MOSS-TTS-Nano is an open-source **multilingual tiny speech generation model** from MOSI.AI and the OpenMOSS team. With only **0.1B parameters**, it is designed for **realtime speech generation**, can run directly on **CPU without a GPU**, and keeps the deployment stack simple enough for local demos, web serving, and lightweight product integration. * 2026.4.10: We release **MOSS-TTS-Nano**. A demo Space is available at OpenMOSS-Team/MOSS-TTS-Nano. You can also view the demo and more details at openmoss.github.io/MOSS-TTS-Nano-Demo/. - Online Demo: https://openmoss.github.io/MOSS-TTS-Nano-Demo/ - Hugging Face Space: OpenMOSS-Team/MOSS-TTS-Nano
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MOSS‑TTS Family is an open‑source **speech and sound generation model family** from MOSI.AI and the OpenMOSS team. It is designed for **high‑fidelity**, **high‑expressiveness**, and **complex real‑world scenarios**, covering stable long‑form speech, multi‑speaker dialogue, voice/character design, environmental sound effects, and real‑time streaming TTS. This model is based on the research presented in the paper: **MOSS-Audio-Tokenizer: Scaling Audio Tokenizers for Future Audio Foundation Models**. When a single piece of audio needs to **sound like a real person**, **pronounce every word accurately**, **switch speaking styles across content**, **remain stable over tens of minutes**, and **support dialogue, role‑play, and real‑time interaction**, a single TTS model is often not enough. The **MOSS‑TTS Family** breaks the workflow into five production‑ready models that can be used independently or composed into a complete pipeline.
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**MOSS-TTS-v1.5** is continued from MOSS-TTS 1.0. It preserves the main 1.0 capabilities, including zero-shot voice cloning, long-form speech generation, token-level duration control, Pinyin/IPA pronunciation control, multilingual synthesis, and code-switching. For the full 1.0 feature walkthrough, input schema, decoding hyperparameters, and evaluation tables, please refer to the MOSS-TTS 1.0 README. - **Stronger multilingual synthesis with language tags**: when the `language` field is omitted, v1.5 may improve some languages and regress slightly on others compared with 1.0. When the language is specified, v1.5 is stronger than 1.0 on almost all supported languages. Set the tag when building the user message, for example `processor.build_user_message(text=text_fr, language="French")`. - **More stable voice cloning**: v1.5 improves speaker similarity and reduces cloning variance, making repeated generations more consistent. - **Better long-reference, short-text cloning**: v1.5 handles scenarios where the reference audio is much longer than the target text more reliably than 1.0. - **More stable punctuation-following prosody**: v1.5 follows punctuation-driven pauses more closely, especially in long sentences. - **Explicit pause control**: v1.5 supports inline pause markers such as `"[pause 3.2s]"`. For example, `我今天学习了一首中国的古诗，它的名字是[pause 3.2s]静夜思！` inserts an explicit 3.2s pause before `静夜思`. MOSS-TTS-v1.5 currently supports **31 languages**. It keeps the 20 languages supported by MOSS-TTS 1.0 and extends multilingual continued training to additional languages including Cantonese, Dutch, Finnish, Hindi, Macedonian, Malay, Romanian, Swahili, Tagalog, Thai, and Vietnamese.
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MOSS‑TTS Family is an open‑source **speech and sound generation model family** from MOSI.AI and the OpenMOSS team. It is designed for **high‑fidelity**, **high‑expressiveness**, and **complex real‑world scenarios**, covering stable long‑form speech, multi‑speaker dialogue, voice/character design, environmental sound effects, and real‑time streaming TTS. The model architecture and tokenizer are detailed in the paper MOSS-Audio-Tokenizer: Scaling Audio Tokenizers for Future Audio Foundation Models. When a single piece of audio needs to **sound like a real person**, **pronounce every word accurately**, **switch speaking styles across content**, **remain stable over tens of minutes**, and **support dialogue, role‑play, and real‑time interaction**, a single TTS model is often not enough. The **MOSS‑TTS Family** breaks the workflow into five production‑ready models that can be used independently or composed into a complete pipeline.
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MOSS‑TTS Family is an open‑source **speech and sound generation model family** from MOSI.AI and the OpenMOSS team. It is designed for **high‑fidelity**, **high‑expressiveness**, and **complex real‑world scenarios**, covering stable long‑form speech, multi‑speaker dialogue, voice/character design, environmental sound effects, and real‑time streaming TTS. When a single piece of audio needs to **sound like a real person**, **pronounce every word accurately**, **switch speaking styles across content**, **remain stable over tens of minutes**, and **support dialogue, role‑play, and real‑time interaction**, a single TTS model is often not enough. The **MOSS‑TTS Family** breaks the workflow into five production‑ready models that can be used independently or composed into a complete pipeline. **MOSS Voice Generator** is an open-source voice generation system designed to enable the creation of custom speaker timbres from free-form textual descriptions. This model allows users to generate voices that reflect specific characters, personalities, and emotions. It is particularly notable for its ability to produce speech with natural-sounding emotional expressiveness, providing a realistic and nuanced listening experience. As an open-source tool, MOSS Voice Generator is suitable for a variety of applications, such as audiobooks, game dubbing, role-playing agents, and conversational assistants.
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We introduce **MOVA** (**MO**SS **V**ideo and **A**udio), a foundation model designed to break the "silent era" of open-source video generation. Unlike cascaded pipelines that generate sound as an afterthought, MOVA synthesizes video and audio simultaneously for perfect alignment. 🌟Key Highlights - **Native Bimodal Generation**: Moves beyond clunky cascaded pipelines. MOVA generates high-fidelity video and synchronized audio in a single inference pass, eliminating error accumulation. - **Precise Lip-Sync & Sound FX**: Achieves state-of-the-art performance in multilingual lip-synchronization and environment-aware sound effects. - **Fully Open-Source**: In a field dominated by closed-source models (Sora 2, Veo 3, Kling), we are releasing model weights, inference code, training pipelines, and LoRA fine-tuning scripts. - **Asymmetric Dual-Tower Architecture**: Leverages the power of pre-trained video and audio towers, fused via a bidirectional cross-attention mechanism for rich modality interaction. MOVA addresses the limitations of proprietary systems like Sora 2 and Veo 3 by offering a fully open-source framework for Image-to-Video-Audio (IT2VA) and Text-to-Video-Audio (T2VA) tasks. The model employs an asymmetric dual-tower architecture fused via a bidirectional cross-attention mechanism, leveraging a Mixture-of-Experts (MoE) design with 32B total parameters (18B active during inference) to ensure high-quality synthesis with efficient deployment. Alongside the model weights, we provide a fine-grained bimodal data pipeline and support for LoRA fine-tuning, empowering the community to advance research in synchronized cinematic synthesis.
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