diff --git "a/guitar_generation_test.ipynb" "b/guitar_generation_test.ipynb" new file mode 100644--- /dev/null +++ "b/guitar_generation_test.ipynb" @@ -0,0 +1,13832 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "31564f4e-1c78-4adc-bab5-d7536ca38356", + "metadata": {}, + "outputs": [], + "source": [ + "from audio_diffusion_pytorch import AudioDiffusionModel\n", + "import torch\n", + "from torch import Tensor, nn, optim\n", + "from tqdm import tqdm\n", + "from IPython.display import Audio\n", + "from pathlib import Path\n", + "import torchaudio\n", + "import torchaudio.transforms as T\n", + "import pytorch_lightning as pl\n", + "from torch.utils.data import random_split, DataLoader, Dataset\n", + "import torch.nn.functional as F\n", + "import wandb\n", + "from pytorch_lightning.loggers import WandbLogger\n", + "from einops import rearrange" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "c25ed44a-3271-436a-aa5e-1de9290461c5", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mmattricesound\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n" + ] + }, + { + "data": { + "text/html": [ + "Tracking run with wandb version 0.13.7" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "Run data is saved locally in ./wandb/run-20230110_005416-34njk5ue" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "Syncing run bumbling-night-20 to Weights & Biases (docs)
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "wandb_logger = WandbLogger(project=\"RemFX\", save_dir=\"./\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "5d6cb44c-94bd-4f61-92b4-76f359670679", + "metadata": {}, + "outputs": [], + "source": [ + "SAMPLE_RATE = 22050\n", + "LENGTH = 2**17#round(5 * SAMPLE_RATE) 6 seconds" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "5578eec7-7a22-410a-bb8b-075e62b579ba", + "metadata": {}, + "outputs": [], + "source": [ + "class GuitarDataset(Dataset):\n", + " def __init__(self, root, length=LENGTH):\n", + " self.files = list(Path().glob(f\"{root}/**/*.wav\"))\n", + " self.resampler = T.Resample(48000, SAMPLE_RATE)\n", + " \n", + " def __len__(self):\n", + " return len(self.files)\n", + " \n", + " def __getitem__(self, idx):\n", + " x, sr = torchaudio.load(self.files[idx])\n", + "# x = x.view() # Duplicate channel\n", + " resampled_x = self.resampler(x)\n", + " if resampled_x.shape[1] < LENGTH:\n", + " resampled_x = F.pad(resampled_x, (0, LENGTH - resampled_x.shape[1]))\n", + " elif resampled_x.shape[1] > LENGTH:\n", + " resampled_x = resampled_x[:, :LENGTH]\n", + " return resampled_x" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "401b1525-b375-484c-ad28-4d3469b29520", + "metadata": {}, + "outputs": [], + "source": [ + "g = GuitarDataset(Path(\"Clean\"))\n", + "\n", + "train_split = 0.8\n", + "train_size = int(train_split * len(g))\n", + "val_size = len(g) - train_size\n", + "\n", + "train_dataset, val_dataset = torch.utils.data.random_split(g, [train_size, val_size])\n", + "train = DataLoader(train_dataset, batch_size=2)\n", + "val = DataLoader(val_dataset, batch_size=2)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "077b2bc8-c8b0-4f8e-86d7-aa3b944c107b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([2, 1, 131072])\n", + "690 552 138\n" + ] + } + ], + "source": [ + "dataiter = iter(train)\n", + "x = next(dataiter)\n", + "print(x.shape)\n", + "print(len(g), train_size, val_size)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0f9c71be-05d7-472f-84fc-7c95afa892c0", + "metadata": {}, + "outputs": [], + "source": [ + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "ff5f6e99-0d17-47b8-bfea-06271a7e1c40", + "metadata": {}, + "outputs": [], + "source": [ + "# model = AudioDiffusionModel(in_channels=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "44423c82-91e7-4295-9cb8-611562967d75", + "metadata": {}, + "outputs": [], + "source": [ + "class WrapperModel(pl.LightningModule):\n", + " def __init__(self):\n", + " super().