mirror of
https://github.com/modelscope/FunASR
synced 2025-09-15 14:48:36 +08:00
578 lines
21 KiB
Python
578 lines
21 KiB
Python
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.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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from funasr.models.model_class_factory import *
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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.utils.load_utils import load_audio_text_image_video, 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.transformer.utils.nets_utils import get_transducer_task_io
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class Transducer(nn.Module):
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"""ESPnet2ASRTransducerModel module definition."""
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def __init__(
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self,
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frontend: Optional[str] = None,
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frontend_conf: Optional[Dict] = None,
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specaug: Optional[str] = None,
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specaug_conf: Optional[Dict] = None,
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normalize: str = None,
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normalize_conf: Optional[Dict] = None,
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encoder: str = None,
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encoder_conf: Optional[Dict] = None,
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decoder: str = None,
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decoder_conf: Optional[Dict] = None,
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joint_network: str = None,
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joint_network_conf: Optional[Dict] = None,
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transducer_weight: float = 1.0,
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fastemit_lambda: float = 0.0,
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auxiliary_ctc_weight: float = 0.0,
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auxiliary_ctc_dropout_rate: float = 0.0,
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auxiliary_lm_loss_weight: float = 0.0,
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auxiliary_lm_loss_smoothing: float = 0.0,
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input_size: int = 80,
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vocab_size: int = -1,
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ignore_id: int = -1,
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blank_id: int = 0,
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sos: int = 1,
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eos: int = 2,
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lsm_weight: float = 0.0,
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length_normalized_loss: bool = False,
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# report_cer: bool = True,
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# report_wer: bool = True,
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# sym_space: str = "<space>",
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# sym_blank: str = "<blank>",
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# extract_feats_in_collect_stats: bool = True,
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share_embedding: bool = False,
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# preencoder: Optional[AbsPreEncoder] = None,
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# postencoder: Optional[AbsPostEncoder] = None,
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**kwargs,
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):
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super().__init__()
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if frontend is not None:
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frontend_class = frontend_classes.get_class(frontend)
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frontend = frontend_class(**frontend_conf)
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if specaug is not None:
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specaug_class = specaug_classes.get_class(specaug)
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specaug = specaug_class(**specaug_conf)
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if normalize is not None:
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normalize_class = normalize_classes.get_class(normalize)
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normalize = normalize_class(**normalize_conf)
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encoder_class = encoder_classes.get_class(encoder)
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encoder = encoder_class(input_size=input_size, **encoder_conf)
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encoder_output_size = encoder.output_size()
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decoder_class = decoder_classes.get_class(decoder)
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decoder = decoder_class(
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vocab_size=vocab_size,
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encoder_output_size=encoder_output_size,
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**decoder_conf,
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)
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decoder_output_size = decoder.output_size
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joint_network_class = joint_network_classes.get_class(decoder)
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joint_network = joint_network_class(
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vocab_size,
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encoder_output_size,
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decoder_output_size,
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**joint_network_conf,
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)
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self.criterion_transducer = None
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self.error_calculator = None
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self.use_auxiliary_ctc = auxiliary_ctc_weight > 0
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self.use_auxiliary_lm_loss = auxiliary_lm_loss_weight > 0
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if self.use_auxiliary_ctc:
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self.ctc_lin = torch.nn.Linear(encoder.output_size(), vocab_size)
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self.ctc_dropout_rate = auxiliary_ctc_dropout_rate
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if self.use_auxiliary_lm_loss:
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self.lm_lin = torch.nn.Linear(decoder.output_size, vocab_size)
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self.lm_loss_smoothing = auxiliary_lm_loss_smoothing
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self.transducer_weight = transducer_weight
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self.fastemit_lambda = fastemit_lambda
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self.auxiliary_ctc_weight = auxiliary_ctc_weight
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self.auxiliary_lm_loss_weight = auxiliary_lm_loss_weight
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self.blank_id = blank_id
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self.sos = sos if sos is not None else vocab_size - 1
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self.eos = eos if eos is not None else vocab_size - 1
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self.vocab_size = vocab_size
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self.ignore_id = ignore_id
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self.frontend = frontend
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self.specaug = specaug
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self.normalize = normalize
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self.encoder = encoder
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self.decoder = decoder
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self.joint_network = joint_network
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self.criterion_att = LabelSmoothingLoss(
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size=vocab_size,
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padding_idx=ignore_id,
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smoothing=lsm_weight,
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normalize_length=length_normalized_loss,
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)
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#
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# if report_cer or report_wer:
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# self.error_calculator = ErrorCalculator(
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# token_list, sym_space, sym_blank, report_cer, report_wer
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# )
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#
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self.length_normalized_loss = length_normalized_loss
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self.beam_search = None
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def forward(
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self,
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speech: torch.Tensor,
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speech_lengths: torch.Tensor,
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text: torch.Tensor,
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text_lengths: torch.Tensor,
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**kwargs,
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) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]:
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"""Encoder + Decoder + Calc loss
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Args:
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speech: (Batch, Length, ...)
