vllm-omni/recipes/OpenMOSS/MOSS-TTS.md

MOSS-TTS

Summary

• Vendor: OpenMOSS
• Models: OpenMOSS-Team/MOSS-TTS (8B), OpenMOSS-Team/MOSS-TTS-v1.5 (8B), OpenMOSS-Team/MOSS-TTS-Realtime (1.7B), OpenMOSS-Team/MOSS-TTSD-v1.0 (8B), OpenMOSS-Team/MOSS-SoundEffect (8B), OpenMOSS-Team/MOSS-VoiceGenerator (1.7B)
• Task: Text-to-speech synthesis, sound effect generation, zero-shot voice design
• Mode: Online serving via the OpenAI-compatible /v1/audio/speech API; offline inference
• Maintainer: Community

When to use this recipe

Use this recipe for 24 kHz multilingual TTS with voice cloning (20 languages including Chinese and English). Choose a variant based on your latency and quality requirements:

Model	Params	Use case
MOSS-TTS	8B	General TTS, highest quality
MOSS-TTS-v1.5	8B	General TTS upgrade of 1.0: 31 languages, steadier cloning, [pause Xs] markers (set language for best results); same MossTTSDelay API
MOSS-TTS-Realtime	1.7B	Lowest latency (TTFB ~180 ms), streaming-first
MOSS-TTSD-v1.0	8B	Multi-turn dialogue TTS
MOSS-SoundEffect	8B	Sound effect synthesis from text description
MOSS-VoiceGenerator	1.7B	Zero-shot voice design

All variants share the same codec (OpenMOSS-Team/MOSS-Audio-Tokenizer, ~7 GB) and output 24 kHz mono audio.

References

• Offline inference example: examples/offline_inference/text_to_speech/moss_tts/
• Deploy configs: vllm_omni/deploy/moss_tts.yaml and variants
• HuggingFace org: https://huggingface.co/OpenMOSS-Team

Hardware Support

GPU

1x H100 80GB — MOSS-TTS (8B)

Environment

• OS: Linux
• Python: 3.11+
• CUDA 12.8
• vLLM-Omni version: see vllm_omni/__version__.py

Command

# The codec is loaded automatically from OpenMOSS-Team/MOSS-Audio-Tokenizer.
# Override the path with MOSS_TTS_CODEC_PATH if you have a local copy.
vllm serve OpenMOSS-Team/MOSS-TTS --omni --port 8091

Verification

Voice cloning (provide a reference audio clip):

curl -X POST http://localhost:8091/v1/audio/speech \
    -H "Content-Type: application/json" \
    -d '{
        "model": "OpenMOSS-Team/MOSS-TTS",
        "input": "Hello, this is a voice cloning test.",
        "voice": "default",
        "ref_audio": "https://raw.githubusercontent.com/OpenMOSS/MOSS-TTS/main/assets/audio/zh_1.wav",
        "response_format": "wav"
    }' --output output.wav

Notes

• Peak GPU memory: ~18 GB for the talker (8B) + ~8 GB for the codec decoder on the same device. Use gpu_memory_utilization: 0.85 in moss_tts.yaml (default).
• Output: 24 kHz mono WAV.
• The MOSS_TTS_CODEC_PATH environment variable overrides the codec checkpoint location.

---

1x A10G 24GB — MOSS-TTS-Realtime (1.7B)

Environment

• OS: Linux
• Python: 3.11+
• CUDA 12.8

Command

vllm serve OpenMOSS-Team/MOSS-TTS-Realtime --omni --port 8091

Verification

curl -X POST http://localhost:8091/v1/audio/speech \
    -H "Content-Type: application/json" \
    -d '{
        "model": "OpenMOSS-Team/MOSS-TTS-Realtime",
        "input": "This is a low-latency streaming TTS test.",
        "voice": "default",
        "ref_audio": "https://raw.githubusercontent.com/OpenMOSS/MOSS-TTS/main/assets/audio/zh_1.wav",
        "response_format": "wav",
        "stream": true,
        "stream_format": "audio"
    }' --output output.wav

Notes

• Peak GPU memory: ~6 GB for the talker (1.7B) + ~8 GB for the codec decoder.
• First-audio latency (TTFB): ~180 ms on A10G.
• codec_chunk_frames: 15 in moss_tts_realtime.yaml for lower TTFA than the 8B variant.

---

1x A10G 24GB — MOSS-SoundEffect (8B, sound effect synthesis)

Command

vllm serve OpenMOSS-Team/MOSS-SoundEffect --omni --port 8091

Verification

Sound effect synthesis takes a text description instead of reference audio:

curl -X POST http://localhost:8091/v1/audio/speech \
    -H "Content-Type: application/json" \
    -d '{
        "model": "OpenMOSS-Team/MOSS-SoundEffect",
        "input": "Thunder rumbling, rain pattering on a tin roof.",
        "response_format": "wav"
    }' --output thunder.wav

Notes

• No ref_audio required or accepted for MOSS-SoundEffect.
• Input field maps to the ambient_sound parameter in the upstream processor.
• Rate: ~12.5 tokens per second; longer descriptions produce longer audio.