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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
ESMFold2 builds upon its predecessor, ESMFold, by establishing a new benchmark in single-sequence structure prediction and facilitating the creation of novel functional proteins via exploration of the latent space within the ESMC model. This advanced model is capable of forecasting high-resolution, all-atom 3D structures of biomolecular complexes straight from the amino acid sequence, and it allows for the incorporation of multiple sequence alignments to improve accuracy on difficult targets. Tailored for predicting structures through both sequence and structure modalities, it employs ESM representations that drive a series of looped folding layers while a diffusion model translates pairwise representations into atomic-resolution outcomes. ESMFold2 excels in predicting protein structures from amino acid sequences, providing detailed structural data, including precise all-atom coordinates for both backbone and side chains, along with confidence metrics and optional distogram predictions for in-depth structural evaluation. Furthermore, its innovative approach enhances the understanding of protein folding dynamics and functional implications, making it a valuable tool for researchers in the field.
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Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
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Live Rep (24/7)
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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
Biohub
Founded
2016
Country
United States
Website
biohub.ai/models/esmfold2