mirror of
https://github.com/modelscope/FunASR
synced 2025-09-15 14:48:36 +08:00
513 lines
19 KiB
Python
513 lines
19 KiB
Python
import os
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import logging
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from contextlib import contextmanager
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from distutils.version import LooseVersion
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from typing import Dict
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from typing import List
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from typing import Optional
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from typing import Tuple
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from typing import Union
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import tempfile
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import codecs
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import requests
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import re
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import copy
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import torch
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import torch.nn as nn
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import random
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import numpy as np
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import time
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# from funasr.layers.abs_normalize import AbsNormalize
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from funasr.losses.label_smoothing_loss import (
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LabelSmoothingLoss, # noqa: H301
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)
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# from funasr.models.ctc import CTC
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# from funasr.models.decoder.abs_decoder import AbsDecoder
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# from funasr.models.e2e_asr_common import ErrorCalculator
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# from funasr.models.encoder.abs_encoder import AbsEncoder
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# from funasr.frontends.abs_frontend import AbsFrontend
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# from funasr.models.postencoder.abs_postencoder import AbsPostEncoder
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from funasr.models.paraformer.cif_predictor import mae_loss
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# from funasr.models.preencoder.abs_preencoder import AbsPreEncoder
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# from funasr.models.specaug.abs_specaug import AbsSpecAug
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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.metrics.compute_acc import th_accuracy
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from funasr.train_utils.device_funcs import force_gatherable
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# from funasr.models.base_model import FunASRModel
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# from funasr.models.paraformer.cif_predictor import CifPredictorV3
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from funasr.models.paraformer.search import Hypothesis
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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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from funasr.datasets.audio_datasets.load_audio_extract_fbank import load_audio, extract_fbank
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from funasr.utils import postprocess_utils
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from funasr.utils.datadir_writer import DatadirWriter
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from funasr.models.paraformer.model import Paraformer
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from funasr.utils.register import register_class, registry_tables
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@register_class("model_classes", "SeacoParaformer")
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class SeacoParaformer(Paraformer):
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"""
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Author: Speech Lab of DAMO Academy, Alibaba Group
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SeACo-Paraformer: A Non-Autoregressive ASR System with Flexible and Effective Hotword Customization Ability
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https://arxiv.org/abs/2308.03266
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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.inner_dim = kwargs.get("inner_dim", 256)
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self.bias_encoder_type = kwargs.get("bias_encoder_type", "lstm")
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bias_encoder_dropout_rate = kwargs.get("bias_encoder_dropout_rate", 0.0)
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bias_encoder_bid = kwargs.get("bias_encoder_bid", False)
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seaco_lsm_weight = kwargs.get("seaco_lsm_weight", 0.0)
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seaco_length_normalized_loss = kwargs.get("seaco_length_normalized_loss", True)
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# bias encoder
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if self.bias_encoder_type == 'lstm':
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logging.warning("enable bias encoder sampling and contextual training")
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self.bias_encoder = torch.nn.LSTM(self.inner_dim,
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self.inner_dim,
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2,
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batch_first=True,
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dropout=bias_encoder_dropout_rate,
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bidirectional=bias_encoder_bid)
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if bias_encoder_bid:
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self.lstm_proj = torch.nn.Linear(self.inner_dim*2, self.inner_dim)
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else:
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self.lstm_proj = None
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self.bias_embed = torch.nn.Embedding(self.vocab_size, self.inner_dim)
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elif self.bias_encoder_type == 'mean':
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logging.warning("enable bias encoder sampling and contextual training")
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self.bias_embed = torch.nn.Embedding(self.vocab_size, self.inner_dim)
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else:
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logging.error("Unsupport bias encoder type: {}".format(self.bias_encoder_type))
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# seaco decoder
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seaco_decoder = kwargs.get("seaco_decoder", None)
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if seaco_decoder is not None:
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seaco_decoder_conf = kwargs.get("seaco_decoder_conf")
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seaco_decoder_class = registry_tables.decoder_classes.get(seaco_decoder.lower())
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self.seaco_decoder = seaco_decoder_class(
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vocab_size=self.vocab_size,
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encoder_output_size=self.inner_dim,
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**seaco_decoder_conf,
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)
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self.hotword_output_layer = torch.nn.Linear(self.inner_dim, self.vocab_size)
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self.criterion_seaco = LabelSmoothingLoss(
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size=self.vocab_size,
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padding_idx=self.ignore_id,
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smoothing=seaco_lsm_weight,
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normalize_length=seaco_length_normalized_loss,
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)
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self.train_decoder = kwargs.get("train_decoder", False)
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self.NO_BIAS = kwargs.get("NO_BIAS", 8377)
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def forward(
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self,
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speech: torch.Tensor,
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speech_lengths: torch.Tensor,
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text: torch.Tensor,
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text_lengths: torch.Tensor,
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**kwargs,
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) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]:
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"""Frontend + Encoder + Decoder + Calc loss
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Args:
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speech: (Batch, Length, ...)
