export

funasr/bin/punctuation_infer.py

@@ -23,7 +23,7 @@ from funasr.torch_utils.set_all_random_seed import set_all_random_seed
 from funasr.utils import config_argparse
 from funasr.utils.types import str2triple_str
 from funasr.utils.types import str_or_none
-from funasr.punctuation.text_preprocessor import split_to_mini_sentence
+from funasr.datasets.preprocessor import split_to_mini_sentence
 
 
 class Text2Punc:

funasr/bin/punctuation_infer_vadrealtime.py

@@ -23,7 +23,7 @@ from funasr.torch_utils.set_all_random_seed import set_all_random_seed
 from funasr.utils import config_argparse
 from funasr.utils.types import str2triple_str
 from funasr.utils.types import str_or_none
-from funasr.punctuation.text_preprocessor import split_to_mini_sentence
+from funasr.datasets.preprocessor import split_to_mini_sentence
 
 
 class Text2Punc:

funasr/datasets/preprocessor.py

@@ -800,3 +800,17 @@ class PuncTrainTokenizerCommonPreprocessor(CommonPreprocessor):
                     data[self.vad_name] = np.array([vad], dtype=np.int64)
                 text_ints = self.token_id_converter[i].tokens2ids(tokens)
                 data[text_name] = np.array(text_ints, dtype=np.int64)
+
+
+def split_to_mini_sentence(words: list, word_limit: int = 20):
+    assert word_limit > 1
+    if len(words) <= word_limit:
+        return [words]
+    sentences = []
+    length = len(words)
+    sentence_len = length // word_limit
+    for i in range(sentence_len):
+        sentences.append(words[i * word_limit:(i + 1) * word_limit])
+    if length % word_limit > 0:
+        sentences.append(words[sentence_len * word_limit:])
+    return sentences

funasr/export/models/__init__.py

@@ -3,10 +3,10 @@ from funasr.export.models.e2e_asr_paraformer import Paraformer as Paraformer_exp
 from funasr.export.models.e2e_asr_paraformer import BiCifParaformer as BiCifParaformer_export
 from funasr.models.e2e_vad import E2EVadModel
 from funasr.export.models.e2e_vad import E2EVadModel as E2EVadModel_export
-from funasr.punctuation.target_delay_transformer import TargetDelayTransformer
+from funasr.models.target_delay_transformer import TargetDelayTransformer
 from funasr.export.models.target_delay_transformer import TargetDelayTransformer as TargetDelayTransformer_export
-from funasr.punctuation.espnet_model import ESPnetPunctuationModel
-from funasr.punctuation.vad_realtime_transformer import VadRealtimeTransformer
+from funasr.train.abs_model import PunctuationModel
+from funasr.models.vad_realtime_transformer import VadRealtimeTransformer
 from funasr.export.models.vad_realtime_transformer import VadRealtimeTransformer as VadRealtimeTransformer_export
 
 def get_model(model, export_config=None):
@@ -16,7 +16,7 @@ def get_model(model, export_config=None):
         return Paraformer_export(model, **export_config)
     elif isinstance(model, E2EVadModel):
         return E2EVadModel_export(model, **export_config)
-    elif isinstance(model, ESPnetPunctuationModel):
+    elif isinstance(model, PunctuationModel):
         if isinstance(model.punc_model, TargetDelayTransformer):
             return TargetDelayTransformer_export(model.punc_model, **export_config)
         elif isinstance(model.punc_model, VadRealtimeTransformer):

funasr/export/models/target_delay_transformer.py

@@ -1,18 +1,8 @@
-from typing import Any
-from typing import List
 from typing import Tuple
 
 import torch
 import torch.nn as nn
 
-from funasr.export.utils.torch_function import MakePadMask
-from funasr.export.utils.torch_function import sequence_mask
-#from funasr.models.encoder.sanm_encoder import SANMEncoder as Encoder
-from funasr.punctuation.sanm_encoder import SANMEncoder
-from funasr.export.models.encoder.sanm_encoder import SANMEncoder as SANMEncoder_export
-from funasr.punctuation.abs_model import AbsPunctuation
-
-
 class TargetDelayTransformer(nn.Module):
 
     def __init__(
@@ -32,85 +22,10 @@ class TargetDelayTransformer(nn.Module):
         self.feats_dim = self.embed.embedding_dim
         self.num_embeddings = self.embed.num_embeddings
         self.model_name = model_name
-        from typing import Any
-        from typing import List
-        from typing import Tuple
 
-        import torch
-        import torch.nn as nn
-
-        from funasr.export.utils.torch_function import MakePadMask
-        from funasr.export.utils.torch_function import sequence_mask
         # from funasr.models.encoder.sanm_encoder import SANMEncoder as Encoder
-        from funasr.punctuation.sanm_encoder import SANMEncoder
+        from funasr.models.encoder.sanm_encoder import SANMEncoder
         from funasr.export.models.encoder.sanm_encoder import SANMEncoder as SANMEncoder_export
-        from funasr.punctuation.abs_model import AbsPunctuation
-
-        # class TargetDelayTransformer(nn.Module):
-        #
-        #     def __init__(
-        #             self,
-        #             model,
-        #             max_seq_len=512,
-        #             model_name='punc_model',
-        #             **kwargs,
-        #     ):
-        #         super().__init__()
-        #         onnx = False
-        #         if "onnx" in kwargs:
-        #             onnx = kwargs["onnx"]
-        #         self.embed = model.embed
-        #         self.decoder = model.decoder
-        #         self.model = model
-        #         self.feats_dim = self.embed.embedding_dim
-        #         self.num_embeddings = self.embed.num_embeddings
-        #         self.model_name = model_name
-        #
-        #         if isinstance(model.encoder, SANMEncoder):
-        #             self.encoder = SANMEncoder_export(model.encoder, onnx=onnx)
-        #         else:
-        #             assert False, "Only support samn encode."
-        #
-        #     def forward(self, input: torch.Tensor, text_lengths: torch.Tensor) -> Tuple[torch.Tensor, None]:
-        #         """Compute loss value from buffer sequences.
-        #
-        #         Args:
-        #             input (torch.Tensor): Input ids. (batch, len)
-        #             hidden (torch.Tensor): Target ids. (batch, len)
-        #
-        #         """
-        #         x = self.embed(input)
-        #         # mask = self._target_mask(input)
-        #         h, _ = self.encoder(x, text_lengths)
-        #         y = self.decoder(h)
-        #         return y
-        #
-        #     def get_dummy_inputs(self):
-        #         length = 120
-        #         text_indexes = torch.randint(0, self.embed.num_embeddings, (2, length))
-        #         text_lengths = torch.tensor([length - 20, length], dtype=torch.int32)
-        #         return (text_indexes, text_lengths)
-        #
-        #     def get_input_names(self):
-        #         return ['input', 'text_lengths']
-        #
-        #     def get_output_names(self):
-        #         return ['logits']
-        #
-        #     def get_dynamic_axes(self):
-        #         return {
-        #             'input': {
-        #                 0: 'batch_size',
-        #                 1: 'feats_length'
-        #             },
-        #             'text_lengths': {
-        #                 0: 'batch_size',
-        #             },
-        #             'logits': {
-        #                 0: 'batch_size',
-        #                 1: 'logits_length'
-        #             },
-        #         }
 
