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.register import tables

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.utils.register import register_class, registry_tables



@register_class("predictor_classes", "CifPredictor")

class CifPredictor(nn.Module):

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

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

@@ -136,8 +138,8 @@
        predictor_alignments_length = predictor_alignments.sum(-1).type(encoder_sequence_length.dtype)

        return predictor_alignments.detach(), predictor_alignments_length.detach()



@register_class("predictor_classes", "CifPredictorV2")

class CifPredictorV2(nn.Module):

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

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:

@@ -205,7 +207,8 @@


        return acoustic_embeds, token_num, alphas, cif_peak



    def forward_chunk(self, hidden, cache=None):

    def forward_chunk(self, hidden, cache=None, **kwargs):

        is_final = kwargs.get("is_final", False)

        batch_size, len_time, hidden_size = hidden.shape

        h = hidden

        context = h.transpose(1, 2)

@@ -226,14 +229,14 @@


        if cache is not None and "chunk_size" in cache:

            alphas[:, :cache["chunk_size"][0]] = 0.0

            if "is_final" in cache and not cache["is_final"]:

            if not is_final:

                alphas[:, sum(cache["chunk_size"][:2]):] = 0.0

        if cache is not None and "cif_alphas" in cache and "cif_hidden" in cache:

            cache["cif_hidden"] = to_device(cache["cif_hidden"], device=hidden.device)

            cache["cif_alphas"] = to_device(cache["cif_alphas"], device=alphas.device)

            hidden = torch.cat((cache["cif_hidden"], hidden), dim=1)

            alphas = torch.cat((cache["cif_alphas"], alphas), dim=1)

        if cache is not None and "is_final" in cache and cache["is_final"]:

        if cache is not None and is_final:

            tail_hidden = torch.zeros((batch_size, 1, hidden_size), device=hidden.device)

            tail_alphas = torch.tensor([[self.tail_threshold]], device=alphas.device)

            tail_alphas = torch.tile(tail_alphas, (batch_size, 1))

@@ -277,7 +280,7 @@


        max_token_len = max(token_length)

        if max_token_len == 0:

             return hidden, torch.stack(token_length, 0)

             return hidden, torch.stack(token_length, 0), None, None

        list_ls = []

        for b in range(batch_size):

            pad_frames = torch.zeros((max_token_len - token_length[b], hidden_size), device=alphas.device)

@@ -291,7 +294,7 @@
        cache["cif_alphas"] = torch.unsqueeze(cache["cif_alphas"], axis=0)

        cache["cif_hidden"] = torch.stack(cache_hiddens, axis=0)

        cache["cif_hidden"] = torch.unsqueeze(cache["cif_hidden"], axis=0)

        return torch.stack(list_ls, 0), torch.stack(token_length, 0)

        return torch.stack(list_ls, 0), torch.stack(token_length, 0), None, None





    def tail_process_fn(self, hidden, alphas, token_num=None, mask=None):

@@ -425,7 +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__()