From 0871fa6e0d986115e3056878d2eec9dcac2ba43d Mon Sep 17 00:00:00 2001
From: 语帆 <yf352572@alibaba-inc.com>
Date: 星期四, 22 二月 2024 14:15:35 +0800
Subject: [PATCH] atsr
---
funasr/models/lcbnet/model.py | 454 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++
1 files changed, 454 insertions(+), 0 deletions(-)
diff --git a/funasr/models/lcbnet/model.py b/funasr/models/lcbnet/model.py
new file mode 100644
index 0000000..c68ccd7
--- /dev/null
+++ b/funasr/models/lcbnet/model.py
@@ -0,0 +1,454 @@
+import logging
+from typing import Union, Dict, List, Tuple, Optional
+
+import time
+import torch
+import torch.nn as nn
+from torch.cuda.amp import autocast
+
+from funasr.losses.label_smoothing_loss import LabelSmoothingLoss
+from funasr.models.ctc.ctc import CTC
+from funasr.models.transformer.utils.add_sos_eos import add_sos_eos
+from funasr.metrics.compute_acc import th_accuracy
+# from funasr.models.e2e_asr_common import ErrorCalculator
+from funasr.train_utils.device_funcs import force_gatherable
+from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank
+from funasr.utils import postprocess_utils
+from funasr.utils.datadir_writer import DatadirWriter
+from funasr.register import tables
+
+@tables.register("model_classes", "Transformer")
+class Transformer(nn.Module):
+ """CTC-attention hybrid Encoder-Decoder model"""
+
+
+ def __init__(
+ self,
+ specaug: str = None,
+ specaug_conf: dict = None,
+ normalize: str = None,
+ normalize_conf: dict = None,
+ encoder: str = None,
+ encoder_conf: dict = None,
+ decoder: str = None,
+ decoder_conf: dict = None,
+ ctc: str = None,
+ ctc_conf: dict = None,
+ ctc_weight: float = 0.5,
+ interctc_weight: float = 0.0,
+ input_size: int = 80,
+ vocab_size: int = -1,
+ ignore_id: int = -1,
+ blank_id: int = 0,
+ sos: int = 1,
+ eos: int = 2,
+ lsm_weight: float = 0.0,
+ length_normalized_loss: bool = False,
+ report_cer: bool = True,
+ report_wer: bool = True,
+ sym_space: str = "<space>",
+ sym_blank: str = "<blank>",
+ # extract_feats_in_collect_stats: bool = True,
+ share_embedding: bool = False,
+ # preencoder: Optional[AbsPreEncoder] = None,
+ # postencoder: Optional[AbsPostEncoder] = None,
+ **kwargs,
+ ):
+
+ super().__init__()
+
+ if specaug is not None:
+ specaug_class = tables.specaug_classes.get(specaug)
+ specaug = specaug_class(**specaug_conf)
+ if normalize is not None:
+ normalize_class = tables.normalize_classes.get(normalize)
+ normalize = normalize_class(**normalize_conf)
+ encoder_class = tables.encoder_classes.get(encoder)
+ encoder = encoder_class(input_size=input_size, **encoder_conf)
+ encoder_output_size = encoder.output_size()
+ if decoder is not None:
+ decoder_class = tables.decoder_classes.get(decoder)
+ decoder = decoder_class(
+ vocab_size=vocab_size,
+ encoder_output_size=encoder_output_size,
+ **decoder_conf,
+ )
+ if ctc_weight > 0.0:
+
+ if ctc_conf is None:
+ ctc_conf = {}
+
+ ctc = CTC(
+ odim=vocab_size, encoder_output_size=encoder_output_size, **ctc_conf
+ )
+
+ self.blank_id = blank_id
+ self.sos = sos if sos is not None else vocab_size - 1
+ self.eos = eos if eos is not None else vocab_size - 1
+ self.vocab_size = vocab_size
+ self.ignore_id = ignore_id
+ self.ctc_weight = ctc_weight
+ self.specaug = specaug
+ self.normalize = normalize
+ self.encoder = encoder
+
+ if not hasattr(self.encoder, "interctc_use_conditioning"):
+ self.encoder.interctc_use_conditioning = False
+ if self.encoder.interctc_use_conditioning:
+ self.encoder.conditioning_layer = torch.nn.Linear(
+ vocab_size, self.encoder.output_size()
+ )
+ self.interctc_weight = interctc_weight
+
+ # self.error_calculator = None
+ if ctc_weight == 1.0:
+ self.decoder = None
+ else:
+ self.decoder = decoder
