AudioLM

AudioLM

Google
ESMFold2

ESMFold2

Biohub
+
+

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About

AudioLM is a pure audio language model that generates high‑fidelity, long‑term coherent speech and piano music by learning from raw audio alone, without requiring any text transcripts or symbolic representations. It represents audio hierarchically using two types of discrete tokens, semantic tokens extracted from a self‑supervised model to capture phonetic or melodic structure and global context, and acoustic tokens from a neural codec to preserve speaker characteristics and fine waveform details, and chains three Transformer stages to predict first semantic tokens for high‑level structure, then coarse and finally fine acoustic tokens for detailed synthesis. The resulting pipeline allows AudioLM to condition on a few seconds of input audio and produce seamless continuations that retain voice identity, prosody, and recording conditions in speech or melody, harmony, and rhythm in music. Human evaluations show that synthetic continuations are nearly indistinguishable from real recordings.

About

ESMFold2 is the successor to ESMFold, setting a new state of the art for single-sequence structure prediction and enabling the generation of new functional proteins through searching the ESMC model’s latent space. The model predicts high-resolution, all-atom 3D structures of biomolecular complexes directly from sequence, with optional multiple sequence alignment input for enhanced accuracy on challenging targets. It is designed for structure prediction using sequence and structure modalities, with ESM representations powering a series of looped folding layers and a diffusion model projecting pairwise representations to atomic-resolution predictions. ESMFold2 predicts protein structures directly from amino acid sequences and outputs comprehensive structural information, including all-atom coordinates for backbone and side chains, confidence metrics, and optional distogram predictions for detailed structural analysis.

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Audience

Audio researchers and developers needing a solution for creating realistic speech and music continuations directly from raw audio

Audience

Structural biology researchers who need fast, high-resolution protein structure prediction from sequence for analysis, exploration, and experimental planning

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

API

Offers API

API

Offers API

Screenshots and Videos

Screenshots and Videos

Pricing

No information available.
Free Version
Free Trial

Pricing

Free
Free Version
Free Trial

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

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Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

Google
United States
research.google/blog/audiolm-a-language-modeling-approach-to-audio-generation/

Company Information

Biohub
Founded: 2016
United States
biohub.ai/models/esmfold2

Alternatives

AudioCraft

AudioCraft

Meta AI

Alternatives

ESMC

ESMC

Biohub
Seed-Music

Seed-Music

ByteDance
Qwen3-TTS

Qwen3-TTS

Alibaba
Evo 2

Evo 2

Arc Institute
Melodea

Melodea

Audoir
HyperProtein

HyperProtein

Hypercube

Categories

Categories

Integrations

Biohub
Google Opal
Python

Integrations

Biohub
Google Opal
Python
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