From 5f25e809c5acb3a24d9eca942e7540f5ddf6d361 Mon Sep 17 00:00:00 2001
From: zhifu gao <zhifu.gzf@alibaba-inc.com>
Date: 星期二, 05 十一月 2024 16:33:27 +0800
Subject: [PATCH] Update version.txt
---
funasr/models/scama/chunk_utilis.py | 383 ++++++++++++++++++++++++++++++++++-------------------
1 files changed, 244 insertions(+), 139 deletions(-)
diff --git a/funasr/models/scama/chunk_utilis.py b/funasr/models/scama/chunk_utilis.py
index 245d282..2fe3fa4 100644
--- a/funasr/models/scama/chunk_utilis.py
+++ b/funasr/models/scama/chunk_utilis.py
@@ -7,14 +7,16 @@
from funasr.models.transformer.utils.nets_utils import make_pad_mask
-class overlap_chunk():
+class overlap_chunk:
"""
Author: Speech Lab of DAMO Academy, Alibaba Group
San-m: Memory equipped self-attention for end-to-end speech recognition
https://arxiv.org/abs/2006.01713
"""
- def __init__(self,
+
+ def __init__(
+ self,
chunk_size: tuple = (16,),
stride: tuple = (10,),
pad_left: tuple = (0,),
@@ -24,10 +26,25 @@
):
pad_left = self.check_chunk_size_args(chunk_size, pad_left)
- encoder_att_look_back_factor = self.check_chunk_size_args(chunk_size, encoder_att_look_back_factor)
- decoder_att_look_back_factor = self.check_chunk_size_args(chunk_size, decoder_att_look_back_factor)
- self.chunk_size, self.stride, self.pad_left, self.encoder_att_look_back_factor, self.decoder_att_look_back_factor \
- = chunk_size, stride, pad_left, encoder_att_look_back_factor, decoder_att_look_back_factor
+ encoder_att_look_back_factor = self.check_chunk_size_args(
+ chunk_size, encoder_att_look_back_factor
+ )
+ decoder_att_look_back_factor = self.check_chunk_size_args(
+ chunk_size, decoder_att_look_back_factor
+ )
+ (
+ self.chunk_size,
+ self.stride,
+ self.pad_left,
+ self.encoder_att_look_back_factor,
+ self.decoder_att_look_back_factor,
+ ) = (
+ chunk_size,
+ stride,
+ pad_left,
+ encoder_att_look_back_factor,
+ decoder_att_look_back_factor,
+ )
self.shfit_fsmn = shfit_fsmn
self.x_add_mask = None
self.x_rm_mask = None
@@ -37,23 +54,50 @@
self.mask_att_chunk_encoder = None
self.mask_shift_att_chunk_decoder = None
self.chunk_outs = None
- self.chunk_size_cur, self.stride_cur, self.pad_left_cur, self.encoder_att_look_back_factor_cur, self.chunk_size_pad_shift_cur \
- = None, None, None, None, None
+ (
+ self.chunk_size_cur,
+ self.stride_cur,
+ self.pad_left_cur,
+ self.encoder_att_look_back_factor_cur,
+ self.chunk_size_pad_shift_cur,
+ ) = (None, None, None, None, None)
def check_chunk_size_args(self, chunk_size, x):
if len(x) < len(chunk_size):
x = [x[0] for i in chunk_size]
return x
- def get_chunk_size(self,
- ind: int = 0
- ):
+ def get_chunk_size(self, ind: int = 0):
# with torch.no_grad:
- chunk_size, stride, pad_left, encoder_att_look_back_factor, decoder_att_look_back_factor = \
- self.chunk_size[ind], self.stride[ind], self.pad_left[ind], self.encoder_att_look_back_factor[ind], self.decoder_att_look_back_factor[ind]
