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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "bcbbe26c",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import sys\n",
"sys.path.insert(0, os.path.dirname(os.path.abspath(\"\")))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b451ab22",
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"import random\n",
"import numpy as np\n",
"from PIL import Image\n",
"from datasets import load_dataset\n",
"from IPython.display import Audio\n",
"from diffusers import AutoencoderKL\n",
"from audiodiffusion.mel import Mel"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "324cef44",
"metadata": {},
"outputs": [],
"source": [
"mel = Mel()\n",
"vae = AutoencoderKL.from_pretrained('../models/autoencoder-kl')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "da55ce79",
"metadata": {},
"outputs": [],
"source": [
"vae.config"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5fea99ff",
"metadata": {},
"outputs": [],
"source": [
"ds = load_dataset('teticio/audio-diffusion-256')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "426c6edd",
"metadata": {},
"outputs": [],
"source": [
"image = random.choice(ds['train'])['image']\n",
"display(image)\n",
"Audio(data=mel.image_to_audio(image), rate=mel.get_sample_rate())"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d123f8a0",
"metadata": {},
"outputs": [],
"source": [
"# encode\n",
"input_image = np.frombuffer(image.convert('RGB').tobytes(), dtype=\"uint8\").reshape(\n",
" (image.height, image.width, 3))\n",
"input_image = ((input_image / 255) * 2 - 1).transpose(2, 0, 1)\n",
"posterior = vae.encode(torch.tensor([input_image], dtype=torch.float32)).latent_dist\n",
"latents = posterior.sample()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "482c458f",
"metadata": {},
"outputs": [],
"source": [
"# reconstruct\n",
"output_image = vae.decode(latents)['sample']\n",
"output_image = torch.clamp(output_image, -1., 1.)\n",
"output_image = (output_image + 1.0) / 2.0 # -1,1 -> 0,1; c,h,w\n",
"output_image = (output_image.detach().cpu().numpy() *\n",
" 255).round().astype(\"uint8\").transpose(0, 2, 3, 1)[0]\n",
"output_image = Image.fromarray(output_image).convert('L')\n",
"display(output_image)\n",
"Audio(data=mel.image_to_audio(output_image), rate=mel.get_sample_rate())"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f10db020",
"metadata": {},
"outputs": [],
"source": [
"# sample\n",
"output_image = vae.decode(torch.randn_like(posterior.sample()))['sample']\n",
"output_image = torch.clamp(output_image, -1., 1.)\n",
"output_image = (output_image + 1.0) / 2.0 # -1,1 -> 0,1; c,h,w\n",
"output_image = (output_image.detach().cpu().numpy() *\n",
" 255).round().astype(\"uint8\").transpose(0, 2, 3, 1)[0]\n",
"output_image = Image.fromarray(output_image).convert('L')\n",
"display(output_image)\n",
"Audio(data=mel.image_to_audio(output_image), rate=mel.get_sample_rate())"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "46019770",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "huggingface",
"language": "python",
"name": "huggingface"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.6"
},
"toc": {
"base_numbering": 1,
"nav_menu": {},
"number_sections": true,
"sideBar": true,
"skip_h1_title": false,
"title_cell": "Table of Contents",
"title_sidebar": "Contents",
"toc_cell": false,
"toc_position": {},
"toc_section_display": true,
"toc_window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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