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speech_lengths: (Batch, )
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text: (Batch, Length)
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text_lengths: (Batch,)
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"""
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assert text_lengths.dim() == 1, text_lengths.shape
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# Check that batch_size is unified
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assert (
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speech.shape[0]
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== speech_lengths.shape[0]
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== text.shape[0]
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== text_lengths.shape[0]
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), (speech.shape, speech_lengths.shape, text.shape, text_lengths.shape)
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hotword_pad = kwargs.get("hotword_pad")
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hotword_lengths = kwargs.get("hotword_lengths")
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dha_pad = kwargs.get("dha_pad")
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batch_size = speech.shape[0]
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self.step_cur += 1
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# for data-parallel
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text = text[:, : text_lengths.max()]
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speech = speech[:, :speech_lengths.max()]
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# 1. Encoder
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encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
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if self.predictor_bias == 1:
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_, ys_pad = add_sos_eos(text, self.sos, self.eos, self.ignore_id)
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ys_lengths = text_lengths + self.predictor_bias
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stats = dict()
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loss_seaco = self._calc_seaco_loss(encoder_out,
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encoder_out_lens,
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ys_pad,
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ys_lengths,
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hotword_pad,
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hotword_lengths,
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dha_pad,
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)
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if self.train_decoder:
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loss_att, acc_att = self._calc_att_loss(
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encoder_out, encoder_out_lens, text, text_lengths
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)
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loss = loss_seaco + loss_att
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stats["loss_att"] = torch.clone(loss_att.detach())
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stats["acc_att"] = acc_att
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else:
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loss = loss_seaco
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stats["loss_seaco"] = torch.clone(loss_seaco.detach())
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stats["loss"] = torch.clone(loss.detach())
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# force_gatherable: to-device and to-tensor if scalar for DataParallel
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if self.length_normalized_loss:
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batch_size = (text_lengths + self.predictor_bias).sum().type_as(batch_size)
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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 _merge(self, cif_attended, dec_attended):
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return cif_attended + dec_attended
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def _calc_seaco_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_lengths: torch.Tensor,
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hotword_pad: torch.Tensor,
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hotword_lengths: torch.Tensor,
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dha_pad: torch.Tensor,
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):
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# predictor forward
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encoder_out_mask = (~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]).to(
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encoder_out.device)
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pre_acoustic_embeds, _, _, _ = self.predictor(encoder_out, ys_pad, encoder_out_mask,
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ignore_id=self.ignore_id)
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# decoder forward
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decoder_out, _ = self.decoder(encoder_out, encoder_out_lens, pre_acoustic_embeds, ys_lengths, return_hidden=True)
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selected = self._hotword_representation(hotword_pad,
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hotword_lengths)
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contextual_info = selected.squeeze(0).repeat(encoder_out.shape[0], 1, 1).to(encoder_out.device)
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num_hot_word = contextual_info.shape[1]
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_contextual_length = torch.Tensor([num_hot_word]).int().repeat(encoder_out.shape[0]).to(encoder_out.device)
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# dha core
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cif_attended, _ = self.seaco_decoder(contextual_info, _contextual_length, pre_acoustic_embeds, ys_lengths)
