We introduce a language modeling approach for text to speech synthesis (TTS). Specifically, we train a neural codec language model (called Vall-E) using discrete codes derived from an off-the-shelf neural audio codec model, and regard TTS as a conditional language modeling task rather than ...
We introduce a language modeling approach for text to speech synthesis (TTS). Specifically, we train a neural codec language model (called Vall-E) using discrete codes derived from an off-the-shelf neural audio codec model, and regard TTS as a conditional language modeling task rather than con...
Personalized Lao language synthesis via disentangled neural codec language modeldoi:10.1007/s13042-025-02535-xThe task of personalized speech synthesis aims to generate speech that mimics the voice characteristics of a specific speaker. Recent advancements in large speech models, such as VALL-E, have...
We introduce a language modeling approach for text to speech synthesis (TTS). Specifically, we train a neural codec language model (called VALL-E ) using discrete codes derived from an off-the-shelf neural audio codec model, and regard TTS as a conditional language modeling task rather than ...
Neural audio codec tokens serve as the fundamental building blocks for speech language model (SLM)-based speech generation. However, there is no systematic understanding on how the codec system affects the speech generation performance of the SLM. In this work, we examine...
of quantizers at variable frame rates, the codec adapts to the audio structure across multiple timescales. This leads to more efficient compression, as demonstrated by extensive objective and subjective evaluations. The code and model weights are open-sourced at https://github.com/hubertsiuzdak/sna...
5.3.3Neural Network Model Aneural networkmodel is represented by its architecture that shows how to transform two or more inputs into an output. The transformation is given in the form of a learning algorithm. In this work, the feed-forward architecture used is amultilayer perceptron(MLP) that...
Model Encoder & Decoder Architecture Encoder-Decoder Non-streamable. Streamable. Language Modeling and Entropy Coding Entropy Encoding. Abstract We introduce a state-of-the-art real-time, high-fidelity, audio codec leveraging neural networks.It consists in a streaming encoder-decoder architecture...
Richard, Audiodec: an open-source streaming high-fidelity neural audio codec. 2023 IEEE international conference on acoustics, speech and signal processing (ICASSP), 1–5. (2023) R. Yamamoto, E. Song, J. Kim, Parallel wavegan: a fast waveform generation model based on generative adversarial ...
This can not only save on bitrate, but more importantly this might be very useful for language modeling approaches to audio generation. E.g. with coarse tokens of ~10 Hz and a context window of 2048 you can effectively model a consistent structure of an audio track for ~3 minutes. ...