neural-amp-modeler

Neural network emulator for guitar amplifiers
Log | Files | Refs | README | LICENSE

commit 1c53cba5ddd5a2d61a74ba63ff51478299c568c2
parent f16eb61a2fa321cc526ea05a82e9109306746840
Author: Steven Atkinson <steven@atkinson.mn>
Date:   Sun, 17 Jul 2022 19:09:00 -0400

Version 0.2.1 (#34)

PR: https://github.com/sdatkinson/neural-amp-modeler/pull/34

* Colab

* Update Python package workflow

* Fix Issue 15, tqdm when loading multiple datasets

* Requriements in setup.py

* Bump version to 0.2.1
Diffstat:
M.github/workflows/python-package.yml | 44++++++++++++++++++++++----------------------
MREADME.md | 18+++++++++++++++---
Abin/train/colab.ipynb | 3999+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
Mnam/_version.py | 2+-
Mnam/data.py | 45++++++++++++++++++++++++++++++++++++++++++---
Msetup.py | 18++++++++++--------
6 files changed, 4089 insertions(+), 37 deletions(-)

diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml @@ -5,13 +5,12 @@ name: Python package on: push: - branches: [ main ] + branches: [main, dev] pull_request: - branches: [ main ] + branches: [main, dev] jobs: build: - runs-on: ubuntu-latest strategy: fail-fast: false @@ -19,22 +18,23 @@ jobs: python-version: ["3.8", "3.9", "3.10"] steps: - - uses: actions/checkout@v3 - - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v3 - with: - python-version: ${{ matrix.python-version }} - - name: Install dependencies - run: | - python -m pip install --upgrade pip - python -m pip install flake8 pytest - if [ -f requirements.txt ]; then pip install -r requirements.txt; fi - - name: Lint with flake8 - run: | - # stop the build if there are Python syntax errors or undefined names - flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics - # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide - flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics - - name: Test with pytest - run: | - pytest + - uses: actions/checkout@v3 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v3 + with: + python-version: ${{ matrix.python-version }} + - name: Install dependencies + run: | + python -m pip install --upgrade pip + python -m pip install flake8 pytest + if [ -f requirements.txt ]; then pip install -r requirements.txt; fi + python -m pip install . + - name: Lint with flake8 + run: | + # stop the build if there are Python syntax errors or undefined names + flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics + # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide + flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics + - name: Test with pytest + run: | + pytest diff --git a/README.md b/README.md @@ -4,11 +4,23 @@ This is the training part of NAM. For the code to create the plugin with a trained model, see my [iPlug2 fork](https://github.com/sdatkinson/iPlug2). -## How to use - This repository handles training, reamping, and exporting the weights of a model (to use with [the iPlug2 plugin]()) +## How to use (Google Colab) + +If you don't have a good computer for training ML models, you can run the +notebook located at `bin/train/colab.ipynb` in the cloud using Google Colab--no +local installation required! + +Go to [colab.research.google.com](https://colab.research.google.com), open the +notebook using the "GitHub" tab, and go! + +## How to use (Local) + +Alternatively, the you can clone this repo and use it in the following ways on +your own computer: + ### Train a model You'll need at least two mono wav files: the input (DI) and the amped sound (without the cab). @@ -70,7 +82,7 @@ If you want to mess with the model architecture and end up with a different rece field (e.g. by messing with the dilation pattern), then you need to make sure that `nx` is changed accordingly in the data setup. The default architecture has a receptive field of 8191 samples, so `nx` is `8191`. -Generally, for the conv net architecture the receptive field is one less than the sum of the `dilations`. +Generally, for the conv net architecture the receptive field is one elss than the sum of the `dilations`. You can train for shorter or longer. 