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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

SpeechData.ai operates as a commercial service that curates and provides collections of conversational speech data, which are essential for developing and assessing Automatic Speech Recognition (ASR), Text-to-Speech (TTS), and various voice-AI technologies. Their platform features an extensive catalogue of 60 conversational datasets in 60 different languages and dialects, encompassing a remarkable total of 73,000 hours of audio recorded from 7,250 distinct speakers. Each listing provides details about spontaneous two-person dialogues, includes dual-channel audio, and offers time-aligned, human-verified transcripts along with speaker metadata and consent documentation, with pricing ranging from USD 60 to 95 per audio hour. Customers have the option to request audio samples and can also commission additional data collection tailored to specific requirements. Beyond this, the service includes transcription and audio annotation capabilities. Typical purchasers comprise teams focused on acquiring speech data for training and evaluating their models. The availability of datasets, licensing agreements, and final costs are influenced by factors such as the chosen language, the extent of data collection, and the specific terms negotiated for the project. Additionally, the platform aims to facilitate seamless access to high-quality speech data for a variety of applications in the voice technology sector.

API Access

Has API No 

API Access

Has API No 

Screenshots View All

Screenshots View All

No images available

Integrations

Google Opal Yes 

Integrations

Google Opal No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

USD 60 per audio hour
Free Trial No 
Free Version No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based No 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours Yes 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person Yes 

Types of Training

Training Docs No 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

Google

Country

United States

Website

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

Vendor Details

Company Name

Spirelight ApS

Founded

2019

Country

Denmark

Website

www.speechdata.ai/

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Product Features

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