5229a254b7
* Set up JS project * Finalise JS library * Update README * Fix package.json repository url * Rename package -> `kokoro-js` * Fix samples in README * Cleanup README * Bump `phonemizer` version * Create web demo * Run prettier * Link to model used in demo * Enable multithreading in HF space demo (~40% faster) * Add link to demo in README * Bump to v1.0.1 * Update voices * Update versions * Update phonemize JSDoc * Use updated voice pack * Update versions * Update demo (v1.0 & WebGPU support) * Update README * Enforce maximum number of tokens * Update README * [version] Update to 1.1.1 * Create simple sentence splitter * Update `npm run test` * Update API to use sync and async iterators * Add support for streamed generation in kokoro.js * Always split on newlines * Remove debug line * Improvements * Add more matching puntuation marks * Update comments * nits * Export TextSplitterStream too * Update splitter.js * Update README * [version] Update to 1.2.0
120 lines
6.2 KiB
Markdown
120 lines
6.2 KiB
Markdown
# Kokoro TTS
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<p align="center">
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<a href="https://www.npmjs.com/package/kokoro-js"><img alt="NPM" src="https://img.shields.io/npm/v/kokoro-js"></a>
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<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://www.jsdelivr.com/package/npm/kokoro-js"><img alt="jsDelivr Hits" src="https://img.shields.io/jsdelivr/npm/hw/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>
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<a href="https://huggingface.co/spaces/webml-community/kokoro-webgpu"><img alt="Demo" src="https://img.shields.io/badge/Hugging_Face-demo-green"></a>
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</p>
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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)!
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## Usage
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First, install the `kokoro-js` library from [NPM](https://npmjs.com/package/kokoro-js) using:
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```bash
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npm i kokoro-js
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```
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You can then generate speech as follows:
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```js
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import { KokoroTTS } from "kokoro-js";
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const model_id = "onnx-community/Kokoro-82M-v1.0-ONNX";
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const tts = await KokoroTTS.from_pretrained(model_id, {
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dtype: "q8", // Options: "fp32", "fp16", "q8", "q4", "q4f16"
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device: "wasm", // Options: "wasm", "webgpu" (web) or "cpu" (node). If using "webgpu", we recommend using dtype="fp32".
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});
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const text = "Life is like a box of chocolates. You never know what you're gonna get.";
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const audio = await tts.generate(text, {
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// Use `tts.list_voices()` to list all available voices
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voice: "af_heart",
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});
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audio.save("audio.wav");
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```
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Or if you'd prefer to stream the output, you can do that with:
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```js
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import { KokoroTTS, TextSplitterStream } from "kokoro-js";
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const model_id = "onnx-community/Kokoro-82M-v1.0-ONNX";
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const tts = await KokoroTTS.from_pretrained(model_id, {
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dtype: "fp32", // Options: "fp32", "fp16", "q8", "q4", "q4f16"
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// device: "webgpu", // Options: "wasm", "webgpu" (web) or "cpu" (node).
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});
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// First, set up the stream
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const splitter = new TextSplitterStream();
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const stream = tts.stream(splitter);
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(async () => {
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let i = 0;
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for await (const { text, phonemes, audio } of stream) {
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console.log({ text, phonemes });
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audio.save(`audio-${i++}.wav`);
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}
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})();
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// Next, add text to the stream. Note that the text can be added at different times.
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// For this example, let's pretend we're consuming text from an LLM, one word at a time.
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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!";
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const tokens = text.match(/\s*\S+/g);
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for (const token of tokens) {
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splitter.push(token);
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await new Promise((resolve) => setTimeout(resolve, 10));
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}
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// Finally, close the stream to signal that no more text will be added.
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splitter.close();
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// Alternatively, if you'd like to keep the stream open, but flush any remaining text, you can use the `flush` method.
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// splitter.flush();
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```
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## Voices/Samples
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> [!TIP]
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> 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.
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### American English
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| Name | Traits | Target Quality | Training Duration | Overall Grade |
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| ------------ | ------ | -------------- | ----------------- | ------------- |
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| **af_heart** | 🚺❤️ | | | **A** |
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| af_alloy | 🚺 | B | MM minutes | C |
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| af_aoede | 🚺 | B | H hours | C+ |
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| af_bella | 🚺🔥 | **A** | **HH hours** | **A-** |
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| af_jessica | 🚺 | C | MM minutes | D |
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| af_kore | 🚺 | B | H hours | C+ |
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| af_nicole | 🚺🎧 | B | **HH hours** | B- |
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| af_nova | 🚺 | B | MM minutes | C |
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| af_river | 🚺 | C | MM minutes | D |
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| af_sarah | 🚺 | B | H hours | C+ |
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| af_sky | 🚺 | B | _M minutes_ 🤏 | C- |
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| am_adam | 🚹 | D | H hours | F+ |
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| am_echo | 🚹 | C | MM minutes | D |
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| am_eric | 🚹 | C | MM minutes | D |
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| am_fenrir | 🚹 | B | H hours | C+ |
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| am_liam | 🚹 | C | MM minutes | D |
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| am_michael | 🚹 | B | H hours | C+ |
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| am_onyx | 🚹 | C | MM minutes | D |
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| am_puck | 🚹 | B | H hours | C+ |
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| am_santa | 🚹 | C | _M minutes_ 🤏 | D- |
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### British English
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| Name | Traits | Target Quality | Training Duration | Overall Grade |
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| ----------- | ------ | -------------- | ----------------- | ------------- |
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| bf_alice | 🚺 | C | MM minutes | D |
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| bf_emma | 🚺 | B | **HH hours** | B- |
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| bf_isabella | 🚺 | B | MM minutes | C |
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| bf_lily | 🚺 | C | MM minutes | D |
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| bm_daniel | 🚹 | C | MM minutes | D |
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| bm_fable | 🚹 | B | MM minutes | C |
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| bm_george | 🚹 | B | MM minutes | C |
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| bm_lewis | 🚹 | C | H hours | D+ |
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