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:
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",
+ "âš **Warning**âš \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/",
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+ },
+ "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": {
+ "collapsed_sections": [],
+ "name": "colab.ipynb",
+ "provenance": []
+ },
+ "kernelspec": {
+ "display_name": "Python 3",
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diff --git a/nam/_version.py b/nam/_version.py
@@ -1 +1 @@
-__version__ = "0.2.0"
+__version__ = "0.2.1"
diff --git a/nam/data.py b/nam/data.py
@@ -3,6 +3,7 @@
# Author: Steven Atkinson (steven@atkinson.mn)
import abc
+from collections import namedtuple
from dataclasses import dataclass
from enum import Enum
from pathlib import Path
@@ -12,6 +13,7 @@ import numpy as np
import torch
import wavio
from 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",