- self.chunk_size_cur, self.stride_cur, self.pad_left_cur, self.encoder_att_look_back_factor_cur, self.chunk_size_pad_shift_cur, self.decoder_att_look_back_factor_cur \
- = chunk_size, stride, pad_left, encoder_att_look_back_factor, chunk_size + self.shfit_fsmn, decoder_att_look_back_factor
- return self.chunk_size_cur, self.stride_cur, self.pad_left_cur, self.encoder_att_look_back_factor_cur, self.chunk_size_pad_shift_cur
+ chunk_size, stride, pad_left, encoder_att_look_back_factor, decoder_att_look_back_factor = (
+ self.chunk_size[ind],
+ self.stride[ind],
+ self.pad_left[ind],
+ self.encoder_att_look_back_factor[ind],
+ self.decoder_att_look_back_factor[ind],
+ )
+ (
+ self.chunk_size_cur,
+ self.stride_cur,
+ self.pad_left_cur,
+ self.encoder_att_look_back_factor_cur,
+ self.chunk_size_pad_shift_cur,
+ self.decoder_att_look_back_factor_cur,
+ ) = (
+ chunk_size,
+ stride,
+ pad_left,
+ encoder_att_look_back_factor,
+ chunk_size + self.shfit_fsmn,
+ decoder_att_look_back_factor,
+ )
+ return (
+ self.chunk_size_cur,
+ self.stride_cur,
+ self.pad_left_cur,
+ self.encoder_att_look_back_factor_cur,
+ self.chunk_size_pad_shift_cur,
+ )
def random_choice(self, training=True, decoding_ind=None):
chunk_num = len(self.chunk_size)
@@ -65,25 +109,31 @@
return ind
-
-
-
def gen_chunk_mask(self, x_len, ind=0, num_units=1, num_units_predictor=1):
with torch.no_grad():
x_len = x_len.cpu().numpy()
x_len_max = x_len.max()
- chunk_size, stride, pad_left, encoder_att_look_back_factor, chunk_size_pad_shift = self.get_chunk_size(ind)
+ chunk_size, stride, pad_left, encoder_att_look_back_factor, chunk_size_pad_shift = (
+ self.get_chunk_size(ind)
+ )
shfit_fsmn = self.shfit_fsmn
pad_right = chunk_size - stride - pad_left
- chunk_num_batch = np.ceil(x_len/stride).astype(np.int32)
- x_len_chunk = (chunk_num_batch-1) * chunk_size_pad_shift + shfit_fsmn + pad_left + 0 + x_len - (chunk_num_batch-1) * stride
+ chunk_num_batch = np.ceil(x_len / stride).astype(np.int32)
+ x_len_chunk = (
+ (chunk_num_batch - 1) * chunk_size_pad_shift
+ + shfit_fsmn
+ + pad_left
+ + 0
+ + x_len
+ - (chunk_num_batch - 1) * stride
+ )
x_len_chunk = x_len_chunk.astype(x_len.dtype)
x_len_chunk_max = x_len_chunk.max()
- chunk_num = int(math.ceil(x_len_max/stride))
+ chunk_num = int(math.ceil(x_len_max / stride))
dtype = np.int32
max_len_for_x_mask_tmp = max(chunk_size, x_len_max + pad_left)
x_add_mask = np.zeros([0, max_len_for_x_mask_tmp], dtype=dtype)
@@ -91,7 +141,7 @@
mask_shfit_chunk = np.zeros([0, num_units], dtype=dtype)
mask_chunk_predictor = np.zeros([0, num_units_predictor], dtype=dtype)
mask_shift_att_chunk_decoder = np.zeros([0, 1], dtype=dtype)
- mask_att_chunk_encoder = np.zeros([0, chunk_num*chunk_size_pad_shift], dtype=dtype)
+ mask_att_chunk_encoder = np.zeros([0, chunk_num * chunk_size_pad_shift], dtype=dtype)
