Fix the variable name (#2328)

majic31

2024-12-24 23e7ddebccd3b05cf7ef89809bcfe565ad6dfa1f

 funasr/models/transducer/model.py

@@ -1,42 +1,26 @@
#!/usr/bin/env python3
# -*- encoding: utf-8 -*-
# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
#  MIT License  (https://opensource.org/licenses/MIT)

import time
import torch
import logging
from contextlib import contextmanager
from typing import Dict, Optional, Tuple
from distutils.version import LooseVersion
from typing import Dict
from typing import List
from typing import Optional
from typing import Tuple
from typing import Union
import tempfile
import codecs
import requests
import re
import copy
import torch
import torch.nn as nn
import random
import numpy as np
import time
from funasr.losses.label_smoothing_loss import (
    LabelSmoothingLoss,  # noqa: H301
)
# from funasr.models.ctc import CTC
# from funasr.models.decoder.abs_decoder import AbsDecoder
# from funasr.models.e2e_asr_common import ErrorCalculator
# from funasr.models.encoder.abs_encoder import AbsEncoder
# from funasr.frontends.abs_frontend import AbsFrontend
# from funasr.models.postencoder.abs_postencoder import AbsPostEncoder
from funasr.models.paraformer.cif_predictor import mae_loss
# from funasr.models.preencoder.abs_preencoder import AbsPreEncoder
# from funasr.models.specaug.abs_specaug import AbsSpecAug
from funasr.models.transformer.utils.add_sos_eos import add_sos_eos
from funasr.models.transformer.utils.nets_utils import make_pad_mask, pad_list
from funasr.metrics.compute_acc import th_accuracy
from funasr.train_utils.device_funcs import force_gatherable
# from funasr.models.base_model import FunASRModel
# from funasr.models.paraformer.cif_predictor import CifPredictorV3
from funasr.models.paraformer.search import Hypothesis

from funasr.models.model_class_factory import *
from funasr.register import tables
from funasr.utils import postprocess_utils
from funasr.utils.datadir_writer import DatadirWriter
from funasr.train_utils.device_funcs import force_gatherable
from funasr.models.transformer.scorers.ctc import CTCPrefixScorer
from funasr.losses.label_smoothing_loss import LabelSmoothingLoss
from funasr.models.transformer.scorers.length_bonus import LengthBonus
from funasr.models.transformer.utils.nets_utils import get_transducer_task_io
from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank
from funasr.models.transducer.beam_search_transducer import BeamSearchTransducer


if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"):
    from torch.cuda.amp import autocast
@@ -45,16 +29,10 @@
    @contextmanager
    def autocast(enabled=True):
        yield
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.models.transformer.utils.nets_utils import get_transducer_task_io


class Transducer(nn.Module):
    """ESPnet2ASRTransducerModel module definition."""

    
@tables.register("model_classes", "Transducer")
class Transducer(torch.nn.Module):
    def __init__(
        self,
        frontend: Optional[str] = None,
@@ -96,53 +74,48 @@

        super().__init__()

        if frontend is not None:
            frontend_class = frontend_classes.get_class(frontend)
            frontend = frontend_class(**frontend_conf)
        if specaug is not None:
            specaug_class = specaug_classes.get_class(specaug)
            specaug_class = tables.specaug_classes.get(specaug)
            specaug = specaug_class(**specaug_conf)
        if normalize is not None:
            normalize_class = normalize_classes.get_class(normalize)
            normalize_class = tables.normalize_classes.get(normalize)
            normalize = normalize_class(**normalize_conf)
        encoder_class = encoder_classes.get_class(encoder)
        encoder_class = tables.encoder_classes.get(encoder)
        encoder = encoder_class(input_size=input_size, **encoder_conf)
        encoder_output_size = encoder.output_size()

        decoder_class = decoder_classes.get_class(decoder)
        decoder_class = tables.decoder_classes.get(decoder)
        decoder = decoder_class(
            vocab_size=vocab_size,
            encoder_output_size=encoder_output_size,
            **decoder_conf,
        )
        decoder_output_size = decoder.output_size

        joint_network_class = joint_network_classes.get_class(decoder)
        joint_network_class = tables.joint_network_classes.get(joint_network)
        joint_network = joint_network_class(
            vocab_size,
            encoder_output_size,
            decoder_output_size,
            **joint_network_conf,
        )
        
        

        self.criterion_transducer = None
        self.error_calculator = None
        

        self.use_auxiliary_ctc = auxiliary_ctc_weight > 0
        self.use_auxiliary_lm_loss = auxiliary_lm_loss_weight > 0
        

        if self.use_auxiliary_ctc:
            self.ctc_lin = torch.nn.Linear(encoder.output_size(), vocab_size)
            self.ctc_dropout_rate = auxiliary_ctc_dropout_rate
        

        if self.use_auxiliary_lm_loss:
            self.lm_lin = torch.nn.Linear(decoder.output_size, vocab_size)
            self.lm_loss_smoothing = auxiliary_lm_loss_smoothing
        

        self.transducer_weight = transducer_weight
        self.fastemit_lambda = fastemit_lambda
        

        self.auxiliary_ctc_weight = auxiliary_ctc_weight
        self.auxiliary_lm_loss_weight = auxiliary_lm_loss_weight
        self.blank_id = blank_id
@@ -157,24 +130,18 @@
        self.decoder = decoder
        self.joint_network = joint_network


