#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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import torch
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from torch import nn
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def sequence_mask(lengths, maxlen=None, dtype=torch.float32, device=None):
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if maxlen is None:
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maxlen = lengths.max()
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row_vector = torch.arange(0, maxlen, 1).to(lengths.device)
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matrix = torch.unsqueeze(lengths, dim=-1)
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mask = row_vector < matrix
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mask = mask.detach()
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return mask.type(dtype).to(device) if device is not None else mask.type(dtype)
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def sequence_mask_scripts(lengths, maxlen:int):
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row_vector = torch.arange(0, maxlen, 1).type(lengths.dtype).to(lengths.device)
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matrix = torch.unsqueeze(lengths, dim=-1)
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mask = row_vector < matrix
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return mask.type(torch.float32).to(lengths.device)
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class CifPredictorV2(nn.Module):
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def __init__(self, model):
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super().__init__()
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self.pad = model.pad
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self.cif_conv1d = model.cif_conv1d
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self.cif_output = model.cif_output
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self.threshold = model.threshold
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self.smooth_factor = model.smooth_factor
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self.noise_threshold = model.noise_threshold
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self.tail_threshold = model.tail_threshold
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def forward(self, hidden: torch.Tensor,
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mask: torch.Tensor,
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):
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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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output = torch.relu(self.cif_conv1d(queries))
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output = output.transpose(1, 2)
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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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mask = mask.transpose(-1, -2).float()
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alphas = alphas * mask
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alphas = alphas.squeeze(-1)
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token_num = alphas.sum(-1)
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mask = mask.squeeze(-1)
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hidden, alphas, token_num = self.tail_process_fn(hidden, alphas, mask=mask)
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acoustic_embeds, cif_peak = cif(hidden, alphas, self.threshold)
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return acoustic_embeds, token_num, alphas, cif_peak
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def tail_process_fn(self, hidden, alphas, token_num=None, mask=None):
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b, t, d = hidden.size()
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tail_threshold = self.tail_threshold
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zeros_t = torch.zeros((b, 1), dtype=torch.float32, device=alphas.device)
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ones_t = torch.ones_like(zeros_t)
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mask_1 = torch.cat([mask, zeros_t], dim=1)
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mask_2 = torch.cat([ones_t, mask], dim=1)
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mask = mask_2 - mask_1
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tail_threshold = mask * tail_threshold
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alphas = torch.cat([alphas, zeros_t], dim=1)
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alphas = torch.add(alphas, tail_threshold)
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zeros = torch.zeros((b, 1, d), dtype=hidden.dtype).to(hidden.device)
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hidden = torch.cat([hidden, zeros], dim=1)
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token_num = alphas.sum(dim=-1)
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token_num_floor = torch.floor(token_num)
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return hidden, alphas, token_num_floor
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# @torch.jit.script
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# def cif(hidden, alphas, threshold: float):
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# batch_size, len_time, hidden_size = hidden.size()
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# threshold = torch.tensor([threshold], dtype=alphas.dtype).to(alphas.device)
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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.floor(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([int(max_label_len - l.size(0)), int(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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@torch.jit.script
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def cif(hidden, alphas, threshold: float):
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batch_size, len_time, hidden_size = hidden.size()
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threshold = torch.tensor([threshold], dtype=alphas.dtype).to(alphas.device)
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# loop varss
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integrate = torch.zeros([batch_size], dtype=alphas.dtype, device=hidden.device)
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frame = torch.zeros([batch_size, hidden_size], dtype=hidden.dtype, 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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for t in range(len_time):
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alpha = alphas[:, t]
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distribution_completion = torch.ones([batch_size], dtype=alphas.dtype, device=hidden.device) - integrate
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integrate += alpha
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list_fires.append(integrate)
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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], dtype=alphas.dtype, 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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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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fires = torch.stack(list_fires, 1)
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frames = torch.stack(list_frames, 1)
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fire_idxs = fires >= threshold
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frame_fires = torch.zeros_like(hidden)
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max_label_len = frames[0, fire_idxs[0]].size(0)
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for b in range(batch_size):
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frame_fire = frames[b, fire_idxs[b]]