1000 gives pretty great results, but if you're impatient you can sometimes get away with diff --git a/bin/train/colab.ipynb b/bin/train/colab.ipynb @@ -0,0 +1,3999 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "TC3XkMetGWtK" + }, + "source": [ + "# Neural Amp Modeler (Trainer)\n", + "This notebook allows you to train a neural amp model based on a pair of input/output WAV files that you have of the amp you want to model.\n", + "\n", + "**To use this notebook**:\n", + "Go to [colab.research.google.com](https://colab.research.google.com/), select the \"GitHub\" tab, and select this notebook. Or, if you've cloned the repo, you can upload it from your computer.\n", + "\n", + "🔶**Before you run**🔶\n", + "\n", + "Make sure to get a GPU! (Runtime->Change runtime type->Select \"GPU\" from the \"Hardware accelerator dropdown menu)\n", + "\n", + "⚠**Warning**⚠\n", + "\n", + "Google Colab GPU instances only last for 12 hours.\n", + "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" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2g_4GtFuGlO8" + }, + "source": [ + "## Step 0: Install\n", + "Install `nam` and the other Python packages it depends on." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "vYQIpWr5EYRb", + "outputId": "52530563-78ad-4fb4-8669-1ba0ddd351d1" + }, + "outputs": [], + "source": [ + "!pip install git+https://github.com/sdatkinson/neural-amp-modeler.git@dev" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "-6GUkLz3EayL" + }, + "outputs": [], + "source": [ + "from time import time\n", + "from typing import Optional, Union\n", + "\n", + "import matplotlib.pyplot as plt\n", + "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", + "from nam.models import Model" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5CQleTk7GJV8" + }, + "source": [ + "## Step 1: Upload audio files\n", + "Upload the input (DI) and output (amped) files you want to use.\n", + "\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", + "\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", + "\n", + "If you files are named differently, don't worry--you can modify the names in the data config below." + ] + }, + { + "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": "01632616-e5bc-4fa0-f4c0-5bca42c78d87" + }, + "outputs": [], + "source": [ + "uploaded = files.upload()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "j5fN10s3GwVz" + }, + "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." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Y6gl6RoNJ_6I" + }, + "outputs": [], + "source": [ + "data_config = {\n", + " \"train\": {\n", + " \"x_path\": \"x_train.wav\",\n", + " \"y_path\": \"y_train.wav\",\n", + " \"ny\": 1024\n", + " },\n", + " \"validation\": {\n", + " \"x_path\": \"x_validation.wav\",\n", + " \"y_path\": \"y_validation.wav\",\n", + " \"ny\": None\n", + " },\n", + " \"common\": {\n", + " \"nx\": 8191\n", + " }\n", + "}\n", + "model_config = {\n", + " \"net\": {\n", + " \"name\": \"ConvNet\",\n", + " \"config\": {\n", + " \"channels\": 16,\n", + " \"dilations\": [\n", + " 1,\n", + " 2,\n", + " 4,\n", + " 8,\n", + " 16,\n", + " 32,\n", + " 64,\n", + " 128,\n", + " 256,\n", + " 512,\n", + " 1024,\n", + " 2048,\n", + " 1,\n", + " 2,\n", + " 4,\n", + " 8,\n", + " 16,\n", + " 32,\n", + " 64,\n", + " 128,\n", + " 256,\n", + " 512,\n", + " 1024,\n", + " 2048\n", + " ],\n", + " \"batchnorm\": True,\n", + " \"activation\": \"Tanh\",\n", + " }\n", + " },\n", + " \"optimizer\": {\n", + " \"lr\": 0.003\n", + " },\n", + " \"lr_scheduler\": {\n", + " \"class\": \"ReduceLROnPlateau\",\n", + " \"kwargs\": {\n", + " \"factor\": 0.7,\n", + " \"patience\": 20,\n", + " \"cooldown\": 20,\n", + " \"min_lr\": 1e-05,\n", + " \"verbose\": True\n", + " },\n", + " \"monitor\": \"val_loss\"\n", + " }\n", + "}\n", + "learning_config = {\n", + " \"train_dataloader\": {\n", + " \"batch_size\": 16,\n", + " \"shuffle\": True,\n", + " \"pin_memory\": True,\n", + " \"drop_last\": True,\n", + " \"num_workers\": 2\n", + " },\n", + " \"val_dataloader\": {\n", + " },\n", + " \"trainer\": {\n", + " \"gpus\": 1,\n", + " \"max_epochs\": 100\n", + " },\n", + " \"trainer_fit_kwargs\": {\n", + " }\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "pNga-MNTMQAa" + }, + "source": [ + "## Step 3: Run!