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@@ -13,6 +13,7 @@ from distutils.version import LooseVersion
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from funasr.register import tables
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from funasr.utils import postprocess_utils
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from funasr.utils.datadir_writer import DatadirWriter
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+from funasr.models.transducer.model import Transducer
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from funasr.train_utils.device_funcs import force_gatherable
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from funasr.models.transformer.scorers.ctc import CTCPrefixScorer
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from funasr.losses.label_smoothing_loss import LabelSmoothingLoss
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@@ -32,488 +33,5 @@ else:
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@tables.register("model_classes", "BAT") # TODO: BAT training
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-class BAT(torch.nn.Module):
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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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-
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- super().__init__()
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-
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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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-
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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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- **decoder_conf,
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- )
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- decoder_output_size = decoder.output_size
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-
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- joint_network_class = tables.joint_network_classes.get(joint_network)
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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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-
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- self.criterion_transducer = None
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- self.error_calculator = None
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-
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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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-
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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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-
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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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-
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- self.transducer_weight = transducer_weight
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- self.fastemit_lambda = fastemit_lambda
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-
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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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-
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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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- self.length_normalized_loss = length_normalized_loss
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- self.beam_search = None
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- self.ctc = None
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- self.ctc_weight = 0.0
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-
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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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-
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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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-
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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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-
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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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-
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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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-
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- loss_ctc, loss_lm = 0.0, 0.0
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-
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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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-
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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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-
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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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-
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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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-
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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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-
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- return loss, stats, weight
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-
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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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-
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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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-
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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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-
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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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- 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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-
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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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-
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- return encoder_out, encoder_out_lens
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-
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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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-
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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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-
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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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- """
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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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-
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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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-
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- log_probs = torch.log_softmax(joint_out, dim=-1)
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-
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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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-
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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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-
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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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-
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- cer_transducer, wer_transducer = self.error_calculator(encoder_out, target, t_len)
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-
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- return loss_transducer, cer_transducer, wer_transducer
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-
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- return loss_transducer, None, None
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-
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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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-
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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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-
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- Return:
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- loss_ctc: CTC loss value.
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-
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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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-
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- target_mask = target != 0
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- ctc_target = target[target_mask].cpu()
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-
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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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-
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- return loss_ctc
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-
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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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-
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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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-
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- Return:
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- loss_lm: LM loss value.
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-
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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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-
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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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-
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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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-
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- true_dist.scatter_(1, lm_target.unsqueeze(1), (1 - self.lm_loss_smoothing))
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-
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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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-
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- return loss_lm
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-
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- def init_beam_search(self,
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- **kwargs,
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- ):
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-
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- # 1. Build ASR model
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- scorers = {}
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-
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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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-
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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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|
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-
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- beam_search = BeamSearchTransducer(
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- self.decoder,
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- self.joint_network,
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- kwargs.get("beam_size", 2),
|
|
|
- nbest=1,
|
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- )
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- # beam_search.to(device=kwargs.get("device", "cpu"), dtype=getattr(torch, kwargs.get("dtype", "float32"))).eval()
|
|
|
- # 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
|
|
|
-
|
|
|
- def inference(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,
|
|
|
- ):
|
|
|
-
|
|
|
- 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)
|
|
|
-
|
|
|
- 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))
|
|
|
- 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)
|
|
|
- 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
|
|
|
-
|
|
|
- speech = speech.to(device=kwargs["device"])
|
|
|
- speech_lengths = speech_lengths.to(device=kwargs["device"])
|
|
|
-
|
|
|
- # Encoder
|
|
|
- encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
|
|
|
- if isinstance(encoder_out, tuple):
|
|
|
- encoder_out = encoder_out[0]
|
|
|
-
|
|
|
- # c. Passed the encoder result and the beam search
|
|
|
- nbest_hyps = self.beam_search(encoder_out[0], is_final=True)
|
|
|
- nbest_hyps = nbest_hyps[: self.nbest]
|
|
|
-
|
|
|
- results = []
|
|
|
- b, n, d = encoder_out.size()
|
|
|
- for i in range(b):
|
|
|
-
|
|
|
- for nbest_idx, hyp in enumerate(nbest_hyps):
|
|
|
- ibest_writer = None
|
|
|
- if kwargs.get("output_dir") is not None:
|
|
|
- if not hasattr(self, "writer"):
|
|
|
- self.writer = DatadirWriter(kwargs.get("output_dir"))
|
|
|
- ibest_writer = self.writer[f"{nbest_idx + 1}best_recog"]
|
|
|
- # remove sos/eos and get results
|
|
|
- last_pos = -1
|
|
|
- if isinstance(hyp.yseq, list):
|
|
|
- token_int = hyp.yseq#[1:last_pos]
|
|
|
- else:
|
|
|
- token_int = hyp.yseq#[1:last_pos].tolist()
|
|
|
-
|
|
|
- # remove blank symbol id, which is assumed to be 0
|
|
|
- token_int = list(filter(lambda x: x != self.eos and x != self.sos and x != self.blank_id, token_int))
|
|
|
-
|
|
|
- # Change integer-ids to tokens
|
|
|
- token = tokenizer.ids2tokens(token_int)
|
|
|
- text = tokenizer.tokens2text(token)
|
|
|
-
|
|
|
- text_postprocessed, _ = postprocess_utils.sentence_postprocess(token)
|
|
|
- result_i = {"key": key[i], "token": token, "text": text, "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
|
|
|
-
|
|
|
+class BAT(Transducer):
|
|
|
+ pass
|
游雁