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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
Microsoft has developed E5 Text Embeddings, which are sophisticated models that transform textual information into meaningful vector forms, thereby improving functionalities such as semantic search and information retrieval. Utilizing weakly-supervised contrastive learning, these models are trained on an extensive dataset comprising over one billion pairs of texts, allowing them to effectively grasp complex semantic connections across various languages. The E5 model family features several sizes—small, base, and large—striking a balance between computational efficiency and the quality of embeddings produced. Furthermore, multilingual adaptations of these models have been fine-tuned to cater to a wide array of languages, making them suitable for use in diverse global environments. Rigorous assessments reveal that E5 models perform comparably to leading state-of-the-art models that focus exclusively on English, regardless of size. This indicates that the E5 models not only meet high standards of performance but also broaden the accessibility of advanced text embedding technology worldwide.
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Web-Based
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On-Premises
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Windows
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Training Docs
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Vendor Details
Company Name
Country
United States
Website
research.google/blog/audiolm-a-language-modeling-approach-to-audio-generation/
Vendor Details
Company Name
Microsoft
Founded
1975
Country
United States
Website
github.com/microsoft/unilm/tree/master/e5