commit 102333bf1c2c9f4255d73b8712ad3ecb0d8af275
parent 3deefe5194619cb87d93724ba593b08332919b3c
Author: Steven Atkinson <steven@atkinson.mn>
Date: Sat, 27 Jul 2024 14:59:07 -0700
[BREAKING] Remove `colab.ipynb` (#446)
* rm colab.ipynb
* Update docs
Diffstat:
2 files changed, 2 insertions(+), 185 deletions(-)
diff --git a/colab.ipynb b/colab.ipynb
@@ -1,182 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "TC3XkMetGWtK"
- },
- "source": [
- "# Neural Amp Modeler (\"Easy Mode\" Trainer)\n",
- "**Note**:\n",
- "This notebook is meant to be used on [Google Colab](https://colab.research.google.com/github/sdatkinson/neural-amp-modeler/blob/main/bin/train/easy_colab.ipynb).\n",
- "\n",
- "🔶**Before you run**🔶\n",
- "\n",
- "Make sure to get a GPU! (From the upper-left menu, click Runtime->Change runtime type->Select \"GPU\" from the \"Hardware accelerator dropdown menu)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "5CQleTk7GJV8"
- },
- "source": [
- "## Step 1: Get data\n",
- "* **Download the reamp signal.** Here: [v3_0_0.wav](https://drive.google.com/file/d/1Pgf8PdE0rKB1TD4TRPKbpNo1ByR3IOm9/view?usp=drive_link).\n",
- "* **Reamp your gear.** Then reamp the gear you want to model using it. Save that reamp as \"output.wav\". *Note: Use 48kHz, 24-bit, mono.* For other sample rates, use [the CLI trainer](https://github.com/sdatkinson/neural-amp-modeler).\n",
- "* **Upload your files.** 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 or by dragging the files into the panel."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "7tRCyI_YjZjj"
- },
- "source": [
- "## Step 2: Train!\n",
- "Configure your training run below, then hit the Play button to start training!\n",
- "\n",
- "🕙NOTE: At default settings, training will take about 10 minutes.🕙"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "cellView": "form",
- "id": "zrXbQY7vjZjk"
- },
- "outputs": [],
- "source": [
- "try:\n",
- " import nam\n",
- "except ImportError as e:\n",
- " print(\"Installing NAM into Colab. This should take under 2 minutes.\")\n",
- " # Check what we're starting with (Issue 399)\n",
- " !if [ ! -d logs ]; then mkdir logs; fi\n",
- " !pip list > logs/packages.log\n",
- " !pip install neural-amp-modeler > logs/install.log\n",
- " # Hint: use the next line instead for the very latest!\n",
- " # !pip install git+https://github.com/sdatkinson/neural-amp-modeler.git@main\n",
- "\n",
- "from nam.train.colab import run\n",
- "from nam.models.metadata import GearType, ToneType, UserMetadata\n",
- "\n",
- "%load_ext tensorboard\n",
- "\n",
- "from functools import partial\n",
- "\n",
- "import ipywidgets as widgets\n",
- "\n",
- "# NOTE: Enums need to be handled carefully since the values need to be supplied literally here!\n",
- "\n",
- "#@markdown # Training parameters\n",
- "epochs = 100 #@param {type: \"number\"}\n",
- "architecture = \"standard\" #@param [\"standard\", \"lite\", \"feather\", \"nano\"] {type: \"string\"}\n",
- "latency_samples = \"auto\" #@param {type: \"string\"}\n",
- "fit_cab = False #@param {type: \"boolean\"}\n",
- "ignore_checks = False #@param {type: \"boolean\"}\n",
- "\n",
- "#@markdown # Metadata\n",
- "use_metadata = False #@param {type: \"boolean\"}\n",
- "name = \"My model\" #@param {type:\"string\"}\n",
- "modeled_by = \"Your name\" #@param {type:\"string\"}\n",
- "gear_make = \"GearCo\" #@param {type:\"string\"}\n",
- "gear_model = \"GearName\" #@param {type:\"string\"}\n",
- "gear_type = \"amp\" #@param [\"amp\", \"pedal\", \"pedal_amp\", \"amp_cab\", \"amp_pedal_cab\", \"preamp\", \"studio\"] {type:\"string\"}\n",