\n", + "Let's rock" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "OV4gLukTMjdD" + }, + "outputs": [], + "source": [ + "dataset_train = init_dataset(data_config, Split.TRAIN)\n", + "dataset_validation = init_dataset(data_config, Split.VALIDATION)\n", + "train_dataloader = DataLoader(dataset_train, **learning_config[\"train_dataloader\"])\n", + "val_dataloader = DataLoader(dataset_validation, **learning_config[\"val_dataloader\"])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "UAChcygdMTF4" + }, + "outputs": [], + "source": [ + "model = Model.init_from_config(model_config)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "vyhMf0ZyM4kt", + "outputId": "42fde986-1d31-4dd3-c6f8-585206e0c8e0" + }, + "outputs": [], + "source": [ + "trainer = pl.Trainer(\n", + " callbacks=[\n", + " pl.callbacks.model_checkpoint.ModelCheckpoint(\n", + " filename=\"{epoch}_{val_loss:.6f}\",\n", + " save_top_k=3,\n", + " monitor=\"val_loss\",\n", + " every_n_epochs=1,\n", + " ),\n", + " pl.callbacks.model_checkpoint.ModelCheckpoint(\n", + " filename=\"checkpoint_last_{epoch:04d}\", every_n_epochs=1\n", + " ),\n", + " ],\n", + " **learning_config[\"trainer\"],\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 223, + "referenced_widgets": [ + "b85136e683cd45109dc7db1ee974280c", + 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"1efe3c6b1a0d40a08bf90569a0abde49", + "b5f030d06a0242d9bd0c1622d3e23d98", + "cd013abb526f481eaf47eb380e918ce2", + "c8a88f2adcf74340807f8a7c2ff5746a", + "54c24b1d56324c12abe27dbe606e1760" + ] + }, + "id": "a8WLIx33M7c6", + "outputId": "1cdc152b-458b-4a8c-a522-84366412f625" + }, + "outputs": [], + "source": [ + "# Here we go!\n", + "trainer.fit(\n", + " model,\n", + " train_dataloader,\n", + " val_dataloader,\n", + " **learning_config.get(\"trainer_fit_kwargs\", {}),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "JzGltwwJNAkI" + }, + "outputs": [], + "source": [ + "# Go to best checkpoint\n", + "best_checkpoint = trainer.checkpoint_callback.best_model_path\n", + "if best_checkpoint != \"\":\n", + " model = Model.load_from_checkpoint(\n", + " trainer.checkpoint_callback.best_model_path,\n", + " **Model.parse_config(model_config),\n", + " )\n", + "model.eval()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QvuJEYxJNGn7" + }, + "source": [ + "# Step 4: Check\n", + "Let's look at how good our model matches the real thing" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "K0UeoIbaNMxF" + }, + "outputs": [], + "source": [ + "def _rms(x: Union[np.ndarray, torch.Tensor]) -> float:\n", + " if isinstance(x, np.ndarray):\n", + " return np.sqrt(np.mean(np.square(x)))\n", + " elif isinstance(x, torch.Tensor):\n", + " return torch.sqrt(torch.mean(torch.square(x))).item()\n", + " else:\n", + " raise TypeError(type(x))\n", + "\n", + "def plot(\n", + " model,\n", + " ds,\n", + " savefig=None,\n", + " show=True,\n", + " window_start: Optional[int] = None,\n", + " window_end: Optional[int] = None,\n", + "):\n", + " with torch.no_grad():\n", + " tx = len(ds.x) / 48_000\n", + " print(f\"Run (t={tx})\")\n", + " t0 = time()\n", + " output = model(ds.x).flatten().cpu().numpy()\n", + " t1 = time()\n", + " 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", + " plt.savefig(savefig)\n", + " if show:\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "C_NsBdp5NQMC" + }, + "outputs": [], + "source": [ + "plot(\n", + " model,\n", + " 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", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "R__jJFwgNkAl" + }, + "source": [ + "## Step 5: 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." