moss-tts-nano-speech

Expert skill for using MOSS-TTS-Nano, a 0.1B parameter multilingual real-time TTS model that runs on CPU with voice cloning support.

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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 — 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)

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

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

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。"

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

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

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

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)

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)

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)

from infer import MossTTSNanoInference

tts = MossTTSNanoInference()

long_text = """
MOSS-TTS-Nano supports long-form synthesis through automatic chunking.
Each chunk uses the same reference voice, producing consistent speaker identity
across the entire output even for multi-paragraph documents.
"""

audio = tts.infer(
    text=long_text,
    prompt_audio_path="assets/audio/en_sample.wav",
)

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

import requests
import base64
import soundfile as sf
import io
import numpy as np

# Read reference audio as base64
with open("assets/audio/zh_1.wav", "rb") as f:
    ref_audio_b64 = base64.b64encode(f.read()).decode()

response = requests.post(
    "http://127.0.0.1:18083/generate",
    json={
        "text": "你好，这是一个语音合成测试。",
        "prompt_audio_base64": ref_audio_b64,
    },
)

data = response.json()
audio_bytes = base64.b64decode(data["audio_base64"])

audio_array, sr = sf.read(io.BytesIO(audio_bytes))
sf.write("api_output.wav", audio_array, samplerate=sr)

import requests

with open("assets/audio/zh_1.wav", "rb") as f:
    ref_audio_b64 = __import__("base64").b64encode(f.read()).decode()

with requests.post(
    "http://127.0.0.1:18083/generate_stream",
    json={
        "text": "流式语音合成示例，适合实时播放场景。",
        "prompt_audio_base64": ref_audio_b64,
    },
    stream=True,
) as resp:
    with open("stream_output.wav", "wb") as out:
        for chunk in resp.iter_content(chunk_size=4096):
            out.write(chunk)

Code	Language	Code	Language	Code	Language
zh	Chinese	en	English	de	German
es	Spanish	fr	French	ja	Japanese
it	Italian	hu	Hungarian	ko	Korean
ru	Russian	fa	Persian	ar	Arabic
pl	Polish	pt	Portuguese	cs	Czech
da	Danish	sv	Swedish	el	Greek
tr	Turkish

Flag	Alias	Description
`--prompt-audio-path`	—	Path to reference WAV for voice cloning (`infer.py`)
`--prompt-speech`	—	Same purpose in `moss-tts-nano generate` CLI
`--text`	—	Input text string
`--text-file`	—	Path to plain text file for long-form synthesis
`--output`	—	Output WAV file path (default varies by entrypoint)

from infer import MossTTSNanoInference
import soundfile as sf

tts = MossTTSNanoInference()
ref = "assets/audio/zh_1.wav"

sentences = [
    "第一句话，用于批量合成测试。",
    "第二句话，保持相同的音色。",
    "第三句话，输出独立的音频文件。",
]

for i, sentence in enumerate(sentences):
    audio = tts.infer(text=sentence, prompt_audio_path=ref)
    sf.write(f"output_{i:02d}.wav", audio, samplerate=48000)
    print(f"Saved output_{i:02d}.wav")

import sounddevice as sd
import numpy as np
from infer import MossTTSNanoInference

tts = MossTTSNanoInference()

buffer = []
for chunk in tts.infer_stream(
    text="Real-time playback example using sounddevice.",
    prompt_audio_path="assets/audio/en_sample.wav",
):
    buffer.append(chunk)

audio = np.concatenate(buffer)
sd.play(audio, samplerate=48000)
sd.wait()

import gradio as gr
import soundfile as sf
import numpy as np
import io
from infer import MossTTSNanoInference

tts = MossTTSNanoInference()

def synthesize(reference_audio_path: str, text: str):
    audio = tts.infer(text=text, prompt_audio_path=reference_audio_path)
    # Return as (sample_rate, numpy_array) tuple for Gradio Audio component
    return (48000, audio)

demo = gr.Interface(
    fn=synthesize,
    inputs=[
        gr.Audio(type="filepath", label="Reference Voice"),
        gr.Textbox(label="Text to synthesize"),
    ],
    outputs=gr.Audio(label="Generated Speech"),
    title="MOSS-TTS-Nano Voice Clone",
)

demo.launch()

# Use conda to get pynini, then install from source
conda install -c conda-forge pynini=2.1.6.post1 -y
pip install git+https://github.com/WhizZest/WeTextProcessing.git

export HF_ENDPOINT=https://hf-mirror.com
python infer.py --prompt-audio-path assets/audio/zh_1.wav --text "测试"

pip install modelscope

# Re-run editable install inside the active conda env
pip install -e .
which moss-tts-nano   # should resolve now

# Default port is 18083; check what occupies it
lsof -i :18083
# Kill if needed, then relaunch
moss-tts-nano serve