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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Coarse Transformer"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Libraries:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"from audiolm_pytorch import HubertWithKmeans\n",
"from audiolm_pytorch import SemanticTransformer, SemanticTransformerTrainer\n",
"from audiolm_pytorch import CoarseTransformer, CoarseTransformerTrainer\n",
"from audiolm_pytorch import SoundStream, FineTransformer, FineTransformerTrainer\n",
"from audiolm_pytorch import AudioLMSoundStream, AudioLM, MusicLMSoundStream\n",
"import gc\n",
"from musiclm_pytorch import MuLaNEmbedQuantizer\n",
"from musiclm_pytorch import MuLaN, AudioSpectrogramTransformer, TextTransformer"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"checkpoint_path = './models/hubert/hubert_base_ls960.pt'\n",
"kmeans_path = './models/hubert/hubert_base_ls960_L9_km500.bin'\n",
"\n",
"audio_output_dir = './audio'\n",
"batch_size = 1\n",
"data_max_length = 320 * 32\n",
"num_train_steps = 1000"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"spectrogram yielded shape of (65, 86), but had to be cropped to (64, 80) to be patchified for transformer\n"
]
}
],
"source": [
"audio_transformer = AudioSpectrogramTransformer(\n",
" dim = 512,\n",
" depth = 6,\n",
" heads = 8,\n",
" dim_head = 64,\n",
" spec_n_fft = 128,\n",
" spec_win_length = 24,\n",
" spec_aug_stretch_factor = 0.8\n",
")\n",
"\n",
"text_transformer = TextTransformer(\n",
" dim = 512,\n",
" depth = 6,\n",
" heads = 8,\n",
" dim_head = 64\n",
")\n",
"\n",
"mulan = MuLaN(\n",
" audio_transformer = audio_transformer,\n",
" text_transformer = text_transformer\n",
")\n",
"\n",
"quantizer = MuLaNEmbedQuantizer(\n",
" mulan = mulan, \n",
" conditioning_dims = (1024, 1024, 1024), \n",
" namespaces = ('semantic', 'coarse', 'fine')\n",
")\n",
"wavs = torch.randn(2, 1024)\n",
"conds = quantizer(wavs = wavs, namespace = 'semantic')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ANTLR runtime and generated code versions disagree: 4.9.3!=4.8\n",
"ANTLR runtime and generated code versions disagree: 4.9.3!=4.8\n",
"training with dataset of 4806 samples and validating with randomly splitted 253 samples\n",
"0: loss: 90.55248260498047\n",
"0: valid loss 28.765926361083984\n",
"0: saving model to results\n",
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"341: loss: 0.02351052314043045\n",
"342: loss: 0.06904540210962296\n",
"343: loss: 0.02174321562051773\n",
"344: loss: 0.11702124029397964\n",
"345: loss: 0.061455100774765015\n",
"346: loss: 0.03193430230021477\n",
"347: loss: 0.33268794417381287\n",
"348: loss: 0.053275030106306076\n",
"349: loss: 0.009291582740843296\n",
"350: loss: 0.18401774764060974\n",
"351: loss: 0.30571281909942627\n",
"352: loss: 17.913070678710938\n",
"353: loss: 0.2126859426498413\n",
"354: loss: 0.6229326128959656\n",
"355: loss: 11.214807510375977\n",
"356: loss: 0.15888328850269318\n",
"357: loss: 0.662460446357727\n",
"358: loss: 7.345875263214111\n",
"359: loss: 7.803595066070557\n",
"360: loss: 1.2322083711624146\n",
"361: loss: 0.7014895081520081\n",
"362: loss: 0.10298460721969604\n",
"363: loss: 8.574231147766113\n",
"364: loss: 0.03108447603881359\n",
"365: loss: 0.6616091728210449\n",
"366: loss: 4.938299655914307\n",
"367: loss: 5.479018688201904\n",
"368: loss: 6.740688800811768\n",
"369: loss: 3.110865831375122\n",
"370: loss: 4.795236587524414\n",
"371: loss: 1.8502461910247803\n",
"372: loss: 3.737464427947998\n",
"373: loss: 1.9333598613739014\n",
"374: loss: 7.145735740661621\n",
"375: loss: 1.3372946977615356\n",
"376: loss: 5.683573246002197\n",
"377: loss: 1.204305648803711\n",
"378: loss: 0.9289284348487854\n",
"379: loss: 5.174688339233398\n",
