Summary of Vqalattent: a Transparent Speech Generation Pipeline Based on Transformer-learned Vq-vae Latent Space, by Armani Rodriguez and Silvija Kokalj-filipovic
VQalAttent: a Transparent Speech Generation Pipeline based on Transformer-learned VQ-VAE Latent Space
by Armani Rodriguez, Silvija Kokalj-Filipovic
First submitted to arxiv on: 22 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Audio and Speech Processing (eess.AS)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces VQalAttent, a lightweight generative model designed to efficiently produce high-quality fake speech. The model employs a two-step architecture consisting of a vector quantized autoencoder (VQ-VAE) and a decoder-only transformer. The VQ-VAE compresses audio spectrograms into discrete latent representations, while the transformer generates similar latent sequences that can be converted to audio spectrograms using the VQ-VAE decoder. The model is trained on the AudioMNIST dataset, which consists of human utterances of decimal digits (0-9). The paper demonstrates VQalAttent’s ability to generate intelligible speech samples with limited computational resources and provides insights for refining larger generative models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes a fake speech generator that sounds like real people. It uses a special kind of AI model called VQalAttent, which has two parts: one that squishes down big audio files into tiny pieces, and another that puts those pieces back together to make new audio files. The model was trained on recordings of people saying numbers (0-9). This means it can generate fake speech sounds like “hello” or “goodbye”. The paper shows how this AI can be used to improve even bigger and more powerful AI models, making them better at making fake but realistic speech. |
Keywords
» Artificial intelligence » Autoencoder » Decoder » Generative model » Transformer
