Best AI tools for< Localizing Videos >

Dubformer is an AI-powered dubbing and video localization provider that offers a secure and end-to-end solution for the media industry. With a focus on quality and speed, Dubformer's technology enables the creation of realistic and natural-sounding voice-overs in multiple languages, making video content more accessible and engaging for diverse audiences. The platform combines AI-driven processes with human quality control to ensure broadcast-quality results. Dubformer's services include AI dubbing, accurate and culturally sensitive translations, AI mixing for immersive soundscapes, and AI-powered subtitles and closed captions.

Wavel AI is an advanced AI tool offering a wide range of text-to-speech voice solutions for videos and localization needs. With features like AI Voice Generator, Voice cloning, Subtitles generation, Translation, Transcription, and more, Wavel AI empowers users to create high-quality audio and video content in over 20 languages. The platform also provides tools for dubbing, script editing, and video manipulation, making it a comprehensive solution for content creators, marketers, educators, and various industries. Wavel AI's innovative technology combines speech generation and TTS capabilities to offer realistic voiceovers and accurate translations, catering to a global audience.

AudioShake is a cloud-based audio processing platform that uses artificial intelligence (AI) to separate audio into its component parts, such as vocals, music, and effects. This technology can be used for a variety of applications, including mixing and mastering, localization and captioning, interactive audio, and sync licensing.

Auto Localize is a macOS application powered by OpenAI's advanced GPT models, designed to simplify the localization process for application projects, App Store information, XCLOC, and XLIFF files. It offers features such as automatic processing, editing XLIFF files, localizing projects in various programming tools, designing and exporting localizable screenshots and images, and localizing App Store information. The tool aims to minimize the effort and time spent on localization tasks, enabling users to support multiple languages and expand their target audience efficiently.

Awesome-LLM-Interpretability is a curated list of materials related to LLM (Large Language Models) interpretability, covering tutorials, code libraries, surveys, videos, papers, and blogs. It includes resources on transformer mechanistic interpretability, visualization, interventions, probing, fine-tuning, feature representation, learning dynamics, knowledge editing, hallucination detection, and redundancy analysis. The repository aims to provide a comprehensive overview of tools, techniques, and methods for understanding and interpreting the inner workings of large language models.

Awesome-LLMs-for-Video-Understanding is a repository dedicated to exploring Video Understanding with Large Language Models. It provides a comprehensive survey of the field, covering models, pretraining, instruction tuning, and hybrid methods. The repository also includes information on tasks, datasets, and benchmarks related to video understanding. Contributors are encouraged to add new papers, projects, and materials to enhance the repository.

This repository is a collection of resources focused on interpretability in large language models (LLMs). It aims to help beginners get started in the area and keep researchers updated on the latest progress. It includes libraries, blogs, tutorials, forums, tools, programs, papers, and more related to interpretability in LLMs.

AIlice is a fully autonomous, general-purpose AI agent that aims to create a standalone artificial intelligence assistant, similar to JARVIS, based on the open-source LLM. AIlice achieves this goal by building a "text computer" that uses a Large Language Model (LLM) as its core processor. Currently, AIlice demonstrates proficiency in a range of tasks, including thematic research, coding, system management, literature reviews, and complex hybrid tasks that go beyond these basic capabilities. AIlice has reached near-perfect performance in everyday tasks using GPT-4 and is making strides towards practical application with the latest open-source models. We will ultimately achieve self-evolution of AI agents. That is, AI agents will autonomously build their own feature expansions and new types of agents, unleashing LLM's knowledge and reasoning capabilities into the real world seamlessly.

Recent LLM (Large Language Models)-based CV and multi-modal works.

Awesome-Segment-Anything is a powerful tool for segmenting and extracting information from various types of data. It provides a user-friendly interface to easily define segmentation rules and apply them to text, images, and other data formats. The tool supports both supervised and unsupervised segmentation methods, allowing users to customize the segmentation process based on their specific needs. With its versatile functionality and intuitive design, Awesome-Segment-Anything is ideal for data analysts, researchers, content creators, and anyone looking to efficiently extract valuable insights from complex datasets.

A comprehensive repository focusing on 'Model Merging in LLMs, MLLMs, and Beyond', providing an exhaustive overview of model merging methods, theories, applications, and future research directions. The repository covers various advanced methods, applications in foundation models, different machine learning subfields, and tasks like pre-merging methods, architecture transformation, weight alignment, basic merging methods, and more.

A collection of papers related to knowledge distillation of large language models (LLMs). The repository focuses on techniques to transfer advanced capabilities from proprietary LLMs to smaller models, compress open-source LLMs, and refine their performance. It covers various aspects of knowledge distillation, including algorithms, skill distillation, verticalization distillation in fields like law, medical & healthcare, finance, science, and miscellaneous domains. The repository provides a comprehensive overview of the research in the area of knowledge distillation of LLMs.

Do-Not-Answer is an open-source dataset curated to evaluate Large Language Models' safety mechanisms at a low cost. It consists of prompts to which responsible language models do not answer. The dataset includes human annotations and model-based evaluation using a fine-tuned BERT-like evaluator. The dataset covers 61 specific harms and collects 939 instructions across five risk areas and 12 harm types. Response assessment is done for six models, categorizing responses into harmfulness and action categories. Both human and automatic evaluations show the safety of models across different risk areas. The dataset also includes a Chinese version with 1,014 questions for evaluating Chinese LLMs' risk perception and sensitivity to specific words and phrases.

Deepgram JavaScript SDK. Power your apps with world-class speech and Language AI models.

This repository tracks the latest research on machine unlearning in large language models (LLMs). It offers a comprehensive list of papers, datasets, and resources relevant to the topic.

Agentless is an open-source tool designed for automatically solving software development problems. It follows a two-phase process of localization and repair to identify faults in specific files, classes, and functions, and generate candidate patches for fixing issues. The tool is aimed at simplifying the software development process by automating issue resolution and patch generation.

Expert in localizing & QA'ing content with Excel-friendly output.