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Single-Codec: Single-Codebook Speech Codec towards High-Performance Speech Generation
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The multi-codebook speech codec enables the application of large language models (LLM) in TTS but bottlenecks efficiency and robustness due to multi-sequence prediction. To avoid this obstacle, we propose Single-Codec, a single-codebook single-sequence codec, which employs a disentangled VQ-VAE to decouple speech into a time-invariant embedding and a phonetically-rich discrete sequence. Furthermore, the encoder is enhanced with 1) contextual modeling with a BLSTM module to exploit the temporal information, 2) a hybrid sampling module to alleviate distortion from upsampling and downsampling, and 3) a resampling module to encourage discrete units to carry more phonetic information. Compared with multi-codebook codecs, e.g., EnCodec and TiCodec, Single-Codec demonstrates higher reconstruction quality with a lower bandwidth of only 304bps. The effectiveness of Single-Code is further validated by LLM-TTS experiments, showing improved naturalness and intelligibility.
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Cited by 2 Pith papers
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UniTTS: An end-to-end TTS system without decoupling of acoustic and semantic information
The authors propose DistilCodec, a 32,768-code single-codebook audio codec, and UniTTS, a Qwen2.5-7B TTS model trained with audio, text, and cross-modal autoregressive tasks on interleaved prompts.
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DS-Codec: Dual-Stage Training with Mirror-to-NonMirror Architecture Switching for Speech Codec
DS-Codec improves low-bitrate speech codec quality by first training a mirrored codec and then switching to a non-mirrored decoder, while using product quantization to form one large codebook.
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