        
        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.length_normalized_loss = length_normalized_loss
        self.beam_search = None
    
        self.ctc = None
        self.ctc_weight = 0.0

    def forward(
        self,
        speech: torch.Tensor,
@@ -190,35 +157,35 @@
                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)
        if hasattr(self.encoder, 'overlap_chunk_cls') and self.encoder.overlap_chunk_cls is not None:
            encoder_out, encoder_out_lens = self.encoder.overlap_chunk_cls.remove_chunk(encoder_out, encoder_out_lens,
                                                                                        chunk_outs=None)
        if (
            hasattr(self.encoder, "overlap_chunk_cls")
            and self.encoder.overlap_chunk_cls is not None
        ):
            encoder_out, encoder_out_lens = self.encoder.overlap_chunk_cls.remove_chunk(
                encoder_out, encoder_out_lens, chunk_outs=None
            )
        # 2. Transducer-related I/O preparation
        decoder_in, target, t_len, u_len = get_transducer_task_io(
            text,
            encoder_out_lens,
            ignore_id=self.ignore_id,
        )
        

        # 3. Decoder
        self.decoder.set_device(encoder_out.device)
        decoder_out = self.decoder(decoder_in, u_len)
        

        # 4. Joint Network
        joint_out = self.joint_network(
            encoder_out.unsqueeze(2), decoder_out.unsqueeze(1)
        )
        
        joint_out = self.joint_network(encoder_out.unsqueeze(2), decoder_out.unsqueeze(1))

        # 5. Losses
        loss_trans, cer_trans, wer_trans = self._calc_transducer_loss(
            encoder_out,
@@ -227,9 +194,9 @@
            t_len,
            u_len,
        )
        

        loss_ctc, loss_lm = 0.0, 0.0
        

        if self.use_auxiliary_ctc:
            loss_ctc = self._calc_ctc_loss(
                encoder_out,
@@ -237,16 +204,16 @@
                t_len,
                u_len,
            )
        

        if self.use_auxiliary_lm_loss:
            loss_lm = self._calc_lm_loss(decoder_out, target)
        

        loss = (
            self.transducer_weight * loss_trans
            + self.auxiliary_ctc_weight * loss_ctc
            + self.auxiliary_lm_loss_weight * loss_lm
        )
        

        stats = dict(
            loss=loss.detach(),
            loss_transducer=loss_trans.detach(),
@@ -255,14 +222,17 @@
            cer_transducer=cer_trans,
            wer_transducer=wer_trans,
        )
        

        # force_gatherable: to-device and to-tensor if scalar for DataParallel
        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,
        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:
@@ -275,30 +245,25 @@
            # 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)
        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_transducer_loss(
        self,
        encoder_out: torch.Tensor,
@@ -325,17 +290,17 @@
        if self.criterion_transducer is None:
            try:
                from warp_rnnt import rnnt_loss as RNNTLoss

                self.criterion_transducer = RNNTLoss
            

            except ImportError:
                logging.error(
                    "warp-rnnt was not installed."
                    "Please consult the installation documentation."
                    "warp-rnnt was not installed." "Please consult the installation documentation."
                )
                exit(1)
        

        log_probs = torch.log_softmax(joint_out, dim=-1)
        

        loss_transducer = self.criterion_transducer(
            log_probs,
            target,
@@ -346,11 +311,11 @@
            fastemit_lambda=self.fastemit_lambda,
            gather=True,
        )
        

        if not self.training and (self.report_cer or self.report_wer):
            if self.error_calculator is None:
                from funasr.metrics import ErrorCalculatorTransducer as ErrorCalculator
                

                self.error_calculator = ErrorCalculator(
                    self.decoder,
                    self.joint_network,
@@ -360,13 +325,13 @@
                    report_cer=self.report_cer,
                    report_wer=self.report_wer,
                )
            

            cer_transducer, wer_transducer = self.error_calculator(encoder_out, target, t_len)
            

            return loss_transducer, cer_transducer, wer_transducer
        

        return loss_transducer, None, None
    

    def _calc_ctc_loss(
        self,
        encoder_out: torch.Tensor,
@@ -386,14 +351,12 @@
            loss_ctc: CTC loss value.

