commit b95be59f002acad89bea34525809a88cb164d326
parent 5a08768b6f4fbdd52f9f79d741e9f340217e95f1
Author: Steven Atkinson <[email protected]>
Date: Sun, 4 Dec 2022 17:23:21 -0800
Easy Colab
Diffstat:
3 files changed, 2109 insertions(+), 0 deletions(-)
diff --git a/bin/train/easy_colab.ipynb b/bin/train/easy_colab.ipynb
@@ -0,0 +1,1875 @@
+{
+ "cells": [
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+ "cell_type": "markdown",
+ "metadata": {
+ "id": "TC3XkMetGWtK"
+ },
+ "source": [
+ "# Neural Amp Modeler (\"Easy mode\" 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",
+ "1. Get your data\n",
+ "2. Installation\n",
+ "3. Train!\n",
+ "4. Check the results and download your model"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "5CQleTk7GJV8"
+ },
+ "source": [
+ "## Step 1: Get data\n",
+ "We're gonna need data. \"Easy mode\" takes out a lot of the guesswork.\n",
+ "\n",
+ "### Step 1.1: Download the capture signal\n",
+ "\"Easy mode\" uses a pre-crafted \"capture signal\".\n",
+ "Download it [here](https://drive.google.com/file/d/1sVHtubguuXmDHRM8w1TmDaJEfB7-H9Vz/view?usp=share_link).\n",
+ "\n",
+ "### Step 1.2 Reamp your gear\n",
+ "Then reamp the gear you want to model using it. Save that reamp as \"output.wav\".\n",
+ "**Please use 48kHz, 24-bit, mono.** We'll support other sample rates etc in the future; sit tight!\n",
+ "\n",
+ "### Step 1.3: upload!\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": "markdown",
+ "metadata": {
+ "id": "2g_4GtFuGlO8"
+ },
+ "source": [
+ "## Step 2: Installation\n",
+ "Install `nam` into this Colab instance by running the next cell:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "vYQIpWr5EYRb",
+ "outputId": "9099af59-62ae-45b2-8cb6-415db054a73a"
+ },
+ "outputs": [],
+ "source": [
+ "!pip install git+https://github.com/sdatkinson/neural-amp-modeler.git@main\n",
+ "\n",
+ "from nam.train.colab import run"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 3: Train!\n",
+ "Here we go!\n",
+ "\n",
+ "🕙Training will go through 100 epochs and take just over 10 minutes.🕙\n",
+ "\n",
+ "If you want a better model, you can try training for more epochs--just put in a \n",
+ "different number before hitting go!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "run(epochs=100)\n",
+ "# Psst! Curious how it's going?\n",
+ "# You can look under lightning_logs/version_0/checkpoints to see it saving its progress.\n",
+ "# Look for the number after \"ESR\" to go down. It will start around 1.0. 0.1 is ok, and \n",
+ "# 0.01 is really good. Anything higher than 0.1 is probably not great."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "823KJ_L0Rchp"
+ },
+ "source": [
+ "## Step 4: 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\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 too.\n",
+ "\n",
+ "# 🎸 **ENJOY!** 🎸"
+ ]
+ }
+ ],
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diff --git a/nam/train/__init__.py b/nam/train/__init__.py
@@ -0,0 +1,3 @@
+# File: __init__.py
+# Created Date: Sunday December 4th 2022
+# Author: Steven Atkinson ([email protected])
diff --git a/nam/train/colab.py b/nam/train/colab.py
@@ -0,0 +1,231 @@
+# File: colab.py
+# Created Date: Sunday December 4th 2022
+# Author: Steven Atkinson ([email protected])
+
+"""
+Hide the mess in Colab to make things look pretty for users.
+"""
+
+from pathlib import Path
+from time import time
+from typing import Optional, Union
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pytorch_lightning as pl
+import torch
+from torch.utils.data import DataLoader
+
+from nam.data import REQUIRED_RATE, Split, init_dataset, wav_to_np
+from nam.models import Model
+
+_INPUT_BASENAME = "v1.wav"
+_OUTPUT_BASENAME = "output.wav"
+
+
+def _check_for_files():
+ print("Checking that we have all of the required audio files...")
+ if not Path(_INPUT_BASENAME).exists():
+ raise FileNotFoundError(
+ f"Didn't find NAM's input audio file. Please upload {_INPUT_BASENAME}"
+ )
+ if not Path(_OUTPUT_BASENAME).exists():
+ raise FileNotFoundError(
+ f"Didn't find your reamped output audio file. Please upload {_OUTPUT_BASENAME}."