for chunk_ids in range(chunk_num):
# x_mask add
fsmn_padding = np.zeros((shfit_fsmn, max_len_for_x_mask_tmp), dtype=dtype)
@@ -104,15 +154,19 @@
x_add_mask = np.concatenate([x_add_mask, x_add_mask_fsmn], axis=0)
# x_mask rm
- fsmn_padding = np.zeros((max_len_for_x_mask_tmp, shfit_fsmn),dtype=dtype)
- padding_mask_left = np.zeros((max_len_for_x_mask_tmp, pad_left),dtype=dtype)
+ fsmn_padding = np.zeros((max_len_for_x_mask_tmp, shfit_fsmn), dtype=dtype)
+ padding_mask_left = np.zeros((max_len_for_x_mask_tmp, pad_left), dtype=dtype)
padding_mask_right = np.zeros((max_len_for_x_mask_tmp, pad_right), dtype=dtype)
x_mask_cur = np.diag(np.ones(stride, dtype=dtype))
- x_mask_cur_pad_top = np.zeros((chunk_ids*stride, stride), dtype=dtype)
+ x_mask_cur_pad_top = np.zeros((chunk_ids * stride, stride), dtype=dtype)
x_mask_cur_pad_bottom = np.zeros((max_len_for_x_mask_tmp, stride), dtype=dtype)
- x_rm_mask_cur = np.concatenate([x_mask_cur_pad_top, x_mask_cur, x_mask_cur_pad_bottom], axis=0)
+ x_rm_mask_cur = np.concatenate(
+ [x_mask_cur_pad_top, x_mask_cur, x_mask_cur_pad_bottom], axis=0
+ )
x_rm_mask_cur = x_rm_mask_cur[:max_len_for_x_mask_tmp, :stride]
- x_rm_mask_cur_fsmn = np.concatenate([fsmn_padding, padding_mask_left, x_rm_mask_cur, padding_mask_right], axis=1)
+ x_rm_mask_cur_fsmn = np.concatenate(
+ [fsmn_padding, padding_mask_left, x_rm_mask_cur, padding_mask_right], axis=1
+ )
x_rm_mask = np.concatenate([x_rm_mask, x_rm_mask_cur_fsmn], axis=1)
# fsmn_padding_mask
@@ -124,22 +178,26 @@
# predictor mask
zeros_1 = np.zeros([shfit_fsmn + pad_left, num_units_predictor], dtype=dtype)
ones_2 = np.ones([stride, num_units_predictor], dtype=dtype)
- zeros_3 = np.zeros([chunk_size - stride - pad_left, num_units_predictor], dtype=dtype)
+ zeros_3 = np.zeros(
+ [chunk_size - stride - pad_left, num_units_predictor], dtype=dtype
+ )
ones_zeros = np.concatenate([ones_2, zeros_3], axis=0)
mask_chunk_predictor_cur = np.concatenate([zeros_1, ones_zeros], axis=0)
- mask_chunk_predictor = np.concatenate([mask_chunk_predictor, mask_chunk_predictor_cur], axis=0)
+ mask_chunk_predictor = np.concatenate(
+ [mask_chunk_predictor, mask_chunk_predictor_cur], axis=0
+ )
# encoder att mask
- zeros_1_top = np.zeros([shfit_fsmn, chunk_num*chunk_size_pad_shift], dtype=dtype)
+ zeros_1_top = np.zeros([shfit_fsmn, chunk_num * chunk_size_pad_shift], dtype=dtype)
zeros_2_num = max(chunk_ids - encoder_att_look_back_factor, 0)
- zeros_2 = np.zeros([chunk_size, zeros_2_num*chunk_size_pad_shift], dtype=dtype)
+ zeros_2 = np.zeros([chunk_size, zeros_2_num * chunk_size_pad_shift], dtype=dtype)
encoder_att_look_back_num = max(chunk_ids - zeros_2_num, 0)
zeros_2_left = np.zeros([chunk_size, shfit_fsmn], dtype=dtype)
ones_2_mid = np.ones([stride, stride], dtype=dtype)
- zeros_2_bottom = np.zeros([chunk_size-stride, stride], dtype=dtype)
- zeros_2_right = np.zeros([chunk_size, chunk_size-stride], dtype=dtype)
