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#!/usr/bin/env python3
# -*- encoding: utf-8 -*-
# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
# MIT License (https://opensource.org/licenses/MIT)
import torch
from funasr.register import tables
from funasr.models.transformer.utils.nets_utils import make_pad_mask
class mae_loss(torch.nn.Module):
def __init__(self, normalize_length=False):
super(mae_loss, 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, alphas, threshold):
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([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
def cif_wo_hidden(alphas, threshold):
batch_size, len_time = alphas.size()
# loop varss
integrate = torch.zeros([batch_size], 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) * threshold,
integrate,
)
fires = torch.stack(list_fires, 1)
return fires
@tables.register("predictor_classes", "CifPredictorV3")
class CifPredictorV3(torch.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=0.0,
tf2torch_tensor_name_prefix_torch="predictor",
tf2torch_tensor_name_prefix_tf="seq2seq/cif",
smooth_factor2=1.0,
noise_threshold2=0,
upsample_times=5,
upsample_type="cnn",
use_cif1_cnn=True,
tail_mask=True,
):
super(CifPredictorV3, self).__init__()
self.pad = torch.nn.ConstantPad1d((l_order, r_order), 0)
self.cif_conv1d = torch.nn.Conv1d(idim, idim, l_order + r_order + 1)
self.cif_output = torch.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
self.tf2torch_tensor_name_prefix_torch = tf2torch_tensor_name_prefix_torch
self.tf2torch_tensor_name_prefix_tf = tf2torch_tensor_name_prefix_tf
self.upsample_times = upsample_times
self.upsample_type = upsample_type
self.use_cif1_cnn = use_cif1_cnn
if torch.cuda.get_device_name() == 'R200-8F' and self.upsample_type != "cnn":
# kunlunxin doesn't support some ops in other two branches
self.upsample_type = "cnn"
if self.upsample_type == "cnn":
self.upsample_cnn = torch.nn.ConvTranspose1d(
idim, idim, self.upsample_times, self.upsample_times
)
self.cif_output2 = torch.nn.Linear(idim, 1)
elif self.upsample_type == "cnn_blstm":
self.upsample_cnn = torch.nn.ConvTranspose1d(
idim, idim, self.upsample_times, self.upsample_times
)
self.blstm = torch.nn.LSTM(
idim, idim, 1, bias=True, batch_first=True, dropout=0.0, bidirectional=True
)
self.cif_output2 = torch.nn.Linear(idim * 2, 1)
elif self.upsample_type == "cnn_attn":
self.upsample_cnn = torch.nn.ConvTranspose1d(
idim, idim, self.upsample_times, self.upsample_times
)
from funasr.models.transformer.encoder import EncoderLayer as TransformerEncoderLayer
from funasr.models.transformer.attention import MultiHeadedAttention
from funasr.models.transformer.positionwise_feed_forward import PositionwiseFeedForward
positionwise_layer_args = (
idim,
idim * 2,
0.1,
)
self.self_attn = TransformerEncoderLayer(
idim,
MultiHeadedAttention(4, idim, 0.1),
PositionwiseFeedForward(*positionwise_layer_args),
0.1,
True, # normalize_before,
False, # concat_after,
)
self.cif_output2 = torch.nn.Linear(idim, 1)
self.smooth_factor2 = smooth_factor2
self.noise_threshold2 = noise_threshold2
def forward(
self,
hidden,
target_label=None,
mask=None,
ignore_id=-1,
mask_chunk_predictor=None,
target_label_length=None,
):
h = hidden
context = h.transpose(1, 2)
queries = self.pad(context)
output = torch.relu(self.cif_conv1d(queries))
# alphas2 is an extra head for timestamp prediction
if not self.use_cif1_cnn:
_output = context
else:
_output = output
if self.upsample_type == "cnn":
output2 = self.upsample_cnn(_output)
output2 = output2.transpose(1, 2)
elif self.upsample_type == "cnn_blstm":
output2 = self.upsample_cnn(_output)
output2 = output2.transpose(1, 2)
output2, (_, _) = self.blstm(output2)
elif self.upsample_type == "cnn_attn":
output2 = self.upsample_cnn(_output)
output2 = output2.transpose(1, 2)
output2, _ = self.self_attn(output2, mask)
alphas2 = torch.sigmoid(self.cif_output2(output2))
