![youtube download subtitles youtube download subtitles](https://hello-sunil.in/wp-content/uploads/2020/05/Download-YouTube-Subtitles-YouTube-advanced-options-for-video.png)
Contrary to text translation, subtitling is subject to spatial and temporal constraints, which greatly increase the post-processing effort required to restore the NMT output to a proper subtitle format.
![youtube download subtitles youtube download subtitles](https://sites.google.com/site/ytsrtdownloader/_/rsrc/1409755768873/home/srtdownloader.png)
Growing needs in localising multimedia content for global audiences have resulted in Neural Machine Translation (NMT) gradually becoming an established practice in the field of subtitling in order to reduce costs and turn-around times. Experiments demonstrate that UANOA ensures a USV arrives at the target points with optimal path planning in complex ocean environments without any collisions occurring, and UANOA outperforms deep Q-network (DQN) and random control policy in convergence speed, sailing distance, rudder angle steering consumption, and other performance measurements. To alleviate the decision bias caused by partial observable of USVs, we use the long short-term memory (LSTM) networks to enhance the ability to remember the ocean environment of USVs. In our work, we employ a double Q-network to achieve end-to-end control from raw sensor input to output of discrete rudder action, and design a set of reward functions that can be adapted to USV navigation and obstacle avoidance. The UANOA achieves the autonomous navigation task of USVs by real-time sensing of partially complex ocean information around and real-time output of rudder angle control commands of USVs. In this paper, a deep reinforcement learning-based UANOA (USVs autonomous navigation and obstacle avoidance) method is proposed. However, fine modeling of conventional algorithms cannot meet the real-time precise behavior control strategy of USVs in complex environments, which poses a great challenge to autonomous control policy. Autonomous navigation and obstacle avoidance, as the essential technology of USVs, are the key conditions for successful mission execution. Unmanned surface vehicles (USVs) have been widely used in research and exploration, patrol, and defense. The official baselines are beaten by large margins. By framing the problem as a binary tagging task using the outlined architecture we are able to achieve competitive results on the official test set across all languages, with Recall, Precision, F1 ranging between 0.91 and 0.96 which makes us joint winners for Recall in two of the languages. The difference to that work is that here we use language-specific BERT models for each featured language. We had recently demonstrated, that such an approach achieves state-of-the-art performance when identifying end-of-sentence markers on automatically transcribed texts. Our submissions are based on pre-trained BERT models that have been fine-tuned to the task at hand. We participated in Subtask 1 (fully un-punctuated sentences-full stop detection) and submitted a run for every featured language (English, German, French, Italian). This paper describes our approach (UR-mSBD) to address the shared task on Sentence End and Punctuation Prediction in NLG Text (SEPP-NLG) organised as part of SwissText 2021. In addition, we expect that language barriers in online education will be more easily broken by achieving more accurate translations of numerous video lectures in English. Our research will provide people with more accurate translations of subtitles. We build a model with the training data using the LSTM-RNN (Long-Short Term Memory – Recurrent Neural Networks) and predict the position of the period mark, resulting in prediction accuracy of 70.84%. Since this lecture video provides complete sentence caption data, it can be used as training data by transforming the subtitles into general YouTube-like caption data. For this study, we use the 27,826 sentence subtitles provided by Stanford University’s courses as data. In this paper, we propose a method to divide text into sentences and generate period marks to improve the accuracy of automatic translation of English subtitles. Since the generated subtitles are separated by time units rather than sentence units, and are translated, it is very difficult to understand the translation result as a whole. However, when extracting subtitles from video using Speech to Text, it is impossible to accurately translate the sentence because all sentences are generated without periods. This method creates subtitles suitable for the running time. Currently, the automatic caption system extracts voice data when uploading a video and provides a subtitle file converted into text. Using this, YouTube, a video-sharing site, provides captions in many languages. Recently, with the development of Speech to Text, which converts voice to text, and machine translation, technologies for simultaneously translating the captions of video into other languages have been developed.