_chunk_size
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decoder_att_look_back_factor = (
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self.encoder.overlap_chunk_cls.decoder_att_look_back_factor_cur
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)
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mask_shift_att_chunk_decoder = (
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self.encoder.overlap_chunk_cls.get_mask_shift_att_chunk_decoder(
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None, device=encoder_out.device, batch_size=encoder_out.size(0)
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)
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)
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scama_mask = self.build_scama_mask_for_cross_attention_decoder_fn(
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predictor_alignments=predictor_alignments,
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encoder_sequence_length=encoder_out_lens,
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chunk_size=1,
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encoder_chunk_size=encoder_chunk_size,
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attention_chunk_center_bias=attention_chunk_center_bias,
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attention_chunk_size=attention_chunk_size,
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attention_chunk_type=self.decoder_attention_chunk_type,
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step=None,
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predictor_mask_chunk_hopping=mask_chunk_predictor,
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decoder_att_look_back_factor=decoder_att_look_back_factor,
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mask_shift_att_chunk_decoder=mask_shift_att_chunk_decoder,
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target_length=ys_in_lens,
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is_training=self.training,
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)
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# try:
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# 1. Forward decoder
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decoder_out, _ = self.decoder(
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encoder_out,
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encoder_out_lens,
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ys_in_pad,
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ys_in_lens,
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chunk_mask=scama_mask,
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pre_acoustic_embeds=pre_acoustic_embeds,
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)
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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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# predictor loss
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loss_pre = self.criterion_pre(ys_in_lens.type_as(pre_token_length), pre_token_length)
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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, loss_pre
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def calc_predictor_mask(
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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 = None,
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ys_pad_lens: torch.Tensor = None,
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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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ys_out_pad, ys_in_lens = None, None
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encoder_out_mask = sequence_mask(
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encoder_out_lens,
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maxlen=encoder_out.size(1),
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dtype=encoder_out.dtype,
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device=encoder_out.device,
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)[:, None, :]
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mask_chunk_predictor = None
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mask_chunk_predictor = self.encoder.overlap_chunk_cls.get_mask_chunk_predictor(
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None, device=encoder_out.device, batch_size=encoder_out.size(0)
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)
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mask_shfit_chunk = self.encoder.overlap_chunk_cls.get_mask_shfit_chunk(
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None, device=encoder_out.device, batch_size=encoder_out.size(0)
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)
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encoder_out = encoder_out * mask_shfit_chunk
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pre_acoustic_embeds, pre_token_length, pre_alphas, _ = self.predictor(
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encoder_out,
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ys_out_pad,
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encoder_out_mask,
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ignore_id=self.ignore_id,
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mask_chunk_predictor=mask_chunk_predictor,
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target_label_length=ys_in_lens,
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)
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predictor_alignments, predictor_alignments_len = self.predictor.gen_frame_alignments(
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pre_alphas, encoder_out_lens
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)
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encoder_chunk_size = self.encoder.overlap_chunk_cls.chunk_size_pad_shift_cur
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attention_chunk_center_bias = 0
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attention_chunk_size = encoder_chunk_size
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decoder_att_look_back_factor = (
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self.encoder.overlap_chunk_cls.decoder_att_look_back_factor_cur
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)
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mask_shift_att_chunk_decoder = (
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self.encoder.overlap_chunk_cls.get_mask_shift_att_chunk_decoder(
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None, device=encoder_out.device, batch_size=encoder_out.size(0)
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)
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)
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scama_mask = self.build_scama_mask_for_cross_attention_decoder_fn(
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predictor_alignments=predictor_alignments,
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encoder_sequence_length=encoder_out_lens,
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chunk_size=1,
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encoder_chunk_size=encoder_chunk_size,
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attention_chunk_center_bias=attention_chunk_center_bias,
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attention_chunk_size=attention_chunk_size,
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attention_chunk_type=self.decoder_attention_chunk_type,
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step=None,
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predictor_mask_chunk_hopping=mask_chunk_predictor,
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decoder_att_look_back_factor=decoder_att_look_back_factor,
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mask_shift_att_chunk_decoder=mask_shift_att_chunk_decoder,
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target_length=ys_in_lens,
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is_training=self.training,
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)
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return (
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pre_acoustic_embeds,
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pre_token_length,
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predictor_alignments,
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predictor_alignments_len,
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scama_mask,
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)
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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.scama.beam_search import BeamSearchScamaStreaming