"380: loss: 1.458616852760315\n",
"381: loss: 0.9457168579101562\n",
"382: loss: 0.4627819359302521\n",
"383: loss: 0.2658665180206299\n",
"384: loss: 4.429558753967285\n",
"385: loss: 1.2449607849121094\n",
"386: loss: 1.3288488388061523\n",
"387: loss: 6.628821849822998\n",
"388: loss: 0.4825551211833954\n",
"389: loss: 0.6510865688323975\n",
"390: loss: 0.36395493149757385\n",
"391: loss: 0.18036174774169922\n",
"392: loss: 0.3237663209438324\n",
"393: loss: 6.840792655944824\n",
"394: loss: 1.6587960720062256\n",
"395: loss: 7.458000659942627\n",
"396: loss: 0.8729283809661865\n",
"397: loss: 0.6731876134872437\n",
"398: loss: 0.1747300773859024\n",
"399: loss: 0.5882076621055603\n",
"400: loss: 0.6982569098472595\n",
"400: valid loss 0.4763210713863373\n",
"401: loss: 0.4763210713863373\n",
"402: loss: 0.46096739172935486\n",
"403: loss: 4.166454792022705\n",
"404: loss: 0.44991931319236755\n",
"405: loss: 4.830379009246826\n",
"406: loss: 0.5408239364624023\n",
"407: loss: 0.2607786953449249\n",
"408: loss: 0.13067474961280823\n",
"409: loss: 4.062631130218506\n",
"410: loss: 5.5028300285339355\n",
"411: loss: 1.2942296266555786\n",
"412: loss: 1.4390389919281006\n",
"413: loss: 5.374651908874512\n",
"414: loss: 1.2929461002349854\n",
"415: loss: 0.643798291683197\n",
"416: loss: 0.6353816986083984\n",
"417: loss: 5.8032636642456055\n",
"418: loss: 3.3737053871154785\n",
"419: loss: 1.8712362051010132\n",
"420: loss: 1.0622261762619019\n",
"421: loss: 0.8681365847587585\n",
"422: loss: 0.6761938333511353\n",
"423: loss: 4.074782371520996\n",
"424: loss: 0.4106965661048889\n"
]
},
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[4], line 49\u001b[0m\n\u001b[0;32m 46\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m coarse_transformer, trainer, wav2vec, soundstream\n\u001b[0;32m 47\u001b[0m gc\u001b[38;5;241m.\u001b[39mcollect()\n\u001b[1;32m---> 49\u001b[0m \u001b[43mtrain_coarse_transformer\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
"Cell \u001b[1;32mIn[4], line 43\u001b[0m, in \u001b[0;36mtrain_coarse_transformer\u001b[1;34m()\u001b[0m\n\u001b[0;32m 23\u001b[0m coarse_transformer \u001b[38;5;241m=\u001b[39m CoarseTransformer(\n\u001b[0;32m 24\u001b[0m num_semantic_tokens\u001b[38;5;241m=\u001b[39mwav2vec\u001b[38;5;241m.\u001b[39mcodebook_size,\n\u001b[0;32m 25\u001b[0m codebook_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1024\u001b[39m,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 29\u001b[0m audio_text_condition\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m 30\u001b[0m )\n\u001b[0;32m 32\u001b[0m trainer \u001b[38;5;241m=\u001b[39m CoarseTransformerTrainer(\n\u001b[0;32m 33\u001b[0m transformer\u001b[38;5;241m=\u001b[39mcoarse_transformer,\n\u001b[0;32m 34\u001b[0m codec\u001b[38;5;241m=\u001b[39msoundstream,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 40\u001b[0m num_train_steps\u001b[38;5;241m=\u001b[39mnum_train_steps\n\u001b[0;32m 41\u001b[0m )\n\u001b[1;32m---> 43\u001b[0m \u001b[43mtrainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 44\u001b[0m torch\u001b[38;5;241m.\u001b[39msave(coarse_transformer\u001b[38;5;241m.\u001b[39mstate_dict(), \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcoarse_transformer.pth\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m 45\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msave coarse_transformer.pth\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
