commit e6ac3b64879ae7b633b74f32edac8bd0f056fe52
parent 5fa1e35bfed8c42003da29ec3395cbe4ef8e7e7c
Author: Steven Atkinson <steven@atkinson.mn>
Date: Sat, 3 Dec 2022 00:00:11 -0800
Update Colab notebook
Diffstat:
M | bin/train/colab.ipynb | | | 35381 | +++---------------------------------------------------------------------------- |
1 file changed, 928 insertions(+), 34453 deletions(-)
diff --git a/bin/train/colab.ipynb b/bin/train/colab.ipynb
@@ -49,7 +49,7 @@
"base_uri": "https://localhost:8080/"
},
"id": "vYQIpWr5EYRb",
- "outputId": "ce378c94-da2a-47f5-f805-870a6b1284cd"
+ "outputId": "1b5db1b0-af68-4ed2-88af-82216a5ab1ef"
},
"outputs": [],
"source": [
@@ -64,6 +64,7 @@
},
"outputs": [],
"source": [
+ "from pathlib import Path\n",
"from time import time\n",
"from typing import Optional, Union\n",
"\n",
@@ -71,7 +72,6 @@
"import numpy as np\n",
"import pytorch_lightning as pl\n",
"import torch\n",
- "from google.colab import files\n",
"from torch.utils.data import DataLoader\n",
"\n",
"from nam.data import Split, init_dataset\n",
@@ -85,45 +85,37 @@
},
"source": [
"## Step 1: Upload audio files\n",
- "Upload the input (DI) and output (amped) files you want to use.\n",
+ "We're gonna need data. **Read this because it's important. Your model lives and dies with its data!**\n",
"\n",
+ "### Some tips for making good data:\n",
+ "I'm going to assume you know about proper gain staging for reamping. Beyond that, here are a few things that are less obvious:\n",
+ "* **Show your model everything!** The model is going to learn from your examples, so demonstrate everything! Play loud, play soft, play single notes, chords, different pickups, play through an overdrive pedal (you wanted your model to understand how pedals sound, right?), etc etc. Just think: You'll ask \"But can the model clean up like the real thing?\" _Just show it!_ (**Don't riff(!!!)** It sounds weird, but riffs are repetitive, and repetition is wasted data. Instead, just play every fret up and down every string. It's boring, but it's good data!)\n",
+ "* **\"How much data?\"** More is better, but there's diminishing returns. About 3 minutes is a good compromise, but up to maybe 15 minutes can still help if you really want the best model possible.\n",
+ "* πΆ**Measure the latency!**πΆ Most interfaces will have a little lag between when they send the signal and when the reamp comes back. Use your DAW to figure out how many samples it is--I'll ask you for it below. _This is important--If there's too much delay, then the model may not learn well. The closer you get this, the better the results will be, but don't over-compensate or else you're effectively asking the model to predict the future!_\n",
+ "\n",
+ "### What you need\n",
"You'll need two pairs of files (4 in total):\n",
"* A training pair (`x_train.wav`, `y_train.wav`) for the model to fit to.\n",
- "* A validation pair, (`x_validation.wav`, `y_validation.wav`) to check the model's performance on a new signal.\n",
+ "* A validation pair, (`x_test.wav`, `y_test.wav`) to check how the model's doing on something new.\n",
"\n",
- "The **default names** for the training data are `x_train.wav` (DI input) and `y_train.wav` (amped output), and for the validation set, `x_validation.wav` and `y_validation.wav`. \n",
+ "`x_train.py` and `x_test.py` should be two (different!) DI files, and `y_train.wav` and `y_test.wav` should be their corresponding outputs that you reamped. **The train files should hold most of the data; the test files can be just a few seconds long.** The point of the test files is to just quickly check if your model gets it right if it sees something new (but not _too_ new--shouldn't you be training on those? β¬)\n",
"\n",
- "If you files are named differently, don't worry--you can modify the names in the data config below."