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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)
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scorers.update(ctc=ctc)
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token_list = kwargs.get("token_list")
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scorers.update(
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decoder=self.decoder,
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length_bonus=LengthBonus(len(token_list)),
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)
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# 3. Build ngram model
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# ngram is not supported now
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ngram = None
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scorers["ngram"] = ngram
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weights = dict(
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decoder=1.0 - kwargs.get("decoding_ctc_weight", 0.0),
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ctc=kwargs.get("decoding_ctc_weight", 0.0),
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lm=kwargs.get("lm_weight", 0.0),
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ngram=kwargs.get("ngram_weight", 0.0),
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length_bonus=kwargs.get("penalty", 0.0),
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)
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beam_search = BeamSearchScamaStreaming(
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beam_size=kwargs.get("beam_size", 2),
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weights=weights,
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scorers=scorers,
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sos=self.sos,
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eos=self.eos,
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vocab_size=len(token_list),
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token_list=token_list,
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pre_beam_score_key=None if self.ctc_weight == 1.0 else "full",
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)
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# beam_search.to(device=kwargs.get("device", "cpu"), dtype=getattr(torch, kwargs.get("dtype", "float32"))).eval()
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# for scorer in scorers.values():
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# if isinstance(scorer, torch.nn.Module):
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# scorer.to(device=kwargs.get("device", "cpu"), dtype=getattr(torch, kwargs.get("dtype", "float32"))).eval()
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self.beam_search = beam_search
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def generate_chunk(
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self,
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speech,
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speech_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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cache = kwargs.get("cache", {})
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speech = speech.to(device=kwargs["device"])
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speech_lengths = speech_lengths.to(device=kwargs["device"])
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# Encoder
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encoder_out, encoder_out_lens = self.encode_chunk(
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speech, speech_lengths, cache=cache, is_final=kwargs.get("is_final", False)
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)
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if isinstance(encoder_out, tuple):
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encoder_out = encoder_out[0]
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if "running_hyps" not in cache:
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running_hyps = self.beam_search.init_hyp(encoder_out)
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cache["running_hyps"] = running_hyps
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# predictor
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predictor_outs = self.calc_predictor_chunk(
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encoder_out,
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encoder_out_lens,
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cache=cache,
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is_final=kwargs.get("is_final", False),
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)
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pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = (
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predictor_outs[0],
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predictor_outs[1],
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predictor_outs[2],
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predictor_outs[3],
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)
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pre_token_length = pre_token_length.round().long()
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if torch.max(pre_token_length) < 1:
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return []
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maxlen = minlen = pre_token_length
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if kwargs.get("is_final", False):
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maxlen += kwargs.get("token_num_relax", 5)
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minlen = max(0, minlen - kwargs.get("token_num_relax", 5))
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# c. Passed the encoder result and the beam search
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nbest_hyps = self.beam_search(
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x=encoder_out[0],
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scama_mask=None,
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pre_acoustic_embeds=pre_acoustic_embeds,
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maxlen=int(maxlen),
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minlen=int(minlen),
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cache=cache,
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)
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cache["running_hyps"] = nbest_hyps
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nbest_hyps = nbest_hyps[: self.nbest]
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results = []
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for hyp in nbest_hyps:
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# assert isinstance(hyp, (Hypothesis)), type(hyp)
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# remove sos/eos and get results
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last_pos = -1
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if isinstance(hyp.yseq, list):
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token_int = hyp.yseq[1:last_pos]
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else:
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token_int = hyp.yseq[1:last_pos].tolist()
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# remove blank symbol id, which is assumed to be 0
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token_int = list(
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filter(
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lambda x: x != self.eos and x != self.sos and x != self.blank_id, token_int
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)
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)
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# Change integer-ids to tokens
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token = tokenizer.ids2tokens(token_int)
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# text = tokenizer.tokens2text(token)
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result_i = token
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results.extend(result_i)
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return results
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def init_cache(self, cache: dict = {}, **kwargs):
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device = kwargs.get("device", "cuda")
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chunk_size = kwargs.get("chunk_size", [0, 10, 5])
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encoder_chunk_look_back = kwargs.get("encoder_chunk_look_back", 0)
