What is LyRuno?
LyRuno leverages advanced AI models, trained specifically on extensive film and television data, to achieve clean and precise separation of audio tracks. It enables users to effortlessly extract distinct dialogue, music, and sound effect stems from a wide range of video and audio file formats. The tool emphasizes simplicity, offering one-click separation designed to integrate smoothly into post-production workflows within the film and video industry.
Engineered with a professional model focused on cinematic audio, LyRuno surpasses traditional music-based separation models in handling the complex audio elements typical in movies and TV shows. It incorporates features like batch processing for handling multiple files efficiently and optional GPU acceleration to speed up the separation process. A built-in audio player allows for immediate auditioning of the separated results.
Features
- AI-Powered Separation: Utilizes advanced AI models trained on film and TV data for precise audio track separation.
- One-Click Processing: Simplifies the separation of dialogue, music, and sound effects stems.
- Batch Processing: Supports processing multiple audio and video files simultaneously.
- Wide Format Support: Compatible with video formats (MP4, MKV, FLV, AVI, WEBM) and audio formats (MP3, WAV, FLAC, ACC, M4A).
- GPU Acceleration: Offers faster processing by optionally leveraging GPU capabilities.
- Built-in Audio Player: Allows immediate preview of separated audio files directly within the tool.
- High-Quality Output (Professional Plan): Supports higher quality audio output formats like WAV and FLAC.
Use Cases
- Extracting clean dialogue for dubbing, subtitling, or restoration.
- Isolating music tracks for remixing, sampling, or analysis.
- Separating sound effects for sound design projects or foley work.
- Enhancing audio clarity in film and video post-production.
- Creating stems for immersive audio experiences.
- Audio archiving and restoration projects.
Related Queries
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LyRuno Uptime Monitor
Average Uptime
100%
Average Response Time
146.77 ms
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