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Models that generate sequences by predicting future tokens based on previously generated ones.
Distinguishing note: Focuses on the autoregressive generation mechanism specifically for audio tokens.
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Bark is a generative audio engine and machine learning inference library designed to convert written text into high-fidelity speech and sound effects. It functions as a text-to-audio transformer, utilizing multi-stage neural network architectures to map semantic input tokens into detailed audio codebooks for synthesis. The system distinguishes itself through a hierarchical transformer stacking approach that separates semantic understanding from acoustic realization. By employing autoregressive token prediction and vector quantized codebook mapping, the engine bridges linguistic and sonic doma
Generates audio by predicting sequences of discrete acoustic tokens one at a time.