wehub-resource-sync
604 lines
22 KiB
Python
604 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 time
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import copy
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import torch
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import logging
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from torch.cuda.amp import autocast
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from typing import Union, Dict, List, Tuple, Optional
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from funasr.register import tables
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from funasr.models.ctc.ctc import CTC
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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.train_utils.device_funcs import to_device
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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.models.paraformer.cif_predictor import mae_loss
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from funasr.train_utils.device_funcs import force_gatherable
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from funasr.losses.label_smoothing_loss import LabelSmoothingLoss
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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
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from funasr.utils.timestamp_tools import ts_prediction_lfr6_standard
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from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank
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from torch.nn.utils.rnn import pad_sequence
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import torchaudio
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@tables.register("model_classes", "Paraformer_v2_community")
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class Paraformer(torch.nn.Module):
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"""
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Author: Speech Lab of DAMO Academy, Alibaba Group
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Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition
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https://arxiv.org/abs/2206.08317
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"""
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def __init__(
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self,
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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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ctc: str = None,
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ctc_conf: Optional[Dict] = None,
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ctc_weight: float = 0.5,
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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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# predictor=None,
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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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use_1st_decoder_loss: bool = False,
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**kwargs,
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):
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"""Initialize Paraformer.
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Args:
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specaug: TODO.
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specaug_conf: Configuration dict for specaug.
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normalize: TODO.
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normalize_conf: Configuration dict for normalize.
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encoder: TODO.
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encoder_conf: Configuration dict for encoder.
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decoder: TODO.
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decoder_conf: Configuration dict for decoder.
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ctc: TODO.
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ctc_conf: Configuration dict for ctc.
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ctc_weight: TODO.
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input_size: Size/dimension parameter.
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vocab_size: Size/dimension parameter.
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ignore_id: TODO.
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blank_id: TODO.
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sos: TODO.
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eos: TODO.
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lsm_weight: TODO.
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length_normalized_loss: TODO.
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share_embedding: TODO.
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use_1st_decoder_loss: TODO.
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**kwargs: Additional keyword arguments.
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"""
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super().__init__()
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if specaug is not None:
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specaug_class = tables.specaug_classes.get(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 = tables.normalize_classes.get(normalize)
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normalize = normalize_class(**normalize_conf)
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encoder_class = tables.encoder_classes.get(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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if decoder is not None:
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decoder_class = tables.decoder_classes.get(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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if ctc_weight > 0.0:
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if ctc_conf is None:
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ctc_conf = {}
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ctc = CTC(odim=vocab_size, encoder_output_size=encoder_output_size, **ctc_conf)
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# note that eos is the same as sos (equivalent ID)
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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.ctc_weight = ctc_weight
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# self.token_list = token_list.copy()
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#
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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.preencoder = preencoder
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# self.postencoder = postencoder
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self.encoder = encoder
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#
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# if not hasattr(self.encoder, "interctc_use_conditioning"):
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# self.encoder.interctc_use_conditioning = False
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# if self.encoder.interctc_use_conditioning:
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# self.encoder.conditioning_layer = torch.nn.Linear(
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# vocab_size, self.encoder.output_size()
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# )
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#
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# self.error_calculator = None
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#
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if ctc_weight == 1.0:
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self.decoder = None
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else:
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self.decoder = decoder
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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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if ctc_weight == 0.0:
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self.ctc = None
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else:
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self.ctc = ctc
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#
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# self.extract_feats_in_collect_stats = extract_feats_in_collect_stats
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self.share_embedding = share_embedding
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if self.share_embedding:
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self.decoder.embed = None
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self.use_1st_decoder_loss = use_1st_decoder_loss
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self.length_normalized_loss = length_normalized_loss
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self.beam_search = None
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self.error_calculator = 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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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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# 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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loss_pre = None
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stats = dict()
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# decoder: 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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# decoder: Attention decoder branch
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loss_att, acc_att, cer_att, wer_att = self._calc_att_loss(
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encoder_out, encoder_out_lens, text, text_lengths
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)
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# 3. CTC-Att loss definition
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if self.ctc_weight == 0.0:
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loss = loss_att
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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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)
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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"] = torch.clone(loss.detach())
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stats["batch_size"] = batch_size
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# force_gatherable: to-device and to-tensor if scalar for DataParallel
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if self.length_normalized_loss:
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batch_size = (text_lengths).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 encode(
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self,
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speech: torch.Tensor,
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speech_lengths: torch.Tensor,
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**kwargs,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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"""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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encoder_out, encoder_out_lens, _ = self.encoder(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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return encoder_out, encoder_out_lens
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def _calc_att_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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):
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# 0. sampler
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"""Internal: calc att loss.
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Args:
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encoder_out: Encoder output tensor.
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encoder_out_lens: Encoder output lengths.
