You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
95 lines
3.1 KiB
95 lines
3.1 KiB
2 years ago
|
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
|
||
|
|
#
|
||
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
||
|
|
# you may not use this file except in compliance with the License.
|
||
|
|
# You may obtain a copy of the License at
|
||
|
|
#
|
||
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
||
|
|
#
|
||
|
|
# Unless required by applicable law or agreed to in writing, software
|
||
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
||
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||
|
|
# See the License for the specific language governing permissions and
|
||
|
|
# limitations under the License.
|
||
|
|
"""Maximum path calculation module.
|
||
|
|
|
||
|
|
This code is based on https://github.com/jaywalnut310/vits.
|
||
|
|
|
||
|
|
"""
|
||
|
|
import warnings
|
||
|
|
|
||
|
|
import numpy as np
|
||
|
|
import paddle
|
||
|
|
from numba import njit
|
||
|
|
from numba import prange
|
||
|
|
|
||
|
|
try:
|
||
|
|
from .core import maximum_path_c
|
||
|
|
|
||
|
|
is_cython_avalable = True
|
||
|
|
except ImportError:
|
||
|
|
is_cython_avalable = False
|
||
|
|
warnings.warn(
|
||
|
|
"Cython version is not available. Fallback to 'EXPERIMETAL' numba version. "
|
||
|
|
"If you want to use the cython version, please build it as follows: "
|
||
|
|
"`cd paddlespeech/t2s/models/vits/monotonic_align; python setup.py build_ext --inplace`"
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
def maximum_path(neg_x_ent: paddle.Tensor,
|
||
|
|
attn_mask: paddle.Tensor) -> paddle.Tensor:
|
||
|
|
"""Calculate maximum path.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
neg_x_ent (Tensor): Negative X entropy tensor (B, T_feats, T_text).
|
||
|
|
attn_mask (Tensor): Attention mask (B, T_feats, T_text).
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
Tensor: Maximum path tensor (B, T_feats, T_text).
|
||
|
|
|
||
|
|
"""
|
||
|
|
dtype = neg_x_ent.dtype
|
||
|
|
neg_x_ent = neg_x_ent.numpy().astype(np.float32)
|
||
|
|
path = np.zeros(neg_x_ent.shape, dtype=np.int32)
|
||
|
|
t_t_max = attn_mask.sum(1)[:, 0].cpu().numpy().astype(np.int32)
|
||
|
|
t_s_max = attn_mask.sum(2)[:, 0].cpu().numpy().astype(np.int32)
|
||
|
|
if is_cython_avalable:
|
||
|
|
maximum_path_c(path, neg_x_ent, t_t_max, t_s_max)
|
||
|
|
else:
|
||
|
|
maximum_path_numba(path, neg_x_ent, t_t_max, t_s_max)
|
||
|
|
|
||
|
|
return paddle.cast(paddle.to_tensor(path), dtype=dtype)
|
||
|
|
|
||
|
|
|
||
|
|
@njit
|
||
|
|
def maximum_path_each_numba(path, value, t_y, t_x, max_neg_val=-np.inf):
|
||
|
|
"""Calculate a single maximum path with numba."""
|
||
|
|
index = t_x - 1
|
||
|
|
for y in range(t_y):
|
||
|
|
for x in range(max(0, t_x + y - t_y), min(t_x, y + 1)):
|
||
|
|
if x == y:
|
||
|
|
v_cur = max_neg_val
|
||
|
|
else:
|
||
|
|
v_cur = value[y - 1, x]
|
||
|
|
if x == 0:
|
||
|
|
if y == 0:
|
||
|
|
v_prev = 0.0
|
||
|
|
else:
|
||
|
|
v_prev = max_neg_val
|
||
|
|
else:
|
||
|
|
v_prev = value[y - 1, x - 1]
|
||
|
|
value[y, x] += max(v_prev, v_cur)
|
||
|
|
|
||
|
|
for y in range(t_y - 1, -1, -1):
|
||
|
|
path[y, index] = 1
|
||
|
|
if index != 0 and (index == y or
|
||
|
|
value[y - 1, index] < value[y - 1, index - 1]):
|
||
|
|
index = index - 1
|
||
|
|
|
||
|
|
|
||
|
|
@njit(parallel=True)
|
||
|
|
def maximum_path_numba(paths, values, t_ys, t_xs):
|
||
|
|
"""Calculate batch maximum path with numba."""
|
||
|
|
for i in prange(paths.shape[0]):
|
||
|
|
maximum_path_each_numba(paths[i], values[i], t_ys[i], t_xs[i])
|