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "yQDcgoi_NrsW" + }, + "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\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "823KJ_L0Rchp" + }, + "source": [ + "## Step 6: Download your artifacts\n", + "We're done! \n", + "Go to the file browser on the left panel ⬅ and collect your artifacts!\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)." + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + 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torch.utils.data import Dataset as _Dataset +from tqdm import tqdm from ._core import InitializableFromConfig @@ -122,6 +124,8 @@ class Dataset(AbstractDataset, InitializableFromConfig): stop: Optional[int] = None, delay: Optional[int] = None, y_scale: float = 1.0, + x_path: Optional[Union[str, Path]] = None, + y_path: Optional[Union[str, Path]] = None, ): """ :param start: In samples @@ -133,10 +137,12 @@ class Dataset(AbstractDataset, InitializableFromConfig): if delay > 0: x = x[:-delay] y = y[delay:] - else: + elif delay < 0: x = x[-delay:] y = y[:delay] y = y * y_scale + self._x_path = x_path + self._y_path = y_path self._validate_inputs(x, y, nx, ny) self._x = x self._y = y @@ -157,6 +163,10 @@ class Dataset(AbstractDataset, InitializableFromConfig): return single_pairs // self._ny @property + def ny(self) -> int: + return self._ny + + @property def x(self): return self._x @@ -186,6 +196,8 @@ class Dataset(AbstractDataset, InitializableFromConfig): "stop": config.get("stop"), "delay": config.get("delay"), "y_scale": config.get("y_scale", 1.0), + "x_path": config["x_path"], + "y_path": config["y_path"], } def _validate_inputs(self, x, y, nx, ny): @@ -195,10 +207,16 @@ class Dataset(AbstractDataset, InitializableFromConfig): assert nx <= len(x) if ny is not None: assert ny <= len(y) - nx + 1 + if torch.abs(y).max() >= 1.0: + msg = "Output clipped." + if self._y_path is not None: + msg += f"Source is {self._y_path}" + raise ValueError(msg) class ConcatDataset(AbstractDataset, InitializableFromConfig): def __init__(self, datasets: Sequence[Dataset]): + self._validate_datasets(datasets) self._datasets = datasets def __getitem__(self, idx: int) -> Tuple[torch.Tensor, torch.Tensor]: @@ -213,7 +231,24 @@ class ConcatDataset(AbstractDataset, InitializableFromConfig): @classmethod def parse_config(cls, config): - return {"datasets": tuple(Dataset.init_from_config(c) for c in config)} + return { + "datasets": tuple( + Dataset.init_from_config(c) + for c in tqdm(config["dataset_configs"], desc="Loading data") + ) + } + + @classmethod + def _validate_datasets(cls, datasets: Sequence[Dataset]): + Reference = namedtuple("Reference", ("index", "val")) + ref_ny = None + for i, d in enumerate(datasets): + ref_ny = Reference(i, d.ny) if ref_ny is None else ref_ny + if d.ny != ref_ny.val: + raise ValueError( + f"Mismatch between ny of datasets {ref_ny.index} ({ref_ny.val}) and" + f" {i} ({d.ny})" + ) def init_dataset(config, split: Split) -> AbstractDataset: @@ -222,4 +257,8 @@ def init_dataset(config, split: Split) -> AbstractDataset: if isinstance(base_config, dict): return Dataset.init_from_config({**common, **base_config}) elif isinstance(base_config, list): - return ConcatDataset.init_from_config([{**common, **c} for c in base_config]) + return ConcatDataset.init_from_config( + {"dataset_configs": [{**common, **c} for c in base_config]} + ) + else: + raise TypeError(f"Unrecognized config type {type(base_config)}") diff --git a/setup.py b/setup.py @@ -10,14 +10,16 @@ ver_path = convert_path("nam/_version.py") with open(ver_path) as ver_file: exec(ver_file.read(), main_ns) -requirements = [] # torch... - -try: - import torch # noqa F401 -except ImportError as e: - raise ImportError( - f"PyTorch not found. Please install it as needed.\nOriginal error: {e}" - ) +requirements = [ + "matplotlib", + "numpy", + "pytorch_lightning", + "scipy", + "sounddevice", + "torch", + "tqdm", + "wavio", +] setup( name="nam",