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speech_lengths: (Batch, )
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text: (Batch, Length)
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text_lengths: (Batch,)
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"""
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# import pdb;
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# pdb.set_trace()
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if len(text_lengths.size()) > 1:
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text_lengths = text_lengths[:, 0]
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if len(speech_lengths.size()) > 1:
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speech_lengths = speech_lengths[:, 0]
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batch_size = speech.shape[0]
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# 1. Encoder
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encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
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if hasattr(self.encoder, 'overlap_chunk_cls') and self.encoder.overlap_chunk_cls is not None:
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encoder_out, encoder_out_lens = self.encoder.overlap_chunk_cls.remove_chunk(encoder_out, encoder_out_lens,
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chunk_outs=None)
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# 2. Transducer-related I/O preparation
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decoder_in, target, t_len, u_len = get_transducer_task_io(
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text,
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encoder_out_lens,
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ignore_id=self.ignore_id,
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)
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# 3. Decoder
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self.decoder.set_device(encoder_out.device)
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decoder_out = self.decoder(decoder_in, u_len)
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# 4. Joint Network
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joint_out = self.joint_network(
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encoder_out.unsqueeze(2), decoder_out.unsqueeze(1)
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)
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# 5. Losses
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loss_trans, cer_trans, wer_trans = self._calc_transducer_loss(
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encoder_out,
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joint_out,
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target,
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t_len,
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u_len,
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)
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loss_ctc, loss_lm = 0.0, 0.0
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if self.use_auxiliary_ctc:
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loss_ctc = self._calc_ctc_loss(
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encoder_out,
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target,
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t_len,
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u_len,
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)
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if self.use_auxiliary_lm_loss:
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loss_lm = self._calc_lm_loss(decoder_out, target)
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loss = (
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self.transducer_weight * loss_trans
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+ self.auxiliary_ctc_weight * loss_ctc
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+ self.auxiliary_lm_loss_weight * loss_lm
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)
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stats = dict(
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loss=loss.detach(),
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loss_transducer=loss_trans.detach(),
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aux_ctc_loss=loss_ctc.detach() if loss_ctc > 0.0 else None,
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aux_lm_loss=loss_lm.detach() if loss_lm > 0.0 else None,
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cer_transducer=cer_trans,
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wer_transducer=wer_trans,
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)
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# force_gatherable: to-device and to-tensor if scalar for DataParallel
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loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
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return loss, stats, weight
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def encode(
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self, speech: torch.Tensor, speech_lengths: torch.Tensor, **kwargs,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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"""Frontend + Encoder. Note that this method is used by asr_inference.py
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Args:
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speech: (Batch, Length, ...)
