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text-to-speech for the web","contributors":[{"name":"Xenova"}],"maintainers":[{"name":"xenova","email":"admin@xenova.com"}],"readme":"# Kokoro TTS\n\n

\n \"NPM\"\n \"NPM\n \"jsDelivr\n \"License\"\n \"Demo\"\n

\n\nKokoro 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 [πŸ€— Transformers.js](https://huggingface.co/docs/transformers.js). Try it out using our [online demo](https://huggingface.co/spaces/webml-community/kokoro-webgpu)!\n\n## Usage\n\nFirst, install the `kokoro-js` library from [NPM](https://npmjs.com/package/kokoro-js) using:\n\n```bash\nnpm i kokoro-js\n```\n\nYou can then generate speech as follows:\n\n```js\nimport { KokoroTTS } from \"kokoro-js\";\n\nconst model_id = \"onnx-community/Kokoro-82M-v1.0-ONNX\";\nconst tts = await KokoroTTS.from_pretrained(model_id, {\n dtype: \"q8\", // Options: \"fp32\", \"fp16\", \"q8\", \"q4\", \"q4f16\"\n device: \"wasm\", // Options: \"wasm\", \"webgpu\" (web) or \"cpu\" (node). If using \"webgpu\", we recommend using dtype=\"fp32\".\n});\n\nconst text = \"Life is like a box of chocolates. You never know what you're gonna get.\";\nconst audio = await tts.generate(text, {\n // Use `tts.list_voices()` to list all available voices\n voice: \"af_heart\",\n});\naudio.save(\"audio.wav\");\n```\n\nOr if you'd prefer to stream the output, you can do that with:\n\n```js\nimport { KokoroTTS, TextSplitterStream } from \"kokoro-js\";\n\nconst model_id = \"onnx-community/Kokoro-82M-v1.0-ONNX\";\nconst tts = await KokoroTTS.from_pretrained(model_id, {\n dtype: \"fp32\", // Options: \"fp32\", \"fp16\", \"q8\", \"q4\", \"q4f16\"\n // device: \"webgpu\", // Options: \"wasm\", \"webgpu\" (web) or \"cpu\" (node).\n});\n\n// First, set up the stream\nconst splitter = new TextSplitterStream();\nconst stream = tts.stream(splitter);\n(async () => {\n let i = 0;\n for await (const { text, phonemes, audio } of stream) {\n console.log({ text, phonemes });\n audio.save(`audio-${i++}.wav`);\n }\n})();\n\n// Next, add text to the stream. Note that the text can be added at different times.\n// For this example, let's pretend we're consuming text from an LLM, one word at a time.\nconst 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!\";\nconst tokens = text.match(/\\s*\\S+/g);\nfor (const token of tokens) {\n splitter.push(token);\n await new Promise((resolve) => setTimeout(resolve, 10));\n}\n\n// Finally, close the stream to signal that no more text will be added.\nsplitter.close();\n\n// Alternatively, if you'd like to keep the stream open, but flush any remaining text, you can use the `flush` method.\n// splitter.flush();\n```\n\n## Voices/Samples\n\n> [!TIP]\n> 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.\n\n### American English\n\n| Name | Traits | Target Quality | Training Duration | Overall Grade |\n| ------------ | ------ | -------------- | ----------------- | ------------- |\n| **af_heart** | 🚺❀️ | | | **A** |\n| af_alloy | 🚺 | B | MM minutes | C |\n| af_aoede | 🚺 | B | H hours | C+ |\n| af_bella | 🚺πŸ”₯ | **A** | **HH hours** | **A-** |\n| af_jessica | 🚺 | C | MM minutes | D |\n| af_kore | 🚺 | B | H hours | C+ |\n| af_nicole | 🚺🎧 | B | **HH hours** | B- |\n| af_nova | 🚺 | B | MM minutes | C |\n| af_river | 🚺 | C | MM minutes | D |\n| af_sarah | 🚺 | B | H hours | C+ |\n| af_sky | 🚺 | B | _M minutes_ 🀏 | C- |\n| am_adam | 🚹 | D | H hours | F+ |\n| am_echo | 🚹 | C | MM minutes | D |\n| am_eric | 🚹 | C | MM minutes | D |\n| am_fenrir | 🚹 | B | H hours | C+ |\n| am_liam | 🚹 | C | MM minutes | D |\n| am_michael | 🚹 | B | H hours | C+ |\n| am_onyx | 🚹 | C | MM minutes | D |\n| am_puck | 🚹 | B | H hours | C+ |\n| am_santa | 🚹 | C | _M minutes_ 🀏 | D- |\n\n### British English\n\n| Name | Traits | Target Quality | Training Duration | Overall Grade |\n| ----------- | ------ | -------------- | ----------------- | ------------- |\n| bf_alice | 🚺 | C | MM minutes | D |\n| bf_emma | 🚺 | B | **HH hours** | B- |\n| bf_isabella | 🚺 | B | MM minutes | C |\n| bf_lily | 🚺 | C | MM minutes | D |\n| bm_daniel | 🚹 | C | MM minutes | D |\n| bm_fable | 🚹 | B | MM minutes | C |\n| bm_george | 🚹 | B | MM minutes | C |\n| bm_lewis | 🚹 | C | H hours | D+ |\n","readmeFilename":"README.md"}