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What is MMAudio?
MMAudio stands out as a groundbreaking solution driven by artificial intelligence, effortlessly transforming any MP4, AVI, or MOV file into superior audio with a single click and no usage restrictions. Leveraging sophisticated video analysis along with open-source AI technologies, it ensures flawless lip-sync alignment between audio and video, adeptly processing eight-second clips in under two seconds. Users can conveniently extract audio from video files or convert written text into spoken words while enjoying the ability to implement both straightforward and intricate sound effects, as well as modify settings like timeline-specific audio cues and sound alterations to match their creative vision. The platform supports simple file uploads and URL submissions, provides browser-based previews of generated audio, and showcases a comprehensive library of user scenarios that encompasses environmental sounds such as ocean waves and wolf howls, as well as mechanical sounds like train movements and drum beats, underlining its versatile nature. Furthermore, frequent updates improve its synchronization technology and expand the array of compatible formats, guaranteeing that users always have access to the latest enhancements and features. Ultimately, this tool acts not only as a valuable resource for audio creation but also as a collaborative partner for those aspiring to enhance their multimedia endeavors, enriching the creative process further.
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.
Integrations Supported
Opal
API Availability
Has API
API Availability
Has API
Pricing Information
Free
Free Trial Offered?
Free Version
Pricing Information
Pricing not provided.
Free Trial Offered?
Free Version
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Company Facts
Organization Name
MMAudio
Company Location
United States
Company Website
mmaudio.pro/
Company Facts
Organization Name
Company Location
United States
Company Website
research.google/blog/audiolm-a-language-modeling-approach-to-audio-generation/
