598 lines
22 KiB
Python
598 lines
22 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 os
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import re
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import time
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import torch
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import codecs
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import logging
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import tempfile
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import requests
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import numpy as np
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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.utils import postprocess_utils
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from funasr.metrics.compute_acc import th_accuracy
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from funasr.models.paraformer.model import Paraformer
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from funasr.utils.datadir_writer import DatadirWriter
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from funasr.models.paraformer.search import Hypothesis
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from funasr.train_utils.device_funcs import force_gatherable
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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", "ContextualParaformer")
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class ContextualParaformer(Paraformer):
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"""
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Author: Speech Lab of DAMO Academy, Alibaba Group
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FunASR: A Fundamental End-to-End Speech Recognition Toolkit
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https://arxiv.org/abs/2305.11013
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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.target_buffer_length = kwargs.get("target_buffer_length", -1)
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inner_dim = kwargs.get("inner_dim", 256)
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bias_encoder_type = kwargs.get("bias_encoder_type", "lstm")
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use_decoder_embedding = kwargs.get("use_decoder_embedding", False)
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crit_attn_weight = kwargs.get("crit_attn_weight", 0.0)
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crit_attn_smooth = kwargs.get("crit_attn_smooth", 0.0)
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bias_encoder_dropout_rate = kwargs.get("bias_encoder_dropout_rate", 0.0)
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if bias_encoder_type == "lstm":
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self.bias_encoder = torch.nn.LSTM(
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inner_dim, inner_dim, 1, batch_first=True, dropout=bias_encoder_dropout_rate
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)
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self.bias_embed = torch.nn.Embedding(self.vocab_size, inner_dim)
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elif bias_encoder_type == "mean":
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self.bias_embed = torch.nn.Embedding(self.vocab_size, inner_dim)
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else:
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logging.error("Unsupport bias encoder type: {}".format(bias_encoder_type))
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if self.target_buffer_length > 0:
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self.hotword_buffer = None
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self.length_record = []
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self.current_buffer_length = 0
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self.use_decoder_embedding = use_decoder_embedding
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self.crit_attn_weight = crit_attn_weight
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if self.crit_attn_weight > 0:
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self.attn_loss = torch.nn.L1Loss()
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self.crit_attn_smooth = crit_attn_smooth
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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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text_lengths = text_lengths.squeeze()
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speech_lengths = speech_lengths.squeeze()
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batch_size = speech.shape[0]
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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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# 1. Encoder
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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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stats = dict()
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# 1. CTC branch
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if self.ctc_weight != 0.0:
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loss_ctc, cer_ctc = self._calc_ctc_loss(
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encoder_out, encoder_out_lens, text, text_lengths
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)
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# Collect CTC branch stats
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stats["loss_ctc"] = loss_ctc.detach() if loss_ctc is not None else None
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stats["cer_ctc"] = cer_ctc
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# 2b. Attention decoder branch
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loss_att, acc_att, cer_att, wer_att, loss_pre, loss_ideal = self._calc_att_clas_loss(
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encoder_out, encoder_out_lens, text, text_lengths, hotword_pad, hotword_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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if loss_ideal is not None:
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loss = loss + loss_ideal * self.crit_attn_weight
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stats["loss_ideal"] = loss_ideal.detach().cpu()
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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 = int((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 _calc_att_clas_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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hotword_pad: torch.Tensor,
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hotword_lengths: 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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pre_acoustic_embeds, pre_token_length, _, _ = self.predictor(
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encoder_out, ys_pad, encoder_out_mask, ignore_id=self.ignore_id
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)
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# -1. bias encoder
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if self.use_decoder_embedding:
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hw_embed = self.decoder.embed(hotword_pad)
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else:
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hw_embed = self.bias_embed(hotword_pad)
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hw_embed, (_, _) = self.bias_encoder(hw_embed)
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_ind = np.arange(0, hotword_pad.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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contextual_info = selected.squeeze(0).repeat(ys_pad.shape[0], 1, 1).to(ys_pad.device)
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# 0. sampler
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decoder_out_1st = None
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if self.sampling_ratio > 0.0:
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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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contextual_info,
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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,
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encoder_out_lens,
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sematic_embeds,
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ys_pad_lens,
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contextual_info=contextual_info,
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)
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decoder_out, _ = decoder_outs[0], decoder_outs[1]
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"""
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if self.crit_attn_weight > 0 and attn.shape[-1] > 1:
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ideal_attn = ideal_attn + self.crit_attn_smooth / (self.crit_attn_smooth + 1.0)