+ "### What to do\n",
+ "Upload the input (DI) and output (amped) files you want to use by clicking the Folder icon on the left β¬
and then clicking the upload icon."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 177,
- "resources": {
- "http://localhost:8080/nbextensions/google.colab/files.js": {
- "data": "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",
- "headers": [
- [
- "content-type",
- "application/javascript"
- ]
- ],
- "ok": true,
- "status": 200,
- "status_text": ""
- }
- }
- },
- "id": "R_filL-5F8HR",
- "outputId": "67289979-ca84-4b4e-b190-24a32efb892d"
+ "id": "R_filL-5F8HR"
},
"outputs": [],
"source": [
- "uploaded = files.upload()"
+ "# I'm just gonna check that you were paying attention ;)\n",
+ "for name in (\"x_train.wav\", \"y_train.wav\", \"x_test.wav\", \"y_test.wav\"):\n",
+ " if not Path(name).exists():\n",
+ " raise RuntimeError(f\"I didn't find all of your data files. Where is {name}?\")"
]
},
{
@@ -133,18 +125,18 @@
},
"source": [
"## Step 2: Settings\n",
- "The defaults are what I tend to start with and should usually work well (except the file names--see above), but if you'd like, you can make changes.\n",
- "\n",
- "πΆ**Important**πΆ\n",
- "\n",
- "If there is a **delay** between your input and output (e.g. if you reamped and your interface has latency that your DAW doesn't factor out), then change that in the `data_config` dict below to the delay, in samples. For example, if the output lags the input by 505 samples, then change it to `\"delay\": 505`. If there's too much delay (e.g. more than 10 samples), then the model may not learn. The closer you get this, the better the results will be, but don't over-compensate or else the model would have to predict the future!"
+ "The defaults are what I tend to start with and should usually work well, but if you'd like, you can make changes."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
- "id": "Y6gl6RoNJ_6I"
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "Y6gl6RoNJ_6I",
+ "outputId": "6a9cb51d-218d-48ad-ac08-33b8d2fcfb65"
},
"outputs": [],
"source": [
@@ -152,46 +144,66 @@
" \"train\": {\n",
" \"x_path\": \"x_train.wav\",\n",
" \"y_path\": \"y_train.wav\",\n",
- " \"ny\": 32768\n",
+ " \"ny\": 8192\n",
" },\n",
" \"validation\": {\n",
- " \"x_path\": \"x_validation.wav\",\n",
- " \"y_path\": \"y_validation.wav\",\n",
+ " \"x_path\": \"x_test.wav\",\n",
+ " \"y_path\": \"y_test.wav\",\n",
" \"ny\": None\n",
" },\n",
" \"common\": {\n",
- " \"delay\": 0\n",
+ " \"delay\": int(input(\"What is the latency (in samples) of your reamp? \"))\n",
" }\n",
"}\n",
"model_config = {\n",
" \"net\": {\n",
- " \"name\": \"LSTM\",\n",
+ " \"name\": \"WaveNet\",\n",
+ " # This should do decently. If you really want a nice model, try turning up \n",
+ " # \"channels\" in the first block and \"input_size\" in the second from 12 to 16.\n",
" \"config\": {\n",
- " \"num_layers\": 3,\n",
- " \"hidden_size\": 24,\n",
- " \"train_burn_in\": 4096,\n",
- " \"train_truncate\": 512 \n",
+ " \"layers_configs\": [\n",
+ " {\n",
+ " \"input_size\": 1,\n",
+ " \"condition_size\": 1,\n",
+ " \"head_size\": 8,\n",
+ " \"channels\": 12,\n",
+ " \"kernel_size\": 3,\n",
+ " \"dilations\": [1,2,4,8,16,32,64,128,256,512],\n",
+ " \"activation\": \"Tanh\",\n",
+ " \"gated\": False,\n",
+ " \"head_bias\": False\n",
+ " },\n",
+ " {\n",
+ " \"input_size\": 12,\n",
+ " \"condition_size\": 1,\n",
+ " \"head_size\": 1,\n",
+ " \"channels\": 8,\n",
+ " \"kernel_size\": 3,\n",
+ " \"dilations\": [1,2,4,8,16,32,64,128,256,512],\n",
+ " \"activation\": \"Tanh\",\n",
+ " \"gated\": False,\n",
+ " \"head_bias\": True\n",
+ " }\n",
+ " ]\n",
" }\n",
" },\n",
" \"loss\": {\n",
- " \"val_loss\": \"mse\",\n",
- " \"mask_first\": 4096, \n",
- " \"pre_emph_weight\": 1.0,\n",
- " \"pre_emph_coef\": 0.85\n",
