moss-tts-nano-speech

The saved excerpt is a snapshot from review. The external source remains the complete and most current version.

---
name: moss-tts-nano-speech
description: Expert skill for using MOSS-TTS-Nano, a 0.1B parameter multilingual real-time TTS model that runs on CPU with voice cloning support.
triggers:
  - generate speech with MOSS TTS
  - text to speech with voice cloning
  - moss tts nano inference
  - run MOSS-TTS-Nano locally
  - multilingual TTS CPU inference
  - clone voice with MOSS
  - streaming audio generation python
  - tiny TTS model deployment
---

# MOSS-TTS-Nano Speech Generation Skill

> Skill by [ara.so](https://ara.so) — Daily 2026 Skills collection.

MOSS-TTS-Nano is an open-source multilingual tiny TTS model (0.1B parameters) from MOSI.AI and the OpenMOSS team. It uses an Audio Tokenizer + LLM autoregressive pipeline to generate 48 kHz stereo speech in real time, supports 20 languages, voice cloning, streaming inference, and runs on CPU without a GPU.

## Installation

### Conda (recommended)

```bash
conda create -n moss-tts-nano python=3.12 -y
conda activate moss-tts-nano

git clone https://github.com/OpenMOSS/MOSS-TTS-Nano.git
cd MOSS-TTS-Nano

pip install -r requirements.txt
pip install -e .
```

### Fix WeTextProcessing if it fails

```bash
conda install -c conda-forge pynini=2.1.6.post1 -y
pip install git+https://github.com/WhizZest/WeTextProcessing.git
```

After `pip install -e .` the `moss-tts-nano` CLI command is available in the active environment.

## Model Weights

Models are auto-downloaded from Hugging Face on first run:
- TTS model: `OpenMOSS-Team/MOSS-TTS-Nano`
- Audio tokenizer: `OpenMOSS-Team/MOSS-Audio-Tokenizer-Nano`

ModelScope mirrors are available at `openmoss/MOSS-TTS-Nano` and `openmoss/MOSS-Audio-Tokenizer-Nano`.

## CLI Commands

### Generate speech (voice clone mode)

```bash
moss-tts-nano generate \
  --prompt-speech assets/audio/zh_1.wav \
  --text "欢迎关注模思智能、上海创智学院与复旦大学自然语言处理实验室。"
```

Output defaults to `generated_audio/moss_tts_nano_output.wav`.

### Generate from a text file (long-form)

```bash
moss-tts-nano generate \
  --prompt-speech assets/audio/zh_1.wav \
  --text-file my_script.txt \
  --output output.wav
```

### Launch local web demo

```bash
moss-tts-nano serve
# or directly:
python app.py
```

Opens at `http://127.0.0.1:18083` — model stays loaded in memory for fast repeated requests.

### Direct Python entrypoint

```bash
python infer.py \
  --prompt-audio-path assets/audio/zh_1.wav \
  --text "Hello, this is a test of MOSS-TTS-Nano."
```

Output: `generated_audio/infer_output.wav`

## Python API Usage

### Basic voice clone inference

```python
from infer import MossTTSNanoInference

# Initialize once (downloads weights on first run)
tts = MossTTSNanoInference()

# Voice clone: synthesize text in the style of the reference audio
audio = tts.infer(
    text="欢迎使用MOSS语音合成系统。",
    prompt_audio_path="assets/audio/zh_1.wav",
)

# Save output
import soundfile as sf
sf.write("output.wav", audio, samplerate=48000)
```

### English voice clone

```python
from infer import MossTTSNanoInference

tts = MossTTSNanoInference()

audio = tts.infer(
    text="Welcome to MOSS TTS Nano, a tiny but capable text to speech model.",
    prompt_audio_path="assets/audio/en_sample.wav",
)

import soundfile as sf
sf.write("english_output.wav", audio, samplerate=48000)
```

### Streaming inference (low latency)

```python
from infer import MossTTSNanoInference
import soundfile as sf
import numpy as np

tts = MossTTSNanoInference()

chunks = []
for audio_chunk in tts.infer_stream(
    text="This sentence is generated chunk by chunk for low latency playback.",
    prompt_audio_path="assets/audio/en_sample.wav",
):
    chunks.append(audio_chunk)
    # process or play chunk in real time here

full_audio = np.concatenate(chunks)
sf.write("streamed_output.wav", full_audio, samplerate=48000)
```

### Long-text synthesis with chunked voice cloning

```python
from infer import MossTTSNanoInference

tts = MossTTSNanoInference()

long_text = """
MOSS-TTS-Nano supports long-form synthesis through automatic chunkin