+
+ self.criterion_att = LabelSmoothingLoss(
+ size=vocab_size,
+ padding_idx=ignore_id,
+ smoothing=lsm_weight,
+ normalize_length=length_normalized_loss,
+ )
+ #
+ # if report_cer or report_wer:
+ # self.error_calculator = ErrorCalculator(
+ # token_list, sym_space, sym_blank, report_cer, report_wer
+ # )
+ #
+ self.error_calculator = None
+ if ctc_weight == 0.0:
+ self.ctc = None
+ else:
+ self.ctc = ctc
+
+ self.share_embedding = share_embedding
+ if self.share_embedding:
+ self.decoder.embed = None
+
+ self.length_normalized_loss = length_normalized_loss
+ self.beam_search = None
+
+ def forward(
+ self,
+ speech: torch.Tensor,
+ speech_lengths: torch.Tensor,
+ text: torch.Tensor,
+ text_lengths: torch.Tensor,
+ **kwargs,
+ ) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]:
+ """Encoder + Decoder + Calc loss
+ Args:
+ speech: (Batch, Length, ...)
+ speech_lengths: (Batch, )
+ text: (Batch, Length)
+ text_lengths: (Batch,)
+ """
+ # import pdb;
+ # pdb.set_trace()
+ if len(text_lengths.size()) > 1:
+ text_lengths = text_lengths[:, 0]
+ if len(speech_lengths.size()) > 1:
+ speech_lengths = speech_lengths[:, 0]
+
+ batch_size = speech.shape[0]
+
+ # 1. Encoder
+ encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
+ intermediate_outs = None
+ if isinstance(encoder_out, tuple):
+ intermediate_outs = encoder_out[1]
+ encoder_out = encoder_out[0]
+
+ loss_att, acc_att, cer_att, wer_att = None, None, None, None
+ loss_ctc, cer_ctc = None, None
+ stats = dict()
+
+ # decoder: CTC branch
+ if self.ctc_weight != 0.0:
+ loss_ctc, cer_ctc = self._calc_ctc_loss(
+ encoder_out, encoder_out_lens, text, text_lengths
+ )
+
+ # Collect CTC branch stats
+ stats["loss_ctc"] = loss_ctc.detach() if loss_ctc is not None else None
+ stats["cer_ctc"] = cer_ctc
+
+ # Intermediate CTC (optional)
+ loss_interctc = 0.0
+ if self.interctc_weight != 0.0 and intermediate_outs is not None:
+ for layer_idx, intermediate_out in intermediate_outs:
+ # we assume intermediate_out has the same length & padding
+ # as those of encoder_out
+ loss_ic, cer_ic = self._calc_ctc_loss(
+ intermediate_out, encoder_out_lens, text, text_lengths
+ )
+ loss_interctc = loss_interctc + loss_ic
+
+ # Collect Intermedaite CTC stats
+ stats["loss_interctc_layer{}".format(layer_idx)] = (
+ loss_ic.detach() if loss_ic is not None else None
+ )
+ stats["cer_interctc_layer{}".format(layer_idx)] = cer_ic
+
+ loss_interctc = loss_interctc / len(intermediate_outs)
+
+ # calculate whole encoder loss
+ loss_ctc = (
+ 1 - self.interctc_weight
+ ) * loss_ctc + self.interctc_weight * loss_interctc
+
+ # decoder: Attention decoder branch
+ loss_att, acc_att, cer_att, wer_att = self._calc_att_loss(
+ encoder_out, encoder_out_lens, text, text_lengths
+ )
+
+ # 3. CTC-Att loss definition
+ if self.ctc_weight == 0.0:
+ loss = loss_att
+ elif self.ctc_weight == 1.0:
+ loss = loss_ctc
+ else:
+ loss = self.ctc_weight * loss_ctc + (1 - self.ctc_weight) * loss_att
+
+ # Collect Attn branch stats
+ stats["loss_att"] = loss_att.detach() if loss_att is not None else None
+ stats["acc"] = acc_att
+ stats["cer"] = cer_att
+ stats["wer"] = wer_att
+
+ # Collect total loss stats
+ stats["loss"] = torch.clone(loss.detach())
+
+ # force_gatherable: to-device and to-tensor if scalar for DataParallel
+ if self.length_normalized_loss:
+ batch_size = int((text_lengths + 1).sum())
+ loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
+ return loss, stats, weight
+
+
+ def encode(
+ self, speech: torch.Tensor, speech_lengths: torch.Tensor, **kwargs,
+ ) -> Tuple[torch.Tensor, torch.Tensor]:
+ """Frontend + Encoder. Note that this method is used by asr_inference.py
+ Args:
+ speech: (Batch, Length, ...)
