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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

Seeduplex represents a cutting-edge full-duplex speech large language model that operates on an innovative “listen while speaking” paradigm to facilitate more natural, fluid, and accurately timed voice interactions. Unlike conventional half-duplex systems that switch between listening and responding, it continually processes and comprehends audio from the user, enabling simultaneous listening and speaking while being aware of the surrounding acoustic environment. Its advanced interference suppression capabilities effectively differentiate genuine user input from background distractions such as noise, broadcasts, navigation cues, and overlapping conversations, thereby minimizing incorrect responses and disruptions in intricate scenarios. Furthermore, Seeduplex integrates both speech and semantic features for dynamic endpoint detection, allowing it to discern when a user is contemplating, pausing, correcting themselves, or has completed their statement. This model exhibits the ability to patiently endure reflective silences, provide swift responses immediately after an utterance concludes, and seamlessly cease speaking when interrupted, ensuring a more engaging interaction. Ultimately, the design of Seeduplex aims to enhance user experience by making voice communication feel more intuitive and responsive.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Google Opal

Integrations

Google Opal

Pricing Details

No price information available.
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

Google

Country

United States

Website

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

Vendor Details

Company Name

ByteDance

Founded

2012

Country

China

Website

seed.bytedance.com/en/seeduplex

Product Features

Product Features

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