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Description
Audio Muse serves as a versatile online platform for audio processing, providing a wide range of tools for tasks such as music editing, AI-driven music creation, vocal extraction, and background noise elimination. Its user-friendly interface caters to individuals with varying degrees of expertise, enabling them to effortlessly trim, merge, and convert audio files, as well as modify key and BPM, apply effects, and create royalty-free music with the help of advanced AI technology.
With AI Music Generation, users can effortlessly design unique music tracks or songs that align with specific vibes, moods, or styles utilizing cutting-edge AI capabilities. The platform also boasts a comprehensive selection of audio editing utilities, including an Audio Trimmer, Audio Merger, and Audio Converter, alongside effects like Fade In and Fade Out to enhance the listening experience.
Additionally, the advanced Vocal Removal and Noise Reduction features empower users to either extract vocal elements or effectively eliminate unwanted background noise from their audio recordings. Overall, the intuitive design of the platform ensures that navigating through its diverse features is a smooth experience for everyone, enhancing creativity in music production.
Description
AudioCraft serves as a comprehensive codebase tailored for all your generative audio requirements, including music, sound effects, and compression, following its training on raw audio signals. By utilizing AudioCraft, we enhance the design of generative audio models significantly compared to earlier methodologies. Both MusicGen and AudioGen rely on a unified autoregressive Language Model (LM) that functions across streams of compressed discrete music representations known as tokens. We propose a straightforward technique to exploit the intrinsic structure of the parallel token streams, demonstrating that with a single model and a refined interleaving pattern, we can effectively model audio sequences while capturing long-term dependencies, resulting in the generation of high-quality audio outputs. Our models utilize the EnCodec neural audio codec to derive discrete audio tokens from the raw waveform, with EnCodec transforming the audio signal into multiple parallel streams of discrete tokens. This innovative approach not only streamlines audio generation but also enhances the overall efficiency and quality of the output.
API Access
Has API
API Access
Has API
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Integrations
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Integrations
No details available.
Pricing Details
$9.90/month
Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Audio Muse
Founded
2024
Country
China
Website
audiomuse.ai/
Vendor Details
Company Name
Meta AI
Founded
2004
Country
United States
Website
audiocraft.metademolab.com