{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import sys\n", "sys.path.append(\"/home/minz/glockenspiel/musicfm-training\")\n", "import torch\n", "import torch.nn.functional as F\n", "\n", "from einops import rearrange\n", "from torch import nn\n", "from torch.utils.data import DataLoader\n", "from musicfm.data_loaders.mertlong import MERTDataset\n", "from musicfm.models.musicfm_mertlong import MusicFM_MERTLong\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "There are 1414224 audio files longer than 30.0 seconds. The dataset is 22621 hours in total.\n", "There are 74691 audio files longer than 30.0 seconds. The dataset is 1194 hours in total.\n" ] } ], "source": [ "# data loaders\n", "train_dataset = MERTDataset(split=\"train\")\n", "valid_dataset = MERTDataset(split=\"valid\")\n", "train_loader = DataLoader(dataset=train_dataset, batch_size=8, shuffle=True)\n", "valid_loader = DataLoader(dataset=valid_dataset, batch_size=8, shuffle=False)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "class Encoder(nn.Module):\n", " def __init__(\n", " self, \n", " input_dim=1024, \n", " hidden_dim=512, \n", " latent_dim=128,\n", " ):\n", " super(Encoder, self).__init__()\n", "\n", " self.linear_1 = nn.Linear(input_dim, hidden_dim)\n", " self.linear_2 = nn.Linear(hidden_dim, hidden_dim)\n", "\n", " self.res_1 = nn.Linear(hidden_dim, hidden_dim)\n", " self.res_2 = nn.Linear(hidden_dim, latent_dim)\n", "\n", " def forward(self, emb):\n", " # encoder\n", " emb = self.linear_1(emb)\n", " emb = self.linear_2(emb)\n", "\n", " emb = F.relu(emb)\n", " emb = emb + self.res_1(emb)\n", "\n", " emb = F.relu(emb)\n", " emb = self.res_2(emb)\n", "\n", " return emb\n" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "class VQEmbeddingEMA(nn.Module):\n", " def __init__(self, n_embeddings=4096, embedding_dim=128, commitment_cost=0.25, decay=0.999, epsilon=1e-5):\n", " super(VQEmbeddingEMA, self).__init__()\n", " self.commitment_cost = commitment_cost\n", " self.decay = decay\n", " self.epsilon = epsilon\n", " \n", " init_bound = 1 / n_embeddings\n", " embedding = torch.Tensor(n_embeddings, embedding_dim)\n", " embedding.uniform_(-init_bound, init_bound)\n", " self.register_buffer(\"embedding\", embedding)\n", " self.register_buffer(\"ema_count\", torch.zeros(n_embeddings))\n", " self.register_buffer(\"ema_weight\", self.embedding.clone())\n", "\n", " def encode(self, x):\n", " M, D = self.embedding.size()\n", " x_flat = x.detach().reshape(-1, D)\n", "\n", " distances = torch.addmm(torch.sum(self.embedding ** 2, dim=1) +\n", " torch.sum(x_flat ** 2, dim=1, keepdim=True),\n", " x_flat, self.embedding.t(),\n", " alpha=-2.0, beta=1.0)\n", "\n", " indices = torch.argmin(distances.float(), dim=-1)\n", " quantized = F.embedding(indices, self.embedding)\n", " quantized = quantized.view_as(x)\n", " return quantized, indices.view(x.size(0), x.size(1))\n", " \n", " def retrieve_random_codebook(self, random_indices):\n", " quantized = F.embedding(random_indices, self.embedding)\n", " quantized = quantized.transpose(1, 3)\n", " \n", " return quantized\n", "\n", " def forward(self, x):\n", " M, D = self.embedding.size()\n", " x_flat = x.detach().reshape(-1, D)\n", " \n", " distances = torch.addmm(torch.sum(self.embedding ** 2, dim=1) +\n", " torch.sum(x_flat ** 2, dim=1, keepdim=True),\n", " x_flat, self.embedding.t(),\n", " alpha=-2.0, beta=1.0)\n", "\n", " indices = torch.argmin(distances.float(), dim=-1)\n", " encodings = F.one_hot(indices, M).float()\n", " quantized = F.embedding(indices, self.embedding)\n", " quantized = quantized.view_as(x)\n", " \n", " if self.training:\n", " self.ema_count = self.decay * self.ema_count + (1 - self.decay) * torch.sum(encodings, dim=0)\n", " n = torch.sum(self.ema_count)\n", " self.ema_count = (self.ema_count + self.epsilon) / (n + M * self.epsilon) * n\n", "\n", " dw = torch.matmul(encodings.t(), x_flat)\n", " self.ema_weight = self.decay * self.ema_weight + (1 - self.decay) * dw\n", " self.embedding = self.ema_weight / self.ema_count.unsqueeze(-1)\n", "\n", " codebook_loss = F.mse_loss(x.detach(), quantized)\n", " e_latent_loss = F.mse_loss(x, quantized.detach())\n", " commitment_loss = self.commitment_cost * e_latent_loss\n", "\n", " quantized = x + (quantized - x).detach()\n", "\n", " avg_probs = torch.mean(encodings, dim=0)\n", " perplexity = torch.exp(-torch.sum(avg_probs * torch.log(avg_probs + 1e-10)))\n", "\n", " return quantized, commitment_loss, codebook_loss, perplexity" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "class Decoder(nn.Module):\n", " def __init__(\n", " self, \n", " input_dim=1024, \n", " hidden_dim=512, \n", " latent_dim=128,\n", " ):\n", " super(Decoder, self).__init__()\n", "\n", " self.linear_1 = nn.Linear(latent_dim, hidden_dim)\n", " self.linear_2 = nn.Linear(hidden_dim, hidden_dim)\n", "\n", " self.res_1 = nn.Linear(hidden_dim, hidden_dim)\n", " self.res_2 = nn.Linear(hidden_dim, input_dim)\n", "\n", " def forward(self, emb):\n", " # encoder\n", " emb = self.linear_1(emb)\n", " emb = self.linear_2(emb)\n", "\n", " emb = F.relu(emb)\n", " emb = emb + self.res_1(emb)\n", "\n", " emb = F.relu(emb)\n", " emb = self.res_2(emb)\n", "\n", " return emb\n" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "class Model(nn.Module):\n", " def __init__(self):\n", " super(Model, self).__init__()\n", " self.pretrained = MusicFM_MERTLong(is_flash=True)\n", " self.encoder = Encoder()\n", " self.codebook = VQEmbeddingEMA()\n", " self.decoder = Decoder()\n", "\n", " def forward(self, wav):\n", " # musicfm\n", " self.pretrained.eval()\n", " with torch.no_grad():\n", " emb = self.pretrained.bfloat16().get_latent(wav.bfloat16(), 7)\n", "\n", " # rearrange\n", " emb = rearrange(emb, \"b t c -> (b t) c\").float()\n", "\n", " # encoder\n", " z = self.encoder(emb)\n", " z_quantized, commitment_loss, codebook_loss, perplexity = self.codebook(z)\n", " x_hat = self.decoder(z_quantized)\n", "\n", " return x_hat, emb, commitment_loss, codebook_loss, perplexity" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/minz/anaconda3/envs/suno_env/lib/python3.10/site-packages/torch/nn/utils/weight_norm.py:30: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.\n", " warnings.warn(\"torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.\")\n" ] } ], "source": [ "model = Model().cuda()" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "from torch.optim import Adam\n", "mse_loss = nn.MSELoss()\n", "optimizer = Adam(model.parameters(), lr=1e-4)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Start training VQ-VAE...\n", "epoch: 1 step: 1 recon_loss: 1.0677740573883057 perplexity: 58.560935974121094 \n", "\t\tcommit_loss: 0.006217521615326405 codebook loss: 0.02487008646130562 total_loss: 1.098861575126648\n", "epoch: 1 step: 51 recon_loss: 0.9689509868621826 perplexity: 1.6758702993392944 \n", "\t\tcommit_loss: 0.0025498117320239544 codebook loss: 0.010199246928095818 total_loss: 0.98170006275177\n", "epoch: 1 step: 101 recon_loss: 0.9593443274497986 perplexity: 3.3094873428344727 \n", "\t\tcommit_loss: 0.004297384060919285 codebook loss: 0.01718953624367714 total_loss: 0.9808312654495239\n", "epoch: 1 step: 151 recon_loss: 0.9598926305770874 perplexity: 5.776788234710693 \n", "\t\tcommit_loss: 0.008258461020886898 codebook loss: 0.03303384408354759 total_loss: 1.0011849403381348\n", "epoch: 1 step: 201 recon_loss: 0.9419916868209839 perplexity: 13.654998779296875 \n", "\t\tcommit_loss: 0.009389763697981834 codebook loss: 0.03755905479192734 total_loss: 0.9889404773712158\n", "epoch: 1 step: 251 recon_loss: 0.9283761382102966 perplexity: 20.427499771118164 \n", "\t\tcommit_loss: 0.011770899407565594 codebook loss: 0.047083597630262375 total_loss: 0.987230658531189\n", "epoch: 1 step: 301 recon_loss: 0.9211518168449402 perplexity: 27.019771575927734 \n", "\t\tcommit_loss: 0.011905713006854057 codebook loss: 0.04762285202741623 total_loss: 