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frame_len = frame_fire.size(0)
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frame_fires[b, :frame_len, :] = frame_fire
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if frame_len >= max_label_len:
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max_label_len = frame_len
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frame_fires = frame_fires[:, :max_label_len, :]
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return frame_fires, fires
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class CifPredictorV3(nn.Module):
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def __init__(self, model):
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super().__init__()
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self.pad = model.pad
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self.cif_conv1d = model.cif_conv1d
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self.cif_output = model.cif_output
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self.threshold = model.threshold
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self.smooth_factor = model.smooth_factor
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self.noise_threshold = model.noise_threshold
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self.tail_threshold = model.tail_threshold
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self.upsample_times = model.upsample_times
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self.upsample_cnn = model.upsample_cnn
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self.blstm = model.blstm
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self.cif_output2 = model.cif_output2
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self.smooth_factor2 = model.smooth_factor2
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self.noise_threshold2 = model.noise_threshold2
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def forward(self, hidden: torch.Tensor,
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mask: torch.Tensor,
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):
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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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output = torch.relu(self.cif_conv1d(queries))
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output = output.transpose(1, 2)
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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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mask = mask.transpose(-1, -2).float()
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alphas = alphas * mask
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alphas = alphas.squeeze(-1)
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token_num = alphas.sum(-1)
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mask = mask.squeeze(-1)
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hidden, alphas, token_num = self.tail_process_fn(hidden, alphas, mask=mask)
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acoustic_embeds, cif_peak = cif(hidden, alphas, self.threshold)
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return acoustic_embeds, token_num, alphas, cif_peak
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def get_upsample_timestmap(self, hidden, mask=None, token_num=None):
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h = hidden
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b = hidden.shape[0]
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context = h.transpose(1, 2)
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# generate alphas2
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_output = context
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output2 = self.upsample_cnn(_output)
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output2 = output2.transpose(1, 2)
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output2, (_, _) = self.blstm(output2)
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alphas2 = torch.sigmoid(self.cif_output2(output2))
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alphas2 = torch.nn.functional.relu(alphas2 * self.smooth_factor2 - self.noise_threshold2)
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mask = mask.repeat(1, self.upsample_times, 1).transpose(-1, -2).reshape(alphas2.shape[0], -1)
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mask = mask.unsqueeze(-1)
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alphas2 = alphas2 * mask
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alphas2 = alphas2.squeeze(-1)
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_token_num = alphas2.sum(-1)
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alphas2 *= (token_num / _token_num)[:, None].repeat(1, alphas2.size(1))
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# upsampled alphas and cif_peak
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us_alphas = alphas2
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us_cif_peak = cif_wo_hidden(us_alphas, self.threshold - 1e-4)
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return us_alphas, us_cif_peak
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def tail_process_fn(self, hidden, alphas, token_num=None, mask=None):
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b, t, d = hidden.size()
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tail_threshold = self.tail_threshold
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zeros_t = torch.zeros((b, 1), dtype=torch.float32, device=alphas.device)
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ones_t = torch.ones_like(zeros_t)
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mask_1 = torch.cat([mask, zeros_t], dim=1)
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mask_2 = torch.cat([ones_t, mask], dim=1)
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mask = mask_2 - mask_1
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tail_threshold = mask * tail_threshold
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alphas = torch.cat([alphas, zeros_t], dim=1)
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alphas = torch.add(alphas, tail_threshold)
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zeros = torch.zeros((b, 1, d), dtype=hidden.dtype).to(hidden.device)
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hidden = torch.cat([hidden, zeros], dim=1)
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token_num = alphas.sum(dim=-1)
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token_num_floor = torch.floor(token_num)
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return hidden, alphas, token_num_floor
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@torch.jit.script
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def cif_wo_hidden(alphas, threshold: float):
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batch_size, len_time = alphas.size()
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# loop varss
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integrate = torch.zeros([batch_size], dtype=alphas.dtype, device=alphas.device)
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# intermediate vars along time
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list_fires = []
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for t in range(len_time):
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alpha = alphas[:, t]
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integrate += alpha
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list_fires.append(integrate)
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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),
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integrate)
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fires = torch.stack(list_fires, 1)
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return fires
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