Kokoro 82M v1.0 ONNX

Provideronnx-community
Categorytext-to-speech
Licenseapache-2.0
Downloads1.3K
Stars2

Overview

Kokoro 82M v1.0 is a lightweight, high-efficiency text-to-speech model optimized for local deployment via the ONNX runtime. Unlike massive transformer-based TTS systems, this model's compact 82M parameter footprint allows it to run on consumer-grade hardware with minimal latency and low memory overhead. It is designed for developers building real-time voice interfaces, accessibility tools, or embedded applications where cloud API costs and network latency are bottlenecks. By leveraging the ONNX format, it ensures cross-platform compatibility and easy integration into existing C#, Python, or JavaScript pipelines without requiring a heavy deep-learning stack.

Highlights

  • Compact 82M parameter size for low-latency local execution
  • ONNX format ensures seamless cross-platform deployment
  • Apache-2.0 license allows for flexible commercial integration
  • Ideal for edge computing and real-time voice applications

Usage

Install
# Install Hugging Face transformers
pip install transformers torch
SDK Usage
# Load model with transformers
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("onnx-community/Kokoro-82M-v1.0-ONNX")
tokenizer = AutoTokenizer.from_pretrained("onnx-community/Kokoro-82M-v1.0-ONNX")

Hugging Face Download

We recommend downloading the model via the Hugging Face CLI or Hub SDK.

Guidance：Before downloading, install huggingface_hub with:

Guidance 
pip install -U huggingface_hub

CLI Download

Download the full repository

Download the full repository 
huggingface-cli download onnx-community/Kokoro-82M-v1.0-ONNX

Download a single file to a local folder (e.g. config.json into ./dir)

Download a single file to a local folder (e.g. config.json into ./dir) 
huggingface-cli download onnx-community/Kokoro-82M-v1.0-ONNX config.json --local-dir ./dir

See the official docs for more CLI options

SDK Download

SDK Download 
# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('onnx-community/Kokoro-82M-v1.0-ONNX')

Git Download

Make sure git-lfs is installed first

Git Download 
git lfs install
git clone https://huggingface.co/onnx-community/Kokoro-82M-v1.0-ONNX

To skip LFS large-file downloads, use:

Skip LFS 
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/onnx-community/Kokoro-82M-v1.0-ONNX

Model files are hosted on the Hugging Face Hub — download directly via HF CLI / SDK / Git, not through this site.

PyTorch / Transformers Usage

Install Transformers

Install Transformers 
pip install -U transformers torch

Load the model and run inference

Load the model and run inference 
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('onnx-community/Kokoro-82M-v1.0-ONNX')
tokenizer = AutoTokenizer.from_pretrained('onnx-community/Kokoro-82M-v1.0-ONNX')

Model Download

We recommend downloading the model via the ModelScope CLI or SDK.

Guidance：Before downloading, install ModelScope with:

Guidance 
pip install modelscope

CLI Download

Download the full repository

Download the full repository 
modelscope download --model onnx-community/Kokoro-82M-v1.0-ONNX

Download a single file to a local folder (e.g. README.md into ./dir)

Download a single file to a local folder (e.g. README.md into ./dir) 
modelscope download --model onnx-community/Kokoro-82M-v1.0-ONNX README.md --local_dir ./dir

See the docs for more CLI options

SDK Download

SDK Download 
# 模型下载
from modelscope import snapshot_download
model_dir = snapshot_download('onnx-community/Kokoro-82M-v1.0-ONNX')

Git Download

Make sure git-lfs is installed first

Git Download 
git lfs install
git clone https://www.modelscope.cn/onnx-community/Kokoro-82M-v1.0-ONNX.git

To skip LFS large-file downloads, use:

Skip LFS 
GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/onnx-community/Kokoro-82M-v1.0-ONNX.git

ModelScope 模型页直接下载模型文件；无需将模型文件放在本站服务器。

Notebook Quickstart

Install the ModelScope library

Install the ModelScope library 
pip install "modelscope[audio,cv,nlp,multi-modal,science]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html

Load the model and run inference

Load the model and run inference 
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

p = pipeline('text-generation', 'onnx-community/Kokoro-82M-v1.0-ONNX')

Full Documentation

来源: HuggingFace

---
license: apache-2.0
library_name: transformers.js
language:

  • en

base_model:
  • hexgrad/Kokoro-82M

pipeline_tag: text-to-speech
---

Kokoro TTS

Kokoro is a frontier TTS model for its size of 82 million parameters (text in/audio out).

Table of contents

- JavaScript - Python

Usage

JavaScript

First, install the kokoro-js library from NPM using:

bash
npm i kokoro-js

You can then generate speech as follows:

js
import { KokoroTTS } from "kokoro-js";

const model_id = "onnx-community/Kokoro-82M-ONNX";
const tts = await KokoroTTS.from_pretrained(model_id, {
  dtype: "q8", // Options: "fp32", "fp16", "q8", "q4", "q4f16"
});


const text = "Life is like a box of chocolates. You never know what you're gonna get.";
const audio = await tts.generate(text, {
  // Use tts.list_voices() to list all available voices
  voice: "af_bella",
});
audio.save("audio.wav");

Python

python
import os
import numpy as np
from onnxruntime import InferenceSession

You can generate token ids as follows:


  1. Convert input text to phonemes using https://github.com/hexgrad/misaki


  2. Map phonemes to ids using https://huggingface.co/hexgrad/Kokoro-82M/blob/785407d1adfa7ae8fbef8ffd85f34ca127da3039/config.json#L34-L148

tokens = [50, 157, 43, 135, 16, 53, 135, 46, 16, 43, 102, 16, 56, 156, 57, 135, 6, 16, 102, 62, 61, 16, 70, 56, 16, 138, 56, 156, 72, 56, 61, 85, 123, 83, 44, 83, 54, 16, 53, 65, 156, 86, 61, 62, 131, 83, 56, 4, 16, 54, 156, 43, 102, 53, 16, 156, 72, 61, 53, 102, 112, 16, 70, 56, 16, 138, 56, 44, 156, 76, 158, 123, 56, 16, 62, 131, 156, 43, 102, 54, 46, 16, 102, 48, 16, 81, 47, 102, 54, 16, 54, 156, 51, 158, 46, 16, 70, 16, 92, 156, 135, 46, 16, 54, 156, 43, 102, 48, 4, 16, 81, 47, 102, 16, 50, 156, 72, 64, 83, 56, 62, 16, 156, 51, 158, 64, 83, 56, 16, 44, 157, 102, 56, 16, 44, 156, 76, 158, 123, 56, 4]

Context length is 512, but leave room for the pad token 0 at the start & end

assert len(tokens) <= 510, len(tokens)

Style vector based on len(tokens), ref_s has shape (1, 256)

voices = np.fromfile('./voices/af.bin', dtype=np.float32).reshape(-1, 1, 256)
ref_s = voices[len(tokens)]

Add the pad ids, and reshape tokens, should now have shape (1, <=512)

tokens = [[0, *tokens, 0]]

model_name = 'model.onnx' # Options: model.onnx, model_fp16.onnx, model_quantized.onnx, model_q8f16.onnx, model_uint8.onnx, model_uint8f16.onnx, model_q4.onnx, model_q4f16.onnx
sess = InferenceSession(os.path.join('onnx', model_name))


audio = sess.run(None, dict(
    input_ids=tokens,
    style=ref_s,
    speed=np.ones(1, dtype=np.float32),
))[0]

Optionally, save the audio to a file:

py
import scipy.io.wavfile as wavfile
wavfile.write('audio.wav', 24000, audio[0])

Voices/Samples

> Life is like a box of chocolates. You never know what you're gonna get.

| Name | Nationality | Gender | Sample |
| ------------ | ----------- | ------ | --------------------------------------------------------------------------------------------------------------------------------------- |
| af_heart | American | Female | <audio controls src="https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/S_9tkA75BT_QHKOzSX6S-.wav"></audio> |
| af_alloy | American | Female | <audio controls src="https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/wiZ3gvlL--p5pRItO4YRE.wav"></audio> |
| af_aoede | American | Female | <audio controls src="https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/Nv1xMwzjTdF9MR8v0oEEJ.wav"></audio> |
| af_bella | American | Female | <audio controls src="https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/sWN0rnKU6TlLsVdGqRktF.wav"></audio> |
| af_jessica | American | Female | <audio controls src="https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/2Oa4wITWAmiCXJ_Q97-7R.wav"></audio> |
| af_kore | American | Female | <audio controls src="https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/AOIgyspzZWDGpn7oQgwtu.wav"></audio> |
| af_nicole | American | Female | <audio controls src="https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/EY_V2OGr-hzmtTGrTCTyf.wav"></audio> |
| af_nova | American | Female | <audio controls src="https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/X-xdEkx3GPlQG5DK8Gsqd.wav"></audio> |
| af_river | American | Female | <audio controls src="https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/ZqaV2-xGUZdBQmZAF1Xqy.wav"></audio> |
| af_sarah | American | Female | <audio controls src="https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/xzoJBl1HCvkE8Fl8Xu2R4.wav"></audio> |
| af_sky | American | Female | <audio controls src="https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/ubebYQoaseyQk-jDLeWX7.wav"></audio> |
| am_adam | American | Male | <audio controls src="https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/tvauhDVRGvGK98I-4wv3H.wav"></audio> |
| am_echo | American | Male | <audio controls src="https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/qy_KuUB0hXsu-u8XaJJ_Z.wav"></audio> |
| am_eric | American | Male | <audio controls src="https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/JhqPjbpMhraUv5nTSPpwD.wav"></audio> |
| am_fenrir | American | Male | <audio controls src="https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/c0R9caBdBiNjGUUalI_DQ.wav"></audio> |
| am_liam | American | Male | <audio controls src="https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/DFHvulaLeOjXIDKecvNG3.wav"></audio> |
| am_michael | American | Male | <audio controls src="https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/IPKhsnjq1tPh3JmHH8nEg.wav"></audio> |
| am_onyx | American | Male | <audio controls src="https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/ov0pFDfE8NNKZ80LqW6Di.wav"></audio> |
| am_puck | American | Male | <audio controls src="https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/MOC654sLMHWI64g8HWesV.wav"></audio> |
| am_santa | American | Male | <audio controls src="https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/LzA6JmHBvQlhOviy8qVfJ.wav"></audio> |
| bf_alice | British | Female | <audio controls src="https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/9mnYZ3JWq7f6U12plXilA.wav"></audio> |
| bf_emma | British | Female | <audio controls src="https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/_fvGtKMttRI0cZVGqxMh8.wav"></audio> |
| bf_isabella | British | Female | <audio controls src="https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/VzlcJpqGEND_Q3duYnhiu.wav"></audio> |
| bf_lily | British | Female | <audio controls src="https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/qZCoartohiRlVamY8Xpok.wav"></audio> |
| bm_daniel | British | Male | <audio controls src="https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/Eb0TLnLXHDRYOA3TJQKq3.wav"></audio> |
| bm_fable | British | Male | <audio controls src="https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/NT9XkmvlezQ0FJ6Th5hoZ.wav"></audio> |
| bm_george | British |

View official source
Join our Telegram