#!/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):
|
if maxlen is None:
|
maxlen = lengths.max()
|
row_vector = torch.arange(0, maxlen, 1).to(lengths.device)
|
matrix = torch.unsqueeze(lengths, dim=-1)
|
mask = row_vector < matrix
|
mask = mask.detach()
|
|
return mask.type(dtype).to(device) if device is not None else mask.type(dtype)
|
|
|
class CifPredictorV2(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
|
|
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)
|
|
acoustic_embeds, cif_peak = cif(hidden, alphas, self.threshold)
|
|
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
|
|
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)
|
|
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(hidden, alphas, threshold: float):
|
batch_size, len_time, hidden_size = hidden.size()
|
threshold = torch.tensor([threshold], dtype=alphas.dtype).to(alphas.device)
|
|
# loop varss
|
integrate = torch.zeros([batch_size], device=hidden.device)
|
frame = torch.zeros([batch_size, hidden_size], device=hidden.device)
|
# intermediate vars along time
|
list_fires = []
|
list_frames = []
|
|
for t in range(len_time):
|
alpha = alphas[:, t]
|
distribution_completion = torch.ones([batch_size], device=hidden.device) - integrate
|
|
integrate += alpha
|
list_fires.append(integrate)
|
|
fire_place = integrate >= threshold
|
integrate = torch.where(fire_place,
|
integrate - torch.ones([batch_size], device=hidden.device),
|
integrate)
|
cur = torch.where(fire_place,
|
distribution_completion,
|
alpha)
|
remainds = alpha - cur
|
|
frame += cur[:, None] * hidden[:, t, :]
|
list_frames.append(frame)
|
frame = torch.where(fire_place[:, None].repeat(1, hidden_size),
|
remainds[:, None] * hidden[:, t, :],
|
frame)
|
|
fires = torch.stack(list_fires, 1)
|
frames = torch.stack(list_frames, 1)
|
list_ls = []
|
len_labels = torch.round(alphas.sum(-1)).int()
|
max_label_len = len_labels.max()
|
for b in range(batch_size):
|
fire = fires[b, :]
|
l = torch.index_select(frames[b, :, :], 0, torch.nonzero(fire >= threshold).squeeze())
|
pad_l = torch.zeros([int(max_label_len - l.size(0)), int(hidden_size)], device=hidden.device)
|
list_ls.append(torch.cat([l, pad_l], 0))
|
return torch.stack(list_ls, 0), fires
|
