Kokoro-82M-ONNX

Kokoro is a frontier TTS model for its size of 82 million parameters (text in/audio out). - [Samples](#samples) - [Usage](#usage) - [JavaScript](#javascript) - [Python](#python) - [Quantizations](#quantizations) > Life is like a box of chocolates. You never know what you're gonna get. First, install the `kokoro-js` library from [NPM](https://npmjs.com/package/kokoro-js) using: npm i kokoro-js You can then generate speech as follows: 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"); import os import numpy as np from onnxruntime import InferenceSession 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] assert len(tokens) <= 510, len(tokens) voices = np.fromfile('./voices/af.bin', dtype=np.float32).reshape(-1, 1, 256) ref_s = voices[len(tokens)] 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]) 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.