         if isinstance(model.encoder, SANMEncoder):
             self.encoder = SANMEncoder_export(model.encoder, onnx=onnx)

funasr/export/models/vad_realtime_transformer.py

@@ -1,14 +1,9 @@
-from typing import Any
-from typing import List
 from typing import Tuple
 
 import torch
 import torch.nn as nn
 
-from funasr.modules.embedding import SinusoidalPositionEncoder
-from funasr.punctuation.sanm_encoder import SANMVadEncoder as Encoder
-from funasr.punctuation.abs_model import AbsPunctuation
-from funasr.punctuation.sanm_encoder import SANMVadEncoder
+from funasr.models.encoder.sanm_encoder import SANMVadEncoder
 from funasr.export.models.encoder.sanm_encoder import SANMVadEncoder as SANMVadEncoder_export
 
 class VadRealtimeTransformer(nn.Module):

funasr/lm/espnet_model.py

@@ -12,7 +12,7 @@ from funasr.torch_utils.device_funcs import force_gatherable
 from funasr.train.abs_espnet_model import AbsESPnetModel
 
 
-class ESPnetLanguageModel(AbsESPnetModel):
+class LanguageModel(AbsESPnetModel):
     def __init__(self, lm: AbsLM, vocab_size: int, ignore_id: int = 0):
         assert check_argument_types()
         super().__init__()

funasr/models/encoder/sanm_encoder.py

@@ -10,7 +10,7 @@ from funasr.modules.streaming_utils.chunk_utilis import overlap_chunk
 from typeguard import check_argument_types
 import numpy as np
 from funasr.modules.nets_utils import make_pad_mask
-from funasr.modules.attention import MultiHeadedAttention, MultiHeadedAttentionSANM
+from funasr.modules.attention import MultiHeadedAttention, MultiHeadedAttentionSANM, MultiHeadedAttentionSANMwithMask
 from funasr.modules.embedding import SinusoidalPositionEncoder
 from funasr.modules.layer_norm import LayerNorm
 from funasr.modules.multi_layer_conv import Conv1dLinear
@@ -27,7 +27,7 @@ from funasr.modules.subsampling import TooShortUttError
 from funasr.modules.subsampling import check_short_utt
 from funasr.models.ctc import CTC
 from funasr.models.encoder.abs_encoder import AbsEncoder
-
+from funasr.modules.mask import subsequent_mask, vad_mask
 
 class EncoderLayerSANM(nn.Module):
     def __init__(
@@ -958,3 +958,231 @@ class SANMEncoderChunkOpt(AbsEncoder):
                                                                                       var_dict_tf[name_tf].shape))
     
