{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Kokoro-82M ONNX Runtime Inference" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# Import required libraries\n", "import numpy as np\n", "from IPython.display import display, Audio\n", "\n", "# Import the Kokoro model class\n", "from models import Tokenizer, Kokoro" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "model_path = \"weights/kokoro-v0_19.onnx\"\n", "output_filename = \"output.wav\"\n", "style_vector_path = \"voices/af_bella.pt\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Initialize Tokenizer and Kokoro model" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "tokenizer = Tokenizer()\n", "\n", "kokoro = Kokoro(model_path, style_vector_path, tokenizer=tokenizer, lang='en-us')\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "text = (\n", " \"This approach ensures the entire text is processed without exceeding the token limit and outputs seamless audio for the full input. \"\n", " \"Let me know if you need further assistance!\"\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Generate audio" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "audio, sample_rate = kokoro.generate_audio(text, speed=1.0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Display the output on jupyter" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display(Audio(data=audio, rate=24000, autoplay=True))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Save model the output" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Audio saved to output.wav\n" ] } ], "source": [ "import soundfile as sf\n", "\n", "sf.write(output_filename, audio, sample_rate)\n", "print(f\"Audio saved to {output_filename}\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "torch", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.14" } }, "nbformat": 4, "nbformat_minor": 2 }