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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
Edgee operates as an AI intermediary that integrates seamlessly with your application and various large language model providers, functioning as an intelligence layer at the edge that minimizes prompt size before they are sent to the model, ultimately decreasing token consumption, lowering expenses, and enhancing response times without requiring alterations to your current codebase. Users can access Edgee via a single API that is compatible with OpenAI, allowing it to implement various edge policies, including smart token compression, routing, privacy measures, retries, caching, and financial oversight, before passing the requests to chosen providers like OpenAI, Anthropic, Gemini, xAI, and Mistral. The advanced token compression feature efficiently eliminates unnecessary input tokens while maintaining the meaning and context, which can lead to a substantial reduction of up to 50% in input tokens, making it particularly beneficial for extensive contexts, retrieval-augmented generation (RAG) workflows, and multi-turn conversations. Furthermore, Edgee allows users to label their requests with bespoke metadata, facilitating the monitoring of usage and expenses by different criteria such as features, teams, projects, or environments, and it sends notifications when there is an unexpected increase in spending. This comprehensive solution not only streamlines interactions with AI models but also empowers users to manage costs and optimize their application’s performance effectively.
API Access
Has API
API Access
Has API
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Free Trial
Free Version
Pricing Details
Free
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
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Windows
Mac
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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
Country
United States
Website
research.google/blog/audiolm-a-language-modeling-approach-to-audio-generation/
Vendor Details
Company Name
Edgee
Founded
2024
Country
United States
Website
www.edgee.ai/