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attn_non_blank = attn[:,:,:,:-1]
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ideal_attn_non_blank = ideal_attn[:,:,:-1]
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loss_ideal = self.attn_loss(attn_non_blank.max(1)[0], ideal_attn_non_blank.to(attn.device))
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else:
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loss_ideal = None
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"""
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loss_ideal = None
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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, loss_ideal
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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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contextual_info,
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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 = 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]
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else:
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ys_pad_embed = self.decoder.embed(ys_pad)
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with torch.no_grad():
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decoder_outs = self.decoder(
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encoder_out,
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encoder_out_lens,
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pre_acoustic_embeds,
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ys_pad_lens,
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contextual_info=contextual_info,
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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,
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index=torch.randperm(seq_lens[li])[:target_num].to(
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pre_acoustic_embeds.device
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),
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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 cal_decoder_with_predictor(
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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=None,
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clas_scale=1.0,
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):
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if hw_list is None:
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hw_list = [torch.Tensor([1]).long().to(encoder_out.device)] # empty hotword list
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hw_list_pad = pad_list(hw_list, 0)
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if self.use_decoder_embedding:
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hw_embed = self.decoder.embed(hw_list_pad)
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else:
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hw_embed = self.bias_embed(hw_list_pad)
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hw_embed, (h_n, _) = self.bias_encoder(hw_embed)
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hw_embed = h_n.repeat(encoder_out.shape[0], 1, 1)
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else:
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hw_lengths = [len(i) for i in hw_list]
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hw_list_pad = pad_list([torch.Tensor(i).long() for i in hw_list], 0).to(
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encoder_out.device
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)
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if self.use_decoder_embedding:
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hw_embed = self.decoder.embed(hw_list_pad)
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else:
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hw_embed = self.bias_embed(hw_list_pad)
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hw_embed = torch.nn.utils.rnn.pack_padded_sequence(
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hw_embed, hw_lengths, batch_first=True, enforce_sorted=False
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)
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_, (h_n, _) = self.bias_encoder(hw_embed)
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hw_embed = h_n.repeat(encoder_out.shape[0], 1, 1)
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decoder_outs = self.decoder(
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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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contextual_info=hw_embed,
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clas_scale=clas_scale,
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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 inference(
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self,
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data_in,
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data_lengths=None,
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key: list = None,
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tokenizer=None,
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frontend=None,
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**kwargs,
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):
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# 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 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_text_image_video(
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data_in, fs=frontend.fs, audio_fs=kwargs.get("fs", 16000)
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)
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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(
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audio_sample_list, data_type=kwargs.get("data_type", "sound"), frontend=frontend
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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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speech = speech.to(device=kwargs["device"])
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speech_lengths = speech_lengths.to(device=kwargs["device"])
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# hotword
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self.hotword_list = self.generate_hotwords_list(
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kwargs.get("hotword", None), tokenizer=tokenizer, frontend=frontend
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)
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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, 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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decoder_outs = self.cal_decoder_with_predictor(
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encoder_out,
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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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clas_scale=kwargs.get("clas_scale", 1.0),
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)
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decoder_out, ys_pad_lens = 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,
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am_scores=am_scores,
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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([self.sos] + yseq.tolist() + [self.eos], device=yseq.device)
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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 kwargs.get("output_dir") is not None:
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if not hasattr(self, "writer"):
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self.writer = DatadirWriter(kwargs.get("output_dir"))
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ibest_writer = self.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(
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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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if tokenizer is not None:
|
|
# Change integer-ids to tokens
|
|
token = tokenizer.ids2tokens(token_int)
|
|
text = tokenizer.tokens2text(token)
|
|
|
|
text_postprocessed, _ = postprocess_utils.sentence_postprocess(token)
|
|
result_i = {"key": key[i], "text": text_postprocessed}
|
|
|
|
if ibest_writer is not None:
|
|
ibest_writer["token"][key[i]] = " ".join(token)
|
|
ibest_writer["text"][key[i]] = text
|
|
ibest_writer["text_postprocessed"][key[i]] = text_postprocessed
|
|
else:
|
|
result_i = {"key": key[i], "token_int": token_int}
|
|
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
|
|
|
|
def export(
|
|
self,
|
|
**kwargs,
|
|
):
|
|
if "max_seq_len" not in kwargs:
|
|
kwargs["max_seq_len"] = 512
|
|
from .export_meta import export_rebuild_model
|
|
|
|
models = export_rebuild_model(model=self, **kwargs)
|
|
return models
|