"File \u001b[1;32md:\\Sunil\\Mini Project\\MusicLM\\myenv\\Lib\\site-packages\\audiolm_pytorch\\trainer.py:1302\u001b[0m, in \u001b[0;36mCoarseTransformerTrainer.train\u001b[1;34m(self, log_fn)\u001b[0m\n\u001b[0;32m 1299\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mtrain\u001b[39m(\u001b[38;5;28mself\u001b[39m, log_fn \u001b[38;5;241m=\u001b[39m noop):\n\u001b[0;32m 1301\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msteps \u001b[38;5;241m<\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnum_train_steps:\n\u001b[1;32m-> 1302\u001b[0m logs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain_step\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1303\u001b[0m log_fn(logs)\n\u001b[0;32m 1305\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprint(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtraining complete\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
"File \u001b[1;32md:\\Sunil\\Mini Project\\MusicLM\\myenv\\Lib\\site-packages\\audiolm_pytorch\\trainer.py:1244\u001b[0m, in \u001b[0;36mCoarseTransformerTrainer.train_step\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 1238\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maccelerator\u001b[38;5;241m.\u001b[39mautocast(), context():\n\u001b[0;32m 1239\u001b[0m loss \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrain_wrapper(\n\u001b[0;32m 1240\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mdata_kwargs,\n\u001b[0;32m 1241\u001b[0m return_loss \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m 1242\u001b[0m )\n\u001b[1;32m-> 1244\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43maccelerator\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\u001b[43mloss\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgrad_accum_every\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1246\u001b[0m accum_log(logs, {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mloss\u001b[39m\u001b[38;5;124m'\u001b[39m: loss\u001b[38;5;241m.\u001b[39mitem() \u001b[38;5;241m/\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgrad_accum_every})\n\u001b[0;32m 1248\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m exists(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmax_grad_norm):\n",
"File \u001b[1;32md:\\Sunil\\Mini Project\\MusicLM\\myenv\\Lib\\site-packages\\accelerate\\accelerator.py:2151\u001b[0m, in \u001b[0;36mAccelerator.backward\u001b[1;34m(self, loss, **kwargs)\u001b[0m\n\u001b[0;32m 2149\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlomo_backward(loss, learning_rate)\n\u001b[0;32m 2150\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 2151\u001b[0m \u001b[43mloss\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[1;32md:\\Sunil\\Mini Project\\MusicLM\\myenv\\Lib\\site-packages\\torch\\_tensor.py:525\u001b[0m, in \u001b[0;36mTensor.backward\u001b[1;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[0;32m 515\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m has_torch_function_unary(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m 516\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m handle_torch_function(\n\u001b[0;32m 517\u001b[0m Tensor\u001b[38;5;241m.\u001b[39mbackward,\n\u001b[0;32m 518\u001b[0m (\u001b[38;5;28mself\u001b[39m,),\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 523\u001b[0m inputs\u001b[38;5;241m=\u001b[39minputs,\n\u001b[0;32m 524\u001b[0m )\n\u001b[1;32m--> 525\u001b[0m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mautograd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 526\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgradient\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs\u001b[49m\n\u001b[0;32m 527\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[1;32md:\\Sunil\\Mini Project\\MusicLM\\myenv\\Lib\\site-packages\\torch\\autograd\\__init__.py:267\u001b[0m, in \u001b[0;36mbackward\u001b[1;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[0;32m 262\u001b[0m retain_graph \u001b[38;5;241m=\u001b[39m create_graph\n\u001b[0;32m 264\u001b[0m \u001b[38;5;66;03m# The reason we repeat the same comment below is