#!/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 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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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", "ParaformerStreaming")
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class ParaformerStreaming(Paraformer):
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"""
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Author: Speech Lab of DAMO Academy, Alibaba Group
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Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition
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https://arxiv.org/abs/2206.08317
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"""
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def __init__(
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self,
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*args,
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**kwargs,
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):
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super().__init__(*args, **kwargs)
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self.sampling_ratio = kwargs.get("sampling_ratio", 0.2)
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self.scama_mask = None
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if (
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hasattr(self.encoder, "overlap_chunk_cls")
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and self.encoder.overlap_chunk_cls is not None
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):
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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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if hasattr(self.encoder, "overlap_chunk_cls"):
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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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else:
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encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
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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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if hasattr(self.encoder, "overlap_chunk_cls"):
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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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else:
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encoder_out_ctc, encoder_out_lens_ctc = encoder_out, encoder_out_lens
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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, pre_loss_att = 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["pre_loss_att"] = pre_loss_att.detach() if pre_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_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_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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encoder_out_mask = (
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~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]
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).to(encoder_out.device)
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if self.predictor_bias == 1:
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_, ys_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id)
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ys_pad_lens = ys_pad_lens + self.predictor_bias
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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_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_pad_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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scama_mask = None
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if (
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self.encoder.overlap_chunk_cls is not None
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and self.decoder_attention_chunk_type == "chunk"
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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_pad_lens,
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is_training=self.training,
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)
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elif self.encoder.overlap_chunk_cls is not None:
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encoder_out, encoder_out_lens = 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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# 0. sampler
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decoder_out_1st = None
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pre_loss_att = None
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if self.sampling_ratio > 0.0:
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if self.use_1st_decoder_loss:
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sematic_embeds, decoder_out_1st, pre_loss_att = self.sampler_with_grad(
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encoder_out,
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encoder_out_lens,
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ys_pad,
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ys_pad_lens,
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pre_acoustic_embeds,
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scama_mask,
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)
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else:
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sematic_embeds, decoder_out_1st = self.sampler(
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encoder_out,
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encoder_out_lens,
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ys_pad,
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ys_pad_lens,
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pre_acoustic_embeds,
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scama_mask,
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)
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else:
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sematic_embeds = pre_acoustic_embeds
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# 1. Forward decoder
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decoder_outs = self.decoder(
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encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens, scama_mask
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)
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decoder_out, _ = decoder_outs[0], decoder_outs[1]
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if decoder_out_1st is None:
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decoder_out_1st = decoder_out
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# 2. Compute attention loss
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loss_att = self.criterion_att(decoder_out, ys_pad)
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acc_att = th_accuracy(
