update the minimum size of audio

huangmingming

2023-01-30 adcee8828ef5d78b575043954deb662a35e318f7

 funasr/models/predictor/cif.py

@@ -1,10 +1,12 @@
import torch

from torch import nn



import logging

import numpy as np

from funasr.modules.nets_utils import make_pad_mask

from funasr.modules.streaming_utils.utils import sequence_mask



class CifPredictor(nn.Module):

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

    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(CifPredictor, self).__init__()



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

@@ -14,6 +16,7 @@
        self.threshold = threshold

        self.smooth_factor = smooth_factor

        self.noise_threshold = noise_threshold

        self.tail_threshold = tail_threshold



    def forward(self, hidden, target_label=None, mask=None, ignore_id=-1, mask_chunk_predictor=None,

                target_label_length=None):

@@ -29,10 +32,12 @@
        alphas = torch.sigmoid(output)

        alphas = torch.nn.functional.relu(alphas * self.smooth_factor - self.noise_threshold)

        if mask is not None:

            alphas = alphas * mask.transpose(-1, -2).float()

            mask = mask.transpose(-1, -2).float()

            alphas = alphas * mask

        if mask_chunk_predictor is not None:

            alphas = alphas * mask_chunk_predictor

        alphas = alphas.squeeze(-1)

        mask = mask.squeeze(-1)

        if target_label_length is not None:

            target_length = target_label_length

        elif target_label is not None:

@@ -42,8 +47,39 @@
        token_num = alphas.sum(-1)

        if target_length is not None:

            alphas *= (target_length / token_num)[:, None].repeat(1, alphas.size(1))

        elif self.tail_threshold > 0.0:

            hidden, alphas, token_num = self.tail_process_fn(hidden, alphas, token_num, mask=mask)

            
        acoustic_embeds, cif_peak = cif(hidden, alphas, self.threshold)

        
        if target_length is None and self.tail_threshold > 0.0:

            token_num_int = torch.max(token_num).type(torch.int32).item()

            acoustic_embeds = acoustic_embeds[:, :token_num_int, :]

            
        return acoustic_embeds, token_num, alphas, cif_peak



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

        b, t, d = hidden.size()

        tail_threshold = self.tail_threshold

        if mask is not None:

            zeros_t = torch.zeros((b, 1), dtype=torch.float32, device=alphas.device)

            ones_t = torch.ones_like(zeros_t)

            mask_1 = torch.cat([mask, zeros_t], dim=1)

            mask_2 = torch.cat([ones_t, mask], dim=1)

            mask = mask_2 - mask_1

            tail_threshold = mask * tail_threshold

            alphas = torch.cat([alphas, tail_threshold], dim=1)

        else:

            tail_threshold = torch.tensor([tail_threshold], dtype=alphas.dtype).to(alphas.device)

            tail_threshold = torch.reshape(tail_threshold, (1, 1))

            alphas = torch.cat([alphas, tail_threshold], dim=1)

        zeros = torch.zeros((b, 1, d), dtype=hidden.dtype).to(hidden.device)

        hidden = torch.cat([hidden, zeros], dim=1)

        token_num = alphas.sum(dim=-1)

        token_num_floor = torch.floor(token_num)



        return hidden, alphas, token_num_floor





    def gen_frame_alignments(self,

                             alphas: torch.Tensor = None,

@@ -95,8 +131,19 @@




class CifPredictorV2(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=0.0):

    def __init__(self,

                 idim,

                 l_order,

                 r_order,

                 threshold=1.0,

                 dropout=0.1,

                 smooth_factor=1.0,

                 noise_threshold=0,

                 tail_threshold=0.0,

                 tf2torch_tensor_name_prefix_torch="predictor",

                 tf2torch_tensor_name_prefix_tf="seq2seq/cif",

                 tail_mask=True,

                 ):

        super(CifPredictorV2, self).__init__()



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

@@ -107,6 +154,9 @@
        self.smooth_factor = smooth_factor

        self.noise_threshold = noise_threshold

        self.tail_threshold = tail_threshold

        self.tf2torch_tensor_name_prefix_torch = tf2torch_tensor_name_prefix_torch

        self.tf2torch_tensor_name_prefix_tf = tf2torch_tensor_name_prefix_tf

        self.tail_mask = tail_mask



    def forward(self, hidden, target_label=None, mask=None, ignore_id=-1, mask_chunk_predictor=None,

                target_label_length=None):