+ zeros_2_bottom = np.zeros([chunk_size - stride, stride], dtype=dtype)
+ zeros_2_right = np.zeros([chunk_size, chunk_size - stride], dtype=dtype)
ones_2 = np.concatenate([ones_2_mid, zeros_2_bottom], axis=0)
ones_2 = np.concatenate([zeros_2_left, ones_2, zeros_2_right], axis=1)
ones_2 = np.tile(ones_2, [1, encoder_att_look_back_num])
@@ -149,21 +207,25 @@
ones_3 = np.concatenate([zeros_3_left, ones_3_right], axis=1)
zeros_remain_num = max(chunk_num - 1 - chunk_ids, 0)
- zeros_remain = np.zeros([chunk_size, zeros_remain_num*chunk_size_pad_shift], dtype=dtype)
+ zeros_remain = np.zeros(
+ [chunk_size, zeros_remain_num * chunk_size_pad_shift], dtype=dtype
+ )
ones2_bottom = np.concatenate([zeros_2, ones_2, ones_3, zeros_remain], axis=1)
mask_att_chunk_encoder_cur = np.concatenate([zeros_1_top, ones2_bottom], axis=0)
- mask_att_chunk_encoder = np.concatenate([mask_att_chunk_encoder, mask_att_chunk_encoder_cur], axis=0)
-
+ mask_att_chunk_encoder = np.concatenate(
+ [mask_att_chunk_encoder, mask_att_chunk_encoder_cur], axis=0
+ )
# decoder fsmn_shift_att_mask
zeros_1 = np.zeros([shfit_fsmn, 1])
ones_1 = np.ones([chunk_size, 1])
mask_shift_att_chunk_decoder_cur = np.concatenate([zeros_1, ones_1], axis=0)
mask_shift_att_chunk_decoder = np.concatenate(
- [mask_shift_att_chunk_decoder, mask_shift_att_chunk_decoder_cur], axis=0)
+ [mask_shift_att_chunk_decoder, mask_shift_att_chunk_decoder_cur], axis=0
+ )
- self.x_add_mask = x_add_mask[:x_len_chunk_max, :x_len_max+pad_left]
+ self.x_add_mask = x_add_mask[:x_len_chunk_max, : x_len_max + pad_left]
self.x_len_chunk = x_len_chunk
self.x_rm_mask = x_rm_mask[:x_len_max, :x_len_chunk_max]
self.x_len = x_len
@@ -171,17 +233,18 @@
self.mask_chunk_predictor = mask_chunk_predictor[:x_len_chunk_max, :]
self.mask_att_chunk_encoder = mask_att_chunk_encoder[:x_len_chunk_max, :x_len_chunk_max]
self.mask_shift_att_chunk_decoder = mask_shift_att_chunk_decoder[:x_len_chunk_max, :]
- self.chunk_outs = (self.x_add_mask,
+ self.chunk_outs = (
+ self.x_add_mask,
self.x_len_chunk,
self.x_rm_mask,
self.x_len,
self.mask_shfit_chunk,
self.mask_chunk_predictor,
self.mask_att_chunk_encoder,
- self.mask_shift_att_chunk_decoder)
+ self.mask_shift_att_chunk_decoder,
+ )
return self.chunk_outs
-
def split_chunk(self, x, x_len, chunk_outs):
"""
@@ -190,10 +253,9 @@
:param ind: int
:return:
"""
- x = x[:, :x_len.max(), :]
+ x = x[:, : x_len.max(), :]
b, t, d = x.size()
- x_len_mask = (~make_pad_mask(x_len, maxlen=t)).to(
- x.device)
+ x_len_mask = (~make_pad_mask(x_len, maxlen=t)).to(x.device)
x *= x_len_mask[:, :, None]
x_add_mask = self.get_x_add_mask(chunk_outs, x.device, dtype=x.dtype)
@@ -209,10 +271,9 @@
return x_chunk, x_len_chunk
def remove_chunk(self, x_chunk, x_len_chunk, chunk_outs):
- x_chunk = x_chunk[:, :x_len_chunk.max(), :]
+ x_chunk = x_chunk[:, : x_len_chunk.max(), :]