that\u001b[39;00m\n\u001b[0;32m 265\u001b[0m \u001b[38;5;66;03m# some Python versions print out the first line of a multi-line function\u001b[39;00m\n\u001b[0;32m 266\u001b[0m \u001b[38;5;66;03m# calls in the traceback and some print out the last line\u001b[39;00m\n\u001b[1;32m--> 267\u001b[0m \u001b[43m_engine_run_backward\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 268\u001b[0m \u001b[43m \u001b[49m\u001b[43mtensors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 269\u001b[0m \u001b[43m \u001b[49m\u001b[43mgrad_tensors_\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 270\u001b[0m \u001b[43m \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 271\u001b[0m \u001b[43m \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 272\u001b[0m \u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 273\u001b[0m \u001b[43m \u001b[49m\u001b[43mallow_unreachable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m 274\u001b[0m \u001b[43m \u001b[49m\u001b[43maccumulate_grad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m 275\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[1;32md:\\Sunil\\Mini Project\\MusicLM\\myenv\\Lib\\site-packages\\torch\\autograd\\graph.py:744\u001b[0m, in \u001b[0;36m_engine_run_backward\u001b[1;34m(t_outputs, *args, **kwargs)\u001b[0m\n\u001b[0;32m 742\u001b[0m unregister_hooks \u001b[38;5;241m=\u001b[39m _register_logging_hooks_on_whole_graph(t_outputs)\n\u001b[0;32m 743\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 744\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mVariable\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_execution_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_backward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# Calls into the C++ engine to run the backward pass\u001b[39;49;00m\n\u001b[0;32m 745\u001b[0m \u001b[43m \u001b[49m\u001b[43mt_outputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[0;32m 746\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# Calls into the C++ engine to run the backward pass\u001b[39;00m\n\u001b[0;32m 747\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m 748\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m attach_logging_hooks:\n",
"\u001b[1;31mKeyboardInterrupt\u001b[0m: "
]
}
],
"source": [
"def train_coarse_transformer():\n",
" wav2vec = HubertWithKmeans(\n",
" checkpoint_path=checkpoint_path,\n",
" kmeans_path=kmeans_path\n",
" )\n",
" soundstream = MusicLMSoundStream(\n",
" codebook_size=1024, # Add this line to specify the codebook size\n",
" strides=(3, 4, 5, 8),\n",
" target_sample_hz=24000,\n",
" rq_num_quantizers=8\n",
" )\n",
"\n",
" if torch.cuda.is_available():\n",
" coarse_transformer = CoarseTransformer(\n",
" num_semantic_tokens=wav2vec.codebook_size,\n",
" codebook_size=1024,\n",
" num_coarse_quantizers=4,\n",
" dim=1024,\n",
" depth=6,\n",
" audio_text_condition=True\n",
" ).cuda()\n",
" else:\n",
" coarse_transformer = CoarseTransformer(\n",
" num_semantic_tokens=wav2vec.codebook_size,\n",
" codebook_size=1024,\n",
" num_coarse_quantizers=4,\n",
" dim=1024,\n",
" depth=6,\n",
" audio_text_condition=True\n",
" )\n",
"\n",
" trainer = CoarseTransformerTrainer(\n",
" transformer=coarse_transformer,\n",
" codec=soundstream,\n",
" wav2vec=wav2vec,\n",
" audio_conditioner=quantizer,\n",
" folder=audio_output_dir,\n",
" batch_size=batch_size,\n",
" data_max_length=data_max_length,\n",
" num_train_steps=num_train_steps\n",
" )\n",
"\n",
" trainer.train()\n",
" torch.save(coarse_transformer.state_dict(), 'coarse_transformer.pth')\n",
" print(\"save coarse_transformer.pth\")\n",
" del coarse_transformer, trainer, wav2vec, soundstream\n",
" gc.collect()\n",
"\n",
"train_coarse_transformer()"
]
}
],
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|