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dec_attended, _ = self.seaco_decoder(contextual_info, _contextual_length, decoder_out, ys_lengths)
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merged = self._merge(cif_attended, dec_attended)
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dha_output = self.hotword_output_layer(merged[:, :-1]) # remove the last token in loss calculation
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loss_att = self.criterion_seaco(dha_output, dha_pad)
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return loss_att
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def _seaco_decode_with_ASF(self,
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encoder_out,
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encoder_out_lens,
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sematic_embeds,
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ys_pad_lens,
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hw_list,
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nfilter=50,
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seaco_weight=1.0):
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# decoder forward
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decoder_out, decoder_hidden, _ = self.decoder(encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens, return_hidden=True, return_both=True)
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decoder_pred = torch.log_softmax(decoder_out, dim=-1)
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if hw_list is not None:
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hw_lengths = [len(i) for i in hw_list]
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hw_list_ = [torch.Tensor(i).long() for i in hw_list]
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hw_list_pad = pad_list(hw_list_, 0).to(encoder_out.device)
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selected = self._hotword_representation(hw_list_pad, torch.Tensor(hw_lengths).int().to(encoder_out.device))
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contextual_info = selected.squeeze(0).repeat(encoder_out.shape[0], 1, 1).to(encoder_out.device)
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num_hot_word = contextual_info.shape[1]
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_contextual_length = torch.Tensor([num_hot_word]).int().repeat(encoder_out.shape[0]).to(encoder_out.device)
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# ASF Core
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if nfilter > 0 and nfilter < num_hot_word:
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for dec in self.seaco_decoder.decoders:
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dec.reserve_attn = True
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# cif_attended, _ = self.decoder2(contextual_info, _contextual_length, sematic_embeds, ys_pad_lens)
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dec_attended, _ = self.seaco_decoder(contextual_info, _contextual_length, decoder_hidden, ys_pad_lens)
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# cif_filter = torch.topk(self.decoder2.decoders[-1].attn_mat[0][0].sum(0).sum(0)[:-1], min(nfilter, num_hot_word-1))[1].tolist()
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hotword_scores = self.seaco_decoder.decoders[-1].attn_mat[0][0].sum(0).sum(0)[:-1]
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# hotword_scores /= torch.sqrt(torch.tensor(hw_lengths)[:-1].float()).to(hotword_scores.device)
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dec_filter = torch.topk(hotword_scores, min(nfilter, num_hot_word-1))[1].tolist()
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add_filter = dec_filter
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add_filter.append(len(hw_list_pad)-1)
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# filter hotword embedding
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selected = selected[add_filter]
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# again
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contextual_info = selected.squeeze(0).repeat(encoder_out.shape[0], 1, 1).to(encoder_out.device)
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num_hot_word = contextual_info.shape[1]
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_contextual_length = torch.Tensor([num_hot_word]).int().repeat(encoder_out.shape[0]).to(encoder_out.device)
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for dec in self.seaco_decoder.decoders:
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dec.attn_mat = []
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dec.reserve_attn = False
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# SeACo Core
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cif_attended, _ = self.seaco_decoder(contextual_info, _contextual_length, sematic_embeds, ys_pad_lens)
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dec_attended, _ = self.seaco_decoder(contextual_info, _contextual_length, decoder_hidden, ys_pad_lens)
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merged = self._merge(cif_attended, dec_attended)
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dha_output = self.hotword_output_layer(merged) # remove the last token in loss calculation
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dha_pred = torch.log_softmax(dha_output, dim=-1)
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# import pdb; pdb.set_trace()
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def _merge_res(dec_output, dha_output):
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lmbd = torch.Tensor([seaco_weight] * dha_output.shape[0])
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dha_ids = dha_output.max(-1)[-1][0]
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dha_mask = (dha_ids == 8377).int().unsqueeze(-1)
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a = (1 - lmbd) / lmbd
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b = 1 / lmbd
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a, b = a.to(dec_output.device), b.to(dec_output.device)