b, t, d = x_chunk.size()
- x_len_chunk_mask = (~make_pad_mask(x_len_chunk, maxlen=t)).to(
- x_chunk.device)
+ x_len_chunk_mask = (~make_pad_mask(x_len_chunk, maxlen=t)).to(x_chunk.device)
x_chunk *= x_len_chunk_mask[:, :, None]
x_rm_mask = self.get_x_rm_mask(chunk_outs, x_chunk.device, dtype=x_chunk.dtype)
@@ -224,77 +285,104 @@
return x, x_len
- def get_x_add_mask(self, chunk_outs=None, device='cpu', idx=0, dtype=torch.float32):
+ def get_x_add_mask(self, chunk_outs=None, device="cpu", idx=0, dtype=torch.float32):
with torch.no_grad():
- x = chunk_outs[idx] if chunk_outs is not None else self.chunk_outs[idx]
+ x = chunk_outs[idx] if chunk_outs is not None else self.chunk_outs[idx]
x = torch.from_numpy(x).type(dtype).to(device)
return x
- def get_x_len_chunk(self, chunk_outs=None, device='cpu', idx=1, dtype=torch.float32):
+ def get_x_len_chunk(self, chunk_outs=None, device="cpu", idx=1, dtype=torch.float32):
with torch.no_grad():
- x = chunk_outs[idx] if chunk_outs is not None else self.chunk_outs[idx]
+ x = chunk_outs[idx] if chunk_outs is not None else self.chunk_outs[idx]
x = torch.from_numpy(x).type(dtype).to(device)
return x
-
- def get_x_rm_mask(self, chunk_outs=None, device='cpu', idx=2, dtype=torch.float32):
+ def get_x_rm_mask(self, chunk_outs=None, device="cpu", idx=2, dtype=torch.float32):
with torch.no_grad():
- x = chunk_outs[idx] if chunk_outs is not None else self.chunk_outs[idx]
+ x = chunk_outs[idx] if chunk_outs is not None else self.chunk_outs[idx]
x = torch.from_numpy(x).type(dtype).to(device)
return x
- def get_x_len(self, chunk_outs=None, device='cpu', idx=3, dtype=torch.float32):
+ def get_x_len(self, chunk_outs=None, device="cpu", idx=3, dtype=torch.float32):
with torch.no_grad():
- x = chunk_outs[idx] if chunk_outs is not None else self.chunk_outs[idx]
+ x = chunk_outs[idx] if chunk_outs is not None else self.chunk_outs[idx]
x = torch.from_numpy(x).type(dtype).to(device)
return x
-
- def get_mask_shfit_chunk(self, chunk_outs=None, device='cpu', batch_size=1, num_units=1, idx=4, dtype=torch.float32):
+ def get_mask_shfit_chunk(
+ self, chunk_outs=None, device="cpu", batch_size=1, num_units=1, idx=4, dtype=torch.float32
+ ):
with torch.no_grad():
- x = chunk_outs[idx] if chunk_outs is not None else self.chunk_outs[idx]
- x = np.tile(x[None, :, :, ], [batch_size, 1, num_units])
+ x = chunk_outs[idx] if chunk_outs is not None else self.chunk_outs[idx]
+ x = np.tile(
+ x[
+ None,
+ :,
+ :,
+ ],
+ [batch_size, 1, num_units],
+ )
x = torch.from_numpy(x).type(dtype).to(device)
return x
- def get_mask_chunk_predictor(self, chunk_outs=None, device='cpu', batch_size=1, num_units=1, idx=5, dtype=torch.float32):
+ def get_mask_chunk_predictor(
+ self, chunk_outs=None, device="cpu", batch_size=1, num_units=1, idx=5, dtype=torch.float32
+ ):
with torch.no_grad():
- x = chunk_outs[idx] if chunk_outs is not None else self.chunk_outs[idx]
