1038lab / Kokoro-82M-zh-MLX

Kokoro-82M-zh-MLX is a fast, high-quality text-to-speech model optimized for Apple Silicon. Built on the official Kokoro architecture with native MLX inference — no GPU required on your Mac.

Features

Quick Start

from mlx_audio.tts.generate import load_model

m = load_model("1038lab/Kokoro-82M-zh-MLX")

results = list(m.generate(
    text="今天天气很好，一起去公园散步吧。",
    voice="zf_001",
    speed=1.0,
    lang_code="z"
))
from mlx_audio.audio_io import write as audio_write
audio_write("output.wav", results[0].audio, 24000, format="wav")

Installation

pip install misaki[zh] phonemizer-fork espeakng-loader mlx-audio

Usage

Speech Generation

from mlx_audio.tts.generate import load_model
from mlx_audio.audio_io import write as audio_write

m = load_model("1038lab/Kokoro-82M-zh-MLX")

# Chinese TTS (lang_code="z")
results = list(m.generate(
    text="你好世界，这是一段中文测试。",
    voice="zm_009",       # male voice
    speed=1.0,
    lang_code="z"         # "z"=Chinese, "a"=English
))
audio_write("output.wav", results[0].audio, 24000, format="wav")

# English TTS
results = list(m.generate(
    text="Hello world!",
    voice="af_heart",
    speed=1.0
))
audio_write("output.wav", results[0].audio, 24000, format="wav")

Streaming

results = list(m.generate(text="A long text here...", voice="zf_001", lang_code="z", streaming=True))
for chunk in results:
    if chunk.audio is not None:
        print(f"Chunk {chunk.segment_idx}: {len(chunk.audio)} samples")

Available Voices

Parameters

Performance

Benchmarked on M-series MacBook Pro:

Lower RTF = faster. RTF < 1.0 means faster than real-time.

Audio Samples

Click any audio below to hear the voice quality:

See samples/index.html for a full interactive voice index with all 15 demo voices.

License

This model is released under the Apache 2.0 License. The underlying model architecture is from hexgrad/Kokoro. The voice embeddings are converted from the official kokoro-v1.1-zh models.

Acknowledgements

• Original model by hexgrad
• Training data by 龙猫数据 LongMaoData
• MLX conversion and voice embedding extraction by 1038lab

1038lab / Kokoro-82M-zh-MLX

Kokoro-82M-zh-MLX 是一个专为 Apple Silicon (M1/M2/M3/M4) 优化的开源语音合成模型。基于原生 MLX 架构实现，无需额外 GPU 即可在 Mac 上快速运行。

功能特点

快速开始

from mlx_audio.tts.generate import load_model

m = load_model("1038lab/Kokoro-82M-zh-MLX")

results = list(m.generate(
    text="今天天气很好，一起去公园散步吧。",
    voice="zf_001",
    speed=1.0,
    lang_code="z"
))
from mlx_audio.audio_io import write as audio_write
audio_write("output.wav", results[0].audio, 24000, format="wav")

安装依赖

pip install misaki[zh] phonemizer-fork espeakng-loader mlx-audio

使用说明

语音合成

from mlx_audio.tts.generate import load_model
from mlx_audio.audio_io import write as audio_write

m = load_model("1038lab/Kokoro-82M-zh-MLX")

# 中文发音
results = list(m.generate(
    text="你好世界，这是一段中文测试。",
    voice="zm_009",       # 男声
    speed=1.0,
    lang_code="z"         # 语言代码："z"=中文, "a"=英文
))
audio_write("output.wav", results[0].audio, 24000, format="wav")

# 英文发音
results = list(m.generate(
    text="Hello world!",
    voice="af_heart",
    speed=1.0
))
audio_write("output.wav", results[0].audio, 24000, format="wav")

流式输出

results = list(m.generate(text="这是一段长文本...", voice="zf_001", lang_code="z", streaming=True))
for chunk in results:
    if chunk.audio is not None:
        print(f"音频块 {chunk.segment_idx}: {len(chunk.audio)} samples")

可用声音

参数说明

性能表现

在 M 系列 MacBook Pro 上的实测结果：

RTF 越小越快。RTF < 1.0 表示比实时更快。

示例音频

点击查看各个声音的演示音频：samples/index.html

许可证

本项目基于 Apache 2.0 License 发布。底层模型架构来自 hexgrad/Kokoro。声音嵌入文件由官方 kokoro-v1.1-zh 模型转换而来。

致谢

• 原始模型由 hexgrad 开发
• 训练数据由 龙猫数据 LongMaoData 提供
• MLX 转换及声音提取由 1038lab 完成