Show HN: Dub Any Podcast in Your Language Using Local Models (github.com)

A new open-source project enables users to dub podcasts into different languages utilizing local models. Designed to work primarily on M-series Macs with a minimum of 16 GB of RAM, this pipeline integrates automatic speaker separation, fluent LLM translation, and voice-cloned text-to-speech (TTS) capabilities for up to four speakers. Key functionalities include the ability to maintain the original audio track while applying deep-ducking techniques, thus enhancing the overall dubbing quality. Users can easily customize the configuration via a `dub.toml` file, which specifies source and target languages, and run the process using a straightforward command-line interface. This initiative holds significant implications for the AI/ML community, particularly in democratizing multilingual content creation and making it more accessible for non-native speakers. By leveraging local models, it allows for experimentation with different large language models (LLMs) while sidestepping reliance on cloud services. The underlying architecture, including components like ASR (automatic speech recognition) and a customizable translation pipeline, opens avenues for further research, particularly in exploring the use of multimodal LLMs that process audio directly. This can potentially streamline translation processes, bringing fresh opportunities for content creators and researchers focused on enhancing automated language translation systems.