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dha_mask = (dha_mask + a.reshape(-1, 1, 1)) / b.reshape(-1, 1, 1)
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# logits = dec_output * dha_mask + dha_output[:,:,:-1] * (1-dha_mask)
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logits = dec_output * dha_mask + dha_output[:,:,:] * (1-dha_mask)
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return logits
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merged_pred = _merge_res(decoder_pred, dha_pred)
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return merged_pred
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else:
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return decoder_pred
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def _hotword_representation(self,
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hotword_pad,
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hotword_lengths):
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if self.bias_encoder_type != 'lstm':
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logging.error("Unsupported bias encoder type")
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hw_embed = self.decoder.embed(hotword_pad)
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hw_embed, (_, _) = self.bias_encoder(hw_embed)
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if self.lstm_proj is not None:
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hw_embed = self.lstm_proj(hw_embed)
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_ind = np.arange(0, hw_embed.shape[0]).tolist()
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selected = hw_embed[_ind, [i-1 for i in hotword_lengths.detach().cpu().tolist()]]
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return selected
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def generate(self,
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data_in,
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data_lengths=None,
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key: list = None,
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tokenizer=None,
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frontend=None,
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**kwargs,
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):
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# 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 = kwargs.get("lm_weight", 0.0) > 0.00001 and kwargs.get("lm_file", None) is not None
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if self.beam_search is None and (is_use_lm or is_use_ctc):
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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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meta_data = {}
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# extract fbank feats
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time1 = time.perf_counter()
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audio_sample_list = load_audio(data_in, fs=frontend.fs, audio_fs=kwargs.get("fs", 16000))
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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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speech, speech_lengths = extract_fbank(audio_sample_list, data_type=kwargs.get("data_type", "sound"),
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frontend=frontend)
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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[
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"batch_data_time"] = speech_lengths.sum().item() * frontend.frame_shift * frontend.lfr_n / 1000
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speech.to(device=kwargs["device"]), speech_lengths.to(device=kwargs["device"])
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# hotword
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self.hotword_list = self.generate_hotwords_list(kwargs.get("hotword", None), tokenizer=tokenizer, frontend=frontend)
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# Encoder
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encoder_out, encoder_out_lens = self.encode(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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# predictor
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predictor_outs = self.calc_predictor(encoder_out, encoder_out_lens)
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pre_acoustic_embeds, pre_token_length, _, _ = predictor_outs[0], predictor_outs[1], \
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predictor_outs[2], predictor_outs[3]
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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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decoder_out = self._seaco_decode_with_ASF(encoder_out, encoder_out_lens,
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pre_acoustic_embeds,
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pre_token_length,
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hw_list=self.hotword_list)
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# decoder_out, _ = decoder_outs[0], decoder_outs[1]
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results = []
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b, n, d = decoder_out.size()
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for i in range(b):
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x = encoder_out[i, :encoder_out_lens[i], :]
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am_scores = decoder_out[i, :pre_token_length[i], :]
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if self.beam_search is not None:
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nbest_hyps = self.beam_search(
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x=x, am_scores=am_scores, maxlenratio=kwargs.get("maxlenratio", 0.0),
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minlenratio=kwargs.get("minlenratio", 0.0)
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)
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nbest_hyps = nbest_hyps[: self.nbest]