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decoder_out_1st.view(-1, self.vocab_size),
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ys_pad,
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ignore_label=self.ignore_id,
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)
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loss_pre = self.criterion_pre(ys_pad_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_1st.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, pre_loss_att
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def sampler(
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self,
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encoder_out,
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encoder_out_lens,
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ys_pad,
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ys_pad_lens,
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pre_acoustic_embeds,
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chunk_mask=None,
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):
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tgt_mask = (~make_pad_mask(ys_pad_lens, maxlen=ys_pad_lens.max())[:, :, None]).to(
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ys_pad.device
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)
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ys_pad_masked = ys_pad * tgt_mask[:, :, 0]
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if self.share_embedding:
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ys_pad_embed = self.decoder.output_layer.weight[ys_pad_masked]
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else:
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ys_pad_embed = self.decoder.embed(ys_pad_masked)
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with torch.no_grad():
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decoder_outs = self.decoder(
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encoder_out, encoder_out_lens, pre_acoustic_embeds, ys_pad_lens, chunk_mask
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)
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decoder_out, _ = decoder_outs[0], decoder_outs[1]
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pred_tokens = decoder_out.argmax(-1)
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nonpad_positions = ys_pad.ne(self.ignore_id)
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seq_lens = (nonpad_positions).sum(1)
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same_num = ((pred_tokens == ys_pad) & nonpad_positions).sum(1)
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input_mask = torch.ones_like(nonpad_positions)
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bsz, seq_len = ys_pad.size()
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for li in range(bsz):
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target_num = (
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((seq_lens[li] - same_num[li].sum()).float()) * self.sampling_ratio
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).long()
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if target_num > 0:
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input_mask[li].scatter_(
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dim=0, index=torch.randperm(seq_lens[li])[:target_num].cuda(), value=0
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)
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input_mask = input_mask.eq(1)
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input_mask = input_mask.masked_fill(~nonpad_positions, False)
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input_mask_expand_dim = input_mask.unsqueeze(2).to(pre_acoustic_embeds.device)
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sematic_embeds = pre_acoustic_embeds.masked_fill(
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~input_mask_expand_dim, 0
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) + ys_pad_embed.masked_fill(input_mask_expand_dim, 0)
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return sematic_embeds * tgt_mask, decoder_out * tgt_mask
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def calc_predictor(self, encoder_out, encoder_out_lens):
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encoder_out_mask = (
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~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]
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).to(encoder_out.device)
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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, pre_peak_index = self.predictor(
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encoder_out,
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None,
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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=None,
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)
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predictor_alignments, predictor_alignments_len = self.predictor.gen_frame_alignments(
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pre_alphas,
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encoder_out_lens + 1 if self.predictor.tail_threshold > 0.0 else encoder_out_lens,
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)
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scama_mask = None
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if (
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self.encoder.overlap_chunk_cls is not None
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and self.decoder_attention_chunk_type == "chunk"
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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=None,
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is_training=self.training,
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)
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self.scama_mask = scama_mask
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return pre_acoustic_embeds, pre_token_length, pre_alphas, pre_peak_index
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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 cal_decoder_with_predictor(
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self, encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens
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):
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decoder_outs = self.decoder(
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encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens, self.scama_mask
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)
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decoder_out = decoder_outs[0]
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decoder_out = torch.log_softmax(decoder_out, dim=-1)
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return decoder_out, ys_pad_lens
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def cal_decoder_with_predictor_chunk(
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self, encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens, cache=None