@@ -120,10 +170,12 @@
        alphas = torch.sigmoid(output)

        alphas = torch.nn.functional.relu(alphas * self.smooth_factor - self.noise_threshold)

        if mask is not None:

            alphas = alphas * mask.transpose(-1, -2).float()

            mask = mask.transpose(-1, -2).float()

            alphas = alphas * mask

        if mask_chunk_predictor is not None:

            alphas = alphas * mask_chunk_predictor

        alphas = alphas.squeeze(-1)

        mask = mask.squeeze(-1)

        if target_label_length is not None:

            target_length = target_label_length

        elif target_label is not None:

@@ -134,7 +186,10 @@
        if target_length is not None:

            alphas *= (target_length / token_num)[:, None].repeat(1, alphas.size(1))

        elif self.tail_threshold > 0.0:

            hidden, alphas, token_num = self.tail_process_fn(hidden, alphas, token_num)

            if self.tail_mask:

                hidden, alphas, token_num = self.tail_process_fn(hidden, alphas, token_num, mask=mask)

            else:

                hidden, alphas, token_num = self.tail_process_fn(hidden, alphas, token_num, mask=None)



        acoustic_embeds, cif_peak = cif(hidden, alphas, self.threshold)

        if target_length is None and self.tail_threshold > 0.0:

@@ -143,12 +198,24 @@


        return acoustic_embeds, token_num, alphas, cif_peak



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

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

        b, t, d = hidden.size()

        tail_threshold = self.tail_threshold

        tail_threshold = torch.tensor([tail_threshold], dtype=alphas.dtype).to(alphas.device)

        tail_threshold = tail_threshold.unsqueeze(0).repeat(b, 1)

        alphas = torch.cat([alphas, tail_threshold], dim=1)

        if mask is not None:

            zeros_t = torch.zeros((b, 1), dtype=torch.float32, device=alphas.device)

            ones_t = torch.ones_like(zeros_t)

            mask_1 = torch.cat([mask, zeros_t], dim=1)

            mask_2 = torch.cat([ones_t, mask], dim=1)

            mask = mask_2 - mask_1

            tail_threshold = mask * tail_threshold

            alphas = torch.cat([alphas, tail_threshold], dim=1)

        else:

            tail_threshold = torch.tensor([tail_threshold], dtype=alphas.dtype).to(alphas.device)

            tail_threshold = torch.reshape(tail_threshold, (1, 1))

            if b > 1:

                alphas = torch.cat([alphas, tail_threshold.repeat(b, 1)], dim=1)

            else:

                alphas = torch.cat([alphas, tail_threshold], dim=1)

        zeros = torch.zeros((b, 1, d), dtype=hidden.dtype).to(hidden.device)

        hidden = torch.cat([hidden, zeros], dim=1)

        token_num = alphas.sum(dim=-1)

@@ -203,6 +270,62 @@
        predictor_alignments = index_div_bool_zeros_count_tile_out

        predictor_alignments_length = predictor_alignments.sum(-1).type(encoder_sequence_length.dtype)

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



    def gen_tf2torch_map_dict(self):

    
        tensor_name_prefix_torch = self.tf2torch_tensor_name_prefix_torch

        tensor_name_prefix_tf = self.tf2torch_tensor_name_prefix_tf

        map_dict_local = {

            ## predictor

            "{}.cif_conv1d.weight".format(tensor_name_prefix_torch):

                {"name": "{}/conv1d/kernel".format(tensor_name_prefix_tf),

                 "squeeze": None,

                 "transpose": (2, 1, 0),

                 },  # (256,256,3),(3,256,256)

            "{}.cif_conv1d.bias".format(tensor_name_prefix_torch):

                {"name": "{}/conv1d/bias".format(tensor_name_prefix_tf),

                 "squeeze": None,

                 "transpose": None,

                 },  # (256,),(256,)

            "{}.cif_output.weight".format(tensor_name_prefix_torch):

                {"name": "{}/conv1d_1/kernel".format(tensor_name_prefix_tf),

                 "squeeze": 0,

                 "transpose": (1, 0),

                 },  # (1,256),(1,256,1)

            "{}.cif_output.bias".format(tensor_name_prefix_torch):

                {"name": "{}/conv1d_1/bias".format(tensor_name_prefix_tf),

                 "squeeze": None,

                 "transpose": None,

                 },  # (1,),(1,)

        }

        return map_dict_local



    def convert_tf2torch(self,

                         var_dict_tf,

                         var_dict_torch,

                         ):

        map_dict = self.gen_tf2torch_map_dict()

        var_dict_torch_update = dict()

        for name in sorted(var_dict_torch.keys(), reverse=False):

            names = name.split('.')

