# import torch
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# from torch import nn
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# from torch import Tensor
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# import logging
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# import numpy as np
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# from funasr.train_utils.device_funcs import to_device
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# from funasr.models.transformer.utils.nets_utils import make_pad_mask
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# from funasr.models.scama.utils import sequence_mask
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# from typing import Optional, Tuple
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#
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# from funasr.register import tables
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#
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# class mae_loss(nn.Module):
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#
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# def __init__(self, normalize_length=False):
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# super(mae_loss, self).__init__()
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# self.normalize_length = normalize_length
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# self.criterion = torch.nn.L1Loss(reduction='sum')
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#
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# def forward(self, token_length, pre_token_length):
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# loss_token_normalizer = token_length.size(0)
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# if self.normalize_length:
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# loss_token_normalizer = token_length.sum().type(torch.float32)
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# loss = self.criterion(token_length, pre_token_length)
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# loss = loss / loss_token_normalizer
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# return loss
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#
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#
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# def cif(hidden, alphas, threshold):
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# batch_size, len_time, hidden_size = hidden.size()
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#
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# # loop varss
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# integrate = torch.zeros([batch_size], device=hidden.device)
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# frame = torch.zeros([batch_size, hidden_size], device=hidden.device)
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# # intermediate vars along time
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# list_fires = []
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# list_frames = []
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#
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# for t in range(len_time):
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# alpha = alphas[:, t]
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# distribution_completion = torch.ones([batch_size], device=hidden.device) - integrate
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#
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# integrate += alpha
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# list_fires.append(integrate)
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#
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# fire_place = integrate >= threshold
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# integrate = torch.where(fire_place,
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# integrate - torch.ones([batch_size], device=hidden.device),
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# integrate)
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# cur = torch.where(fire_place,
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# distribution_completion,
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# alpha)
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# remainds = alpha - cur
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#
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# frame += cur[:, None] * hidden[:, t, :]
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# list_frames.append(frame)
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# frame = torch.where(fire_place[:, None].repeat(1, hidden_size),
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# remainds[:, None] * hidden[:, t, :],
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# frame)
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#
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# fires = torch.stack(list_fires, 1)
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# frames = torch.stack(list_frames, 1)
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# list_ls = []
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# len_labels = torch.round(alphas.sum(-1)).int()
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# max_label_len = len_labels.max()
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# for b in range(batch_size):
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# fire = fires[b, :]
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# l = torch.index_select(frames[b, :, :], 0, torch.nonzero(fire >= threshold).squeeze())
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# pad_l = torch.zeros([max_label_len - l.size(0), hidden_size], device=hidden.device)
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# list_ls.append(torch.cat([l, pad_l], 0))
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# return torch.stack(list_ls, 0), fires
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#
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#
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# def cif_wo_hidden(alphas, threshold):
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# batch_size, len_time = alphas.size()
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#
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# # loop varss
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# integrate = torch.zeros([batch_size], device=alphas.device)
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# # intermediate vars along time
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# list_fires = []
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#
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# for t in range(len_time):
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# alpha = alphas[:, t]
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#
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# integrate += alpha
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# list_fires.append(integrate)
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#
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# fire_place = integrate >= threshold
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# integrate = torch.where(fire_place,
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# integrate - torch.ones([batch_size], device=alphas.device)*threshold,
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# integrate)
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#
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# fires = torch.stack(list_fires, 1)
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# return fires
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#
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# @tables.register("predictor_classes", "BATPredictor")
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# class BATPredictor(nn.Module):
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# def __init__(self, idim, l_order, r_order, threshold=1.0, dropout=0.1, smooth_factor=1.0, noise_threshold=0, return_accum=False):
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# super(BATPredictor, self).__init__()
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#
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# self.pad = nn.ConstantPad1d((l_order, r_order), 0)
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# self.cif_conv1d = nn.Conv1d(idim, idim, l_order + r_order + 1, groups=idim)
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# self.cif_output = nn.Linear(idim, 1)
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# self.dropout = torch.nn.Dropout(p=dropout)
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# self.threshold = threshold
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# self.smooth_factor = smooth_factor
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# self.noise_threshold = noise_threshold
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# self.return_accum = return_accum
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#
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# def cif(