+ " \"val_loss\": \"esr\"\n",
" },\n",
" \"optimizer\": {\n",
- " \"lr\": 0.01\n",
+ " \"lr\": 0.001\n",
" },\n",
" \"lr_scheduler\": {\n",
" \"class\": \"ExponentialLR\",\n",
" \"kwargs\": {\n",
- " \"gamma\": 0.995\n",
+ " \"gamma\": 0.993\n",
" }\n",
- " }\n",
+ " },\n",
+ " \"checkpoint_path\": \"lightning_logs/version_5/checkpoints/epoch=0099_step=6500_ESR=2.990e-02_MSE=1.805e-04.ckpt\"\n",
"}\n",
"learning_config = {\n",
" \"train_dataloader\": {\n",
- " \"batch_size\": 32,\n",
+ " \"batch_size\": 16,\n",
" \"shuffle\": True,\n",
" \"pin_memory\": True,\n",
" \"drop_last\": True,\n",
@@ -201,8 +213,7 @@
" \"trainer\": {\n",
" \"gpus\": 1,\n",
" \"max_epochs\": 100\n",
- " },\n",
- " \"trainer_fit_kwargs\": {}\n",
+ " }\n",
"}"
]
},
@@ -260,7 +271,7 @@
"base_uri": "https://localhost:8080/"
},
"id": "vyhMf0ZyM4kt",
- "outputId": "2a4ebf5a-9bbe-4119-f1fe-2bc1d6d665f5"
+ "outputId": "a7a9e60d-89d4-4a13-987b-c79195a02626"
},
"outputs": [],
"source": [
@@ -281,1153 +292,114 @@
]
},
{
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Lmn32oiGzrf6"
+ },
+ "source": [
+ "Here we go!\n",
+ "\n",
+ "πFor a 3 minutes of training data, training will take just over 10 minutes.π \n",
+ "\n",
+ "But, if you want to stop early, you can always press the stop button.\n",
+ "\n",
+ "If you want to train shorter or longer, you can also change the `\"max_epochs\"` above."
+ ]
+ },
+ {
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
- "height": 295,
+ "height": 283,
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},
"id": "a8WLIx33M7c6",
- "outputId": "4e5ac39e-601c-4521-d15e-b67b82d12f15"
+ "outputId": "35e925bd-90b8-4ac1-ba93-bff3f2cfe825"
},
"outputs": [],
"source": [
- "# Here we go!\n",
- "trainer.fit(\n",
- " model,\n",
- " train_dataloader,\n",
- " val_dataloader,\n",
- " **learning_config.get(\"trainer_fit_kwargs\", {}),\n",
- ")\n",
+ "trainer.fit(model, train_dataloader, val_dataloader)\n",
"# Monitor the progress in lightning_logs/version_0/checkpoints.\n",
- "# Tthe ESR may plateau at 1 for about 20 iterations, but if it hasn't started decreasing\n",
- "# By then, then something might be wrong e.g. mismatched data, or an incorrect delay \n",
- "# (see above).\n",
"#\n",
"# Many models can get a good result (rule of thumb: look for ESR<0.01) in about 15 \n",
"# minutes of training, but if you're more patient, it'll probably keep getting better."
@@ -1441,7 +413,7 @@
"base_uri": "https://localhost:8080/"
},
"id": "JzGltwwJNAkI",
- "outputId": "1759b50a-f855-47e1-d7e4-54287917e33a"
+ "outputId": "a4c4c0ae-ddb9-4f83-e0ca-a7a2a49b70c1"
},
"outputs": [],
"source": [
@@ -1498,12 +470,8 @@
" print(f\"Took {t1 - t0} ({tx / (t1 - t0):.2f}x)\")\n",
"\n",
" plt.figure(figsize=(16, 5))\n",
- " # plt.plot(ds.x[window_start:window_end], label=\"Input\")\n",
" plt.plot(output[window_start:window_end], label=\"Prediction\")\n",
" plt.plot(ds.y[window_start:window_end], linestyle=\"--\", label=\"Target\")\n",
- " # plt.plot(\n",
- " # ds.y[window_start:window_end] - output[window_start:window_end], label=\"Error\"\n",
- " # )\n",
" plt.title(f\"NRMSE={100.0 * _rms(torch.Tensor(output) - ds.y) / _rms(ds.y):2.1f}%\")\n",
" plt.legend()\n",
" if savefig is not None:\n",
@@ -1518,10 +486,10 @@
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
- "height": 287
+ "height": 338
},
"id": "C_NsBdp5NQMC",
- "outputId": "9f49bae0-f657-4140-cfde-807c6bb0550b"
+ "outputId": "b074ce12-82fe-4e23-c216-238126d5c079"
},
"outputs": [],
"source": [
@@ -1530,9 +498,7 @@
" dataset_validation,\n",
" window_start=100_000, # Start of the plotting window, in samples\n",
" window_end=101_000, # End of the plotting window, in samples\n",
- ")\n",
- "# Don't worry if the LSTM runs slower than 1x. Python's interpreter is slow, and the C++\n",
- "# version is a lot faster."