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decoder_chunk_look_back = kwargs.get("decoder_chunk_look_back", 0)
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batch_size = 1
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enc_output_size = kwargs["encoder_conf"]["output_size"]
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feats_dims = kwargs["frontend_conf"]["n_mels"] * kwargs["frontend_conf"]["lfr_m"]
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cache_encoder = {
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"start_idx": 0,
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"cif_hidden": torch.zeros((batch_size, 1, enc_output_size)).to(device=device),
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"cif_alphas": torch.zeros((batch_size, 1)).to(device=device),
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"chunk_size": chunk_size,
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"encoder_chunk_look_back": encoder_chunk_look_back,
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"last_chunk": False,
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"opt": None,
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"feats": torch.zeros((batch_size, chunk_size[0] + chunk_size[2], feats_dims)).to(
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device=device
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),
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"tail_chunk": False,
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}
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cache["encoder"] = cache_encoder
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cache_decoder = {
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"decode_fsmn": None,
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"decoder_chunk_look_back": decoder_chunk_look_back,
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"opt": None,
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"chunk_size": chunk_size,
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}
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cache["decoder"] = cache_decoder
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cache["frontend"] = {}
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cache["prev_samples"] = torch.empty(0).to(device=device)
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return cache
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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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cache: dict = {},
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**kwargs,
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):
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# init beamsearch
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is_use_ctc = kwargs.get("decoding_ctc_weight", 0.0) > 0.00001 and self.ctc != None
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is_use_lm = (
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kwargs.get("lm_weight", 0.0) > 0.00001 and kwargs.get("lm_file", None) is not None
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)
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if self.beam_search is None:
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logging.info("enable beam_search")
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self.init_beam_search(**kwargs)
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self.nbest = kwargs.get("nbest", 1)
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if len(cache) == 0:
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self.init_cache(cache, **kwargs)
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meta_data = {}
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chunk_size = kwargs.get("chunk_size", [0, 10, 5])
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chunk_stride_samples = int(chunk_size[1] * 960) # 600ms
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time1 = time.perf_counter()
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cfg = {"is_final": kwargs.get("is_final", False)}
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audio_sample_list = load_audio_text_image_video(
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data_in,
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fs=frontend.fs,
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audio_fs=kwargs.get("fs", 16000),
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data_type=kwargs.get("data_type", "sound"),
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tokenizer=tokenizer,
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cache=cfg,
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)
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_is_final = cfg["is_final"] # if data_in is a file or url, set is_final=True
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time2 = time.perf_counter()
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meta_data["load_data"] = f"{time2 - time1:0.3f}"
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assert len(audio_sample_list) == 1, "batch_size must be set 1"
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audio_sample = torch.cat((cache["prev_samples"], audio_sample_list[0]))
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n = int(len(audio_sample) // chunk_stride_samples + int(_is_final))
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m = int(len(audio_sample) % chunk_stride_samples * (1 - int(_is_final)))
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tokens = []
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for i in range(n):
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kwargs["is_final"] = _is_final and i == n - 1
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audio_sample_i = audio_sample[i * chunk_stride_samples : (i + 1) * chunk_stride_samples]
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# extract fbank feats
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speech, speech_lengths = extract_fbank(
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[audio_sample_i],
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data_type=kwargs.get("data_type", "sound"),
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frontend=frontend,
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cache=cache["frontend"],
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is_final=kwargs["is_final"],
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)
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time3 = time.perf_counter()
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meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
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meta_data["batch_data_time"] = (
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speech_lengths.sum().item() * frontend.frame_shift * frontend.lfr_n / 1000
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)
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tokens_i = self.generate_chunk(
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speech,
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speech_lengths,
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key=key,
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tokenizer=tokenizer,
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cache=cache,
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frontend=frontend,
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**kwargs,
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)
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tokens.extend(tokens_i)
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text_postprocessed, _ = postprocess_utils.sentence_postprocess(tokens)
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result_i = {"key": key[0], "text": text_postprocessed}
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result = [result_i]
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cache["prev_samples"] = audio_sample[:-m]
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if _is_final:
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self.init_cache(cache, **kwargs)
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if kwargs.get("output_dir"):
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writer = DatadirWriter(kwargs.get("output_dir"))
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ibest_writer = writer[f"{1}best_recog"]
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ibest_writer["token"][key[0]] = " ".join(tokens)
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ibest_writer["text"][key[0]] = text_postprocessed
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return result, meta_data
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