- x = np.tile(x[None, :, :, ], [batch_size, 1, num_units])
+ x = chunk_outs[idx] if chunk_outs is not None else self.chunk_outs[idx]
+ x = np.tile(
+ x[
+ None,
+ :,
+ :,
+ ],
+ [batch_size, 1, num_units],
+ )
x = torch.from_numpy(x).type(dtype).to(device)
return x
- def get_mask_att_chunk_encoder(self, chunk_outs=None, device='cpu', batch_size=1, idx=6, dtype=torch.float32):
+ def get_mask_att_chunk_encoder(
+ self, chunk_outs=None, device="cpu", batch_size=1, idx=6, dtype=torch.float32
+ ):
with torch.no_grad():
- x = chunk_outs[idx] if chunk_outs is not None else self.chunk_outs[idx]
- x = np.tile(x[None, :, :, ], [batch_size, 1, 1])
+ x = chunk_outs[idx] if chunk_outs is not None else self.chunk_outs[idx]
+ x = np.tile(
+ x[
+ None,
+ :,
+ :,
+ ],
+ [batch_size, 1, 1],
+ )
x = torch.from_numpy(x).type(dtype).to(device)
return x
- def get_mask_shift_att_chunk_decoder(self, chunk_outs=None, device='cpu', batch_size=1, idx=7, dtype=torch.float32):
+ def get_mask_shift_att_chunk_decoder(
+ self, chunk_outs=None, device="cpu", batch_size=1, idx=7, dtype=torch.float32
+ ):
with torch.no_grad():
- x = chunk_outs[idx] if chunk_outs is not None else self.chunk_outs[idx]
+ x = chunk_outs[idx] if chunk_outs is not None else self.chunk_outs[idx]
x = np.tile(x[None, None, :, 0], [batch_size, 1, 1])
x = torch.from_numpy(x).type(dtype).to(device)
return x
-
def build_scama_mask_for_cross_attention_decoder(
- predictor_alignments: torch.Tensor,
- encoder_sequence_length: torch.Tensor,
- chunk_size: int = 5,
- encoder_chunk_size: int = 5,
- attention_chunk_center_bias: int = 0,
- attention_chunk_size: int = 1,
- attention_chunk_type: str = 'chunk',
- step=None,
- predictor_mask_chunk_hopping: torch.Tensor = None,
- decoder_att_look_back_factor: int = 1,
- mask_shift_att_chunk_decoder: torch.Tensor = None,
- target_length: torch.Tensor = None,
- is_training=True,
- dtype: torch.dtype = torch.float32):
+ predictor_alignments: torch.Tensor,
+ encoder_sequence_length: torch.Tensor,
+ chunk_size: int = 5,
+ encoder_chunk_size: int = 5,
+ attention_chunk_center_bias: int = 0,
+ attention_chunk_size: int = 1,
+ attention_chunk_type: str = "chunk",
+ step=None,
+ predictor_mask_chunk_hopping: torch.Tensor = None,
+ decoder_att_look_back_factor: int = 1,
+ mask_shift_att_chunk_decoder: torch.Tensor = None,
+ target_length: torch.Tensor = None,
+ is_training=True,
+ dtype: torch.dtype = torch.float32,
+):
with torch.no_grad():
device = predictor_alignments.device
batch_size, chunk_num = predictor_alignments.size()
@@ -304,85 +392,102 @@
target_length = predictor_alignments.sum(dim=-1).type(encoder_sequence_length.dtype)
maximum_target_length = target_length.max()
predictor_alignments_cumsum = torch.cumsum(predictor_alignments, dim=1)
- predictor_alignments_cumsum = predictor_alignments_cumsum[:, None, :].repeat(1, maximum_target_length, 1)
