kokoro-js
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High-quality text-to-speech for the web
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# Kokoro TTS
<p align="center">
<a href="https://www.npmjs.com/package/kokoro-js"><img alt="NPM" src="https://img.shields.io/npm/v/kokoro-js"></a>
<a href="https://www.npmjs.com/package/kokoro-js"><img alt="NPM Downloads" src="https://img.shields.io/npm/dw/kokoro-js"></a>
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<a href="https://github.com/hexgrad/kokoro/blob/main/LICENSE"><img alt="License" src="https://img.shields.io/github/license/hexgrad/kokoro?color=blue"></a>
<a href="https://huggingface.co/spaces/webml-community/kokoro-webgpu"><img alt="Demo" src="https://img.shields.io/badge/Hugging_Face-demo-green"></a>
</p>
Kokoro is a frontier TTS model for its size of 82 million parameters (text in/audio out). This JavaScript library allows the model to be run 100% locally in the browser thanks to [🤗 Transformers.js](https://huggingface.co/docs/transformers.js). Try it out using our [online demo](https://huggingface.co/spaces/webml-community/kokoro-webgpu)!
## Usage
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-v1.0-ONNX";
const tts = await KokoroTTS.from_pretrained(model_id, {
dtype: "q8", // Options: "fp32", "fp16", "q8", "q4", "q4f16"
device: "wasm", // Options: "wasm", "webgpu" (web) or "cpu" (node). If using "webgpu", we recommend using dtype="fp32".
});
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_heart",
});
audio.save("audio.wav");
```
Or if you'd prefer to stream the output, you can do that with:
```js
import { KokoroTTS, TextSplitterStream } from "kokoro-js";
const model_id = "onnx-community/Kokoro-82M-v1.0-ONNX";
const tts = await KokoroTTS.from_pretrained(model_id, {
dtype: "fp32", // Options: "fp32", "fp16", "q8", "q4", "q4f16"
// device: "webgpu", // Options: "wasm", "webgpu" (web) or "cpu" (node).
});
// First, set up the stream
const splitter = new TextSplitterStream();
const stream = tts.stream(splitter);
(async () => {
let i = 0;
for await (const { text, phonemes, audio } of stream) {
console.log({ text, phonemes });
audio.save(`audio-${i++}.wav`);
}
})();
// Next, add text to the stream. Note that the text can be added at different times.
// For this example, let's pretend we're consuming text from an LLM, one word at a time.
const text = "Kokoro is an open-weight TTS model with 82 million parameters. Despite its lightweight architecture, it delivers comparable quality to larger models while being significantly faster and more cost-efficient. With Apache-licensed weights, Kokoro can be deployed anywhere from production environments to personal projects. It can even run 100% locally in your browser, powered by Transformers.js!";
const tokens = text.match(/\s*\S+/g);
for (const token of tokens) {
splitter.push(token);
await new Promise((resolve) => setTimeout(resolve, 10));
}
// Finally, close the stream to signal that no more text will be added.
splitter.close();
// Alternatively, if you'd like to keep the stream open, but flush any remaining text, you can use the `flush` method.
// splitter.flush();
```
## Voices/Samples
> [!TIP]
> You can find samples for each of the voices in the [model card](https://huggingface.co/onnx-community/Kokoro-82M-v1.0-ONNX#samples) on Hugging Face.
### American English
| Name | Traits | Target Quality | Training Duration | Overall Grade |
| ------------ | ------ | -------------- | ----------------- | ------------- |
| **af_heart** | 🚺❤️ | | | **A** |
| af_alloy | 🚺 | B | MM minutes | C |
| af_aoede | 🚺 | B | H hours | C+ |
| af_bella | 🚺🔥 | **A** | **HH hours** | **A-** |
| af_jessica | 🚺 | C | MM minutes | D |
| af_kore | 🚺 | B | H hours | C+ |
| af_nicole | 🚺🎧 | B | **HH hours** | B- |
| af_nova | 🚺 | B | MM minutes | C |
| af_river | 🚺 | C | MM minutes | D |
| af_sarah | 🚺 | B | H hours | C+ |
| af_sky | 🚺 | B | _M minutes_ 🤏 | C- |
| am_adam | 🚹 | D | H hours | F+ |
| am_echo | 🚹 | C | MM minutes | D |
| am_eric | 🚹 | C | MM minutes | D |
| am_fenrir | 🚹 | B | H hours | C+ |
| am_liam | 🚹 | C | MM minutes | D |
| am_michael | 🚹 | B | H hours | C+ |
| am_onyx | 🚹 | C | MM minutes | D |
| am_puck | 🚹 | B | H hours | C+ |
| am_santa | 🚹 | C | _M minutes_ 🤏 | D- |
### British English
| Name | Traits | Target Quality | Training Duration | Overall Grade |
| ----------- | ------ | -------------- | ----------------- | ------------- |
| bf_alice | 🚺 | C | MM minutes | D |
| bf_emma | 🚺 | B | **HH hours** | B- |
| bf_isabella | 🚺 | B | MM minutes | C |
| bf_lily | 🚺 | C | MM minutes | D |
| bm_daniel | 🚹 | C | MM minutes | D |
| bm_fable | 🚹 | B | MM minutes | C |
| bm_george | 🚹 | B | MM minutes | C |
| bm_lewis | 🚹 | C | H hours | D+ |