MOSS-TTS
github.com/openmoss/moss-tts- Category
- AI Tools
- Rank
- No. 851Tools index
Previous survey · No. 831 ·
- Type
- APP
- Builder
- openmoss
- GitHub
- 4.1k stars
- Date
About
An open-source speech and sound generation model family from MOSS.
What it does
MOSS-TTS turns text into spoken audio through several specialized models. It handles single-speaker narration, voice cloning, multi-speaker dialogue, designed character voices, sound effects, and context-aware streaming speech. Its models predict discrete audio tokens, which an audio tokenizer decodes into waveforms. Separate architectures target long-form stability or streaming-friendly generation.
Why it's ranked here
This is a broad, unusually controllable audio toolkit rather than a narrow text reader. The flagship supports 31 languages, code-switching, pronunciation guidance, duration control, explicit pauses, and generation lasting up to one hour. Dedicated dialogue, voice-design, sound-effect, and realtime models make the family compelling, although deployments must navigate several large checkpoints and backend choices.
What's good
The family separates distinct production jobs instead of forcing one model to cover everything. Voice cloning needs no speaker-specific fine-tuning. Voice design works from written descriptions without reference speech. Dialogue generation supports one to five speakers, while the realtime model preserves textual and acoustic history across turns. A torch-free inference route supports lighter on-device deployment.
Tradeoffs
The recommended production checkpoint has eight billion parameters, and the default runtime pins a CUDA-specific PyTorch stack. Loading examples permit remote model code, which deserves normal supply-chain scrutiny. Realtime server mode supports only single-item batches. Voice design covers Chinese and English, narrower than the flagship model. Different tasks require separate checkpoints, codecs, and tuning settings.
How to use it well
Use it for teams building narration, dubbing, podcasts, character voices, multilingual speech, or responsive voice agents that need direct control over delivery. Start with the flagship for general synthesis, then adopt a specialist model only when dialogue, designed voices, effects, or streaming justify it. It generates audio, but does not provide conversational reasoning or speech recognition.
Technical notes+
pyproject.toml defines a setuptools package requiring Python 3.10 or newer, with optional groups for FlashAttention, fine-tuning, CUDA PyTorch, llama.cpp, ONNX Runtime, and TensorRT. It exposes moss-tts-llama-cpp through moss_tts_delay.llama_cpp.pipeline:main. clis/moss_tts_app.py implements a Gradio interface for direct generation, cloning, continuation, language tags, and duration control; clis/moss_ttsd_app.py adds one-to-five-speaker dialogue handling. moss_tts_realtime/app.py implements buffered incremental decoding, warmup state, and streaming sessions. docs/moss_tts_model_card.md, docs/moss_ttsd_model_card.md, docs/moss_sound_effect_model_card.md, docs/moss_tts_realtime_model_card.md, and docs/moss_voice_generator_model_card.md document the specialist workflows.
Observed
- Primary language
- Python
- Packaging
- Setuptools package requiring Python 3.10 or newer
- Install surface
- Base dependencies plus optional groups for fine-tuning, CUDA PyTorch, llama.cpp, ONNX Runtime, and TensorRT
- Interfaces
- Python model usage, Gradio applications, a realtime FastAPI server, and a llama.cpp command-line entry point
- Model scope
- General TTS, multi-speaker dialogue, voice design, sound effects, and realtime streaming speech
- License declaration
- Package metadata declares a repository LICENSE file
Read from README.md, pyproject.toml, docs/moss_tts_model_card.md, docs/moss_ttsd_model_card.md, docs/moss_sound_effect_model_card.md, docs/moss_tts_realtime_model_card.md, docs/moss_voice_generator_model_card.md, clis/moss_tts_app.py, clis/moss_ttsd_app.py, moss_tts_realtime/app.py.
Tech Stack
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