-
-
+ predictor_alignments_cumsum = predictor_alignments_cumsum[:, None, :].repeat(
+ 1, maximum_target_length, 1
+ )
+
index = torch.ones([batch_size, maximum_target_length], dtype=int_type).to(device)
index = torch.cumsum(index, dim=1)
index = index[:, :, None].repeat(1, 1, chunk_num)
-
+
index_div = torch.floor(torch.divide(predictor_alignments_cumsum, index)).type(int_type)
index_div_bool_zeros = index_div == 0
index_div_bool_zeros_count = torch.sum(index_div_bool_zeros.type(int_type), dim=-1) + 1
-
+
index_div_bool_zeros_count = torch.clip(index_div_bool_zeros_count, min=1, max=chunk_num)
-
+
index_div_bool_zeros_count *= chunk_size
index_div_bool_zeros_count += attention_chunk_center_bias
- index_div_bool_zeros_count = torch.clip(index_div_bool_zeros_count-1, min=0, max=maximum_encoder_length)
+ index_div_bool_zeros_count = torch.clip(
+ index_div_bool_zeros_count - 1, min=0, max=maximum_encoder_length
+ )
index_div_bool_zeros_count_ori = index_div_bool_zeros_count
-
- index_div_bool_zeros_count = (torch.floor(index_div_bool_zeros_count / encoder_chunk_size)+1)*encoder_chunk_size
+
+ index_div_bool_zeros_count = (
+ torch.floor(index_div_bool_zeros_count / encoder_chunk_size) + 1
+ ) * encoder_chunk_size
max_len_chunk = math.ceil(maximum_encoder_length / encoder_chunk_size) * encoder_chunk_size
-
+
mask_flip, mask_flip2 = None, None
if attention_chunk_size is not None:
index_div_bool_zeros_count_beg = index_div_bool_zeros_count - attention_chunk_size
- index_div_bool_zeros_count_beg = torch.clip(index_div_bool_zeros_count_beg, 0, max_len_chunk)
- index_div_bool_zeros_count_beg_mask = sequence_mask(index_div_bool_zeros_count_beg, maxlen=max_len_chunk, dtype=int_type, device=device)
+ index_div_bool_zeros_count_beg = torch.clip(
+ index_div_bool_zeros_count_beg, 0, max_len_chunk
+ )
+ index_div_bool_zeros_count_beg_mask = sequence_mask(
+ index_div_bool_zeros_count_beg, maxlen=max_len_chunk, dtype=int_type, device=device
+ )
mask_flip = 1 - index_div_bool_zeros_count_beg_mask
- attention_chunk_size2 = attention_chunk_size * (decoder_att_look_back_factor+1)
+ attention_chunk_size2 = attention_chunk_size * (decoder_att_look_back_factor + 1)
index_div_bool_zeros_count_beg = index_div_bool_zeros_count - attention_chunk_size2
-
- index_div_bool_zeros_count_beg = torch.clip(index_div_bool_zeros_count_beg, 0, max_len_chunk)
- index_div_bool_zeros_count_beg_mask = sequence_mask(index_div_bool_zeros_count_beg, maxlen=max_len_chunk, dtype=int_type, device=device)
+
+ index_div_bool_zeros_count_beg = torch.clip(
+ index_div_bool_zeros_count_beg, 0, max_len_chunk
+ )
+ index_div_bool_zeros_count_beg_mask = sequence_mask(
+ index_div_bool_zeros_count_beg, maxlen=max_len_chunk, dtype=int_type, device=device
+ )
mask_flip2 = 1 - index_div_bool_zeros_count_beg_mask
-
- mask = sequence_mask(index_div_bool_zeros_count, maxlen=max_len_chunk, dtype=dtype, device=device)