        """
        ctc_in = self.ctc_lin(
            torch.nn.functional.dropout(encoder_out, p=self.ctc_dropout_rate)
        )
        ctc_in = self.ctc_lin(torch.nn.functional.dropout(encoder_out, p=self.ctc_dropout_rate))
        ctc_in = torch.log_softmax(ctc_in.transpose(0, 1), dim=-1)
        

        target_mask = target != 0
        ctc_target = target[target_mask].cpu()
        

        with torch.backends.cudnn.flags(deterministic=True):
            loss_ctc = torch.nn.functional.ctc_loss(
                ctc_in,
@@ -404,9 +367,9 @@
                reduction="sum",
            )
        loss_ctc /= target.size(0)
        

        return loss_ctc
    

    def _calc_lm_loss(
        self,
        decoder_out: torch.Tensor,
@@ -424,107 +387,98 @@
        """
        lm_loss_in = self.lm_lin(decoder_out[:, :-1, :]).view(-1, self.vocab_size)
        lm_target = target.view(-1).type(torch.int64)
        

        with torch.no_grad():
            true_dist = lm_loss_in.clone()
            true_dist.fill_(self.lm_loss_smoothing / (self.vocab_size - 1))
            

            # Ignore blank ID (0)
            ignore = lm_target == 0
            lm_target = lm_target.masked_fill(ignore, 0)
            

            true_dist.scatter_(1, lm_target.unsqueeze(1), (1 - self.lm_loss_smoothing))
        

        loss_lm = torch.nn.functional.kl_div(
            torch.log_softmax(lm_loss_in, dim=1),
            true_dist,
            reduction="none",
        )
        loss_lm = loss_lm.masked_fill(ignore.unsqueeze(1), 0).sum() / decoder_out.size(
            0
        )
        
        loss_lm = loss_lm.masked_fill(ignore.unsqueeze(1), 0).sum() / decoder_out.size(0)

        return loss_lm
    
    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
    

    def init_beam_search(
        self,
        **kwargs,
    ):

        # 1. Build ASR model
        scorers = {}
        

        if self.ctc != None:
            ctc = CTCPrefixScorer(ctc=self.ctc, eos=self.eos)
            scorers.update(
                ctc=ctc
            )
            scorers.update(ctc=ctc)
        token_list = kwargs.get("token_list")
        scorers.update(
            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"),
            ctc=kwargs.get("decoding_ctc_weight", 0.0),
            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", 2),
            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",

        beam_search = BeamSearchTransducer(
            self.decoder,
            self.joint_network,
            kwargs.get("beam_size", 2),
            nbest=1,
        )
        # beam_search.to(device=kwargs.get("device", "cpu"), dtype=getattr(torch, kwargs.get("dtype", "float32"))).eval()
        # for scorer in scorers.values():
        #     if isinstance(scorer, torch.nn.Module):
        #         scorer.to(device=kwargs.get("device", "cpu"), dtype=getattr(torch, kwargs.get("dtype", "float32"))).eval()
        self.beam_search = beam_search
        
    def generate(self,
             data_in: list,
             data_lengths: list=None,
             key: list=None,
             tokenizer=None,
             **kwargs,
             ):
        

    def inference(
        self,
        data_in: list,
        data_lengths: list = None,
        key: list = None,
        tokenizer=None,
        **kwargs,
    ):

        if kwargs.get("batch_size", 1) > 1:
            raise NotImplementedError("batch decoding is not implemented")
        

        # 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)
        
        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)

        meta_data = {}
        # extract fbank feats
        time1 = time.perf_counter()
        audio_sample_list = load_audio_text_image_video(data_in, fs=self.frontend.fs, audio_fs=kwargs.get("fs", 16000))
        audio_sample_list = load_audio_text_image_video(
            data_in, fs=self.frontend.fs, audio_fs=kwargs.get("fs", 16000)
        )
        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=self.frontend)
        speech, speech_lengths = extract_fbank(
            audio_sample_list, data_type=kwargs.get("data_type", "sound"), frontend=self.frontend
        )
        time3 = time.perf_counter()
        meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
        meta_data["batch_data_time"] = speech_lengths.sum().item() * self.frontend.frame_shift * self.frontend.lfr_n / 1000
        
        meta_data["batch_data_time"] = (
            speech_lengths.sum().item() * self.frontend.frame_shift * self.frontend.lfr_n / 1000
        )

        speech = speech.to(device=kwargs["device"])
        speech_lengths = speech_lengths.to(device=kwargs["device"])

@@ -532,14 +486,10 @@
        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]

        # c. Passed the encoder result and the beam search
        nbest_hyps = self.beam_search(encoder_out[0], is_final=True)
        nbest_hyps = nbest_hyps[: self.nbest]

        results = []
        b, n, d = encoder_out.size()
@@ -547,31 +497,40 @@

            for nbest_idx, hyp in enumerate(nbest_hyps):
                ibest_writer = None
                if ibest_writer is None and kwargs.get("output_dir") is not None:
                    writer = DatadirWriter(kwargs.get("output_dir"))
                    ibest_writer = writer[f"{nbest_idx+1}best_recog"]
                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]
                    token_int = hyp.yseq  # [1:last_pos]
                else:
                    token_int = hyp.yseq[1:last_pos].tolist()
                    
                    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))
                
                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, "text_postprocessed": text_postprocessed}
                result_i = {
                    "key": key[i],
                    "token": token,
                    "text": text,
                    "text_postprocessed": 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
                    ibest_writer["text_postprocessed"][key[i]] = text_postprocessed
        
        return results, meta_data

        return results, meta_data