+ ")"
]
},
{
@@ -1553,10 +519,8 @@
},
"outputs": [],
"source": [
- "# This isn't used right now, but I might use it in the future :)\n",
- "# model.export(\".\")\n",
- "\n",
- "model.net.export_cpp_header(\"HardCodedModel.h\")"
+ "Path(\"exported_model\").mkdir()\n",
+ "model.net.export(\"exported_model\")"
]
},
{
@@ -1567,38 +531,39 @@
"source": [
"## Step 6: Download your artifacts\n",
"We're done! \n",
- "Go to the file browser on the left panel β¬
and collect your artifacts!\n",
+ "Go to the file browser on the left panel β¬
and download the contents of `exported_model`. You'll need `config.json` (the architecture) and `weights.npy` (the weights)--these are the information that the NAM plugin needs to run your model!\n",
+ "\n",
+ "Additionally, if you want to continue to train this model later you can download the lightning model artifacts from `lightning_logs`. If not, that's fine.\n",
"\n",
- "Be sure to download the lightning model artifacts (in case you want to continue training later) and your exported model (so that you can put it into a plugin)."
+ "# πΈ **ENJOY!** πΈ"
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
- "collapsed_sections": [],
"name": "colab.ipynb",
"provenance": []
},
"gpuClass": "standard",
"kernelspec": {
- "display_name": "Python 3.10.4 ('nam-dev')",
+ "display_name": "Python 3.10.6 ('nam')",
"language": "python",
"name": "python3"
},
"language_info": {
"name": "python",
- "version": "3.10.4"
+ "version": "3.10.6"
},
"orig_nbformat": 4,
"vscode": {
"interpreter": {
- "hash": "806b1ffcfc1136d2cb83a025e53d737ae1674e4fb75a9a1fd5fb23a0ac3e6e42"
+ "hash": "920df60c69944ba95f8c12adb41fedfdc8090c370a20d39253c7705973dd37db"
}
},
"widgets": {
"application/vnd.jupyter.widget-state+json": {
- "002917cefb1c4e65b07c93d703c59655": {
+ "2431a564a1be405c8aea25539b9b7350": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -1650,7 +615,37 @@
"width": null
}
},
- "00bc774dd0324286bcc358dd74c6371e": {
+ "2748d196b8fd49c4baeb0cdb247caa76": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "DescriptionStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "DescriptionStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "description_width": ""
+ }
+ },
+ "298f367934954fdfaac4248e5775da98": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "DescriptionStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "DescriptionStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "description_width": ""
+ }
+ },
+ "34133793632d4cb59c841190aa246fcb": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -1667,9 +662,9 @@
"align_self": null,
"border": null,
"bottom": null,
- "display": null,
+ "display": "inline-flex",
"flex": null,
- "flex_flow": null,
+ "flex_flow": "row wrap",
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
@@ -1699,10 +694,69 @@
"right": null,
"top": null,
"visibility": null,
- "width": null
+ "width": "100%"
+ }
+ },
+ "34b3d51b030e441c9b102ae587119ac5": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "ProgressStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "ProgressStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "bar_color": null,
+ "description_width": ""
+ }
+ },
+ "37334126cfb64f2799f1461095ca2c45": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "HBoxModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "HBoxModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_a8e810d8e7c94e079409f5b62f583763",
+ "IPY_MODEL_45e51921c7594d5e9ae1d76a52e7dff6",
+ "IPY_MODEL_c010016c34f2418b8cece0703db1605b"
+ ],
+ "layout": "IPY_MODEL_34133793632d4cb59c841190aa246fcb"
+ }
+ },
+ "409b527bb26544a78b3c18d297a194e2": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "HTMLModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "HTMLModel",
+ "_view_count": null,
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