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else:
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yseq = am_scores.argmax(dim=-1)
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score = am_scores.max(dim=-1)[0]
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score = torch.sum(score, dim=-1)
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# pad with mask tokens to ensure compatibility with sos/eos tokens
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yseq = torch.tensor(
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[self.sos] + yseq.tolist() + [self.eos], device=yseq.device
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)
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nbest_hyps = [Hypothesis(yseq=yseq, score=score)]
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for nbest_idx, hyp in enumerate(nbest_hyps):
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ibest_writer = None
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if ibest_writer is None and kwargs.get("output_dir") is not None:
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writer = DatadirWriter(kwargs.get("output_dir"))
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ibest_writer = writer[f"{nbest_idx + 1}best_recog"]
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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(lambda x: x != self.eos and x != self.sos and x != self.blank_id, token_int))
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if tokenizer is not None:
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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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text_postprocessed, _ = postprocess_utils.sentence_postprocess(token)
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result_i = {"key": key[i], "token": token, "text": text, "text_postprocessed": text_postprocessed}
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if ibest_writer is not None:
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ibest_writer["token"][key[i]] = " ".join(token)
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ibest_writer["text"][key[i]] = text
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ibest_writer["text_postprocessed"][key[i]] = text_postprocessed
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else:
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result_i = {"key": key[i], "token_int": token_int}
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|
results.append(result_i)
|
|
|
|
return results, meta_data
|
|
|
|
|
|
def generate_hotwords_list(self, hotword_list_or_file, tokenizer=None, frontend=None):
|
|
def load_seg_dict(seg_dict_file):
|
|
seg_dict = {}
|
|
assert isinstance(seg_dict_file, str)
|
|
with open(seg_dict_file, "r", encoding="utf8") as f:
|
|
lines = f.readlines()
|
|
for line in lines:
|
|
s = line.strip().split()
|
|
key = s[0]
|
|
value = s[1:]
|
|
seg_dict[key] = " ".join(value)
|
|
return seg_dict
|
|
|
|
def seg_tokenize(txt, seg_dict):
|
|
pattern = re.compile(r'^[\u4E00-\u9FA50-9]+$')
|
|
out_txt = ""
|
|
for word in txt:
|
|
word = word.lower()
|
|
if word in seg_dict:
|
|
out_txt += seg_dict[word] + " "
|
|
else:
|
|
if pattern.match(word):
|
|
for char in word:
|
|
if char in seg_dict:
|
|
out_txt += seg_dict[char] + " "
|
|
else:
|
|
out_txt += "<unk>" + " "
|
|
else:
|
|
out_txt += "<unk>" + " "
|
|
return out_txt.strip().split()
|
|
|
|
seg_dict = None
|
|
if frontend.cmvn_file is not None:
|
|
model_dir = os.path.dirname(frontend.cmvn_file)
|
|
seg_dict_file = os.path.join(model_dir, 'seg_dict')
|
|
if os.path.exists(seg_dict_file):
|
|
seg_dict = load_seg_dict(seg_dict_file)
|
|
else:
|
|
seg_dict = None
|
|
# for None
|
|
if hotword_list_or_file is None:
|
|
hotword_list = None
|
|
# for local txt inputs
|
|
elif os.path.exists(hotword_list_or_file) and hotword_list_or_file.endswith('.txt'):
|
|
logging.info("Attempting to parse hotwords from local txt...")
|
|
hotword_list = []
|
|
hotword_str_list = []
|
|
with codecs.open(hotword_list_or_file, 'r') as fin:
|
|
for line in fin.readlines():
|
|
hw = line.strip()
|
|
hw_list = hw.split()
|
|
if seg_dict is not None:
|
|
hw_list = seg_tokenize(hw_list, seg_dict)
|
|
hotword_str_list.append(hw)
|
|
hotword_list.append(tokenizer.tokens2ids(hw_list))
|
|
hotword_list.append([self.sos])
|
|
hotword_str_list.append('<s>')
|
|
logging.info("Initialized hotword list from file: {}, hotword list: {}."
|
|
.format(hotword_list_or_file, hotword_str_list))
|
|
# for url, download and generate txt
|
|
elif hotword_list_or_file.startswith('http'):
|
|
logging.info("Attempting to parse hotwords from url...")
|
|
work_dir = tempfile.TemporaryDirectory().name
|
|
if not os.path.exists(work_dir):
|
|
os.makedirs(work_dir)
|
|
text_file_path = os.path.join(work_dir, os.path.basename(hotword_list_or_file))
|
|
local_file = requests.get(hotword_list_or_file)
|
|
open(text_file_path, "wb").write(local_file.content)
|
|
hotword_list_or_file = text_file_path
|
|
hotword_list = []
|
|
hotword_str_list = []
|
|
with codecs.open(hotword_list_or_file, 'r') as fin:
|
|
for line in fin.readlines():
|
|
hw = line.strip()
|
|
hw_list = hw.split()
|
|
if seg_dict is not None:
|
|
hw_list = seg_tokenize(hw_list, seg_dict)
|
|
hotword_str_list.append(hw)
|
|
hotword_list.append(tokenizer.tokens2ids(hw_list))
|
|
hotword_list.append([self.sos])
|
|
hotword_str_list.append('<s>')
|
|
logging.info("Initialized hotword list from file: {}, hotword list: {}."
|
|
.format(hotword_list_or_file, hotword_str_list))
|
|
# for text str input
|
|
elif not hotword_list_or_file.endswith('.txt'):
|
|
logging.info("Attempting to parse hotwords as str...")
|
|
hotword_list = []
|
|
hotword_str_list = []
|
|
for hw in hotword_list_or_file.strip().split():
|
|
hotword_str_list.append(hw)
|
|
hw_list = hw.strip().split()
|
|
if seg_dict is not None:
|
|
hw_list = seg_tokenize(hw_list, seg_dict)
|
|
hotword_list.append(tokenizer.tokens2ids(hw_list))
|
|
hotword_list.append([self.sos])
|
|
hotword_str_list.append('<s>')
|
|
logging.info("Hotword list: {}.".format(hotword_str_list))
|
|
else:
|
|
hotword_list = None
|
|
return hotword_list
|
|
|