| # Licensed to the Apache Software Foundation (ASF) under one |
| # or more contributor license agreements. See the NOTICE file |
| # distributed with this work for additional information |
| # regarding copyright ownership. The ASF licenses this file |
| # to you 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. |
| |
| """ |
| Module: align |
| This is used when the data is genrated by LipsDataset |
| """ |
| |
| import numpy as np |
| from .common import word_to_vector |
| |
| |
| class Align(object): |
| """ |
| Preprocess for Align |
| """ |
| skip_list = ['sil', 'sp'] |
| |
| def __init__(self, align_path): |
| self.build(align_path) |
| |
| def build(self, align_path): |
| """ |
| Build the align array |
| """ |
| file = open(align_path, 'r') |
| lines = file.readlines() |
| file.close() |
| # words: list([op, ed, word]) |
| words = [] |
| for line in lines: |
| _op, _ed, word = line.strip().split(' ') |
| if word not in Align.skip_list: |
| words.append((int(_op), int(_ed), word)) |
| self.words = words |
| self.n_words = len(words) |
| self.sentence_str = " ".join([w[2] for w in self.words]) |
| self.sentence_length = len(self.sentence_str) |
| |
| def sentence(self, padding=75): |
| """ |
| Get sentence |
| """ |
| vec = word_to_vector(self.sentence_str) |
| vec += [-1] * (padding - self.sentence_length) |
| return np.array(vec, dtype=np.int32) |
| |
| def word(self, _id, padding=75): |
| """ |
| Get words |
| """ |
| word = self.words[_id][2] |
| vec = word_to_vector(word) |
| vec += [-1] * (padding - len(vec)) |
| return np.array(vec, dtype=np.int32) |
| |
| def word_length(self, _id): |
| """ |
| Get the length of words |
| """ |
| return len(self.words[_id][2]) |
| |
| def word_frame_pos(self, _id): |
| """ |
| Get the position of words |
| """ |
| left = int(self.words[_id][0]/1000) |
| right = max(left+1, int(self.words[_id][1]/1000)) |
| return (left, right) |