+ speech_lengths: (Batch, )
+ ind: int
+ """
+ with autocast(False):
+
+ # Data augmentation
+ if self.specaug is not None and self.training:
+ speech, speech_lengths = self.specaug(speech, speech_lengths)
+
+ # Normalization for feature: e.g. Global-CMVN, Utterance-CMVN
+ if self.normalize is not None:
+ speech, speech_lengths = self.normalize(speech, speech_lengths)
+
+ # Forward encoder
+ # feats: (Batch, Length, Dim)
+ # -> encoder_out: (Batch, Length2, Dim2)
+ if self.encoder.interctc_use_conditioning:
+ encoder_out, encoder_out_lens, _ = self.encoder(
+ speech, speech_lengths, ctc=self.ctc
+ )
+ else:
+ encoder_out, encoder_out_lens, _ = self.encoder(speech, speech_lengths)
+ intermediate_outs = None
+ if isinstance(encoder_out, tuple):
+ intermediate_outs = encoder_out[1]
+ encoder_out = encoder_out[0]
+
+ if intermediate_outs is not None:
+ return (encoder_out, intermediate_outs), encoder_out_lens
+
+ return encoder_out, encoder_out_lens
+
+ def _calc_att_loss(
+ self,
+ encoder_out: torch.Tensor,
+ encoder_out_lens: torch.Tensor,
+ ys_pad: torch.Tensor,
+ ys_pad_lens: torch.Tensor,
+ ):
+ ys_in_pad, ys_out_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id)
+ ys_in_lens = ys_pad_lens + 1
+
+ # 1. Forward decoder
+ decoder_out, _ = self.decoder(
+ encoder_out, encoder_out_lens, ys_in_pad, ys_in_lens
+ )
+
+ # 2. Compute attention loss
+ loss_att = self.criterion_att(decoder_out, ys_out_pad)
+ acc_att = th_accuracy(
+ decoder_out.view(-1, self.vocab_size),
+ ys_out_pad,
+ ignore_label=self.ignore_id,
+ )
+
+ # Compute cer/wer using attention-decoder
+ if self.training or self.error_calculator is None:
+ cer_att, wer_att = None, None
+ else:
+ ys_hat = decoder_out.argmax(dim=-1)
+ cer_att, wer_att = self.error_calculator(ys_hat.cpu(), ys_pad.cpu())
+
+ return loss_att, acc_att, cer_att, wer_att
+
+ def _calc_ctc_loss(
+ self,
+ encoder_out: torch.Tensor,
+ encoder_out_lens: torch.Tensor,
+ ys_pad: torch.Tensor,
+ ys_pad_lens: torch.Tensor,
+ ):
+ # Calc CTC loss
+ loss_ctc = self.ctc(encoder_out, encoder_out_lens, ys_pad, ys_pad_lens)
+
+ # Calc CER using CTC
+ cer_ctc = None
+ if not self.training and self.error_calculator is not None:
+ ys_hat = self.ctc.argmax(encoder_out).data
+ cer_ctc = self.error_calculator(ys_hat.cpu(), ys_pad.cpu(), is_ctc=True)
+ return loss_ctc, cer_ctc
+
+ def init_beam_search(self,
+ **kwargs,
+ ):
+ from funasr.models.transformer.search import BeamSearch
+ from funasr.models.transformer.scorers.ctc import CTCPrefixScorer
+ from funasr.models.transformer.scorers.length_bonus import LengthBonus
+
+ # 1. Build ASR model
+ scorers = {}
+
+ if self.ctc != None:
+ ctc = CTCPrefixScorer(ctc=self.ctc, eos=self.eos)
+ scorers.update(
+ ctc=ctc
+ )
+ token_list = kwargs.get("token_list")
+ scorers.update(
+ decoder=self.decoder,
+ length_bonus=LengthBonus(len(token_list)),
+ )
+
+
+ # 3. Build ngram model
+ # ngram is not supported now
+ ngram = None
+ scorers["ngram"] = ngram
+
+ weights = dict(
+ decoder=1.0 - kwargs.get("decoding_ctc_weight", 0.5),
