onnx-community

onnx-community/Kokoro-82M-ONNX

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Model Documentation

Kokoro TTS

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

Samples

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

| Voice | Nationality | Gender | Sample | |--------------------------|-------------|--------|-----------------------------------------------------------------------------------------------------------------------------------------| | Default (af) | American | Female |

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

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:

py
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 |