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:

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
af_heart	American	Female
af_alloy	American	Female
af_aoede	American	Female
af_bella	American	Female
af_jessica	American	Female
af_kore	American	Female
af_nicole	American	Female
af_nova	American	Female
af_river	American	Female
af_sarah	American	Female
af_sky	American	Female
am_adam	American	Male
am_echo	American	Male
am_eric	American	Male
am_fenrir	American	Male
am_liam	American	Male
am_michael	American	Male
am_onyx	American	Male
am_puck	American	Male
am_santa	American	Male
bf_alice	British	Female
bf_emma	British	Female
bf_isabella	British	Female
bf_lily	British	Female
bm_daniel	British	Male
bm_fable	British	Male
bm_george	British	Male
bm_lewis	British	Male

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

cshbli/Moxin-TTS

Kokoro TTS

Table of contents

Usage

JavaScript

Python

Voices/Samples

Quantizations

cshbli/
Moxin-TTS