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speech_lengths: (Batch, )
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ind: int
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"""
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with autocast(False):
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# Data augmentation
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if self.specaug is not None and self.training:
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speech, speech_lengths = self.specaug(speech, speech_lengths)
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# Normalization for feature: e.g. Global-CMVN, Utterance-CMVN
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if self.normalize is not None:
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speech, speech_lengths = self.normalize(speech, speech_lengths)
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# Forward encoder
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# feats: (Batch, Length, Dim)
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# -> encoder_out: (Batch, Length2, Dim2)
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if self.encoder.interctc_use_conditioning:
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encoder_out, encoder_out_lens, _ = self.encoder(
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speech, speech_lengths, ctc=self.ctc
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)
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else:
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encoder_out, encoder_out_lens, _ = self.encoder(speech, speech_lengths)
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intermediate_outs = None
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if isinstance(encoder_out, tuple):
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intermediate_outs = encoder_out[1]
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encoder_out = encoder_out[0]
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if intermediate_outs is not None:
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return (encoder_out, intermediate_outs), encoder_out_lens
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return encoder_out, encoder_out_lens
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def _calc_transducer_loss(
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self,
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encoder_out: torch.Tensor,
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joint_out: torch.Tensor,
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target: torch.Tensor,
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t_len: torch.Tensor,
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u_len: torch.Tensor,
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) -> Tuple[torch.Tensor, Optional[float], Optional[float]]:
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"""Compute Transducer loss.
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Args:
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encoder_out: Encoder output sequences. (B, T, D_enc)
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joint_out: Joint Network output sequences (B, T, U, D_joint)
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target: Target label ID sequences. (B, L)
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t_len: Encoder output sequences lengths. (B,)
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u_len: Target label ID sequences lengths. (B,)
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Return:
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loss_transducer: Transducer loss value.
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cer_transducer: Character error rate for Transducer.
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wer_transducer: Word Error Rate for Transducer.
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"""
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if self.criterion_transducer is None:
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try:
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from warp_rnnt import rnnt_loss as RNNTLoss
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self.criterion_transducer = RNNTLoss
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except ImportError:
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logging.error(
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"warp-rnnt was not installed."
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"Please consult the installation documentation."
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)
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exit(1)
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log_probs = torch.log_softmax(joint_out, dim=-1)
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loss_transducer = self.criterion_transducer(
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log_probs,
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target,
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t_len,
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u_len,
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reduction="mean",
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blank=self.blank_id,
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fastemit_lambda=self.fastemit_lambda,
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gather=True,
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)
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if not self.training and (self.report_cer or self.report_wer):
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if self.error_calculator is None:
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from funasr.metrics import ErrorCalculatorTransducer as ErrorCalculator
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self.error_calculator = ErrorCalculator(
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self.decoder,
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self.joint_network,
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self.token_list,
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self.sym_space,
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self.sym_blank,
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report_cer=self.report_cer,
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report_wer=self.report_wer,
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)
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cer_transducer, wer_transducer = self.error_calculator(encoder_out, target, t_len)
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return loss_transducer, cer_transducer, wer_transducer
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return loss_transducer, None, None
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def _calc_ctc_loss(
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self,
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encoder_out: torch.Tensor,
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target: torch.Tensor,
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t_len: torch.Tensor,
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u_len: torch.Tensor,
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) -> torch.Tensor:
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"""Compute CTC loss.
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Args:
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encoder_out: Encoder output sequences. (B, T, D_enc)
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target: Target label ID sequences. (B, L)
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t_len: Encoder output sequences lengths. (B,)
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u_len: Target label ID sequences lengths. (B,)
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Return:
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loss_ctc: CTC loss value.
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"""
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ctc_in = self.ctc_lin(
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torch.nn.functional.dropout(encoder_out, p=self.ctc_dropout_rate)
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)
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ctc_in = torch.log_softmax(ctc_in.transpose(0, 1), dim=-1)
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target_mask = target != 0
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ctc_target = target[target_mask].cpu()
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with torch.backends.cudnn.flags(deterministic=True):
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loss_ctc = torch.nn.functional.ctc_loss(
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ctc_in,
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ctc_target,
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t_len,
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u_len,
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zero_infinity=True,
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reduction="sum",
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)
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loss_ctc /= target.size(0)
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return loss_ctc
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def _calc_lm_loss(
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self,
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decoder_out: torch.Tensor,
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target: torch.Tensor,
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) -> torch.Tensor:
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"""Compute LM loss.