-
+
+ mask = sequence_mask(
+ index_div_bool_zeros_count, maxlen=max_len_chunk, dtype=dtype, device=device
+ )
+
if predictor_mask_chunk_hopping is not None:
- b, k, t = mask.size()
- predictor_mask_chunk_hopping = predictor_mask_chunk_hopping[:, None, :, 0].repeat(1, k, 1)
-
- mask_mask_flip = mask
- if mask_flip is not None:
- mask_mask_flip = mask_flip * mask
-
- def _fn():
- mask_sliced = mask[:b, :k, encoder_chunk_size:t]
- zero_pad_right = torch.zeros([b, k, encoder_chunk_size], dtype=mask_sliced.dtype).to(device)
- mask_sliced = torch.cat([mask_sliced, zero_pad_right], dim=2)
- _, _, tt = predictor_mask_chunk_hopping.size()
- pad_right_p = max_len_chunk - tt
- predictor_mask_chunk_hopping_pad = torch.nn.functional.pad(predictor_mask_chunk_hopping, [0, pad_right_p], "constant", 0)
- masked = mask_sliced * predictor_mask_chunk_hopping_pad
-
- mask_true = mask_mask_flip + masked
- return mask_true
-
- mask = _fn() if t > chunk_size else mask_mask_flip
-
-
-
+ b, k, t = mask.size()
+ predictor_mask_chunk_hopping = predictor_mask_chunk_hopping[:, None, :, 0].repeat(
+ 1, k, 1
+ )
+
+ mask_mask_flip = mask
+ if mask_flip is not None:
+ mask_mask_flip = mask_flip * mask
+
+ def _fn():
+ mask_sliced = mask[:b, :k, encoder_chunk_size:t]
+ zero_pad_right = torch.zeros(
+ [b, k, encoder_chunk_size], dtype=mask_sliced.dtype
+ ).to(device)
+ mask_sliced = torch.cat([mask_sliced, zero_pad_right], dim=2)
+ _, _, tt = predictor_mask_chunk_hopping.size()
+ pad_right_p = max_len_chunk - tt
+ predictor_mask_chunk_hopping_pad = torch.nn.functional.pad(
+ predictor_mask_chunk_hopping, [0, pad_right_p], "constant", 0
+ )
+ masked = mask_sliced * predictor_mask_chunk_hopping_pad
+
+ mask_true = mask_mask_flip + masked
+ return mask_true
+
+ mask = _fn() if t > chunk_size else mask_mask_flip
+
if mask_flip2 is not None:
mask *= mask_flip2
-
- mask_target = sequence_mask(target_length, maxlen=maximum_target_length, dtype=mask.dtype, device=device)
+
+ mask_target = sequence_mask(
+ target_length, maxlen=maximum_target_length, dtype=mask.dtype, device=device
+ )
mask = mask[:, :maximum_target_length, :] * mask_target[:, :, None]
-
-
-
- mask_len = sequence_mask(encoder_sequence_length, maxlen=maximum_encoder_length, dtype=mask.dtype, device=device)
+
+ mask_len = sequence_mask(
+ encoder_sequence_length, maxlen=maximum_encoder_length, dtype=mask.dtype, device=device
+ )
mask = mask[:, :, :maximum_encoder_length] * mask_len[:, None, :]
-
-
-
-
- if attention_chunk_type == 'full':
+
+ if attention_chunk_type == "full":
mask = torch.ones_like(mask).to(device)
if mask_shift_att_chunk_decoder is not None:
mask = mask * mask_shift_att_chunk_decoder
mask = mask[:, :maximum_target_length, :maximum_encoder_length].type(dtype).to(device)
return mask
-
--
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