- "tone_type = \"clean\" #@param [\"clean\", \"overdrive\", \"crunch\", \"hi_gain\", \"fuzz\"] {type:\"string\"}\n",
- "\n",
- "def _verbose_enum(E, val):\n",
- " try:\n",
- " return E(val)\n",
- " except ValueError as e:\n",
- " raise ValueError(\n",
- " str(e)\n",
- " + \"\\nValid choices are: \"\n",
- " + \", \".join(list(x.value for x in E))\n",
- " )\n",
- "\n",
- "def _parse_latency(ls: str):\n",
- " if ls.lower() == \"auto\":\n",
- " return None\n",
- " try:\n",
- " return int(ls)\n",
- " except ValueError as e:\n",
- " raise ValueError(\n",
- " f\"Invalid value for latency {ls} was given. Either use 'auto' or provide \"\n",
- " f\"the reamp latency, in samples.\\nOriginal error:\\n\\n{e}\"\n",
- " )\n",
- "\n",
- "user_metadata = None if not use_metadata else UserMetadata(\n",
- " name=name,\n",
- " modeled_by=modeled_by,\n",
- " gear_make=gear_make,\n",
- " gear_model=gear_model,\n",
- " gear_type=_verbose_enum(GearType, gear_type.lower()),\n",
- " tone_type=_verbose_enum(ToneType, tone_type.lower())\n",
- ")\n",
- "run_partial = partial(run, user_metadata=user_metadata)\n",
- "\n",
- "%tensorboard --logdir /content/lightning_logs\n",
- "run(\n",
- " epochs=epochs,\n",
- " architecture=architecture,\n",
- " fit_cab=fit_cab, # Change me to True for full-rig modeling!\n",
- " ignore_checks=ignore_checks, # Change to True to train anyways with potentially bad data\n",
- " user_metadata=user_metadata,\n",
- " delay=_parse_latency(latency_samples)\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "823KJ_L0Rchp"
- },
- "source": [
- "## Step 3: Check the results and download your model\n",
- "We're done!\n",
- "\n",
- "Have a look at the plot above to see how your model compares to the real gear you're modeling.\n",
- "Hopefully it looks good!\n",
- "Go to the file browser on the left panel ⬅ and download `model.nam` from the `exported_model` directory (you may need to hit the refresh button).\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 too.\n",
- "\n",
- "# 🎸 **ENJOY!** 🎸"
- ]
- }
- ],
- "metadata": {
- "accelerator": "GPU",
- "colab": {
- "provenance": []
- },
- "gpuClass": "standard",
- "kernelspec": {
- "display_name": "nam",
- "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": "3.10.6"
- },
- "orig_nbformat": 4,
- "vscode": {
- "interpreter": {
- "hash": "920df60c69944ba95f8c12adb41fedfdc8090c370a20d39253c7705973dd37db"
- }
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
-\ No newline at end of file
diff --git a/docs/source/tutorials/colab.rst b/docs/source/tutorials/colab.rst
@@ -3,7 +3,7 @@ Training in the cloud with Google Colab
If you don't have a good computer for training ML models, you use Google Colab
to train in the cloud using the pre-made Jupyter notebook at
-`colab.ipynb <https://github.com/sdatkinson/neural-amp-modeler/blob/main/colab.ipynb>`_,
+`colab.ipynb <https://github.com/sdatkinson/NAMTrainerColab/blob/main/notebook.ipynb>`_,
which is designed to be used with
`Google Colab <https://colab.research.google.com/>`_.
@@ -11,7 +11,7 @@ Opening the notebook
--------------------
To open the notebook in Colab, follow
-`this link <https://colab.research.google.com/github/sdatkinson/neural-amp-modeler/blob/d248a71/blob/main/colab.ipynb>`_.
+`this link <https://colab.research.google.com/github/sdatkinson/NAMTrainerColab/blob/main/notebook.ipynb>`_.
.. note:: Most browsers work, but Firefox can be a bit temperamental. This isn't
NAM's fault; Google Colab just prefers Chrome (unsurprisingly).
