{
"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
}