Spaces:
Sleeping
Sleeping
| # Copyright (c) 2021, NVIDIA CORPORATION. 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. | |
| import numpy as np | |
| from numba import jit, prange | |
| def mas(log_attn_map, width=1): | |
| # assumes mel x text | |
| opt = np.zeros_like(log_attn_map) | |
| log_attn_map = log_attn_map.copy() | |
| log_attn_map[0, 1:] = -np.inf | |
| log_p = np.zeros_like(log_attn_map) | |
| log_p[0, :] = log_attn_map[0, :] | |
| prev_ind = np.zeros_like(log_attn_map, dtype=np.int64) | |
| for i in range(1, log_attn_map.shape[0]): | |
| for j in range(log_attn_map.shape[1]): # for each text dim | |
| prev_j = np.arange(max(0, j-width), j+1) | |
| prev_log = np.array([log_p[i-1, prev_idx] for prev_idx in prev_j]) | |
| ind = np.argmax(prev_log) | |
| log_p[i, j] = log_attn_map[i, j] + prev_log[ind] | |
| prev_ind[i, j] = prev_j[ind] | |
| # now backtrack | |
| curr_text_idx = log_attn_map.shape[1]-1 | |
| for i in range(log_attn_map.shape[0]-1, -1, -1): | |
| opt[i, curr_text_idx] = 1 | |
| curr_text_idx = prev_ind[i, curr_text_idx] | |
| opt[0, curr_text_idx] = 1 | |
| return opt | |
| def mas_width1(log_attn_map): | |
| """mas with hardcoded width=1""" | |
| # assumes mel x text | |
| neg_inf = log_attn_map.dtype.type(-np.inf) | |
| log_p = log_attn_map.copy() | |
| log_p[0, 1:] = neg_inf | |
| for i in range(1, log_p.shape[0]): | |
| prev_log1 = neg_inf | |
| for j in range(log_p.shape[1]): | |
| prev_log2 = log_p[i-1, j] | |
| log_p[i, j] += max(prev_log1, prev_log2) | |
| prev_log1 = prev_log2 | |
| # now backtrack | |
| opt = np.zeros_like(log_p) | |
| one = opt.dtype.type(1) | |
| j = log_p.shape[1]-1 | |
| for i in range(log_p.shape[0]-1, 0, -1): | |
| opt[i, j] = one | |
| if log_p[i-1, j-1] >= log_p[i-1, j]: | |
| j -= 1 | |
| if j == 0: | |
| opt[1:i, j] = one | |
| break | |
| opt[0, j] = one | |
| return opt | |
| def b_mas(b_log_attn_map, in_lens, out_lens, width=1): | |
| assert width == 1 | |
| attn_out = np.zeros_like(b_log_attn_map) | |
| for b in prange(b_log_attn_map.shape[0]): | |
| out = mas_width1(b_log_attn_map[b, 0, :out_lens[b], :in_lens[b]]) | |
| attn_out[b, 0, :out_lens[b], :in_lens[b]] = out | |
| return attn_out | |