            if names[0] == self.tf2torch_tensor_name_prefix_torch:

                name_tf = map_dict[name]["name"]

                data_tf = var_dict_tf[name_tf]

                if map_dict[name]["squeeze"] is not None:

                    data_tf = np.squeeze(data_tf, axis=map_dict[name]["squeeze"])

                if map_dict[name]["transpose"] is not None:

                    data_tf = np.transpose(data_tf, map_dict[name]["transpose"])

                data_tf = torch.from_numpy(data_tf).type(torch.float32).to("cpu")

                assert var_dict_torch[name].size() == data_tf.size(), "{}, {}, {} != {}".format(name, name_tf,

                                                                                                var_dict_torch[

                                                                                                    name].size(),

                                                                                                data_tf.size())

                var_dict_torch_update[name] = data_tf

                logging.info(

                    "torch tensor: {}, {}, loading from tf tensor: {}, {}".format(name, data_tf.size(), name_tf,

                                                                                  var_dict_tf[name_tf].shape))

    
        return var_dict_torch_update





class mae_loss(nn.Module):

@@ -264,3 +387,271 @@
        pad_l = torch.zeros([max_label_len - l.size(0), hidden_size], device=hidden.device)

        list_ls.append(torch.cat([l, pad_l], 0))

    return torch.stack(list_ls, 0), fires





def cif_wo_hidden(alphas, threshold):

    batch_size, len_time = alphas.size()



    # loop varss

    integrate = torch.zeros([batch_size], device=alphas.device)

    # intermediate vars along time

    list_fires = []



    for t in range(len_time):

        alpha = alphas[:, t]



        integrate += alpha

        list_fires.append(integrate)



        fire_place = integrate >= threshold

        integrate = torch.where(fire_place,

                                integrate - torch.ones([batch_size], device=alphas.device),

                                integrate)



    fires = torch.stack(list_fires, 1)

    return fires





class CifPredictorV3(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=0.0,

                 tf2torch_tensor_name_prefix_torch="predictor",

                 tf2torch_tensor_name_prefix_tf="seq2seq/cif",

                 smooth_factor2=1.0,

                 noise_threshold2=0,

                 upsample_times=5,

                 upsample_type="cnn",

                 use_cif1_cnn=True,

                 tail_mask=True,

                 ):

        super(CifPredictorV3, 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.dropout = torch.nn.Dropout(p=dropout)

        self.threshold = threshold

        self.smooth_factor = smooth_factor

        self.noise_threshold = noise_threshold

        self.tail_threshold = tail_threshold

        self.tf2torch_tensor_name_prefix_torch = tf2torch_tensor_name_prefix_torch

        self.tf2torch_tensor_name_prefix_tf = tf2torch_tensor_name_prefix_tf



        self.upsample_times = upsample_times

        self.upsample_type = upsample_type

        self.use_cif1_cnn = use_cif1_cnn

        if self.upsample_type == 'cnn':

            self.upsample_cnn = nn.ConvTranspose1d(idim, idim, self.upsample_times, self.upsample_times)

            self.cif_output2 = nn.Linear(idim, 1)

        elif self.upsample_type == 'cnn_blstm':

            self.upsample_cnn = nn.ConvTranspose1d(idim, idim, self.upsample_times, self.upsample_times)

            self.blstm = nn.LSTM(idim, idim, 1, bias=True, batch_first=True, dropout=0.0, bidirectional=True)

            self.cif_output2 = nn.Linear(idim*2, 1)

        elif self.upsample_type == 'cnn_attn':

            self.upsample_cnn = nn.ConvTranspose1d(idim, idim, self.upsample_times, self.upsample_times)

            from funasr.models.encoder.transformer_encoder import EncoderLayer as TransformerEncoderLayer

            from funasr.modules.attention import MultiHeadedAttention

            from funasr.modules.positionwise_feed_forward import PositionwiseFeedForward

            positionwise_layer_args = (

                idim,

                idim*2,

                0.1,

            )

            self.self_attn = TransformerEncoderLayer(

                idim,

                MultiHeadedAttention(

                    4, idim, 0.1

                ),

                PositionwiseFeedForward(*positionwise_layer_args),

                0.1,

                True, #normalize_before,

                False, #concat_after,

            )

            self.cif_output2 = nn.Linear(idim, 1)

        self.smooth_factor2 = smooth_factor2

        self.noise_threshold2 = noise_threshold2



    def forward(self, hidden, target_label=None, mask=None, ignore_id=-1, mask_chunk_predictor=None,

                target_label_length=None):

        h = hidden

        context = h.transpose(1, 2)

        queries = self.pad(context)

        output = torch.relu(self.cif_conv1d(queries))