alphas2 = torch.nn.functional.relu(alphas2 * self.smooth_factor2 - self.noise_threshold2)
# repeat the mask in T demension to match the upsampled length
if mask is not None:
mask2 = (
mask.repeat(1, self.upsample_times, 1)
.transpose(-1, -2)
.reshape(alphas2.shape[0], -1)
)
mask2 = mask2.unsqueeze(-1)
alphas2 = alphas2 * mask2
alphas2 = alphas2.squeeze(-1)
token_num2 = alphas2.sum(-1)
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)
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, token_num2
def get_upsample_timestamp(self, hidden, mask=None, token_num=None):
h = hidden
b = hidden.shape[0]
context = h.transpose(1, 2)
queries = self.pad(context)
output = torch.relu(self.cif_conv1d(queries))
# alphas2 is an extra head for timestamp prediction
if not self.use_cif1_cnn:
_output = context
else:
_output = output
if self.upsample_type == "cnn":
output2 = self.upsample_cnn(_output)
output2 = output2.transpose(1, 2)
elif self.upsample_type == "cnn_blstm":
output2 = self.upsample_cnn(_output)
output2 = output2.transpose(1, 2)
output2, (_, _) = self.blstm(output2)
elif self.upsample_type == "cnn_attn":
output2 = self.upsample_cnn(_output)
output2 = output2.transpose(1, 2)
output2, _ = self.self_attn(output2, mask)
alphas2 = torch.sigmoid(self.cif_output2(output2))
alphas2 = torch.nn.functional.relu(alphas2 * self.smooth_factor2 - self.noise_threshold2)
# repeat the mask in T demension to match the upsampled length
if mask is not None:
mask2 = (
mask.repeat(1, self.upsample_times, 1)
.transpose(-1, -2)
.reshape(alphas2.shape[0], -1)
)
mask2 = mask2.unsqueeze(-1)
alphas2 = alphas2 * mask2
alphas2 = alphas2.squeeze(-1)
_token_num = alphas2.sum(-1)
if token_num is not None:
alphas2 *= (token_num / _token_num)[:, None].repeat(1, alphas2.size(1))
# re-downsample
ds_alphas = alphas2.reshape(b, -1, self.upsample_times).sum(-1)
ds_cif_peak = cif_wo_hidden(ds_alphas, self.threshold - 1e-4)
# upsampled alphas and cif_peak
us_alphas = alphas2
us_cif_peak = cif_wo_hidden(us_alphas, self.threshold - 1e-4)
return ds_alphas, ds_cif_peak, 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
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 = torch.tensor([tail_threshold], dtype=alphas.dtype).to(alphas.device)
tail_threshold = torch.reshape(tail_threshold, (1, 1))
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
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, maxlen=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, maxlen=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()
@tables.register("predictor_classes", "CifPredictorV3Export")
class CifPredictorV3Export(torch.nn.Module):
def __init__(self, model, **kwargs):
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_export(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_export(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_export(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], dtype=alphas.dtype, device=hidden.device)
frame = torch.zeros([batch_size, hidden_size], dtype=hidden.dtype, 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], dtype=alphas.dtype, device=hidden.device) - integrate
)
integrate += alpha
list_fires.append(integrate)
fire_place = integrate >= threshold
integrate = torch.where(
fire_place,
integrate - torch.ones([batch_size], dtype=alphas.dtype, 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)
fire_idxs = fires >= threshold
frame_fires = torch.zeros_like(hidden)
max_label_len = frames[0, fire_idxs[0]].size(0)
for b in range(batch_size):
frame_fire = frames[b, fire_idxs[b]]
frame_len = frame_fire.size(0)
frame_fires[b, :frame_len, :] = frame_fire
if frame_len >= max_label_len:
max_label_len = frame_len
frame_fires = frame_fires[:, :max_label_len, :]
return frame_fires, fires
@torch.jit.script
def cif_wo_hidden_export(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) * threshold,
integrate,
)
fires = torch.stack(list_fires, 1)
return fires