         return var_dict_torch_update
+
+
+class SANMVadEncoder(AbsEncoder):
+    """
+    author: Speech Lab, Alibaba Group, China
+
+    """
+
+    def __init__(
+        self,
+        input_size: int,
+        output_size: int = 256,
+        attention_heads: int = 4,
+        linear_units: int = 2048,
+        num_blocks: int = 6,
+        dropout_rate: float = 0.1,
+        positional_dropout_rate: float = 0.1,
+        attention_dropout_rate: float = 0.0,
+        input_layer: Optional[str] = "conv2d",
+        pos_enc_class=SinusoidalPositionEncoder,
+        normalize_before: bool = True,
+        concat_after: bool = False,
+        positionwise_layer_type: str = "linear",
+        positionwise_conv_kernel_size: int = 1,
+        padding_idx: int = -1,
+        interctc_layer_idx: List[int] = [],
+        interctc_use_conditioning: bool = False,
+        kernel_size : int = 11,
+        sanm_shfit : int = 0,
+        selfattention_layer_type: str = "sanm",
+    ):
+        assert check_argument_types()
+        super().__init__()
+        self._output_size = output_size
+
+        if input_layer == "linear":
+            self.embed = torch.nn.Sequential(
+                torch.nn.Linear(input_size, output_size),
+                torch.nn.LayerNorm(output_size),
+                torch.nn.Dropout(dropout_rate),
+                torch.nn.ReLU(),
+                pos_enc_class(output_size, positional_dropout_rate),
+            )
+        elif input_layer == "conv2d":
+            self.embed = Conv2dSubsampling(input_size, output_size, dropout_rate)
+        elif input_layer == "conv2d2":
+            self.embed = Conv2dSubsampling2(input_size, output_size, dropout_rate)
+        elif input_layer == "conv2d6":
+            self.embed = Conv2dSubsampling6(input_size, output_size, dropout_rate)
+        elif input_layer == "conv2d8":
+            self.embed = Conv2dSubsampling8(input_size, output_size, dropout_rate)
+        elif input_layer == "embed":
+            self.embed = torch.nn.Sequential(
+                torch.nn.Embedding(input_size, output_size, padding_idx=padding_idx),
+                SinusoidalPositionEncoder(),
+            )
+        elif input_layer is None:
+            if input_size == output_size:
+                self.embed = None
+            else:
+                self.embed = torch.nn.Linear(input_size, output_size)
+        elif input_layer == "pe":
+            self.embed = SinusoidalPositionEncoder()
+        else:
+            raise ValueError("unknown input_layer: " + input_layer)
+        self.normalize_before = normalize_before
+        if positionwise_layer_type == "linear":
+            positionwise_layer = PositionwiseFeedForward
+            positionwise_layer_args = (
+                output_size,
+                linear_units,
+                dropout_rate,
+            )
+        elif positionwise_layer_type == "conv1d":
+            positionwise_layer = MultiLayeredConv1d
+            positionwise_layer_args = (
+                output_size,
+                linear_units,
+                positionwise_conv_kernel_size,
+                dropout_rate,
+            )
+        elif positionwise_layer_type == "conv1d-linear":
+            positionwise_layer = Conv1dLinear
+            positionwise_layer_args = (
+                output_size,
+                linear_units,
+                positionwise_conv_kernel_size,
+                dropout_rate,
+            )
+        else:
+            raise NotImplementedError("Support only linear or conv1d.")
+
+        if selfattention_layer_type == "selfattn":
+            encoder_selfattn_layer = MultiHeadedAttention
+            encoder_selfattn_layer_args = (
+                attention_heads,
+                output_size,
+                attention_dropout_rate,
+            )
+
+        elif selfattention_layer_type == "sanm":
+            self.encoder_selfattn_layer = MultiHeadedAttentionSANMwithMask
+            encoder_selfattn_layer_args0 = (
+                attention_heads,
+                input_size,
+                output_size,
+                attention_dropout_rate,
+                kernel_size,
+                sanm_shfit,
+            )
+
+            encoder_selfattn_layer_args = (
+                attention_heads,
+                output_size,
+                output_size,
+                attention_dropout_rate,
+                kernel_size,
+                sanm_shfit,
+            )
+
+        self.encoders0 = repeat(
+            1,
+            lambda lnum: EncoderLayerSANM(
+                input_size,
+                output_size,
+                self.encoder_selfattn_layer(*encoder_selfattn_layer_args0),
+                positionwise_layer(*positionwise_layer_args),
+                dropout_rate,
+                normalize_before,
+                concat_after,
+            ),
+        )
+
+        self.encoders = repeat(
+            num_blocks-1,
+            lambda lnum: EncoderLayerSANM(
+                output_size,
+                output_size,
+                self.encoder_selfattn_layer(*encoder_selfattn_layer_args),
+                positionwise_layer(*positionwise_layer_args),
+                dropout_rate,
+                normalize_before,
+                concat_after,
+            ),
+        )
+        if self.normalize_before:
+            self.after_norm = LayerNorm(output_size)
+
+        self.interctc_layer_idx = interctc_layer_idx
+        if len(interctc_layer_idx) > 0:
+            assert 0 < min(interctc_layer_idx) and max(interctc_layer_idx) < num_blocks
+        self.interctc_use_conditioning = interctc_use_conditioning
+        self.conditioning_layer = None
+        self.dropout = nn.Dropout(dropout_rate)
+
+    def output_size(self) -> int:
+        return self._output_size
+
+    def forward(
+        self,
+        xs_pad: torch.Tensor,
+        ilens: torch.Tensor,
+        vad_indexes: torch.Tensor,
+        prev_states: torch.Tensor = None,
+        ctc: CTC = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]:
+        """Embed positions in tensor.
+
+        Args:
+            xs_pad: input tensor (B, L, D)
+            ilens: input length (B)
+            prev_states: Not to be used now.
+        Returns:
+            position embedded tensor and mask
+        """
+        masks = (~make_pad_mask(ilens)[:, None, :]).to(xs_pad.device)
+        sub_masks = subsequent_mask(masks.size(-1), device=xs_pad.device).unsqueeze(0)
+        no_future_masks = masks & sub_masks
+        xs_pad *= self.output_size()**0.5
+        if self.embed is None:
+            xs_pad = xs_pad
+        elif (isinstance(self.embed, Conv2dSubsampling) or isinstance(self.embed, Conv2dSubsampling2)
+              or isinstance(self.embed, Conv2dSubsampling6) or isinstance(self.embed, Conv2dSubsampling8)):
+            short_status, limit_size = check_short_utt(self.embed, xs_pad.size(1))
+            if short_status:
+                raise TooShortUttError(
+                    f"has {xs_pad.size(1)} frames and is too short for subsampling " +
+                    f"(it needs more than {limit_size} frames), return empty results",
+                    xs_pad.size(1),
+                    limit_size,
+                )
+            xs_pad, masks = self.embed(xs_pad, masks)
+        else:
+            xs_pad = self.embed(xs_pad)
+
+        # xs_pad = self.dropout(xs_pad)
+        mask_tup0 = [masks, no_future_masks]
+        encoder_outs = self.encoders0(xs_pad, mask_tup0)
+        xs_pad, _ = encoder_outs[0], encoder_outs[1]
+        intermediate_outs = []
+
+
+        for layer_idx, encoder_layer in enumerate(self.encoders):
+                if layer_idx + 1 == len(self.encoders):
+                    # This is last layer.
+                    coner_mask = torch.ones(masks.size(0),
+                                            masks.size(-1),
+                                            masks.size(-1),
+                                            device=xs_pad.device,
+                                            dtype=torch.bool)
+                    for word_index, length in enumerate(ilens):
+                        coner_mask[word_index, :, :] = vad_mask(masks.size(-1),
+                                                                vad_indexes[word_index],
+                                                                device=xs_pad.device)
+                    layer_mask = masks & coner_mask
+                else:
+                    layer_mask = no_future_masks
+                mask_tup1 = [masks, layer_mask]
+                encoder_outs = encoder_layer(xs_pad, mask_tup1)
+                xs_pad, layer_mask = encoder_outs[0], encoder_outs[1]
+
+        if self.normalize_before:
+            xs_pad = self.after_norm(xs_pad)
+
+        olens = masks.squeeze(1).sum(1)
+        if len(intermediate_outs) > 0:
+            return (xs_pad, intermediate_outs), olens, None
+        return xs_pad, olens, None

funasr/models/target_delay_transformer.py

@@ -5,12 +5,11 @@ from typing import Tuple
 import torch
 import torch.nn as nn
 
-from funasr.modules.embedding import PositionalEncoding
 from funasr.modules.embedding import SinusoidalPositionEncoder
 #from funasr.models.encoder.transformer_encoder import TransformerEncoder as Encoder
 from funasr.punctuation.sanm_encoder import SANMEncoder as Encoder
 #from funasr.modules.mask import subsequent_n_mask
-from funasr.punctuation.abs_model import AbsPunctuation
+from funasr.train.abs_model import AbsPunctuation
 
 
 class TargetDelayTransformer(AbsPunctuation):

funasr/models/vad_realtime_transformer.py

@@ -7,7 +7,7 @@ import torch.nn as nn
 
 from funasr.modules.embedding import SinusoidalPositionEncoder
 from funasr.punctuation.sanm_encoder import SANMVadEncoder as Encoder
-from funasr.punctuation.abs_model import AbsPunctuation
+from funasr.train.abs_model import AbsPunctuation
 
 
 class VadRealtimeTransformer(AbsPunctuation):

