---
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
- [Samples](#samples)
- [Usage](#usage)
- [JavaScript](#javascript)
- [Python](#python)
- [Quantizations](#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](https://npmjs.com/package/kokoro-js) 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:
```
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 | |