MOSS-SoundEffect-v2.0

MOSS-SoundEffect-V2.0 MOSS-SoundEffect v2.0** is a text-to-audio model with a Diffusion Transformer (DiT) backbone trained with the Flow Matching objective, paired with a DAC VAE and a Qwen3 text encoder. It generates high-fidelity environmental, urban, creature, and human-action sound effects from natural-language prompts, with controllable duration up to 30 seconds at 48 kHz. 1. Overview 1.1 TTS Family Positioning Within the MOSS-TTS Family, MOSS-SoundEffect is the dedicated **text-to-sound** model — the family member that turns natural-language captions into non-speech audio (ambience, urban scenes, creatures, human actions, short music-like clips). v2.0 supersedes the v1 discrete-token autoregressive backbone (`MossTTSDelay`) with a continuous-latent **Diffusion Transformer + Flow Matching** design. 1.2 Key Capabilities **Broad SFX coverage**: natural environments, urban environments, animals & creatures, human actions, and short musical/percussive clips. **Long-form generation**: stable audio up to **30 seconds** per call with the duration tag prepended to the prompt at training time. **Bilingual prompts**: trained with both **English and Chinese** captions. 1.3 Released Models | Model | Architecture | DiT Variant | Parameters | |---|---|---|---:| | **MOSS-SoundEffect-V2.0** | DiT + Flow Matching | `1.3B` | 1.3B | Recommended inference hyperparameters** | Parameter | Default | Description | |---|---:|---| | `num_inference_steps` | 100 | Number of flow-match solver steps. | | `cfg_scale` | 4.0 | Classifier-free guidance weight. | | `sigma_shift` | 5.0 | Flow-match scheduler shift applied per call. | | `seconds` | 10.0 | Output duration. Up to 30. | 2. Quick Start Environment Setup We recommend a clean, isolated Python 3.12 environment to avoid dependency conflicts with the top-level MOSS-TTS environment. For a minimal **inference-only** install (still ships the Gradio demo; skips the fine-tuning extras): Basic Usage The underlying DiT is wrapped with `torch.comp