funasr/punctuation/abs_model.py

@@ -1,31 +0,0 @@
-from abc import ABC
-from abc import abstractmethod
-from typing import Tuple
-
-import torch
-
-from funasr.modules.scorers.scorer_interface import BatchScorerInterface
-
-
-class AbsPunctuation(torch.nn.Module, BatchScorerInterface, ABC):
-    """The abstract class
-
-    To share the loss calculation way among different models,
-    We uses delegate pattern here:
-    The instance of this class should be passed to "LanguageModel"
-
-    >>> from funasr.punctuation.abs_model import AbsPunctuation
-    >>> punc = AbsPunctuation()
-    >>> model = ESPnetPunctuationModel(punc=punc)
-
-    This "model" is one of mediator objects for "Task" class.
-
-    """
-
-    @abstractmethod
-    def forward(self, input: torch.Tensor, hidden: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
-        raise NotImplementedError
-
-    @abstractmethod
-    def with_vad(self) -> bool:
-        raise NotImplementedError

funasr/punctuation/sanm_encoder.py

@@ -1,590 +0,0 @@
-from typing import List
-from typing import Optional
-from typing import Sequence
-from typing import Tuple
-from typing import Union
-import logging
-import torch
-import torch.nn as nn
-from funasr.modules.streaming_utils.chunk_utilis import overlap_chunk
-from typeguard import check_argument_types
-import numpy as np
-from funasr.modules.nets_utils import make_pad_mask
-from funasr.modules.attention import MultiHeadedAttention, MultiHeadedAttentionSANM, MultiHeadedAttentionSANMwithMask
-from funasr.modules.embedding import SinusoidalPositionEncoder
-from funasr.modules.layer_norm import LayerNorm
-from funasr.modules.multi_layer_conv import Conv1dLinear
-from funasr.modules.multi_layer_conv import MultiLayeredConv1d
-from funasr.modules.positionwise_feed_forward import (
-    PositionwiseFeedForward,  # noqa: H301
-)
-from funasr.modules.repeat import repeat
-from funasr.modules.subsampling import Conv2dSubsampling
-from funasr.modules.subsampling import Conv2dSubsampling2
-from funasr.modules.subsampling import Conv2dSubsampling6
-from funasr.modules.subsampling import Conv2dSubsampling8
-from funasr.modules.subsampling import TooShortUttError
-from funasr.modules.subsampling import check_short_utt
-from funasr.models.ctc import CTC
-from funasr.models.encoder.abs_encoder import AbsEncoder
-
-from funasr.modules.nets_utils import make_pad_mask
-from funasr.modules.mask import subsequent_mask, vad_mask
-
-class EncoderLayerSANM(nn.Module):
-    def __init__(
-        self,
-        in_size,
-        size,
-        self_attn,
-        feed_forward,
-        dropout_rate,
-        normalize_before=True,
-        concat_after=False,
-        stochastic_depth_rate=0.0,
-    ):
-        """Construct an EncoderLayer object."""
-        super(EncoderLayerSANM, self).__init__()
-        self.self_attn = self_attn
-        self.feed_forward = feed_forward
-        self.norm1 = LayerNorm(in_size)
-        self.norm2 = LayerNorm(size)
-        self.dropout = nn.Dropout(dropout_rate)
-        self.in_size = in_size
-        self.size = size
-        self.normalize_before = normalize_before
-        self.concat_after = concat_after
-        if self.concat_after:
-            self.concat_linear = nn.Linear(size + size, size)
-        self.stochastic_depth_rate = stochastic_depth_rate
-        self.dropout_rate = dropout_rate
-
-    def forward(self, x, mask, cache=None, mask_shfit_chunk=None, mask_att_chunk_encoder=None):
-        """Compute encoded features.
-
-        Args:
-            x_input (torch.Tensor): Input tensor (#batch, time, size).
-            mask (torch.Tensor): Mask tensor for the input (#batch, time).
-            cache (torch.Tensor): Cache tensor of the input (#batch, time - 1, size).
-
-        Returns:
-            torch.Tensor: Output tensor (#batch, time, size).
-            torch.Tensor: Mask tensor (#batch, time).
-
-        """
-        skip_layer = False
-        # with stochastic depth, residual connection `x + f(x)` becomes
-        # `x <- x + 1 / (1 - p) * f(x)` at training time.
-        stoch_layer_coeff = 1.0
-        if self.training and self.stochastic_depth_rate > 0:
-            skip_layer = torch.rand(1).item() < self.stochastic_depth_rate
-            stoch_layer_coeff = 1.0 / (1 - self.stochastic_depth_rate)
-
-        if skip_layer:
-            if cache is not None:
-                x = torch.cat([cache, x], dim=1)
-            return x, mask
-
-        residual = x
-        if self.normalize_before:
-            x = self.norm1(x)
-
-        if self.concat_after:
-            x_concat = torch.cat((x, self.self_attn(x, mask, mask_shfit_chunk=mask_shfit_chunk, mask_att_chunk_encoder=mask_att_chunk_encoder)), dim=-1)
-            if self.in_size == self.size:
-                x = residual + stoch_layer_coeff * self.concat_linear(x_concat)
-            else:
-                x = stoch_layer_coeff * self.concat_linear(x_concat)
-        else:
-            if self.in_size == self.size:
-                x = residual + stoch_layer_coeff * self.dropout(
-                    self.self_attn(x, mask, mask_shfit_chunk=mask_shfit_chunk, mask_att_chunk_encoder=mask_att_chunk_encoder)
-                )
-            else:
-                x = stoch_layer_coeff * self.dropout(
-                    self.self_attn(x, mask, mask_shfit_chunk=mask_shfit_chunk, mask_att_chunk_encoder=mask_att_chunk_encoder)
-                )
-        if not self.normalize_before:
-            x = self.norm1(x)
-
-        residual = x
-        if self.normalize_before:
-            x = self.norm2(x)
-        x = residual + stoch_layer_coeff * self.dropout(self.feed_forward(x))
-        if not self.normalize_before:
-            x = self.norm2(x)
-
-
-        return x, mask, cache, mask_shfit_chunk, mask_att_chunk_encoder
-
-class SANMEncoder(AbsEncoder):
-    """
-    author: Speech Lab, Alibaba Group, China
-
-    """
-
-    def __init__(
-        self,
-        input_size: int,
-        output_size: int = 256,
-        attention_heads: int = 4,
-        linear_units: int = 2048,
-        num_blocks: int = 6,
-        dropout_rate: float = 0.1,
-        positional_dropout_rate: float = 0.1,
-        attention_dropout_rate: float = 0.0,
-        input_layer: Optional[str] = "conv2d",
-        pos_enc_class=SinusoidalPositionEncoder,
-        normalize_before: bool = True,
-        concat_after: bool = False,
-        positionwise_layer_type: str = "linear",
-        positionwise_conv_kernel_size: int = 1,
-        padding_idx: int = -1,
-        interctc_layer_idx: List[int] = [],
-        interctc_use_conditioning: bool = False,
-        kernel_size : int = 11,
-        sanm_shfit : int = 0,
-        selfattention_layer_type: str = "sanm",
-    ):
-        assert check_argument_types()
-        super().__init__()
-        self._output_size = output_size
-
-        if input_layer == "linear":
-            self.embed = torch.nn.Sequential(
-                torch.nn.Linear(input_size, output_size),
-                torch.nn.LayerNorm(output_size),
-                torch.nn.Dropout(dropout_rate),
-                torch.nn.ReLU(),
-                pos_enc_class(output_size, positional_dropout_rate),
-            )
-        elif input_layer == "conv2d":
-            self.embed = Conv2dSubsampling(input_size, output_size, dropout_rate)
-        elif input_layer == "conv2d2":
-            self.embed = Conv2dSubsampling2(input_size, output_size, dropout_rate)
-        elif input_layer == "conv2d6":
-            self.embed = Conv2dSubsampling6(input_size, output_size, dropout_rate)
-        elif input_layer == "conv2d8":
-            self.embed = Conv2dSubsampling8(input_size, output_size, dropout_rate)
-        elif input_layer == "embed":
-            self.embed = torch.nn.Sequential(
-                torch.nn.Embedding(input_size, output_size, padding_idx=padding_idx),
-                SinusoidalPositionEncoder(),
-            )
-        elif input_layer is None:
-            if input_size == output_size:
-                self.embed = None