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Args:
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decoder_out: Decoder output sequences. (B, U, D_dec)
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target: Target label ID sequences. (B, L)
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Return:
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loss_lm: LM loss value.
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"""
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lm_loss_in = self.lm_lin(decoder_out[:, :-1, :]).view(-1, self.vocab_size)
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lm_target = target.view(-1).type(torch.int64)
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with torch.no_grad():
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true_dist = lm_loss_in.clone()
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true_dist.fill_(self.lm_loss_smoothing / (self.vocab_size - 1))
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# Ignore blank ID (0)
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ignore = lm_target == 0
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lm_target = lm_target.masked_fill(ignore, 0)
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true_dist.scatter_(1, lm_target.unsqueeze(1), (1 - self.lm_loss_smoothing))
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loss_lm = torch.nn.functional.kl_div(
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torch.log_softmax(lm_loss_in, dim=1),
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true_dist,
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reduction="none",
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)
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loss_lm = loss_lm.masked_fill(ignore.unsqueeze(1), 0).sum() / decoder_out.size(
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0
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)
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return loss_lm
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def init_beam_search(self,
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**kwargs,
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):
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from funasr.models.transformer.search import BeamSearch
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from funasr.models.transformer.scorers.ctc import CTCPrefixScorer
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from funasr.models.transformer.scorers.length_bonus import LengthBonus
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# 1. Build ASR model
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scorers = {}
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if self.ctc != None:
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ctc = CTCPrefixScorer(ctc=self.ctc, eos=self.eos)
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scorers.update(
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ctc=ctc
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)
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token_list = kwargs.get("token_list")
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scorers.update(
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length_bonus=LengthBonus(len(token_list)),
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)
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# 3. Build ngram model
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# ngram is not supported now
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ngram = None
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scorers["ngram"] = ngram
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weights = dict(
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decoder=1.0 - kwargs.get("decoding_ctc_weight"),
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ctc=kwargs.get("decoding_ctc_weight", 0.0),
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lm=kwargs.get("lm_weight", 0.0),
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ngram=kwargs.get("ngram_weight", 0.0),
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length_bonus=kwargs.get("penalty", 0.0),
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)
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beam_search = BeamSearch(
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beam_size=kwargs.get("beam_size", 2),
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weights=weights,
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scorers=scorers,
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sos=self.sos,
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eos=self.eos,
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vocab_size=len(token_list),
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token_list=token_list,
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pre_beam_score_key=None if self.ctc_weight == 1.0 else "full",
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)
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# beam_search.to(device=kwargs.get("device", "cpu"), dtype=getattr(torch, kwargs.get("dtype", "float32"))).eval()
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# for scorer in scorers.values():
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# if isinstance(scorer, torch.nn.Module):
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# scorer.to(device=kwargs.get("device", "cpu"), dtype=getattr(torch, kwargs.get("dtype", "float32"))).eval()
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self.beam_search = beam_search
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def generate(self,
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data_in: list,
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data_lengths: list=None,
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key: list=None,
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tokenizer=None,
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**kwargs,
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):
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if kwargs.get("batch_size", 1) > 1:
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raise NotImplementedError("batch decoding is not implemented")
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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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|
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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(data_in, fs=self.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"), frontend=self.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["batch_data_time"] = speech_lengths.sum().item() * self.frontend.frame_shift * self.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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|
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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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|
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|
# c. Passed the encoder result and the beam search
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|
nbest_hyps = self.beam_search(
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|
x=encoder_out[0], maxlenratio=kwargs.get("maxlenratio", 0.0), minlenratio=kwargs.get("minlenratio", 0.0)
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|
)
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|
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|
nbest_hyps = nbest_hyps[: self.nbest]
|
|
|
|
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|
results = []
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|
b, n, d = encoder_out.size()
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|
for i in range(b):
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|
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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(filter(lambda x: x != self.eos and x != self.sos and x != self.blank_id, token_int))
|
|
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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)
|
|
|
|
text_postprocessed, _ = postprocess_utils.sentence_postprocess(token)
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|
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
|
|
|