MooER is a speech recognition and translation model based on large language models (LLMs) developed by Moore Threads. It integrates seamlessly with ComfyUI, enabling users to perform automatic speech recognition (ASR) and translation tasks efficiently.
- Supports multiple model downloads from Hugging Face or ModelScope, allowing for flexibility based on user location.
- Offers various modes for processing audio, including options for ASR, AST (Automatic Speech Translation), or both.
- Includes customizable prompts for fine-tuning the model's responses, enhancing user control over output.
Context
MooER is designed to enhance ComfyUI's capabilities by providing advanced speech recognition and translation functionalities. It allows users to leverage LLM technology to convert spoken language into text and translate it into different languages, making it a valuable tool for developers and content creators.
Key Features & Benefits
MooER allows users to download models directly from Hugging Face or ModelScope, streamlining the setup process. The tool supports different operational modes, giving users the option to choose between speech recognition, translation, or both, thus catering to various use cases. Additionally, it offers customizable prompts, allowing users to tailor the model's performance to their specific needs.
Advanced Functionalities
MooER includes specialized features such as the ability to work with different model architectures, primarily supporting the Paraformer model for encoding. Users can also batch process audio files from a specified directory, although this feature is currently untested. The model’s architecture allows for advanced configurations, such as adjusting the downsample rate for audio inputs.
Practical Benefits
By integrating MooER into ComfyUI, users can significantly enhance their workflow by automating speech-to-text and translation tasks. This tool improves control over the output through customizable prompts and supports efficient processing of audio files, ultimately leading to higher quality results and increased productivity.
Credits/Acknowledgments
MooER was developed by Zhenlin Liang, Junhao Xu, Yi Liu, Yichao Hu, Jian Li, Yajun Zheng, Meng Cai, and Hua Wang, with contributions from Moore Threads. The project is open source and can be accessed on GitHub, where further documentation and resources are available.