-            else:
-                self.embed = torch.nn.Linear(input_size, output_size)
-        elif input_layer == "pe":
-            self.embed = SinusoidalPositionEncoder()
-        else:
-            raise ValueError("unknown input_layer: " + input_layer)
-        self.normalize_before = normalize_before
-        if positionwise_layer_type == "linear":
-            positionwise_layer = PositionwiseFeedForward
-            positionwise_layer_args = (
-                output_size,
-                linear_units,
-                dropout_rate,
-            )
-        elif positionwise_layer_type == "conv1d":
-            positionwise_layer = MultiLayeredConv1d
-            positionwise_layer_args = (
-                output_size,
-                linear_units,
-                positionwise_conv_kernel_size,
-                dropout_rate,
-            )
-        elif positionwise_layer_type == "conv1d-linear":
-            positionwise_layer = Conv1dLinear
-            positionwise_layer_args = (
-                output_size,
-                linear_units,
-                positionwise_conv_kernel_size,
-                dropout_rate,
-            )
-        else:
-            raise NotImplementedError("Support only linear or conv1d.")
-
-        if selfattention_layer_type == "selfattn":
-            encoder_selfattn_layer = MultiHeadedAttention
-            encoder_selfattn_layer_args = (
-                attention_heads,
-                output_size,
-                attention_dropout_rate,
-            )
-
-        elif selfattention_layer_type == "sanm":
-            self.encoder_selfattn_layer = MultiHeadedAttentionSANM
-            encoder_selfattn_layer_args0 = (
-                attention_heads,
-                input_size,
-                output_size,
-                attention_dropout_rate,
-                kernel_size,
-                sanm_shfit,
-            )
-
-            encoder_selfattn_layer_args = (
-                attention_heads,
-                output_size,
-                output_size,
-                attention_dropout_rate,
-                kernel_size,
-                sanm_shfit,
-            )
-
-        self.encoders0 = repeat(
-            1,
-            lambda lnum: EncoderLayerSANM(
-                input_size,
-                output_size,
-                self.encoder_selfattn_layer(*encoder_selfattn_layer_args0),
-                positionwise_layer(*positionwise_layer_args),
-                dropout_rate,
-                normalize_before,
-                concat_after,
-            ),
-        )
-
-        self.encoders = repeat(
-            num_blocks-1,
-            lambda lnum: EncoderLayerSANM(
-                output_size,
-                output_size,
-                self.encoder_selfattn_layer(*encoder_selfattn_layer_args),
-                positionwise_layer(*positionwise_layer_args),
-                dropout_rate,
-                normalize_before,
-                concat_after,
-            ),
-        )
-        if self.normalize_before:
-            self.after_norm = LayerNorm(output_size)
-
-        self.interctc_layer_idx = interctc_layer_idx
-        if len(interctc_layer_idx) > 0:
-            assert 0 < min(interctc_layer_idx) and max(interctc_layer_idx) < num_blocks
-        self.interctc_use_conditioning = interctc_use_conditioning
-        self.conditioning_layer = None
-        self.dropout = nn.Dropout(dropout_rate)
-
-    def output_size(self) -> int:
-        return self._output_size
-
-    def forward(
-        self,
-        xs_pad: torch.Tensor,
-        ilens: torch.Tensor,
-        prev_states: torch.Tensor = None,
-        ctc: CTC = None,
-    ) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]:
-        """Embed positions in tensor.
-
-        Args:
-            xs_pad: input tensor (B, L, D)
-            ilens: input length (B)
-            prev_states: Not to be used now.
-        Returns:
-            position embedded tensor and mask
-        """
-        masks = (~make_pad_mask(ilens)[:, None, :]).to(xs_pad.device)
-        xs_pad *= self.output_size()**0.5
-        if self.embed is None:
-            xs_pad = xs_pad
-        elif (
-            isinstance(self.embed, Conv2dSubsampling)
-            or isinstance(self.embed, Conv2dSubsampling2)
-            or isinstance(self.embed, Conv2dSubsampling6)
-            or isinstance(self.embed, Conv2dSubsampling8)
-        ):
-            short_status, limit_size = check_short_utt(self.embed, xs_pad.size(1))
-            if short_status:
-                raise TooShortUttError(
-                    f"has {xs_pad.size(1)} frames and is too short for subsampling "
-                    + f"(it needs more than {limit_size} frames), return empty results",
-                    xs_pad.size(1),
-                    limit_size,
-                )
-            xs_pad, masks = self.embed(xs_pad, masks)
-        else:
-            xs_pad = self.embed(xs_pad)
-
-        # xs_pad = self.dropout(xs_pad)
-        encoder_outs = self.encoders0(xs_pad, masks)
-        xs_pad, masks = encoder_outs[0], encoder_outs[1]
-        intermediate_outs = []
-        if len(self.interctc_layer_idx) == 0:
-            encoder_outs = self.encoders(xs_pad, masks)
-            xs_pad, masks = encoder_outs[0], encoder_outs[1]
-        else:
-            for layer_idx, encoder_layer in enumerate(self.encoders):
-                encoder_outs = encoder_layer(xs_pad, masks)
-                xs_pad, masks = encoder_outs[0], encoder_outs[1]
-
-                if layer_idx + 1 in self.interctc_layer_idx:
-                    encoder_out = xs_pad
-
-                    # intermediate outputs are also normalized
-                    if self.normalize_before:
-                        encoder_out = self.after_norm(encoder_out)
-
-                    intermediate_outs.append((layer_idx + 1, encoder_out))
-
-                    if self.interctc_use_conditioning:
-                        ctc_out = ctc.softmax(encoder_out)
-                        xs_pad = xs_pad + self.conditioning_layer(ctc_out)
-
-        if self.normalize_before:
-            xs_pad = self.after_norm(xs_pad)
-
-        olens = masks.squeeze(1).sum(1)
-        if len(intermediate_outs) > 0:
-            return (xs_pad, intermediate_outs), olens, None
-        return xs_pad, olens, None
-
-class SANMVadEncoder(AbsEncoder):
-    """
-    author: Speech Lab, Alibaba Group, China
-
-    """
-
-    def __init__(
-        self,
-        input_size: int,
-        output_size: int = 256,
-        attention_heads: int = 4,
-        linear_units: int = 2048,
-        num_blocks: int = 6,
-        dropout_rate: float = 0.1,
-        positional_dropout_rate: float = 0.1,
-        attention_dropout_rate: float = 0.0,
-        input_layer: Optional[str] = "conv2d",
-        pos_enc_class=SinusoidalPositionEncoder,
-        normalize_before: bool = True,
-        concat_after: bool = False,
-        positionwise_layer_type: str = "linear",
-        positionwise_conv_kernel_size: int = 1,
-        padding_idx: int = -1,
-        interctc_layer_idx: List[int] = [],
-        interctc_use_conditioning: bool = False,
-        kernel_size : int = 11,
-        sanm_shfit : int = 0,
-        selfattention_layer_type: str = "sanm",
-    ):
-        assert check_argument_types()
-        super().__init__()
-        self._output_size = output_size
-
-        if input_layer == "linear":
-            self.embed = torch.nn.Sequential(
-                torch.nn.Linear(input_size, output_size),
-                torch.nn.LayerNorm(output_size),
-                torch.nn.Dropout(dropout_rate),
-                torch.nn.ReLU(),
-                pos_enc_class(output_size, positional_dropout_rate),
-            )
-        elif input_layer == "conv2d":
-            self.embed = Conv2dSubsampling(input_size, output_size, dropout_rate)
-        elif input_layer == "conv2d2":
-            self.embed = Conv2dSubsampling2(input_size, output_size, dropout_rate)
-        elif input_layer == "conv2d6":