0.9806804060935974\n", "epoch: 1 step: 351 recon_loss: 0.9043430685997009 perplexity: 36.35000991821289 \n", "\t\tcommit_loss: 0.014071691781282425 codebook loss: 0.0562867671251297 total_loss: 0.9747015237808228\n", "epoch: 1 step: 401 recon_loss: 0.9011139273643494 perplexity: 49.29743194580078 \n", "\t\tcommit_loss: 0.020141147077083588 codebook loss: 0.08056458830833435 total_loss: 1.0018196105957031\n", "epoch: 1 step: 451 recon_loss: 0.8969501256942749 perplexity: 57.72582244873047 \n", "\t\tcommit_loss: 0.02076207473874092 codebook loss: 0.08304829895496368 total_loss: 1.000760555267334\n", "epoch: 1 step: 501 recon_loss: 0.892461359500885 perplexity: 63.62979507446289 \n", "\t\tcommit_loss: 0.02313416078686714 codebook loss: 0.09253664314746857 total_loss: 1.0081322193145752\n", "epoch: 1 step: 551 recon_loss: 0.8638562560081482 perplexity: 65.2911148071289 \n", "\t\tcommit_loss: 0.02328726090490818 codebook loss: 0.09314904361963272 total_loss: 0.980292558670044\n", "epoch: 1 step: 601 recon_loss: 0.8769440054893494 perplexity: 81.42245483398438 \n", "\t\tcommit_loss: 0.023160185664892197 codebook loss: 0.09264074265956879 total_loss: 0.9927449226379395\n", "epoch: 1 step: 651 recon_loss: 0.8785597085952759 perplexity: 95.0714111328125 \n", "\t\tcommit_loss: 0.02604922279715538 codebook loss: 0.10419689118862152 total_loss: 1.008805751800537\n", "epoch: 1 step: 701 recon_loss: 0.8699080348014832 perplexity: 97.5772705078125 \n", "\t\tcommit_loss: 0.026551267132163048 codebook loss: 0.10620506852865219 total_loss: 1.00266432762146\n", "epoch: 1 step: 751 recon_loss: 0.8551539778709412 perplexity: 95.57440185546875 \n", "\t\tcommit_loss: 0.032093022018671036 codebook loss: 0.12837208807468414 total_loss: 1.015619158744812\n", "epoch: 1 step: 801 recon_loss: 0.8636468052864075 perplexity: 113.54212188720703 \n", "\t\tcommit_loss: 0.03439086303114891 codebook loss: 0.13756345212459564 total_loss: 1.0356011390686035\n", "epoch: 1 step: 851 recon_loss: 0.8739781975746155 perplexity: 128.9720001220703 \n", "\t\tcommit_loss: 0.035374630242586136 codebook loss: 0.14149852097034454 total_loss: 1.0508513450622559\n", "epoch: 1 step: 901 recon_loss: 0.8657931089401245 perplexity: 131.1429443359375 \n", "\t\tcommit_loss: 0.039343301206827164 codebook loss: 0.15737320482730865 total_loss: 1.0625096559524536\n", "epoch: 1 step: 951 recon_loss: 0.8646793365478516 perplexity: 152.21331787109375 \n", "\t\tcommit_loss: 0.0387103408575058 codebook loss: 0.1548413634300232 total_loss: 1.0582311153411865\n", "epoch: 1 step: 1001 recon_loss: 0.8628396987915039 perplexity: 157.0181884765625 \n", "\t\tcommit_loss: 0.03895613178610802 codebook loss: 0.15582452714443207 total_loss: 1.0576202869415283\n", "epoch: 1 step: 1051 recon_loss: 0.8492342233657837 perplexity: 142.73121643066406 \n", "\t\tcommit_loss: 0.04164862260222435 codebook loss: 0.1665944904088974 total_loss: 1.057477355003357\n", "epoch: 1 step: 1101 recon_loss: 0.8526818156242371 perplexity: 168.5773468017578 \n", "\t\tcommit_loss: 0.04723505675792694 codebook loss: 0.18894022703170776 total_loss: 1.0888571739196777\n", "epoch: 1 step: 1151 recon_loss: 0.8589005470275879 perplexity: 178.60997009277344 \n", "\t\tcommit_loss: 0.04032008722424507 codebook loss: 0.16128034889698029 total_loss: 1.060500979423523\n", "epoch: 1 step: 1201 recon_loss: 0.8543475866317749 perplexity: 184.88565063476562 \n", "\t\tcommit_loss: 0.04600873589515686 codebook loss: 0.18403494358062744 total_loss: 1.0843912363052368\n", "epoch: 1 step: 1251 recon_loss: 0.8434238433837891 perplexity: 183.0250244140625 \n", "\t\tcommit_loss: 0.04902654141187668 codebook loss: 0.1961061656475067 total_loss: 1.0885565280914307\n", "epoch: 1 step: 1301 recon_loss: 0.828915536403656 perplexity: 147.91380310058594 \n", "\t\tcommit_loss: 0.05850980058312416 codebook loss: 0.23403920233249664 total_loss: 1.1214646100997925\n", "epoch: 1 step: 1351 recon_loss: 0.8476969599723816 perplexity: 