update

嘉渊

2023-04-27 9539dec5c7058b4a10f9226d08a8f30e56147dcd

update

 funasr/models/encoder/data2vec_encoder.py

@@ -12,6 +12,7 @@
import torch.nn.functional as F
from typeguard import check_argument_types

from funasr.models.encoder.abs_encoder import AbsEncoder
from funasr.modules.data2vec.data_utils import compute_mask_indices
from funasr.modules.data2vec.ema_module import EMAModule
from funasr.modules.data2vec.grad_multiply import GradMultiply
@@ -28,7 +29,7 @@
    return end - r * pct_remaining


class Data2VecEncoder(torch.nn.Module):
class Data2VecEncoder(AbsEncoder):
    def __init__(
            self,
            # for ConvFeatureExtractionModel
@@ -573,4 +574,4 @@
            )

    def output_size(self) -> int:
        return self.encoder_embed_dim
        return self.encoder_embed_dim

 funasr/models/encoder/resnet34_encoder.py

@@ -1,8 +1,8 @@
import torch
from torch.nn import functional as F
from funasr.models.encoder.abs_encoder import AbsEncoder
from typing import Tuple, Optional
from funasr.models.pooling.statistic_pooling import statistic_pooling, windowed_statistic_pooling
from funasr.models.encoder.abs_encoder import AbsEncoder
from collections import OrderedDict
import logging
import numpy as np
@@ -76,7 +76,7 @@
        return xs_pad, ilens


class ResNet34(torch.nn.Module):
class ResNet34(AbsEncoder):
    def __init__(
            self,
            input_size,
@@ -406,6 +406,12 @@
            tf2torch_tensor_name_prefix_torch="encoder",
            tf2torch_tensor_name_prefix_tf="seq2seq/speech_encoder"
    ):
        """
        Author: Speech Lab, Alibaba Group, China
        SOND: Speaker Overlap-aware Neural Diarization for Multi-party Meeting Analysis
        https://arxiv.org/abs/2211.10243
        """

        super(ResNet34Diar, self).__init__(
            input_size,
            use_head_conv=use_head_conv,
@@ -633,6 +639,12 @@
            tf2torch_tensor_name_prefix_torch="encoder",
            tf2torch_tensor_name_prefix_tf="seq2seq/speech_encoder"
    ):
        """
        Author: Speech Lab, Alibaba Group, China
        TOLD: A Novel Two-Stage Overlap-Aware Framework for Speaker Diarization
        https://arxiv.org/abs/2303.05397
        """

        super(ResNet34SpL2RegDiar, self).__init__(
            input_size,
            use_head_conv=use_head_conv,
@@ -838,4 +850,4 @@
                else:
                    logging.warning("{} is missed from tf checkpoint".format(name))

        return var_dict_torch_update
        return var_dict_torch_update

 funasr/models/encoder/rnn_encoder.py

@@ -1,3 +1,4 @@

from typing import Optional
from typing import Sequence
from typing import Tuple
@@ -9,11 +10,11 @@
from funasr.modules.nets_utils import make_pad_mask
from funasr.modules.rnn.encoders import RNN
from funasr.modules.rnn.encoders import RNNP
from funasr.models.encoder.abs_encoder import AbsEncoder


class RNNEncoder(torch.nn.Module):
class RNNEncoder(AbsEncoder):
    """RNNEncoder class.

    Args:
        input_size: The number of expected features in the input
        output_size: The number of output features
@@ -22,7 +23,6 @@
        use_projection: Use projection layer or not
        num_layers: Number of recurrent layers
        dropout: dropout probability

    """

    def __init__(