{ "cells": [ { "cell_type": "code", "execution_count": 2, "id": "4c52cc1c-91f1-4b79-924b-041d2929ef7b", "metadata": {}, "outputs": [], "source": [ "from audio_diffusion_pytorch import AudioDiffusionModel\n", "import torch\n", "from IPython.display import Audio" ] }, { "cell_type": "code", "execution_count": 3, "id": "a005011f-3019-4d34-bdf2-9a00e5480282", "metadata": {}, "outputs": [], "source": [ "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")" ] }, { "cell_type": "code", "execution_count": 4, "id": "1b689f18-375f-4b40-9ddc-a4ced6a5e5e4", "metadata": {}, "outputs": [], "source": [ "model = AudioDiffusionModel(in_channels=1, \n", " patch_size=1,\n", " multipliers=[1, 2, 4, 4, 4, 4, 4],\n", " factors=[2, 2, 2, 2, 2, 2],\n", " num_blocks=[2, 2, 2, 2, 2, 2],\n", " attentions=[0, 0, 0, 0, 0, 0]\n", " )\n", "model = model.to(device)" ] }, { "cell_type": "code", "execution_count": 7, "id": "bd8a1cb4-42b5-43bc-9a12-f594ce069b33", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Step 300\n", "Step 310\n", "Step 320\n" ] } ], "source": [ "fs = 22050\n", "t = 2 ** 18 / 22050\n", "samples = torch.arange(t * fs) / fs\n", "\n", "for i in range(300, 8000):\n", " f = i\n", " # Create 2 sine waves (one at f=step, other is octave up) \n", " # There is aliasing at higher freq, but since it is sinusoids, that doesn't matter too much\n", " signal1 = torch.sin(2 * torch.pi * f * samples)\n", " signal2 = torch.sin(2 * torch.pi * (f*2) * samples)\n", " stacked_signal = torch.stack((signal1, signal2)).unsqueeze(1)\n", " stacked_signal = stacked_signal.to(device)\n", " loss = model(stacked_signal)\n", " loss.backward() \n", " if i % 10 == 0:\n", " print(\"Step\", i)" ] }, { "cell_type": "code", "execution_count": 8, "id": "71d17c51-842c-40a1-81a1-a53bf358bc8a", "metadata": {}, "outputs": [], "source": [ "# Sample 2 sources given start noise\n", "noise = torch.randn(2, 1, 2 ** 18)\n", "noise = noise.to(device)\n", "sampled = model.sample(\n", " noise=noise,\n", " num_steps=10 # Suggested range: 2-50\n", ") # [2, 1, 2 ** 18]" ] }, { "cell_type": "code", "execution_count": 9, "id": "59d71efa-05ac-4545-84da-8c09c033dfd7", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "z = sampled[1]\n", "Audio(z.cpu(), rate=22050)" ] }, { "cell_type": "code", "execution_count": null, "id": "81eddd71-bba7-4c62-8d50-900b295bb2f8", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 5 }