🔥 MOSS-TTS Technical Report

💡 The MOSS-TTS technical report presents a speech generation model that utilizes discrete audio tokens and autoregressive modeling to achieve voice cloning, pronunciation control, and long-form generation across multiple languages. The model is built on a scalable recipe that includes a causal Transformer tokenizer, which compresses 24 kHz audio to 12.5 fps with variable-bitrate RVQ and unified semantic-acoustic representations. The report releases two complementary generators: MOSS-TTS, which emphasizes structural simplicity, scalability, and long-context/control-oriented deployment, and MOSS-TTS-Local-Transformer, which introduces a frame-local autoregressive module for higher modeling efficiency, stronger speaker preservation, and a shorter time to first audio.

The problem addressed by the report is the need for a speech generation model that can handle multilingual and open-domain settings, and support various features such as voice cloning, pronunciation control, and long-form generation. The method used to address this problem is the development of the MOSS-TTS model, which is built on a combination of discrete audio tokens, autoregressive modeling, and large-scale pretraining.

The results of the report show that the MOSS-TTS model supports zero-shot voice cloning, token-level duration control, phoneme-/pinyin-level pronunciation control, smooth code-switching, and stable long-form generation across multilingual and open-domain settings. The report also summarizes the design, training recipe, and empirical characteristics of the released models, providing a comprehensive overview of the MOSS-TTS model and its capabilities. Overall, the MOSS-TTS model presents a significant contribution to the field of speech generation, offering a scalable and efficient solution for a wide range of applications.

📅 Published on Mar 18

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2603.18090
• PDF: https://arxiv.org/pdf/2603.18090
• Project Page: https://mosi.cn/models/moss-tts

🤖 Models citing this paper:
• https://huggingface.co/OpenMOSS-Team/MOSS-TTS
• https://huggingface.co/OpenMOSS-Team/MOSS-TTS-Nano-100M
• https://huggingface.co/OpenMOSS-Team/MOSS-TTS-Realtime

📊 Datasets citing this paper:
• https://huggingface.co/datasets/somu9/mls_eng_tokens

🚀 Spaces citing this paper:
• https://huggingface.co/spaces/OpenMOSS-Team/MOSS-TTS-v1.5
• https://huggingface.co/spaces/OpenMOSS-Team/MOSS-TTS-Nano
• https://huggingface.co/spaces/OpenMOSS-Team/MOSS-TTS

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📢 By: https://xn--r1a.website/PaperNexus

#SpeechGeneration #VoiceCloning #AutoregressiveModeling #DiscreteAudioTokens #TransformerTokenizer