Neural Codec
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Neural Codec
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CLaM TTS Improving Neural Codec Language Model For Zero Shot Text to
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CLaM TTS Improving Neural Codec Language Model For Zero Shot Text to
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 synthesis. Despite the ongoing rush towards scaling paradigms%2C audio tokenization ironically amplifies the scalability challenge%2C stemming from its long sequence length and the complexity of modelling the multiple sequences. To mitigate these issues%2C we present CLaM-TTS that employs a probabilistic residual vector quantization to (1) achieve superior compression in the token length%2C and (2) allow a language model to generate multiple tokens at once%2C thereby eliminating the need for cascaded modeling to handle the number of token streams. Our experimental results demonstrate that CLaM-TTS is better than or comparable to state-of-the-art neural codec-based TTS models regarding naturalness%2C intelligibility%2C speaker similarity%2C and inference speed. In addition%2C we examine the impact of the pretraining extent of the language models and their text tokenization strategies on performances. "CLaM TTS Improving Neural Codec Language Model For Zero Shot Text to")
CLaM TTS Improving Neural Codec Language Model For Zero Shot Text to
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Speech Neural Codec Language Models Are Zero Shot Text To
Speech Neural Codec Language Models Are Zero Shot Text To