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ys_pad: TODO.
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ys_pad_lens: Lengths of ys_pad.
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"""
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decoder_out_1st = None
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batch_size = encoder_out.size(0)
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ctc_probs_all = self.ctc.softmax(encoder_out)
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compressed_ctc_list = []
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for b in range(batch_size):
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ctc_prob_b = ctc_probs_all[b, :encoder_out_lens[b]]
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text_b = ys_pad[b, :ys_pad_lens[b]]
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with torch.no_grad():
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ctc_log_prob_b = ctc_prob_b.log()
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align_path = self.force_align(ctc_log_prob_b.cpu(), text_b.cpu(), blank_id=self.blank_id)
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align_path = align_path.to(encoder_out.device)
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target_idx_path = self.map_alignment_to_target_index(align_path, self.blank_id)
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ctc_comp = self.average_repeats_training(ctc_prob_b, target_idx_path, ys_pad_lens[b])
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compressed_ctc_list.append(ctc_comp)
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# 4. Pad Batch to [B, U_max, V]
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padded_ctc_input = pad_sequence(compressed_ctc_list, batch_first=True).to(encoder_out.device)
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# 1. Forward decoder
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decoder_outs = self.decoder(encoder_out, encoder_out_lens, padded_ctc_input, ys_pad_lens)
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decoder_out, _ = decoder_outs[0], decoder_outs[1]
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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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# 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
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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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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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):
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# Calc CTC loss
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"""Internal: calc ctc loss.
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Args:
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encoder_out: Encoder output tensor.
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encoder_out_lens: Encoder output lengths.
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ys_pad: TODO.
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ys_pad_lens: Lengths of ys_pad.
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"""
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loss_ctc = self.ctc(encoder_out, encoder_out_lens, ys_pad, ys_pad_lens)
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# Calc CER using CTC
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cer_ctc = None
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if not self.training and self.error_calculator is not None:
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ys_hat = self.ctc.argmax(encoder_out).data
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cer_ctc = self.error_calculator(ys_hat.cpu(), ys_pad.cpu(), is_ctc=True)
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return loss_ctc, cer_ctc
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def map_alignment_to_target_index(self, align_path, blank_id):
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"""
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Robustly map CTC alignment path (Token IDs) to Target Indices.
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Logic:
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Detect boundaries where a new token segment begins.
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A segment starts if the current frame is a Token AND it is different from the previous frame
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(considering CTC topology where repeats are separated by blanks or are distinct tokens).
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Example:
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Text: [A, B]
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Align Path: [A, A, _, B, B]
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Output: [0, 0, -1, 1, 1]
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"""
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# 1. Identify where the path is NOT blank
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is_token = align_path != blank_id
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# 2. Identify transitions
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prev_path = torch.roll(align_path, 1)
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# Handle the very first frame: if it's a token, it must be the start of segment 0.
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prev_path[0] = blank_id # force mismatch for the first element
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# A new segment starts if: It's a token AND (it differs from prev OR prev was blank)
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# Note: If align_path[i] == align_path[i-1] (and not blank), it's the same segment.
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new_segment_start = is_token & (align_path != prev_path)
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# 3. Cumulative sum to assign indices (1..U)
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segment_ids = torch.cumsum(new_segment_start.long(), dim=0) - 1
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# 4. Mask out blank positions with -1
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target_idx_path = torch.where(is_token, segment_ids, -1)
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return target_idx_path
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def force_align(self, ctc_probs: torch.Tensor, y: torch.Tensor, blank_id=0) -> list:
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"""ctc forced alignment.
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Args:
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torch.Tensor ctc_probs: hidden state sequence, 2d tensor (T, D)
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torch.Tensor y: id sequence tensor 1d tensor (L)
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int blank_id: blank symbol index
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Returns:
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torch.Tensor: alignment result
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"""
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ctc_probs = ctc_probs[None].cpu()
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y = y[None].cpu()
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alignments, _ = torchaudio.functional.forced_align(ctc_probs, y, blank=blank_id)
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return alignments[0]
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def average_repeats_training(self, ctc_probs, target_idx_path, target_len):
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"""
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Aggregates frames belonging to the same target index using scatter_add.