__init__()\n", + " self.model = AudioDiffusionModel(in_channels=1)\n", + " \n", + " def forward(self, x: torch.Tensor):\n", + " return self.model(x)\n", + "\n", + " def sample(self, *args, **kwargs) -> Tensor:\n", + " return self.model.sample(*args, **kwargs)\n", + " \n", + " def training_step(self, batch, batch_idx):\n", + " loss = self.common_step(batch, batch_idx, mode=\"train\")\n", + " return loss\n", + "\n", + " def validation_step(self, batch, batch_idx):\n", + " loss = self.common_step(batch, batch_idx, mode=\"val\")\n", + "\n", + " def common_step(self, batch, batch_idx, mode: str = \"train\"):\n", + " loss = self(batch)\n", + " self.log(f\"{mode}_loss\", loss, on_step=True, on_epoch=True)\n", + " return loss\n", + " \n", + " def configure_optimizers(self):\n", + " return torch.optim.Adam(self.parameters(), lr=1e-4, betas=(0.95, 0.999), eps=1e-6, weight_decay=1e-3)\n", + "\n", + " def on_validation_epoch_start(self):\n", + " self.log_next = True\n", + " \n", + " def on_validation_batch_start(\n", + " self, batch, batch_idx, dataloader_idx\n", + " ):\n", + " if self.log_next:\n", + " self.log_sample(batch)\n", + " self.log_next = False\n", + "\n", + " @torch.no_grad()\n", + " def log_sample(self, batch, num_steps=10):\n", + " # is_train = pl_module.training\n", + " # if is_train:\n", + " # pl_module.eval()\n", + " \n", + " # Get start diffusion noise\n", + " noise = torch.randn(batch.shape, device=self.device)\n", + " sampled = self.model.sample(\n", + " noise=noise,\n", + " num_steps=num_steps # Suggested range: 2-50\n", + " ) \n", + " self.log_wandb_audio_batch(\n", + " id=\"sample\",\n", + " samples=sampled,\n", + " sampling_rate=SAMPLE_RATE,\n", + " caption=f\"Sampled in {num_steps} steps\",\n", + " )\n", + "\n", + " # if is_train:\n", + " # pl_module.train()\n", + " \n", + " @torch.no_grad()\n", + " def log_wandb_audio_batch(self, id: str, samples: Tensor, sampling_rate: int, caption: str = \"\"\n", + " ):\n", + " num_items = samples.shape[0]\n", + " samples = rearrange(samples, \"b c t -> b t c\")\n", + " for idx in range(num_items):\n", + " wandb.log(\n", + " {\n", + " f\"sample_{idx}_{id}\": wandb.Audio(\n", + " samples[idx].cpu().numpy(),\n", + " caption=caption,\n", + " sample_rate=sampling_rate,\n", + " )\n", + "\n", + " }\n", + " )\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "db4b3260-406e-4bfc-bdd6-08d8f736d080", + "metadata": {}, + "outputs": [], + "source": [ + "model = WrapperModel()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "55da1120-7392-4e0a-82a5-06d3697d46e3", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "GPU available: True (cuda), used: True\n", + "TPU available: False, using: 0 TPU cores\n", + "IPU available: False, using: 0 IPUs\n", + "HPU available: False, using: 0 HPUs\n" + ] + } + ], + "source": [ + "trainer = pl.Trainer(limit_train_batches=100, max_epochs=1000, accelerator='gpu', devices=[1], logger=wandb_logger)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "fecc3960-539a-4f72-b86a-2a482c393105", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3]\n", + "\n", + " | Name | Type | Params\n", + "----------------------------------------------\n", + "0 | model | AudioDiffusionModel | 106 M \n", + "----------------------------------------------\n", + "106 M Trainable params\n", + "0 Non-trainable params\n", + "106 M Total params\n", + "424.786 Total estimated model params size (MB)\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Sanity Checking: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jovyan/.local/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:224: PossibleUserWarning: The dataloader, val_dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 48 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n", + " rank_zero_warn(\n", + "/home/jovyan/.local/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:224: PossibleUserWarning: The dataloader, train_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 48 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n", + " rank_zero_warn(\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "9c392e9b3b81409baf84f7e4a7a5be3c", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Training: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + 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