--- title: "MOSS-TTS" type: "tool" slug: "openmoss-moss-tts" canonical_url: "https://www.graphcanon.com/tools/openmoss-moss-tts" github_url: "https://github.com/OpenMOSS/MOSS-TTS" homepage_url: "https://mosi.cn/models/moss-tts" stars: 3758 forks: 330 primary_language: "Python" license: "Apache-2.0" archived: false categories: ["model-training", "llm-frameworks", "inference-serving"] tags: ["audio-tokenizer", "voice-cloning", "llm", "text-to-speech", "python", "audio", "multimodal"] updated_at: "2026-07-11T12:07:53.275197+00:00" --- # MOSS-TTS > 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, 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. ## Facts - Repository: https://github.com/OpenMOSS/MOSS-TTS - Homepage: https://mosi.cn/models/moss-tts - Stars: 3,758 · Forks: 330 · Open issues: 12 · Watchers: 19 - Primary language: Python - License: Apache-2.0 - Last pushed: 2026-06-22T11:51:50+00:00 ## Trust & health _Signals computed from public GitHub metadata. Not a security guarantee._ - Maintenance: Active (computed 2026-07-11T12:07:38.808Z) - Security scan: No lockfile (0 critical, 0 high, 0 medium, 0 low) · last scan 2026-07-11T12:07:46.739Z - Full report: [trust report](/tools/openmoss-moss-tts/trust.md) · [JSON](https://www.graphcanon.com/api/graphcanon/tools/openmoss-moss-tts/trust) ## Categories - [Model Training](/categories/model-training.md) - [LLM Frameworks](/categories/llm-frameworks.md) - [Inference & Serving](/categories/inference-serving.md) ## Tags audio-tokenizer, voice-cloning, llm, text-to-speech, python, audio, multimodal ## Category neighbours (exploratory) _Same-category tools for discovery only - not curated alternatives. 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If you use Conda/pip: ```bash pip install --extra-index-url https://download.pytorch.org/whl/cu128 -e ".[torch-runtime,flash-attn]" ``` If your machine has limited RAM and many CPU cores, you can cap build parallelism: ```bash MAX_JOBS=4 pip install --extra-index-url https://download.pytorch.org/whl/cu128 -e ".[torch-runtime,flash-attn]" ``` If you use `uv`: ```bash uv pip install --torch-backend cu128 -e ".[torch-runtime,flash-attn]" ``` If your machine has limited RAM and many CPU cores, you can cap build parallelism: ```bash MAX_JOBS=4 uv pip install --torch-backend cu128 -e ".[torch-runtime,flash-attn]" ``` Notes: - Dependencies are managed in `pyproject.toml`, which currently pins `torch==2.9.1+cu128` and `torchaudio==2.9.1+cu128`. - In `uv`, `--torch-backend cu128` lets uv fetch compatible PyTorch CUDA wheels and resolve the rest from PyPI with the default safe index strategy. - If you need another backend, replace `cu128` with your target (for example, `cpu`, `cu126`). - If FlashAttention 2 fails to build on your machine, you can skip it and use the default attention backend. - FlashAttention 2 is only available on supported GPUs and is typically used with `torch.float16` or `torch.bfloat16`. --- # 1. Install (torch-free) pip install -e ".[llama-cpp-onnx]" --- ### Installation Profiles | Profile | Install Command | Dependencies | Use Case | |---------|----------------|--------------|----------| | **Torch-free (ONNX)** | `pip install -e ".[llama-cpp-onnx]"` | numpy, onnxruntime-gpu, tokenizers | Recommended starting point | | **Torch-free (TRT)** | `pip install -e ".[llama-cpp-trt]"` | numpy, tensorrt, cuda-python | Maximum audio tokenizer speed (build engines yourself) | | **Torch-accelerated** | `pip install -e ".[llama-cpp-onnx,llama-cpp-torch]"` | + torch | GPU-accelerated LM heads (~30x faster) | > **Want to convert weights yourself?** See the [conversion guide](moss_tts_delay/llama_cpp/conversion/README.md) for step-by-step instructions on extracting, converting, and quantizing MOSS-TTS weights with llama.cpp. --- ## LICENSE Models in MOSS-TTS Family are licensed under the Apache License 2.0. ```` --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/tools/openmoss-moss-tts`](/api/graphcanon/tools/openmoss-moss-tts) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_