aishell example

游雁

2024-02-19 94de39dde2e616a01683c518023d0fab72b4e103

 funasr/models/paraformer/cif_predictor.py

@@ -1,23 +1,25 @@
#!/usr/bin/env python3

# -*- encoding: utf-8 -*-

# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.

#  MIT License  (https://opensource.org/licenses/MIT)



import torch

from torch import nn

from torch import Tensor

import logging

import numpy as np

from funasr.train_utils.device_funcs import to_device

from funasr.models.transformer.utils.nets_utils import make_pad_mask

from funasr.models.scama.utils import sequence_mask

from typing import Optional, Tuple



from funasr.register import tables

from funasr.train_utils.device_funcs import to_device

from funasr.models.transformer.utils.nets_utils import make_pad_mask





@tables.register("predictor_classes", "CifPredictor")

class CifPredictor(nn.Module):

class CifPredictor(torch.nn.Module):

    def __init__(self, idim, l_order, r_order, threshold=1.0, dropout=0.1, smooth_factor=1.0, noise_threshold=0, tail_threshold=0.45):

        super().__init__()



        self.pad = nn.ConstantPad1d((l_order, r_order), 0)

        self.cif_conv1d = nn.Conv1d(idim, idim, l_order + r_order + 1, groups=idim)

        self.cif_output = nn.Linear(idim, 1)

        self.pad = torch.nn.ConstantPad1d((l_order, r_order), 0)

        self.cif_conv1d = torch.nn.Conv1d(idim, idim, l_order + r_order + 1, groups=idim)

        self.cif_output = torch.nn.Linear(idim, 1)

        self.dropout = torch.nn.Dropout(p=dropout)

        self.threshold = threshold

        self.smooth_factor = smooth_factor

@@ -137,7 +139,7 @@
        return predictor_alignments.detach(), predictor_alignments_length.detach()



@tables.register("predictor_classes", "CifPredictorV2")

class CifPredictorV2(nn.Module):

class CifPredictorV2(torch.nn.Module):

    def __init__(self,

                 idim,

                 l_order,

@@ -153,9 +155,9 @@
                 ):

        super(CifPredictorV2, self).__init__()



        self.pad = nn.ConstantPad1d((l_order, r_order), 0)

        self.cif_conv1d = nn.Conv1d(idim, idim, l_order + r_order + 1)

        self.cif_output = nn.Linear(idim, 1)

        self.pad = torch.nn.ConstantPad1d((l_order, r_order), 0)

        self.cif_conv1d = torch.nn.Conv1d(idim, idim, l_order + r_order + 1)

        self.cif_output = torch.nn.Linear(idim, 1)

        self.dropout = torch.nn.Dropout(p=dropout)

        self.threshold = threshold

        self.smooth_factor = smooth_factor

@@ -184,7 +186,7 @@
        alphas = alphas.squeeze(-1)

        mask = mask.squeeze(-1)

        if target_label_length is not None:

            target_length = target_label_length

            target_length = target_label_length.squeeze(-1)

        elif target_label is not None:

            target_length = (target_label != ignore_id).float().sum(-1)

        else:

@@ -426,7 +428,7 @@
        return var_dict_torch_update





class mae_loss(nn.Module):

class mae_loss(torch.nn.Module):



    def __init__(self, normalize_length=False):

        super(mae_loss, self).__init__()