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):
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decoder_outs = self.decoder.forward_chunk(encoder_out, sematic_embeds, cache["decoder"])
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decoder_out = decoder_outs
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decoder_out = torch.log_softmax(decoder_out, dim=-1)
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return decoder_out, ys_pad_lens
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def init_cache(self, cache: dict = {}, **kwargs):
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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)
|
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"]
|
cache_encoder = {
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"start_idx": 0,
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"cif_hidden": torch.zeros((batch_size, 1, enc_output_size)),
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"cif_alphas": torch.zeros((batch_size, 1)),
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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)),
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"tail_chunk": False,
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}
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cache["encoder"] = cache_encoder
|
|
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,
|
"chunk_size": chunk_size,
|
}
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cache["decoder"] = cache_decoder
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cache["frontend"] = {}
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cache["prev_samples"] = torch.empty(0)
|
|
return cache
|
|
def generate_chunk(
|
self,
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speech,
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speech_lengths=None,
|
key: list = None,
|
tokenizer=None,
|
frontend=None,
|
**kwargs,
|
):
|
cache = kwargs.get("cache", {})
|
speech = speech.to(device=kwargs["device"])
|
speech_lengths = speech_lengths.to(device=kwargs["device"])
|
|
# Encoder
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encoder_out, encoder_out_lens = self.encode_chunk(
|
speech, speech_lengths, cache=cache, is_final=kwargs.get("is_final", False)
|
)
|
if isinstance(encoder_out, tuple):
|
encoder_out = encoder_out[0]
|
|
# predictor
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predictor_outs = self.calc_predictor_chunk(
|
encoder_out, encoder_out_lens, cache=cache, is_final=kwargs.get("is_final", False)
|
)
|
pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = (
|
predictor_outs[0],
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predictor_outs[1],
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predictor_outs[2],
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predictor_outs[3],
|
)
|
pre_token_length = pre_token_length.round().long()
|
if torch.max(pre_token_length) < 1:
|
return []
|
decoder_outs = self.cal_decoder_with_predictor_chunk(
|
encoder_out, encoder_out_lens, pre_acoustic_embeds, pre_token_length, cache=cache
|
)
|
decoder_out, ys_pad_lens = decoder_outs[0], decoder_outs[1]
|
|
results = []
|
b, n, d = decoder_out.size()
|
if isinstance(key[0], (list, tuple)):
|
key = key[0]
|
for i in range(b):
|
x = encoder_out[i, : encoder_out_lens[i], :]
|
am_scores = decoder_out[i, : pre_token_length[i], :]
|
if self.beam_search is not None:
|
nbest_hyps = self.beam_search(
|
x=x,
|
am_scores=am_scores,
|
maxlenratio=kwargs.get("maxlenratio", 0.0),
|
minlenratio=kwargs.get("minlenratio", 0.0),
|
)
|
|
nbest_hyps = nbest_hyps[: self.nbest]
|
else:
|
|
yseq = am_scores.argmax(dim=-1)
|
score = am_scores.max(dim=-1)[0]
|
score = torch.sum(score, dim=-1)
|
# pad with mask tokens to ensure compatibility with sos/eos tokens
|
yseq = torch.tensor([self.sos] + yseq.tolist() + [self.eos], device=yseq.device)
|
nbest_hyps = [Hypothesis(yseq=yseq, score=score)]
|
for nbest_idx, hyp in enumerate(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 != self.eos and x != self.sos and x != self.blank_id, token_int
|
)
|
)
|
|
# Change integer-ids to tokens
|
token = tokenizer.ids2tokens(token_int)
|
# text = tokenizer.tokens2text(token)
|
|
result_i = token
|
|
results.extend(result_i)
|
|
return results
|
|
def inference(
|
self,
|
data_in,
|
data_lengths=None,
|
key: list = None,
|
tokenizer=None,
|
frontend=None,
|
cache: dict = {},
|
**kwargs,
|
):
|
|
# init beamsearch
|
is_use_ctc = kwargs.get("decoding_ctc_weight", 0.0) > 0.00001 and self.ctc != None
|
is_use_lm = (
|
kwargs.get("lm_weight", 0.0) > 0.00001 and kwargs.get("lm_file", None) is not None
|
)
|
if self.beam_search is None and (is_use_lm or is_use_ctc):
|
logging.info("enable beam_search")
|
self.init_beam_search(**kwargs)
|
self.nbest = kwargs.get("nbest", 1)
|
|
if len(cache) == 0:
|
self.init_cache(cache, **kwargs)
|
|
meta_data = {}
|
chunk_size = kwargs.get("chunk_size", [0, 10, 5])
|
chunk_stride_samples = int(chunk_size[1] * 960) # 600ms
|
|
time1 = time.perf_counter()
|
cfg = {"is_final": kwargs.get("is_final", False)}
|
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,
|
cache=cfg,
|
)
|
_is_final = cfg["is_final"] # if data_in is a file or url, set is_final=True
|
|
time2 = time.perf_counter()
|
meta_data["load_data"] = f"{time2 - time1:0.3f}"
|
assert len(audio_sample_list) == 1, "batch_size must be set 1"
|
|
audio_sample = torch.cat((cache["prev_samples"], audio_sample_list[0]))
|
|
n = int(len(audio_sample) // chunk_stride_samples + int(_is_final))
|
m = int(len(audio_sample) % chunk_stride_samples * (1 - int(_is_final)))
|
tokens = []
|
for i in range(n):
|
kwargs["is_final"] = _is_final and i == n - 1
|
audio_sample_i = audio_sample[i * chunk_stride_samples : (i + 1) * chunk_stride_samples]
|
if kwargs["is_final"] and len(audio_sample_i) < 960:
|
cache["encoder"]["tail_chunk"] = True
|
speech = cache["encoder"]["feats"]
|
speech_lengths = torch.tensor([speech.shape[1]], dtype=torch.int64).to(
|
speech.device
|
)
|
else:
|
# extract fbank feats
|
speech, speech_lengths = extract_fbank(
|
[audio_sample_i],
|
data_type=kwargs.get("data_type", "sound"),
|
frontend=frontend,
|
cache=cache["frontend"],
|
is_final=kwargs["is_final"],
|
)
|
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
|
)
|
|
tokens_i = self.generate_chunk(
|
speech,
|
speech_lengths,
|
key=key,
|
tokenizer=tokenizer,
|
cache=cache,
|
frontend=frontend,
|
**kwargs,
|
)
|
tokens.extend(tokens_i)
|
|
text_postprocessed, _ = postprocess_utils.sentence_postprocess(tokens)
|
|
result_i = {"key": key[0], "text": text_postprocessed}
|
result = [result_i]
|
|
cache["prev_samples"] = audio_sample[:-m]
|
if _is_final:
|
self.init_cache(cache, **kwargs)
|
|
if kwargs.get("output_dir"):
|
if not hasattr(self, "writer"):
|
self.writer = DatadirWriter(kwargs.get("output_dir"))
|
ibest_writer = self.writer[f"{1}best_recog"]
|
ibest_writer["token"][key[0]] = " ".join(tokens)
|
ibest_writer["text"][key[0]] = text_postprocessed
|
|
return result, meta_data
|
|
def export(self, **kwargs):
|
from .export_meta import export_rebuild_model
|
|
models = export_rebuild_model(model=self, **kwargs)
|
return models
|