194.69752502441406 \n", "\t\tcommit_loss: 0.04973212629556656 codebook loss: 0.19892850518226624 total_loss: 1.0963575839996338\n", "epoch: 1 step: 1401 recon_loss: 0.8576015830039978 perplexity: 222.4444580078125 \n", "\t\tcommit_loss: 0.046516090631484985 codebook loss: 0.18606436252593994 total_loss: 1.0901820659637451\n", "epoch: 1 step: 1451 recon_loss: 0.8465639352798462 perplexity: 193.19776916503906 \n", "\t\tcommit_loss: 0.052851591259241104 codebook loss: 0.21140636503696442 total_loss: 1.1108219623565674\n", "epoch: 1 step: 1501 recon_loss: 0.8480766415596008 perplexity: 220.53958129882812 \n", "\t\tcommit_loss: 0.05308759957551956 codebook loss: 0.21235039830207825 total_loss: 1.1135146617889404\n", "epoch: 1 step: 1551 recon_loss: 0.8115909695625305 perplexity: 179.07662963867188 \n", "\t\tcommit_loss: 0.06296391040086746 codebook loss: 0.25185564160346985 total_loss: 1.1264104843139648\n", "epoch: 1 step: 1601 recon_loss: 0.8387894630432129 perplexity: 192.84498596191406 \n", "\t\tcommit_loss: 0.054156240075826645 codebook loss: 0.21662496030330658 total_loss: 1.1095706224441528\n", "epoch: 1 step: 1651 recon_loss: 0.8267682790756226 perplexity: 200.37869262695312 \n", "\t\tcommit_loss: 0.05991891399025917 codebook loss: 0.23967565596103668 total_loss: 1.126362919807434\n", "epoch: 1 step: 1701 recon_loss: 0.839788019657135 perplexity: 241.81053161621094 \n", "\t\tcommit_loss: 0.06050978973507881 codebook loss: 0.24203915894031525 total_loss: 1.1423369646072388\n", "epoch: 1 step: 1751 recon_loss: 0.8280616998672485 perplexity: 218.2129364013672 \n", "\t\tcommit_loss: 0.06738823652267456 codebook loss: 0.26955294609069824 total_loss: 1.1650028228759766\n", "epoch: 1 step: 1801 recon_loss: 0.8359021544456482 perplexity: 228.62109375 \n", "\t\tcommit_loss: 0.06336696445941925 codebook loss: 0.253467857837677 total_loss: 1.1527369022369385\n", "epoch: 1 step: 1851 recon_loss: 0.8469194173812866 perplexity: 248.32516479492188 \n", "\t\tcommit_loss: 0.05965401977300644 codebook loss: 0.23861607909202576 total_loss: 1.1451895236968994\n", "epoch: 1 step: 1901 recon_loss: 0.8148048520088196 perplexity: 218.5003204345703 \n", "\t\tcommit_loss: 0.07166478037834167 codebook loss: 0.2866591215133667 total_loss: 1.1731287240982056\n", "epoch: 1 step: 1951 recon_loss: 0.8378219604492188 perplexity: 273.24053955078125 \n", "\t\tcommit_loss: 0.06567049771547318 codebook loss: 0.2626819908618927 total_loss: 1.1661744117736816\n", "epoch: 1 step: 2001 recon_loss: 0.8416634798049927 perplexity: 255.04014587402344 \n", "\t\tcommit_loss: 0.06472275406122208 codebook loss: 0.2588910162448883 total_loss: 1.1652772426605225\n", "epoch: 1 step: 2051 recon_loss: 0.8286899924278259 perplexity: 263.5976257324219 \n", "\t\tcommit_loss: 0.07274116575717926 codebook loss: 0.29096466302871704 total_loss: 1.192395806312561\n", "epoch: 1 step: 2101 recon_loss: 0.8382840156555176 perplexity: 249.865234375 \n", "\t\tcommit_loss: 0.06874693185091019 codebook loss: 0.27498772740364075 total_loss: 1.1820186376571655\n", "epoch: 1 step: 2151 recon_loss: 0.8418634533882141 perplexity: 274.6426696777344 \n", "\t\tcommit_loss: 0.06540810316801071 codebook loss: 0.26163241267204285 total_loss: 1.1689039468765259\n", "epoch: 1 step: 2201 recon_loss: 0.8347698450088501 perplexity: 252.7014923095703 \n", "\t\tcommit_loss: 0.07189027965068817 codebook loss: 0.2875611186027527 total_loss: 1.1942212581634521\n", "epoch: 1 step: 2251 recon_loss: 0.836129367351532 perplexity: 263.3041687011719 \n", "\t\tcommit_loss: 0.06957303732633591 codebook loss: 0.27829214930534363 total_loss: 1.1839945316314697\n", "epoch: 1 step: 2301 recon_loss: 0.8203662037849426 perplexity: 277.78948974609375 \n", "\t\tcommit_loss: 0.07408475875854492 codebook loss: 0.2963390350341797 total_loss: 1.1907899379730225\n", "epoch: 1 step: 2351 recon_loss: 0.822117269039154 perplexity: 256.7178039550781 \n", "\t\tcommit_loss: 0.07485675811767578 codebook loss: 