+ ctc=kwargs.get("decoding_ctc_weight", 0.5),
+ lm=kwargs.get("lm_weight", 0.0),
+ ngram=kwargs.get("ngram_weight", 0.0),
+ length_bonus=kwargs.get("penalty", 0.0),
+ )
+ beam_search = BeamSearch(
+ beam_size=kwargs.get("beam_size", 10),
+ weights=weights,
+ scorers=scorers,
+ sos=self.sos,
+ eos=self.eos,
+ vocab_size=len(token_list),
+ token_list=token_list,
+ pre_beam_score_key=None if self.ctc_weight == 1.0 else "full",
+ )
+
+ self.beam_search = beam_search
+
+ def inference(self,
+ data_in,
+ data_lengths=None,
+ key: list=None,
+ tokenizer=None,
+ frontend=None,
+ **kwargs,
+ ):
+
+ if kwargs.get("batch_size", 1) > 1:
+ raise NotImplementedError("batch decoding is not implemented")
+
+ # init beamsearch
+ if self.beam_search is None:
+ logging.info("enable beam_search")
+ self.init_beam_search(**kwargs)
+ self.nbest = kwargs.get("nbest", 1)
+
+ meta_data = {}
+ if isinstance(data_in, torch.Tensor) and kwargs.get("data_type", "sound") == "fbank": # fbank
+ speech, speech_lengths = data_in, data_lengths
+ if len(speech.shape) < 3:
+ speech = speech[None, :, :]
+ if speech_lengths is None:
+ speech_lengths = speech.shape[1]
+ else:
+ # extract fbank feats
+ time1 = time.perf_counter()
+ audio_sample_list = load_audio_text_image_video(data_in, fs=frontend.fs, audio_fs=kwargs.get("fs", 16000),
+ data_type=kwargs.get("data_type", "sound"),
+ tokenizer=tokenizer)
+ time2 = time.perf_counter()
+ meta_data["load_data"] = f"{time2 - time1:0.3f}"
+ speech, speech_lengths = extract_fbank(audio_sample_list, data_type=kwargs.get("data_type", "sound"),
+ frontend=frontend)
+ time3 = time.perf_counter()
+ meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
+ meta_data["batch_data_time"] = speech_lengths.sum().item() * frontend.frame_shift * frontend.lfr_n / 1000
+
+ speech = speech.to(device=kwargs["device"])
+ speech_lengths = speech_lengths.to(device=kwargs["device"])
+ # Encoder
+ encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
+ if isinstance(encoder_out, tuple):
+ encoder_out = encoder_out[0]
+
+ # c. Passed the encoder result and the beam search
+ nbest_hyps = self.beam_search(
+ x=encoder_out[0], maxlenratio=kwargs.get("maxlenratio", 0.0), minlenratio=kwargs.get("minlenratio", 0.0)
+ )
+
+ nbest_hyps = nbest_hyps[: self.nbest]
+
+
+ results = []
+ b, n, d = encoder_out.size()
+ for i in range(b):
+
+ for nbest_idx, hyp in enumerate(nbest_hyps):
+ ibest_writer = None
+ if kwargs.get("output_dir") is not None:
+ if not hasattr(self, "writer"):
+ self.writer = DatadirWriter(kwargs.get("output_dir"))
+ ibest_writer = self.writer[f"{nbest_idx + 1}best_recog"]
+
+ # 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)
+
+ text_postprocessed, _ = postprocess_utils.sentence_postprocess(token)
+ result_i = {"key": key[i], "token": token, "text": text_postprocessed}
+ results.append(result_i)
+
+ if ibest_writer is not None:
+ ibest_writer["token"][key[i]] = " ".join(token)
+ ibest_writer["text"][key[i]] = text_postprocessed
+
+ return results, meta_data
+
--
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