Version 0.2.1 (#34) - neural-amp-modeler - Neural network emulator for guitar amplifiers

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 ++++++++++++++++++++++----------------------
M README.md  | 18 +++++++++++++++---
A bin/train/colab.ipynb  | 3999 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
M nam/_version.py  | 2 +-
M nam/data.py  | 45 ++++++++++++++++++++++++++++++++++++++++++---
M setup.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,
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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",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
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+          "state": {
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+            "_model_module_version": "1.5.0",
+            "_model_name": "HTMLModel",
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+            "_view_module_version": "1.5.0",
+            "_view_name": "HTMLView",
+            "description": "",
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+            "placeholder": "",
+            "style": "IPY_MODEL_e7466c9d0f9a47fb9a9c73220d270f6e",
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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",

	neural-amp-modeler Neural network emulator for guitar amplifiers
	Log \| Files \| Refs \| README \| LICENSE

M	.github/workflows/python-package.yml	\|	44	++++++++++++++++++++++----------------------
M	README.md	\|	18	+++++++++++++++---
A	bin/train/colab.ipynb	\|	3999	+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
M	nam/_version.py	\|	2	+-
M	nam/data.py	\|	45	++++++++++++++++++++++++++++++++++++++++++---
M	setup.py	\|	18	++++++++++--------