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# self,
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# input: Tensor,
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# alpha: Tensor,
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# beta: float = 1.0,
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# return_accum: bool = False,
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# ) -> Tuple[Tensor, Tensor, Tensor, Tensor]:
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# B, S, C = input.size()
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# assert tuple(alpha.size()) == (B, S), f"{alpha.size()} != {(B, S)}"
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#
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# dtype = alpha.dtype
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# alpha = alpha.float()
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#
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# alpha_sum = alpha.sum(1)
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# feat_lengths = (alpha_sum / beta).floor().long()
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# T = feat_lengths.max()
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#
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# # aggregate and integrate
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# csum = alpha.cumsum(-1)
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# with torch.no_grad():
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# # indices used for scattering
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# right_idx = (csum / beta).floor().long().clip(max=T)
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# left_idx = right_idx.roll(1, dims=1)
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# left_idx[:, 0] = 0
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#
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# # count # of fires from each source
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# fire_num = right_idx - left_idx
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# extra_weights = (fire_num - 1).clip(min=0)
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# # The extra entry in last dim is for
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# output = input.new_zeros((B, T + 1, C))
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# source_range = torch.arange(1, 1 + S).unsqueeze(0).type_as(input)
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# zero = alpha.new_zeros((1,))
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#
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# # right scatter
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# fire_mask = fire_num > 0
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# right_weight = torch.where(
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# fire_mask,
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# csum - right_idx.type_as(alpha) * beta,
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# zero
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# ).type_as(input)
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# # assert right_weight.ge(0).all(), f"{right_weight} should be non-negative."
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# output.scatter_add_(
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# 1,
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# right_idx.unsqueeze(-1).expand(-1, -1, C),
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# right_weight.unsqueeze(-1) * input
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# )
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#
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# # left scatter
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# left_weight = (
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# alpha - right_weight - extra_weights.type_as(alpha) * beta
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# ).type_as(input)
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# output.scatter_add_(
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# 1,
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# left_idx.unsqueeze(-1).expand(-1, -1, C),
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# left_weight.unsqueeze(-1) * input
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# )
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#
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# # extra scatters
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# if extra_weights.ge(0).any():
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# extra_steps = extra_weights.max().item()
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# tgt_idx = left_idx
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# src_feats = input * beta
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# for _ in range(extra_steps):
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# tgt_idx = (tgt_idx + 1).clip(max=T)
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# # (B, S, 1)
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# src_mask = (extra_weights > 0)
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# output.scatter_add_(
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# 1,
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# tgt_idx.unsqueeze(-1).expand(-1, -1, C),
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# src_feats * src_mask.unsqueeze(2)
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# )
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# extra_weights -= 1
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#
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# output = output[:, :T, :]
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#
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# if return_accum:
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# return output, csum
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# else:
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# return output, alpha
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#
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# def forward(self, hidden, target_label=None, mask=None, ignore_id=-1, mask_chunk_predictor=None, target_label_length=None):
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# h = hidden
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# context = h.transpose(1, 2)
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# queries = self.pad(context)
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# memory = self.cif_conv1d(queries)
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# output = memory + context
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# output = self.dropout(output)
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# output = output.transpose(1, 2)
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# output = torch.relu(output)
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# output = self.cif_output(output)
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# alphas = torch.sigmoid(output)
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# alphas = torch.nn.functional.relu(alphas*self.smooth_factor - self.noise_threshold)
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# if mask is not None:
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# alphas = alphas * mask.transpose(-1, -2).float()
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# if mask_chunk_predictor is not None:
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# alphas = alphas * mask_chunk_predictor
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# alphas = alphas.squeeze(-1)
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# if target_label_length is not None:
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# target_length = target_label_length
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# elif target_label is not None:
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# target_length = (target_label != ignore_id).float().sum(-1)
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# # logging.info("target_length: {}".format(target_length))
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# else:
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# target_length = None
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# token_num = alphas.sum(-1)
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# if target_length is not None:
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# # length_noise = torch.rand(alphas.size(0), device=alphas.device) - 0.5
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# # target_length = length_noise + target_length
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# alphas *= ((target_length + 1e-4) / token_num)[:, None].repeat(1, alphas.size(1))
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# acoustic_embeds, cif_peak = self.cif(hidden, alphas, self.threshold, self.return_accum)
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# return acoustic_embeds, token_num, alphas, cif_peak
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