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What is Decibri?

Decibri is a Rust-based audio library designed for developers who are creating applications centered on real-time speech, voice agents, and audio processing. It provides essential features, including microphone input, speaker output, PCM streaming, and audio file management, complemented by a command-line interface compatible with Python, Node.js, Rust, and browser APIs. Among its extensive capabilities are voice activity detection, acoustic echo cancellation, and optional enhancements like denoising, filtering, automatic gain control, and limiting. Developers can seamlessly stream captured audio to both cloud and local speech-to-text services, while simultaneously utilizing its audio output systems to playback synthesized speech. This library caters to a wide range of applications, such as building voice assistants, transcription services, conversational agents, wake-word detection systems, interactive robotics, full-duplex speech systems, and audio processing pipelines, making it suitable for use in edge devices as well. It comes with prebuilt native packages that support Windows, macOS, and Linux, and the implementations for Python and Node.js are especially user-friendly as they require no additional build tools for installation. Furthermore, the browser-compatible version enhances accessibility by allowing audio capture, energy-based speech detection, and playback, thereby broadening its usability across a variety of platforms. With its robust feature set and adaptability, Decibri stands out as a significant asset for audio application development, empowering creators to innovate within this dynamic field.

What is AudioLM?

AudioLM represents a groundbreaking advancement in audio language modeling, focusing on the generation of high-fidelity, coherent speech and piano music without relying on text or symbolic representations. It arranges audio data hierarchically using two unique types of discrete tokens: semantic tokens, produced by a self-supervised model that captures phonetic and melodic elements alongside broader contextual information, and acoustic tokens, sourced from a neural codec that preserves speaker traits and detailed waveform characteristics. The architecture of this model features a sequence of three Transformer stages, starting with the semantic token prediction to form the structural foundation, proceeding to the generation of coarse tokens, and finishing with the fine acoustic tokens that facilitate intricate audio synthesis. As a result, AudioLM can effectively create seamless audio continuations from merely a few seconds of input, maintaining the integrity of voice identity and prosody in speech as well as the melody, harmony, and rhythm in musical compositions. Notably, human evaluations have shown that the audio outputs are often indistinguishable from genuine recordings, highlighting the remarkable authenticity and dependability of this technology. This innovation in audio generation not only showcases enhanced capabilities but also opens up a myriad of possibilities for future uses in various sectors like entertainment, telecommunications, and beyond, where the necessity for realistic sound reproduction continues to grow. The implications of such advancements could significantly reshape how we interact with and experience audio content in our daily lives.

Media

No images available

Media

Integrations Supported

Amazon Transcribe
AssemblyAI
Azure AI Speech
Deepgram
Google Cloud Speech-to-Text
OpenAI Realtime API
Voxtral

Integrations Supported

Google Opal

API Availability

Has API

API Availability

Pricing Information

$0
The core audio library is free under Apache-2.0. Fees for cloud speech-service providers are separate.
Free Version

Pricing Information

Pricing not provided

Supported Platforms

Windows
Mac
On-Prem
Linux

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

Standard Support
Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub
On-Site Training

Company Facts

Organization Name

Decibri

Date Founded

2008

Company Location

Australia

Company Website

decibri.com

Company Facts

Organization Name

Google

Company Location

United States

Company Website

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

Categories and Features

AI Infrastructure

Not specified

Categories and Features

AI Audio Generators

Not specified

AI Models

Not specified

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