| | |
|---|
| | | #!/usr/bin/env python3
|
|---|
| | | # -*- coding: utf-8 -*-
|
|---|
| | |
|
|---|
| | | import torch
|
|---|
| | | from torch import nn
|
|---|
| | | import logging
|
|---|
| | | import numpy as np
|
|---|
| | |
|
|---|
| | |
|
|---|
| | | def sequence_mask(lengths, maxlen=None, dtype=torch.float32, device=None):
|
|---|
| | |
|---|
| | | max_label_len = frame_len
|
|---|
| | | frame_fires = frame_fires[:, :max_label_len, :]
|
|---|
| | | return frame_fires, fires
|
|---|
| | |
|
|---|
| | |
|
|---|
| | | class CifPredictorV3(nn.Module):
|
|---|
| | | def __init__(self, model):
|
|---|
| | | super().__init__()
|
|---|
| | | |
|---|
| | | self.pad = model.pad
|
|---|
| | | self.cif_conv1d = model.cif_conv1d
|
|---|
| | | self.cif_output = model.cif_output
|
|---|
| | | self.threshold = model.threshold
|
|---|
| | | self.smooth_factor = model.smooth_factor
|
|---|
| | | self.noise_threshold = model.noise_threshold
|
|---|
| | | self.tail_threshold = model.tail_threshold
|
|---|
| | |
|
|---|
| | | self.upsample_times = model.upsample_times
|
|---|
| | | self.upsample_cnn = model.upsample_cnn
|
|---|
| | | self.blstm = model.blstm
|
|---|
| | | self.cif_output2 = model.cif_output2
|
|---|
| | | self.smooth_factor2 = model.smooth_factor2
|
|---|
| | | self.noise_threshold2 = model.noise_threshold2
|
|---|
| | | |
|---|
| | | def forward(self, hidden: torch.Tensor,
|
|---|
| | | mask: torch.Tensor,
|
|---|
| | | ):
|
|---|
| | | h = hidden
|
|---|
| | | context = h.transpose(1, 2)
|
|---|
| | | queries = self.pad(context)
|
|---|
| | | output = torch.relu(self.cif_conv1d(queries))
|
|---|
| | | 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)
|
|---|
| | | mask = mask.transpose(-1, -2).float()
|
|---|
| | | alphas = alphas * mask
|
|---|
| | | alphas = alphas.squeeze(-1)
|
|---|
| | | token_num = alphas.sum(-1)
|
|---|
| | | |
|---|
| | | mask = mask.squeeze(-1)
|
|---|
| | | hidden, alphas, token_num = self.tail_process_fn(hidden, alphas, mask=mask)
|
|---|
| | | acoustic_embeds, cif_peak = cif(hidden, alphas, self.threshold)
|
|---|
| | | |
|---|
| | | return acoustic_embeds, token_num, alphas, cif_peak
|
|---|
| | | |
|---|
| | | def get_upsample_timestmap(self, hidden, mask=None, token_num=None):
|
|---|
| | | h = hidden
|
|---|
| | | b = hidden.shape[0]
|
|---|
| | | context = h.transpose(1, 2)
|
|---|
| | |
|
|---|
| | | # generate alphas2
|
|---|
| | | _output = context
|
|---|
| | | output2 = self.upsample_cnn(_output)
|
|---|
| | | output2 = output2.transpose(1, 2)
|
|---|
| | | output2, (_, _) = self.blstm(output2)
|
|---|
| | | alphas2 = torch.sigmoid(self.cif_output2(output2))
|
|---|
| | | alphas2 = torch.nn.functional.relu(alphas2 * self.smooth_factor2 - self.noise_threshold2)
|
|---|
| | | |
|---|
| | | mask = mask.repeat(1, self.upsample_times, 1).transpose(-1, -2).reshape(alphas2.shape[0], -1)
|
|---|
| | | mask = mask.unsqueeze(-1)
|
|---|
| | | alphas2 = alphas2 * mask
|
|---|
| | | alphas2 = alphas2.squeeze(-1)
|
|---|
| | | _token_num = alphas2.sum(-1)
|
|---|
| | | alphas2 *= (token_num / _token_num)[:, None].repeat(1, alphas2.size(1))
|
|---|
| | | # upsampled alphas and cif_peak
|
|---|
| | | us_alphas = alphas2
|
|---|
| | | us_cif_peak = cif_wo_hidden(us_alphas, self.threshold - 1e-4)
|
|---|
| | | return 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
|
|---|
| | | |
|---|
| | | 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, zeros_t], dim=1)
|
|---|
| | | alphas = torch.add(alphas, tail_threshold)
|
|---|
| | |
|
|---|
| | | 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
|
|---|
| | |
|
|---|
| | |
|
|---|
| | | @torch.jit.script
|
|---|
| | | def cif_wo_hidden(alphas, threshold: float):
|
|---|
| | | batch_size, len_time = alphas.size()
|
|---|
| | |
|
|---|
| | | # loop varss
|
|---|
| | | integrate = torch.zeros([batch_size], dtype=alphas.dtype, 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 |
|---|