0.2994270324707031 total_loss: 1.1964011192321777\n", "epoch: 1 step: 2401 recon_loss: 0.8160398006439209 perplexity: 274.37603759765625 \n", "\t\tcommit_loss: 0.07174580544233322 codebook loss: 0.2869832217693329 total_loss: 1.1747688055038452\n", "epoch: 1 step: 2451 recon_loss: 0.8267545700073242 perplexity: 307.5023498535156 \n", "\t\tcommit_loss: 0.07212787866592407 codebook loss: 0.2885115146636963 total_loss: 1.1873939037322998\n", "epoch: 1 step: 2501 recon_loss: 0.8292704820632935 perplexity: 305.8580627441406 \n", "\t\tcommit_loss: 0.06976823508739471 codebook loss: 0.27907294034957886 total_loss: 1.1781116724014282\n", "epoch: 1 step: 2551 recon_loss: 0.8263665437698364 perplexity: 311.7020568847656 \n", "\t\tcommit_loss: 0.07708801329135895 codebook loss: 0.3083520531654358 total_loss: 1.2118065357208252\n", "epoch: 1 step: 2601 recon_loss: 0.8142561912536621 perplexity: 277.7558288574219 \n", "\t\tcommit_loss: 0.08221229165792465 codebook loss: 0.3288491666316986 total_loss: 1.2253175973892212\n", "epoch: 1 step: 2651 recon_loss: 0.8254061341285706 perplexity: 296.1604309082031 \n", "\t\tcommit_loss: 0.07546131312847137 codebook loss: 0.3018452525138855 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\u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m100\u001b[39m):\n\u001b[1;32m 5\u001b[0m overall_loss \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m----> 6\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m batch_idx, x \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(train_loader):\n\u001b[1;32m 7\u001b[0m x \u001b[38;5;241m=\u001b[39m x\u001b[38;5;241m.\u001b[39mcuda()\n\u001b[1;32m 9\u001b[0m optimizer\u001b[38;5;241m.\u001b[39mzero_grad()\n", "File \u001b[0;32m~/anaconda3/envs/suno_env/lib/python3.10/site-packages/torch/utils/data/dataloader.py:630\u001b[0m, in \u001b[0;36m_BaseDataLoaderIter.__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 627\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sampler_iter \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 628\u001b[0m \u001b[38;5;66;03m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[39;00m\n\u001b[1;32m 629\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reset() \u001b[38;5;66;03m# type: ignore[call-arg]\u001b[39;00m\n\u001b[0;32m--> 630\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_next_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 631\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m 632\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dataset_kind \u001b[38;5;241m==\u001b[39m _DatasetKind\u001b[38;5;241m.\u001b[39mIterable \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[1;32m 633\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[1;32m 634\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m>\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called:\n", "File \u001b[0;32m~/anaconda3/envs/suno_env/lib/python3.10/site-packages/torch/utils/data/dataloader.py:674\u001b[0m, in \u001b[0;36m_SingleProcessDataLoaderIter._next_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 672\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_next_data\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 673\u001b[0m index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_next_index() \u001b[38;5;66;03m# may raise StopIteration\u001b[39;00m\n\u001b[0;32m--> 674\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_dataset_fetcher\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfetch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindex\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# may raise StopIteration\u001b[39;00m\n\u001b[1;32m 675\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pin_memory:\n\u001b[1;32m 676\u001b[0m data \u001b[38;5;241m=\u001b[39m _utils\u001b[38;5;241m.\u001b[39mpin_memory\u001b[38;5;241m.