-            self.embed = Conv2dSubsampling6(input_size, output_size, dropout_rate)
-        elif input_layer == "conv2d8":
-            self.embed = Conv2dSubsampling8(input_size, output_size, dropout_rate)
-        elif input_layer == "embed":
-            self.embed = torch.nn.Sequential(
-                torch.nn.Embedding(input_size, output_size, padding_idx=padding_idx),
-                SinusoidalPositionEncoder(),
-            )
-        elif input_layer is None:
-            if input_size == output_size:
-                self.embed = None
-            else:
-                self.embed = torch.nn.Linear(input_size, output_size)
-        elif input_layer == "pe":
-            self.embed = SinusoidalPositionEncoder()
-        else:
-            raise ValueError("unknown input_layer: " + input_layer)
-        self.normalize_before = normalize_before
-        if positionwise_layer_type == "linear":
-            positionwise_layer = PositionwiseFeedForward
-            positionwise_layer_args = (
-                output_size,
-                linear_units,
-                dropout_rate,
-            )
-        elif positionwise_layer_type == "conv1d":
-            positionwise_layer = MultiLayeredConv1d
-            positionwise_layer_args = (
-                output_size,
-                linear_units,
-                positionwise_conv_kernel_size,
-                dropout_rate,
-            )
-        elif positionwise_layer_type == "conv1d-linear":
-            positionwise_layer = Conv1dLinear
-            positionwise_layer_args = (
-                output_size,
-                linear_units,
-                positionwise_conv_kernel_size,
-                dropout_rate,
-            )
-        else:
-            raise NotImplementedError("Support only linear or conv1d.")
-
-        if selfattention_layer_type == "selfattn":
-            encoder_selfattn_layer = MultiHeadedAttention
-            encoder_selfattn_layer_args = (
-                attention_heads,
-                output_size,
-                attention_dropout_rate,
-            )
-
-        elif selfattention_layer_type == "sanm":
-            self.encoder_selfattn_layer = MultiHeadedAttentionSANMwithMask
-            encoder_selfattn_layer_args0 = (
-                attention_heads,
-                input_size,
-                output_size,
-                attention_dropout_rate,
-                kernel_size,
-                sanm_shfit,
-            )
-
-            encoder_selfattn_layer_args = (
-                attention_heads,
-                output_size,
-                output_size,
-                attention_dropout_rate,
-                kernel_size,
-                sanm_shfit,
-            )
-
-        self.encoders0 = repeat(
-            1,
-            lambda lnum: EncoderLayerSANM(
-                input_size,
-                output_size,
-                self.encoder_selfattn_layer(*encoder_selfattn_layer_args0),
-                positionwise_layer(*positionwise_layer_args),
-                dropout_rate,
-                normalize_before,
-                concat_after,
-            ),
-        )
-
-        self.encoders = repeat(
-            num_blocks-1,
-            lambda lnum: EncoderLayerSANM(
-                output_size,
-                output_size,
-                self.encoder_selfattn_layer(*encoder_selfattn_layer_args),
-                positionwise_layer(*positionwise_layer_args),
-                dropout_rate,
-                normalize_before,
-                concat_after,
-            ),
-        )
-        if self.normalize_before:
-            self.after_norm = LayerNorm(output_size)
-
-        self.interctc_layer_idx = interctc_layer_idx
-        if len(interctc_layer_idx) > 0:
-            assert 0 < min(interctc_layer_idx) and max(interctc_layer_idx) < num_blocks
-        self.interctc_use_conditioning = interctc_use_conditioning
-        self.conditioning_layer = None
-        self.dropout = nn.Dropout(dropout_rate)
-
-    def output_size(self) -> int:
-        return self._output_size
-
-    def forward(
-        self,
-        xs_pad: torch.Tensor,
-        ilens: torch.Tensor,
-        vad_indexes: torch.Tensor,
-        prev_states: torch.Tensor = None,
-        ctc: CTC = None,
-    ) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]:
-        """Embed positions in tensor.
-
-        Args:
-            xs_pad: input tensor (B, L, D)
-            ilens: input length (B)
-            prev_states: Not to be used now.
-        Returns:
-            position embedded tensor and mask
-        """
-        masks = (~make_pad_mask(ilens)[:, None, :]).to(xs_pad.device)
-        sub_masks = subsequent_mask(masks.size(-1), device=xs_pad.device).unsqueeze(0)
-        no_future_masks = masks & sub_masks
-        xs_pad *= self.output_size()**0.5
-        if self.embed is None:
-            xs_pad = xs_pad
-        elif (isinstance(self.embed, Conv2dSubsampling) or isinstance(self.embed, Conv2dSubsampling2)
-              or isinstance(self.embed, Conv2dSubsampling6) or isinstance(self.embed, Conv2dSubsampling8)):
-            short_status, limit_size = check_short_utt(self.embed, xs_pad.size(1))
-            if short_status:
-                raise TooShortUttError(
-                    f"has {xs_pad.size(1)} frames and is too short for subsampling " +
-                    f"(it needs more than {limit_size} frames), return empty results",
-                    xs_pad.size(1),
-                    limit_size,
-                )
-            xs_pad, masks = self.embed(xs_pad, masks)
-        else:
-            xs_pad = self.embed(xs_pad)
-
-        # xs_pad = self.dropout(xs_pad)
-        mask_tup0 = [masks, no_future_masks]
-        encoder_outs = self.encoders0(xs_pad, mask_tup0)
-        xs_pad, _ = encoder_outs[0], encoder_outs[1]
-        intermediate_outs = []
-        #if len(self.interctc_layer_idx) == 0:
-        if False:
-            # Here, we should not use the repeat operation to do it for all layers.
-            encoder_outs = self.encoders(xs_pad, masks)
-            xs_pad, masks = encoder_outs[0], encoder_outs[1]
-        else:
-            for layer_idx, encoder_layer in enumerate(self.encoders):
-                if layer_idx + 1 == len(self.encoders):
-                    # This is last layer.
-                    coner_mask = torch.ones(masks.size(0),
-                                            masks.size(-1),
-                                            masks.size(-1),
-                                            device=xs_pad.device,
-                                            dtype=torch.bool)
-                    for word_index, length in enumerate(ilens):
-                        coner_mask[word_index, :, :] = vad_mask(masks.size(-1),
-                                                                vad_indexes[word_index],
-                                                                device=xs_pad.device)
-                    layer_mask = masks & coner_mask
-                else:
-                    layer_mask = no_future_masks
-                mask_tup1 = [masks, layer_mask]
-                encoder_outs = encoder_layer(xs_pad, mask_tup1)
-                xs_pad, layer_mask = encoder_outs[0], encoder_outs[1]
-
-                if layer_idx + 1 in self.interctc_layer_idx:
-                    encoder_out = xs_pad
-
-                    # intermediate outputs are also normalized
-                    if self.normalize_before:
-                        encoder_out = self.after_norm(encoder_out)
-
-                    intermediate_outs.append((layer_idx + 1, encoder_out))
-
-                    if self.interctc_use_conditioning:
-                        ctc_out = ctc.softmax(encoder_out)
-                        xs_pad = xs_pad + self.conditioning_layer(ctc_out)
-
-        if self.normalize_before:
-            xs_pad = self.after_norm(xs_pad)
-
-        olens = masks.squeeze(1).sum(1)
-        if len(intermediate_outs) > 0:
-            return (xs_pad, intermediate_outs), olens, None
-        return xs_pad, olens, None
-