        # alphas2 is an extra head for timestamp prediction

        if not self.use_cif1_cnn:

            _output = context

        else:

            _output = output

        if self.upsample_type == 'cnn':

            output2 = self.upsample_cnn(_output)

            output2 = output2.transpose(1,2)

        elif self.upsample_type == 'cnn_blstm':

            output2 = self.upsample_cnn(_output)

            output2 = output2.transpose(1,2)

            output2, (_, _) = self.blstm(output2)

        elif self.upsample_type == 'cnn_attn':

            output2 = self.upsample_cnn(_output)

            output2 = output2.transpose(1,2)

            output2, _ = self.self_attn(output2, mask)

        # import pdb; pdb.set_trace()

        alphas2 = torch.sigmoid(self.cif_output2(output2))

        alphas2 = torch.nn.functional.relu(alphas2 * self.smooth_factor2 - self.noise_threshold2)

        # repeat the mask in T demension to match the upsampled length

        if mask is not None:

            mask2 = mask.repeat(1, self.upsample_times, 1).transpose(-1, -2).reshape(alphas2.shape[0], -1)

            mask2 = mask2.unsqueeze(-1)

            alphas2 = alphas2 * mask2

        alphas2 = alphas2.squeeze(-1)

        token_num2 = alphas2.sum(-1)



        output = output.transpose(1, 2)



        output = self.cif_output(output)

        alphas = torch.sigmoid(output)

        alphas = torch.nn.functional.relu(alphas * self.smooth_factor - self.noise_threshold)

        if mask is not None:

            mask = mask.transpose(-1, -2).float()

            alphas = alphas * mask

        if mask_chunk_predictor is not None:

            alphas = alphas * mask_chunk_predictor

        alphas = alphas.squeeze(-1)

        mask = mask.squeeze(-1)

        if target_label_length is not None:

            target_length = target_label_length

        elif target_label is not None:

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

        else:

            target_length = None

        token_num = alphas.sum(-1)



        if target_length is not None:

            alphas *= (target_length / token_num)[:, None].repeat(1, alphas.size(1))

        elif self.tail_threshold > 0.0:

            hidden, alphas, token_num = self.tail_process_fn(hidden, alphas, token_num, mask=mask)



        acoustic_embeds, cif_peak = cif(hidden, alphas, self.threshold)

        if target_length is None and self.tail_threshold > 0.0:

            token_num_int = torch.max(token_num).type(torch.int32).item()

            acoustic_embeds = acoustic_embeds[:, :token_num_int, :]

        return acoustic_embeds, token_num, alphas, cif_peak, token_num2

    
    def get_upsample_timestamp(self, hidden, target_label=None, mask=None, ignore_id=-1, mask_chunk_predictor=None,

                target_label_length=None, token_num=None):

        h = hidden

        b = hidden.shape[0]

        context = h.transpose(1, 2)

        queries = self.pad(context)

        output = torch.relu(self.cif_conv1d(queries))



        # alphas2 is an extra head for timestamp prediction

        if not self.use_cif1_cnn:

            _output = context

        else:

            _output = output

        if self.upsample_type == 'cnn':

            output2 = self.upsample_cnn(_output)

            output2 = output2.transpose(1,2)

        elif self.upsample_type == 'cnn_blstm':

            output2 = self.upsample_cnn(_output)

            output2 = output2.transpose(1,2)

            output2, (_, _) = self.blstm(output2)

        elif self.upsample_type == 'cnn_attn':

            output2 = self.upsample_cnn(_output)

            output2 = output2.transpose(1,2)

            output2, _ = self.self_attn(output2, mask)

        alphas2 = torch.sigmoid(self.cif_output2(output2))

        alphas2 = torch.nn.functional.relu(alphas2 * self.smooth_factor2 - self.noise_threshold2)

        # repeat the mask in T demension to match the upsampled length

        if mask is not None:

            mask2 = mask.repeat(1, self.upsample_times, 1).transpose(-1, -2).reshape(alphas2.shape[0], -1)

            mask2 = mask2.unsqueeze(-1)

            alphas2 = alphas2 * mask2

        alphas2 = alphas2.squeeze(-1)

        _token_num = alphas2.sum(-1)

        if token_num is not None:

            alphas2 *= (token_num / _token_num)[:, None].repeat(1, alphas2.size(1))