\u001b[39mpin_memory(data, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pin_memory_device)\n", "File \u001b[0;32m~/anaconda3/envs/suno_env/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py:51\u001b[0m, in \u001b[0;36m_MapDatasetFetcher.fetch\u001b[0;34m(self, possibly_batched_index)\u001b[0m\n\u001b[1;32m 49\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset\u001b[38;5;241m.\u001b[39m__getitems__(possibly_batched_index)\n\u001b[1;32m 50\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 51\u001b[0m data \u001b[38;5;241m=\u001b[39m [\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset[idx] \u001b[38;5;28;01mfor\u001b[39;00m idx \u001b[38;5;129;01min\u001b[39;00m possibly_batched_index]\n\u001b[1;32m 52\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 53\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset[possibly_batched_index]\n", "File \u001b[0;32m~/anaconda3/envs/suno_env/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py:51\u001b[0m, in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 49\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset\u001b[38;5;241m.\u001b[39m__getitems__(possibly_batched_index)\n\u001b[1;32m 50\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 51\u001b[0m data \u001b[38;5;241m=\u001b[39m [\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdataset\u001b[49m\u001b[43m[\u001b[49m\u001b[43midx\u001b[49m\u001b[43m]\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m idx \u001b[38;5;129;01min\u001b[39;00m possibly_batched_index]\n\u001b[1;32m 52\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 53\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset[possibly_batched_index]\n", "File \u001b[0;32m~/glockenspiel/musicfm-training/musicfm/data_loaders/mertlong.py:44\u001b[0m, in \u001b[0;36mMERTDataset.__getitem__\u001b[0;34m(self, index)\u001b[0m\n\u001b[1;32m 41\u001b[0m length \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdf\u001b[38;5;241m.\u001b[39miloc[index]\u001b[38;5;241m.\u001b[39mlength\n\u001b[1;32m 43\u001b[0m \u001b[38;5;66;03m# load audio\u001b[39;00m\n\u001b[0;32m---> 44\u001b[0m wav, _ \u001b[38;5;241m=\u001b[39m \u001b[43msf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpath\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mjoin\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43maudio\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 46\u001b[0m \u001b[38;5;66;03m# random crop\u001b[39;00m\n\u001b[1;32m 47\u001b[0m input_length \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mint\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfs \u001b[38;5;241m*\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minput_length_s)\n", "File \u001b[0;32m~/anaconda3/envs/suno_env/lib/python3.10/site-packages/soundfile.py:288\u001b[0m, in \u001b[0;36mread\u001b[0;34m(file, frames, start, stop, dtype, always_2d, fill_value, out, samplerate, channels, format, subtype, endian, closefd)\u001b[0m\n\u001b[1;32m 285\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m SoundFile(file, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m'\u001b[39m, samplerate, channels,\n\u001b[1;32m 286\u001b[0m subtype, endian, \u001b[38;5;28mformat\u001b[39m, closefd) \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[1;32m 287\u001b[0m frames \u001b[38;5;241m=\u001b[39m f\u001b[38;5;241m.\u001b[39m_prepare_read(start, stop, frames)\n\u001b[0;32m--> 288\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[43mf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43mframes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43malways_2d\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfill_value\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 289\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m data, f\u001b[38;5;241m.