funasr/punctuation/text_preprocessor.py

@@ -1,12 +1 @@
-def split_to_mini_sentence(words: list, word_limit: int = 20):
-    assert word_limit > 1
-    if len(words) <= word_limit:
-        return [words]
-    sentences = []
-    length = len(words)
-    sentence_len = length // word_limit
-    for i in range(sentence_len):
-        sentences.append(words[i * word_limit:(i + 1) * word_limit])
-    if length % word_limit > 0:
-        sentences.append(words[sentence_len * word_limit:])
-    return sentences
+

funasr/tasks/lm.py

@@ -15,7 +15,7 @@ from typeguard import check_return_type
 from funasr.datasets.collate_fn import CommonCollateFn
 from funasr.datasets.preprocessor import CommonPreprocessor
 from funasr.lm.abs_model import AbsLM
-from funasr.lm.espnet_model import ESPnetLanguageModel
+from funasr.lm.espnet_model import LanguageModel
 from funasr.lm.seq_rnn_lm import SequentialRNNLM
 from funasr.lm.transformer_lm import TransformerLM
 from funasr.tasks.abs_task import AbsTask
@@ -83,7 +83,7 @@ class LMTask(AbsTask):
         group.add_argument(
             "--model_conf",
             action=NestedDictAction,
-            default=get_default_kwargs(ESPnetLanguageModel),
+            default=get_default_kwargs(LanguageModel),
             help="The keyword arguments for model class.",
         )
 
@@ -178,7 +178,7 @@ class LMTask(AbsTask):
         return retval
 
     @classmethod
-    def build_model(cls, args: argparse.Namespace) -> ESPnetLanguageModel:
+    def build_model(cls, args: argparse.Namespace) -> LanguageModel:
         assert check_argument_types()
         if isinstance(args.token_list, str):
             with open(args.token_list, encoding="utf-8") as f:
@@ -201,7 +201,7 @@ class LMTask(AbsTask):
 
         # 2. Build ESPnetModel
         # Assume the last-id is sos_and_eos
-        model = ESPnetLanguageModel(lm=lm, vocab_size=vocab_size, **args.model_conf)
+        model = LanguageModel(lm=lm, vocab_size=vocab_size, **args.model_conf)
 
         # 3. Initialize
         if args.init is not None:

funasr/tasks/punctuation.py

@@ -14,10 +14,10 @@ from typeguard import check_return_type
 
 from funasr.datasets.collate_fn import CommonCollateFn
 from funasr.datasets.preprocessor import PuncTrainTokenizerCommonPreprocessor
-from funasr.punctuation.abs_model import AbsPunctuation
-from funasr.punctuation.espnet_model import ESPnetPunctuationModel
-from funasr.punctuation.target_delay_transformer import TargetDelayTransformer
-from funasr.punctuation.vad_realtime_transformer import VadRealtimeTransformer
+from funasr.train.abs_model import AbsPunctuation
+from funasr.train.abs_model import PunctuationModel
+from funasr.models.target_delay_transformer import TargetDelayTransformer
+from funasr.models.vad_realtime_transformer import VadRealtimeTransformer
 from funasr.tasks.abs_task import AbsTask
 from funasr.text.phoneme_tokenizer import g2p_choices
 from funasr.torch_utils.initialize import initialize
@@ -79,7 +79,7 @@ class PunctuationTask(AbsTask):
         group.add_argument(
             "--model_conf",
             action=NestedDictAction,
-            default=get_default_kwargs(ESPnetPunctuationModel),
+            default=get_default_kwargs(PunctuationModel),
             help="The keyword arguments for model class.",
         )
 