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Args:
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ctc_probs: [T, V]
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target_idx_path: [T], values in [-1, 0, ... U-1]
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target_len: U
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Returns:
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compressed: [U, V]
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"""
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U = target_len
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V = ctc_probs.size(1)
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compressed = torch.zeros((U, V), device=ctc_probs.device, dtype=ctc_probs.dtype)
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counts = torch.zeros((U, 1), device=ctc_probs.device, dtype=ctc_probs.dtype)
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# Filter valid frames (non-blank)
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mask = target_idx_path != -1
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valid_indices = target_idx_path[mask] # [T_valid]
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valid_probs = ctc_probs[mask] # [T_valid, V]
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if valid_indices.numel() == 0:
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return compressed
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# Scatter Add Probs
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index_expanded = valid_indices.unsqueeze(1).repeat(1, V)
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compressed.scatter_add_(0, index_expanded, valid_probs)
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# Scatter Add Counts
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ones = torch.ones((valid_indices.size(0), 1), device=ctc_probs.device)
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counts.scatter_add_(0, valid_indices.unsqueeze(1), ones)
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# Average
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compressed = compressed / (counts + 1e-9)
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return compressed
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def average_repeats_inference(self, ctc_probs, greedy_path):
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"""
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Returns:
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merged_probs: [U', V]
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timestamps: List[Tuple[int, int]] -> [(start_frame, end_frame), ...]
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"""
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if greedy_path.numel() == 0:
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return torch.zeros((0, ctc_probs.size(1)), device=ctc_probs.device)
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# Find consecutive segments in the greedy path
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unique_tokens, counts = torch.unique_consecutive(greedy_path, return_counts=True)
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# Compute start and end indices for each segment
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end_indices = torch.cumsum(counts, dim=0)
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start_indices = torch.cat([torch.tensor([0], device=counts.device), end_indices[:-1]])
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merged_probs = []
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for i, token in enumerate(unique_tokens):
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if token != self.blank_id:
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start = start_indices[i].item()
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end = end_indices[i].item()
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# Extract and average probabilities for the decoder
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avg_prob = ctc_probs[start:end].mean(dim=0)
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merged_probs.append(avg_prob)
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if not merged_probs:
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return torch.zeros((0, ctc_probs.size(1)), device=ctc_probs.device)
|
|
|
|
return torch.stack(merged_probs)
|
|
|
|
def inference(
|
|
self,
|
|
data_in,
|
|
data_lengths=None,
|
|
key: list = None,
|
|
tokenizer=None,
|
|
frontend=None,
|
|
**kwargs,
|
|
):
|
|
|
|
"""Run inference on input data.
|
|
|
|
Args:
|
|
data_in: Input data (audio samples, file paths, or text).
|
|
data_lengths: Lengths of each input sample in the batch.
|
|
key: Sample identifiers.
|
|
tokenizer: Tokenizer instance for text encoding/decoding.
|
|
frontend: Audio frontend for feature extraction.
|
|
**kwargs: Additional keyword arguments.
|
|
"""
|
|
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 not None:
|
|
speech_lengths = speech_lengths.squeeze(-1)
|
|
else:
|
|
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
|
|
if kwargs.get("fp16", False):
|
|
speech = speech.half()
|
|
encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
|
|
if isinstance(encoder_out, tuple):
|
|
encoder_out = encoder_out[0]
|
|
|
|
ctc_probs = self.ctc.softmax(encoder_out)
|
|
ctc_greedy_paths = ctc_probs.argmax(dim=-1)
|
|
|
|
results = []
|
|
batch_size, n, d = encoder_out.size()
|
|
if isinstance(key[0], (list, tuple)):
|
|
key = key[0]
|
|
if len(key) < batch_size:
|
|
key = key * batch_size
|
|
for b in range(batch_size):
|
|
|
|
probs = ctc_probs[b, :encoder_out_lens[b]]
|
|
path = ctc_greedy_paths[b, :encoder_out_lens[b]]
|
|
|
|
# Get compressed probabilities and timestamp indices
|
|
compressed_prob = self.average_repeats_inference(probs, path)
|
|
|
|
# Handling Noise/Silence (Empty Output)
|
|
if compressed_prob.size(0) == 0:
|
|
token_int = []
|
|
else:
|
|
# 4. Decoder Forward
|
|
compressed_prob_in = compressed_prob.unsqueeze(0) # [1, U', V]
|
|
in_lens = torch.tensor([compressed_prob.size(0)], device=encoder_out.device)
|
|
|
|
decoder_out, _ = self.decoder(
|
|
encoder_out[b:b+1],
|
|
encoder_out_lens[b:b+1],
|
|
compressed_prob_in,
|
|
in_lens,)
|
|
|
|
|
|
yseq = decoder_out.argmax(dim=-1)[0]
|
|
token_int = yseq.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
|
|
)
|
|
)
|
|
|
|
result_i = {"key": key[b], "token_int": token_int}
|
|
results.append(result_i)
|
|
|
|
return results, meta_data
|
|
|
|
def export(self, **kwargs):
|
|
"""Export.
|
|
|
|
Args:
|
|
**kwargs: Additional keyword arguments.
|
|
"""
|
|
from .export_meta import export_rebuild_model
|
|
|
|
if "max_seq_len" not in kwargs:
|
|
kwargs["max_seq_len"] = 512
|
|
models = export_rebuild_model(model=self, **kwargs)
|
|
return models
|