Sentencepiece tokenizer Introduction In the rapidly evolving field of natural language processing (NLP), the efficiency and accuracy of language models hinge significantly on how text data is prepared and processed before training. At the heart of this preparation is the process of tokenization, a crucial step where raw text is transformed into a structured format that the models can interpret from. Among the variety of tokenization methods, the sentencepiece tokenizer stands out as a versatile tool that efficiently handles diverse languages without relying on predefined word boundaries.

Audio data is inherently complex and dense. Unlike text, where redundancy is common (think repeated words or phrases), audio signals are continuous streams of varying frequencies and amplitudes. These signals can include everything from human speech and music to ambient noises and complex soundscapes. The richness and variety in audio data make it challenging to identify and eliminate redundancy without losing essential information. Human perception of sound Human ears are sensitive to a wide range of frequencies, from about 20 Hz to 20 kHz.