@@ -183,7 +183,7 @@ class PunctuationTask(AbsTask):
         return retval
 
     @classmethod
-    def build_model(cls, args: argparse.Namespace) -> ESPnetPunctuationModel:
+    def build_model(cls, args: argparse.Namespace) -> PunctuationModel:
         assert check_argument_types()
         if isinstance(args.token_list, str):
             with open(args.token_list, encoding="utf-8") as f:
@@ -218,7 +218,7 @@ class PunctuationTask(AbsTask):
         # Assume the last-id is sos_and_eos
         if "punc_weight" in args.model_conf:
             args.model_conf.pop("punc_weight")
-        model = ESPnetPunctuationModel(punc_model=punc, vocab_size=vocab_size, punc_weight=punc_weight_list, **args.model_conf)
+        model = PunctuationModel(punc_model=punc, vocab_size=vocab_size, punc_weight=punc_weight_list, **args.model_conf)
 
         # FIXME(kamo): Should be done in model?
         # 3. Initialize

funasr/train/abs_model.py

@@ -1,3 +1,9 @@
+from abc import ABC
+from abc import abstractmethod
+from typing import Tuple
+
+import torch
+
 from typing import Dict
 from typing import Optional
 from typing import Tuple
@@ -7,13 +13,34 @@ import torch.nn.functional as F
 from typeguard import check_argument_types
 
 from funasr.modules.nets_utils import make_pad_mask
-from funasr.punctuation.abs_model import AbsPunctuation
 from funasr.torch_utils.device_funcs import force_gatherable
 from funasr.train.abs_espnet_model import AbsESPnetModel
 
+from funasr.modules.scorers.scorer_interface import BatchScorerInterface
 
-class ESPnetPunctuationModel(AbsESPnetModel):
 
+class AbsPunctuation(torch.nn.Module, BatchScorerInterface, ABC):
+    """The abstract class
+
+    To share the loss calculation way among different models,
+    We uses delegate pattern here:
+    The instance of this class should be passed to "LanguageModel"
+
+    This "model" is one of mediator objects for "Task" class.
+
+    """
+
+    @abstractmethod
+    def forward(self, input: torch.Tensor, hidden: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+        raise NotImplementedError
+
+    @abstractmethod
+    def with_vad(self) -> bool:
+        raise NotImplementedError
+
+
+class PunctuationModel(AbsESPnetModel):
+    
     def __init__(self, punc_model: AbsPunctuation, vocab_size: int, ignore_id: int = 0, punc_weight: list = None):
         assert check_argument_types()
         super().__init__()
@@ -21,12 +48,12 @@ class ESPnetPunctuationModel(AbsESPnetModel):
         self.punc_weight = torch.Tensor(punc_weight)
         self.sos = 1
         self.eos = 2
-
+        
         # ignore_id may be assumed as 0, shared with CTC-blank symbol for ASR.
         self.ignore_id = ignore_id
-        #if self.punc_model.with_vad():
+        # if self.punc_model.with_vad():
         #    print("This is a vad puncuation model.")
-
+    
     def nll(
         self,
         text: torch.Tensor,
@@ -54,7 +81,7 @@ class ESPnetPunctuationModel(AbsESPnetModel):
         else:
             text = text[:, :max_length]
             punc = punc[:, :max_length]
-       
+        
         if self.punc_model.with_vad():
             # Should be VadRealtimeTransformer
             assert vad_indexes is not None
@@ -62,7 +89,7 @@ class ESPnetPunctuationModel(AbsESPnetModel):
         else:
             # Should be TargetDelayTransformer,
             y, _ = self.punc_model(text, text_lengths)
-
+        
         # Calc negative log likelihood
         # nll: (BxL,)
         if self.training == False:
@@ -75,7 +102,8 @@ class ESPnetPunctuationModel(AbsESPnetModel):
             return nll, text_lengths
         else:
             self.punc_weight = self.punc_weight.to(punc.device)
-            nll = F.cross_entropy(y.view(-1, y.shape[-1]), punc.view(-1), self.punc_weight, reduction="none", ignore_index=self.ignore_id)
+            nll = F.cross_entropy(y.view(-1, y.shape[-1]), punc.view(-1), self.punc_weight, reduction="none",
+                                  ignore_index=self.ignore_id)
         # nll: (BxL,) -> (BxL,)
         if max_length is None:
             nll.masked_fill_(make_pad_mask(text_lengths).to(nll.device).view(-1), 0.0)
@@ -87,7 +115,7 @@ class ESPnetPunctuationModel(AbsESPnetModel):
         # nll: (BxL,) -> (B, L)
         nll = nll.view(batch_size, -1)
         return nll, text_lengths
-
+    
     def batchify_nll(self,
                      text: torch.Tensor,
                      punc: torch.Tensor,
@@ -113,7 +141,7 @@ class ESPnetPunctuationModel(AbsESPnetModel):
             nlls = []
             x_lengths = []
             max_length = text_lengths.max()
-
+            
             start_idx = 0
             while True:
                 end_idx = min(start_idx + batch_size, total_num)
@@ -132,7 +160,7 @@ class ESPnetPunctuationModel(AbsESPnetModel):
         assert nll.size(0) == total_num
         assert x_lengths.size(0) == total_num
         return nll, x_lengths
-
+    
     def forward(
         self,
         text: torch.Tensor,
@@ -146,15 +174,15 @@ class ESPnetPunctuationModel(AbsESPnetModel):
         ntokens = y_lengths.sum()
         loss = nll.sum() / ntokens
         stats = dict(loss=loss.detach())
-
+        
         # force_gatherable: to-device and to-tensor if scalar for DataParallel
         loss, stats, weight = force_gatherable((loss, stats, ntokens), loss.device)
         return loss, stats, weight
-
+    
     def collect_feats(self, text: torch.Tensor, punc: torch.Tensor,
                       text_lengths: torch.Tensor) -> Dict[str, torch.Tensor]:
         return {}
-
+    
     def inference(self,
                   text: torch.Tensor,
                   text_lengths: torch.Tensor,