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# This module is from [WeNet](https://github.com/wenet-e2e/wenet).
# ## Citations
# ```bibtex
# @inproceedings{yao2021wenet,
# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit},
# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin},
# booktitle={Proc. Interspeech},
# year={2021},
# address={Brno, Czech Republic },
# organization={IEEE}
# }
# @article{zhang2022wenet,
# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit},
# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei},
# journal={arXiv preprint arXiv:2203.15455},
# year={2022}
# }
#
from typing import Optional
import torch
from torch import nn
from modules.wenet_extractor.utils.mask import make_pad_mask
class Predictor(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=0.45,
):
super().__init__()
self.pad = nn.ConstantPad1d((l_order, r_order), 0.0)
self.cif_conv1d = nn.Conv1d(idim, idim, l_order + r_order + 1, groups=idim)
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
def forward(
self,
hidden,
target_label: Optional[torch.Tensor] = None,
mask: torch.Tensor = torch.tensor(0),
ignore_id: int = -1,
mask_chunk_predictor: Optional[torch.Tensor] = None,
target_label_length: Optional[torch.Tensor] = None,
):
h = hidden
context = h.transpose(1, 2)
queries = self.pad(context)
memory = self.cif_conv1d(queries)
output = memory + context
output = self.dropout(output)
output = output.transpose(1, 2)
output = torch.relu(output)
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
def tail_process_fn(
self,
hidden,
alphas,
token_num: Optional[torch.Tensor] = None,
mask: Optional[torch.Tensor] = 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, zeros_t], dim=1)
alphas = torch.add(alphas, tail_threshold)
else:
tail_threshold_tensor = torch.tensor(
[tail_threshold], dtype=alphas.dtype
).to(alphas.device)
tail_threshold_tensor = torch.reshape(tail_threshold_tensor, (1, 1))
alphas = torch.cat([alphas, tail_threshold_tensor], 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, max_len=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, max_len=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()
class MAELoss(nn.Module):
def __init__(self, normalize_length=False):
super(MAELoss, self).__init__()
self.normalize_length = normalize_length
self.criterion = torch.nn.L1Loss(reduction="sum")
def forward(self, token_length, pre_token_length):
loss_token_normalizer = token_length.size(0)
if self.normalize_length:
loss_token_normalizer = token_length.sum().type(torch.float32)
loss = self.criterion(token_length, pre_token_length)
loss = loss / loss_token_normalizer
return loss
def cif(hidden: torch.Tensor, alphas: torch.Tensor, threshold: float):
batch_size, len_time, hidden_size = hidden.size()
# 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)), hidden_size], device=hidden.device
)
list_ls.append(torch.cat([l, pad_l], 0))
return torch.stack(list_ls, 0), fires
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