onnx-community/Kokoro-82M-ONNX

Kokoro TTS

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

Table of contents

• Samples
• Usage
  • JavaScript
  • Python
• Quantizations

Samples

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

Voice	Nationality	Gender	Sample
Default (af)	American	Female
Bella (af_bella)	American	Female
Nicole (af_nicole)	American	Female
Sarah (af_sarah)	American	Female
Sky (af_sky)	American	Female
Adam (am_adam)	American	Male
Michael (am_michael)	American	Male
Emma (bf_emma)	British	Female
Isabella (bf_isabella)	British	Female
George (bm_george)	British	Male
Lewis (bm_lewis)	British	Male

Usage

JavaScript

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

npm i kokoro-js

You can then generate speech as follows:

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

import os
import numpy as np
from onnxruntime import InferenceSession

# Tokens produced by phonemize() and tokenize() in kokoro.py
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:

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

Quantizations

The model is resilient to quantization, enabling efficient high-quality speech synthesis at a fraction of the original model size.

How could I know? It's an unanswerable question. Like asking an unborn child if they'll lead a good life. They haven't even been born.

Model	Size (MB)	Sample
model.onnx (fp32)	326
model_fp16.onnx (fp16)	163
model_quantized.onnx (8-bit)	92.4
model_q8f16.onnx (Mixed precision)	86
model_uint8.onnx (8-bit & mixed precision)	177
model_uint8f16.onnx (Mixed precision)	114
model_q4.onnx (4-bit matmul)	305
model_q4f16.onnx (4-bit matmul & fp16 weights)	154