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Description
AudioLM is an innovative audio language model designed to create high-quality, coherent speech and piano music by solely learning from raw audio data, eliminating the need for text transcripts or symbolic forms. It organizes audio in a hierarchical manner through two distinct types of discrete tokens: semantic tokens, which are derived from a self-supervised model to capture both phonetic and melodic structures along with broader context, and acoustic tokens, which come from a neural codec to maintain speaker characteristics and intricate waveform details. This model employs a series of three Transformer stages, initiating with the prediction of semantic tokens to establish the overarching structure, followed by the generation of coarse tokens, and culminating in the production of fine acoustic tokens for detailed audio synthesis. Consequently, AudioLM can take just a few seconds of input audio to generate seamless continuations that effectively preserve voice identity and prosody in speech, as well as melody, harmony, and rhythm in music. Remarkably, evaluations by humans indicate that the synthetic continuations produced are almost indistinguishable from actual recordings, demonstrating the technology's impressive authenticity and reliability. This advancement in audio generation underscores the potential for future applications in entertainment and communication, where realistic sound reproduction is paramount.
Description
Beets serves as a comprehensive media library management system tailored for dedicated music enthusiasts, functioning as an adaptable automatic metadata corrector and file renamer, while also acting as a batch transcoder for audio files. This tool simplifies the inspection and modification of music metadata across a wide range of audio file formats and is compatible with MPD as a music player. The ultimate goal of beets is to ensure that your music collection is perfectly organized and optimized. It meticulously catalogs your library, enhancing its metadata through integration with the MusicBrainz database. Additionally, it offers a variety of features for managing and accessing your music, allowing for extensive customization. Designed with library functionality in mind, beets can perform nearly any task you can envision for your collection. Through its plugin architecture, beets evolve into a versatile solution, enabling users to obtain or compute an extensive array of metadata, including album art, lyrics, genres, tempos, ReplayGain levels, and acoustic fingerprints. Users can retrieve metadata from sources such as MusicBrainz, Discogs, or Beatport, or derive it by analyzing song filenames or utilizing their acoustic fingerprints. Moreover, this flexibility allows users to maintain a pristine and well-organized music library that evolves alongside their listening preferences.
API Access
Has API
API Access
Has API
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
Free
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
Country
United States
Website
research.google/blog/audiolm-a-language-modeling-approach-to-audio-generation/
Vendor Details
Company Name
beets
Country
United States
Website
beets.io