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