commit 5a08768b6f4fbdd52f9f79d741e9f340217e95f1
parent 09e72a2d068c048ecbec0ca1185db04058643137
Author: Steven Atkinson <steven@atkinson.mn>
Date: Sat, 3 Dec 2022 13:16:23 -0800
Fix bugs in Colab notebook
Diffstat:
| M | bin/train/colab.ipynb | | | 1915 | +++++++++++++++++++++++++------------------------------------------------------ |
1 file changed, 606 insertions(+), 1309 deletions(-)
diff --git a/bin/train/colab.ipynb b/bin/train/colab.ipynb
@@ -22,13 +22,56 @@
"Plan your training accordingly!\n",
"\n",
"## Steps:\n",
- "0. Install everything\n",
"1. Upload audio files\n",
- "2. Settings\n",
- "3. Run!\n",
- "4. Check\n",
- "5. Export\n",
- "6. Download your files"
+ "2. Installation\n",
+ "3. Settings\n",
+ "4. Run!\n",
+ "5. Check\n",
+ "6. Export\n",
+ "7. Download your files"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "5CQleTk7GJV8"
+ },
+ "source": [
+ "## Step 1: Upload audio files\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",
+ "* 🔶**Measure the latency!**🔶 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_test.wav`, `y_test.wav`) to check how the model's doing on something new.\n",
+ "\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? ⬆)\n",
+ "\n",
+ "### What to do\n",
+ "Upload the input (DI) and output (amped) files you want to use by clicking the Folder icon on the left ⬅ and then clicking the upload icon.\n",
+ "\n",
+ "Once you're done, run the next cell and I'll check that everything looks good."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "R_filL-5F8HR"
+ },
+ "outputs": [],
+ "source": [
+ "from pathlib import Path\n",
+ "# 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}?\")"
]
},
{
@@ -37,8 +80,8 @@
"id": "2g_4GtFuGlO8"
},
"source": [
- "## Step 0: Install\n",
- "Install `nam` and the other Python packages it depends on."
+ "## Step 2: Installation\n",
+ "Install `nam` into this Colab instance."
]
},
{
@@ -49,7 +92,7 @@
"base_uri": "https://localhost:8080/"
},
"id": "vYQIpWr5EYRb",
- "outputId": "1b5db1b0-af68-4ed2-88af-82216a5ab1ef"
+ "outputId": "9099af59-62ae-45b2-8cb6-415db054a73a"
},
"outputs": [],
"source": [
@@ -64,7 +107,6 @@
},
"outputs": [],
"source": [
- "from pathlib import Path\n",
"from time import time\n",
"from typing import Optional, Union\n",
"\n",
@@ -81,50 +123,10 @@
{
"cell_type": "markdown",
"metadata": {
- "id": "5CQleTk7GJV8"
- },
- "source": [
- "## Step 1: Upload audio files\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",
- "* 🔶**Measure the latency!**🔶 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_test.wav`, `y_test.wav`) to check how the model's doing on something new.\n",
- "\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? ⬆)\n",
- "\n",
- "### What to do\n",
- "Upload the input (DI) and output (amped) files you want to use by clicking the Folder icon on the left ⬅ and then clicking the upload icon."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "R_filL-5F8HR"
- },
- "outputs": [],
- "source": [
- "# 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}?\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
"id": "j5fN10s3GwVz"
},
"source": [
- "## Step 2: Settings\n",
+ "## Step 3: Settings\n",
"The defaults are what I tend to start with and should usually work well, but if you'd like, you can make changes."
]
},
@@ -136,7 +138,7 @@
"base_uri": "https://localhost:8080/"
},
"id": "Y6gl6RoNJ_6I",
- "outputId": "6a9cb51d-218d-48ad-ac08-33b8d2fcfb65"
+ "outputId": "1f6166d6-87eb-4dfb-9619-3c4a4a7e5147"
},
"outputs": [],
"source": [
@@ -198,8 +200,7 @@
" \"kwargs\": {\n",
" \"gamma\": 0.993\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",
"}\n",
"learning_config = {\n",
" \"train_dataloader\": {\n",
@@ -223,7 +224,7 @@
"id": "pNga-MNTMQAa"
},
"source": [
- "## Step 3: Run!\n",
+ "## Step 4: Run!\n",
"Let's rock"
]
},
@@ -271,7 +272,7 @@
"base_uri": "https://localhost:8080/"
},
"id": "vyhMf0ZyM4kt",
- "outputId": "a7a9e60d-89d4-4a13-987b-c79195a02626"
+ "outputId": "b367dbe3-624a-4e37-ce29-87b70f07424e"
},
"outputs": [],
"source": [
@@ -312,89 +313,67 @@
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
- "height": 283,
+ "height": 301,
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},
"id": "a8WLIx33M7c6",
- "outputId": "35e925bd-90b8-4ac1-ba93-bff3f2cfe825"
+ "outputId": "bd57b7ae-5831-4900-c578-b4070f6a6d1a"
},
"outputs": [],
"source": [
@@ -413,7 +392,7 @@
"base_uri": "https://localhost:8080/"
},
"id": "JzGltwwJNAkI",
- "outputId": "a4c4c0ae-ddb9-4f83-e0ca-a7a2a49b70c1"
+ "outputId": "4e2651d1-0048-4c17-9073-b3180240b9aa"
},
"outputs": [],
"source": [
@@ -433,7 +412,7 @@
"id": "QvuJEYxJNGn7"
},
"source": [
- "# Step 4: Check\n",
+ "# Step 5: Check\n",
"Let's look at how well our model matches the real thing."
]
},
@@ -486,10 +465,10 @@
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
- "height": 338
+ "height": 372
},
"id": "C_NsBdp5NQMC",
- "outputId": "b074ce12-82fe-4e23-c216-238126d5c079"
+ "outputId": "3e3c1812-4520-4943-bd31-d6de579decec"
},
"outputs": [],
"source": [
@@ -507,7 +486,7 @@
"id": "R__jJFwgNkAl"
},
"source": [
- "## Step 5: Export your model\n",
+ "## Step 6: Export your model\n",
"Now we'll use NAM's exporting utility to convert the model from its PyTorch representation to something that you can put into the plugin."
]
},
@@ -529,9 +508,11 @@
"id": "823KJ_L0Rchp"
},
"source": [
- "## Step 6: Download your artifacts\n",
+ "## Step 7: Download your artifacts\n",
"We're done! \n",
- "Go to the file browser on the left panel ⬅ 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",
+ "Go to the file browser on the left panel ⬅ and download the contents of `exported_model` (you may need to hit the refresh button).\n",
+ "\n",
+ "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",
@@ -563,7 +544,53 @@
},
"widgets": {
"application/vnd.jupyter.widget-state+json": {
- "2431a564a1be405c8aea25539b9b7350": {
+ "06d6093b2de54471b6d8ed706a3ec134": {
+ "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": ""
+ }
+ },
+ "0ca1b22f10094010b1de29208e9dc446": {
+ "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": ""
+ }
+ },
+ "1b99071c17b24eb38edc8156e6ce5c82": {
+ "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": ""
+ }
+ },
+ "22ddf8419783483c8f028e7864f70a01": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -580,9 +607,9 @@
"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,
@@ -611,26 +638,35 @@
"padding": null,
"right": null,
"top": null,
- "visibility": null,
- "width": null
+ "visibility": "hidden",
+ "width": "100%"
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