\u001b[39msamplerate\n", "File \u001b[0;32m~/anaconda3/envs/suno_env/lib/python3.10/site-packages/soundfile.py:895\u001b[0m, in \u001b[0;36mSoundFile.read\u001b[0;34m(self, frames, dtype, always_2d, fill_value, out)\u001b[0m\n\u001b[1;32m 893\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m frames \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m0\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m frames \u001b[38;5;241m>\u001b[39m \u001b[38;5;28mlen\u001b[39m(out):\n\u001b[1;32m 894\u001b[0m frames \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(out)\n\u001b[0;32m--> 895\u001b[0m frames \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_array_io\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mread\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mframes\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 896\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(out) \u001b[38;5;241m>\u001b[39m frames:\n\u001b[1;32m 897\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m fill_value \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", "File \u001b[0;32m~/anaconda3/envs/suno_env/lib/python3.10/site-packages/soundfile.py:1344\u001b[0m, in \u001b[0;36mSoundFile._array_io\u001b[0;34m(self, action, array, frames)\u001b[0m\n\u001b[1;32m 1342\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m array\u001b[38;5;241m.\u001b[39mdtype\u001b[38;5;241m.\u001b[39mitemsize \u001b[38;5;241m==\u001b[39m _ffi\u001b[38;5;241m.\u001b[39msizeof(ctype)\n\u001b[1;32m 1343\u001b[0m cdata \u001b[38;5;241m=\u001b[39m _ffi\u001b[38;5;241m.\u001b[39mcast(ctype \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m*\u001b[39m\u001b[38;5;124m'\u001b[39m, array\u001b[38;5;241m.\u001b[39m__array_interface__[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdata\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;241m0\u001b[39m])\n\u001b[0;32m-> 1344\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_cdata_io\u001b[49m\u001b[43m(\u001b[49m\u001b[43maction\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mctype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mframes\u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[0;32m~/anaconda3/envs/suno_env/lib/python3.10/site-packages/soundfile.py:1353\u001b[0m, in \u001b[0;36mSoundFile._cdata_io\u001b[0;34m(self, action, data, ctype, frames)\u001b[0m\n\u001b[1;32m 1351\u001b[0m curr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtell()\n\u001b[1;32m 1352\u001b[0m func \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(_snd, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msf_\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m+\u001b[39m action \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mf_\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m+\u001b[39m ctype)\n\u001b[0;32m-> 1353\u001b[0m frames \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_file\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mframes\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1354\u001b[0m _error_check(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_errorcode)\n\u001b[1;32m 1355\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mseekable():\n", "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ "print(\"Start training VQ-VAE...\")\n", "model.train()\n", "\n", "for epoch in range(100):\n", " overall_loss = 0\n", " for batch_idx, x in enumerate(train_loader):\n", " x = x.cuda()\n", "\n", " optimizer.zero_grad()\n", "\n", " x_hat, x_orig, commitment_loss, codebook_loss, perplexity = model(x)\n", " recon_loss = mse_loss(x_hat, x_orig)\n", " \n", " loss = recon_loss + commitment_loss + codebook_loss\n", " \n", " loss.backward()\n", " optimizer.step()\n", " \n", " if batch_idx % 50 ==0: \n", " print(\"epoch:\", epoch + 1, \" step:\", batch_idx + 1, \" recon_loss:\", recon_loss.item(), \" perplexity: \", perplexity.item(), \n", " \"\\n\\t\\tcommit_loss: \", commitment_loss.item(), \" codebook loss: \", codebook_loss.item(), \" total_loss: \", loss.item())\n", " \n", "print(\"Finish!!\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "suno_env", "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": "3.10.13" } }, "nbformat": 4, "nbformat_minor": 2 }