        # re-downsample

        ds_alphas = alphas2.reshape(b, -1, self.upsample_times).sum(-1)

        ds_cif_peak = cif_wo_hidden(ds_alphas, self.threshold - 1e-4)

        # upsampled alphas and cif_peak

        us_alphas = alphas2

        us_cif_peak = cif_wo_hidden(us_alphas, self.threshold - 1e-4)

        return ds_alphas, ds_cif_peak, us_alphas, us_cif_peak



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

        b, t, d = hidden.size()

        tail_threshold = self.tail_threshold

        if mask is not None:

            zeros_t = torch.zeros((b, 1), dtype=torch.float32, device=alphas.device)

            ones_t = torch.ones_like(zeros_t)

            mask_1 = torch.cat([mask, zeros_t], dim=1)

            mask_2 = torch.cat([ones_t, mask], dim=1)

            mask = mask_2 - mask_1

            tail_threshold = mask * tail_threshold

            alphas = torch.cat([alphas, tail_threshold], dim=1)

        else:

            tail_threshold = torch.tensor([tail_threshold], dtype=alphas.dtype).to(alphas.device)

            tail_threshold = torch.reshape(tail_threshold, (1, 1))

            alphas = torch.cat([alphas, tail_threshold], dim=1)

        zeros = torch.zeros((b, 1, d), dtype=hidden.dtype).to(hidden.device)

        hidden = torch.cat([hidden, zeros], dim=1)

        token_num = alphas.sum(dim=-1)

        token_num_floor = torch.floor(token_num)



        return hidden, alphas, token_num_floor



    def gen_frame_alignments(self,

                             alphas: torch.Tensor = None,

                             encoder_sequence_length: torch.Tensor = None):

        batch_size, maximum_length = alphas.size()

        int_type = torch.int32



        is_training = self.training

        if is_training:

            token_num = torch.round(torch.sum(alphas, dim=1)).type(int_type)

        else:

            token_num = torch.floor(torch.sum(alphas, dim=1)).type(int_type)



        max_token_num = torch.max(token_num).item()



        alphas_cumsum = torch.cumsum(alphas, dim=1)

        alphas_cumsum = torch.floor(alphas_cumsum).type(int_type)

        alphas_cumsum = alphas_cumsum[:, None, :].repeat(1, max_token_num, 1)



        index = torch.ones([batch_size, max_token_num], dtype=int_type)

        index = torch.cumsum(index, dim=1)

        index = index[:, :, None].repeat(1, 1, maximum_length).to(alphas_cumsum.device)



        index_div = torch.floor(torch.true_divide(alphas_cumsum, index)).type(int_type)

        index_div_bool_zeros = index_div.eq(0)

        index_div_bool_zeros_count = torch.sum(index_div_bool_zeros, dim=-1) + 1

        index_div_bool_zeros_count = torch.clamp(index_div_bool_zeros_count, 0, encoder_sequence_length.max())

        token_num_mask = (~make_pad_mask(token_num, maxlen=max_token_num)).to(token_num.device)

        index_div_bool_zeros_count *= token_num_mask



        index_div_bool_zeros_count_tile = index_div_bool_zeros_count[:, :, None].repeat(1, 1, maximum_length)

        ones = torch.ones_like(index_div_bool_zeros_count_tile)

        zeros = torch.zeros_like(index_div_bool_zeros_count_tile)

        ones = torch.cumsum(ones, dim=2)

        cond = index_div_bool_zeros_count_tile == ones

        index_div_bool_zeros_count_tile = torch.where(cond, zeros, ones)



        index_div_bool_zeros_count_tile_bool = index_div_bool_zeros_count_tile.type(torch.bool)

        index_div_bool_zeros_count_tile = 1 - index_div_bool_zeros_count_tile_bool.type(int_type)

        index_div_bool_zeros_count_tile_out = torch.sum(index_div_bool_zeros_count_tile, dim=1)

        index_div_bool_zeros_count_tile_out = index_div_bool_zeros_count_tile_out.type(int_type)

        predictor_mask = (~make_pad_mask(encoder_sequence_length, maxlen=encoder_sequence_length.max())).type(

            int_type).to(encoder_sequence_length.device)

        index_div_bool_zeros_count_tile_out = index_div_bool_zeros_count_tile_out * predictor_mask



        predictor_alignments = index_div_bool_zeros_count_tile_out

        predictor_alignments_length = predictor_alignments.sum(-1).type(encoder_sequence_length.dtype)

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