+ )
+
+
+def _calibrate_delay_v1() -> int:
+ safety_factor = 4
+ # Locations of blips in v1 signal file:
+ i1, i2 = 12_000, 36_000
+ j1_start_looking = i1 - 1_000
+ j2_start_looking = i2 - 1_000
+
+ y = wav_to_np(_OUTPUT_BASENAME)[:48_000]
+
+ background_level = np.max(np.abs(y[:6_000]))
+ trigger_threshold = background_level + 0.01
+ j1 = np.where(np.abs(y[j1_start_looking:j2_start_looking]) > trigger_threshold)[0][
+ 0
+ ]
+ j2 = np.where(np.abs(y[j2_start_looking:]) > trigger_threshold)[0][0]
+
+ delay_1 = (j1 + j1_start_looking) - i1
+ delay_2 = (j2 + j2_start_looking) - i2
+ print(f"Delays: {delay_1}, {delay_2}")
+ delay = int(np.min([delay_1, delay_2])) - safety_factor
+ print(f"Final delay is {delay}")
+ return delay
+
+
+def _calibrate_delay() -> int:
+ print("Calibrating the delay in your data...")
+ return _calibrate_delay_v1()
+
+
+def _get_configs(delay: int, epochs: int, stage_1_channels, stage_2_channels):
+ val_seconds = 9
+ train_val_split = -val_seconds * REQUIRED_RATE
+ data_config = {
+ "train": {"ny": 8192, "stop": train_val_split},
+ "validation": {"ny": None, "start": train_val_split},
+ "common": {
+ "x_path": _INPUT_BASENAME,
+ "y_path": _OUTPUT_BASENAME,
+ "delay": delay,
+ },
+ }
+ model_config = {
+ "net": {
+ "name": "WaveNet",
+ # This should do decently. If you really want a nice model, try turning up
+ # "channels" in the first block and "input_size" in the second from 12 to 16.
+ "config": {
+ "layers_configs": [
+ {
+ "input_size": 1,
+ "condition_size": 1,
+ "head_size": stage_2_channels,
+ "channels": stage_1_channels,
+ "kernel_size": 3,
+ "dilations": [1, 2, 4, 8, 16, 32, 64, 128, 256, 512],
+ "activation": "Tanh",
+ "gated": False,
+ "head_bias": False,
+ },
+ {
+ "input_size": stage_1_channels,
+ "condition_size": 1,
+ "head_size": 1,
+ "channels": stage_2_channels,
+ "kernel_size": 3,
+ "dilations": [1, 2, 4, 8, 16, 32, 64, 128, 256, 512],
+ "activation": "Tanh",
+ "gated": False,
+ "head_bias": True,
+ },
+ ]
+ },
+ },
+ "loss": {"val_loss": "esr"},
+ "optimizer": {"lr": 0.001},
+ "lr_scheduler": {"class": "ExponentialLR", "kwargs": {"gamma": 0.993}},
+ }
+ learning_config = {
+ "train_dataloader": {
+ "batch_size": 16,
+ "shuffle": True,
+ "pin_memory": True,
+ "drop_last": True,
+ "num_workers": 0,
+ },
+ "val_dataloader": {},
+ "trainer": {"accelerator": "gpu", "devices": 1, "max_epochs": epochs},
+ }
+ return data_config, model_config, learning_config
+
+
+def _esr(pred: torch.Tensor, target: torch.Tensor) -> float:
+ return (
+ torch.mean(torch.square(pred - target)).item()
+ / torch.mean(torch.square(target)).item()
+ )
+
+
+def _plot(
+ model, ds, window_start: Optional[int] = None, window_end: Optional[int] = None
+):
+ print("Plotting a comparison of your model with the target output...")
+ with torch.no_grad():
+ tx = len(ds.x) / 48_000
+ print(f"Run (t={tx:.2f} sec)")
+ t0 = time()
+ output = model(ds.x).flatten().cpu().numpy()
+ t1 = time()
+ print(f"Took {t1 - t0:.2f} sec ({tx / (t1 - t0):.2f}x)")
+
+ esr = _esr(torch.Tensor(output), ds.y)
+ # Trying my best to put numbers to it...
+ if esr < 0.01:
+ esr_comment = "Great!"
+ elif esr < 0.035:
+ esr_comment = "Not bad!"
+ elif esr < 0.1:
+ esr_comment = "...This *might* sound ok!"
+ elif esr < 0.3:
+ esr_comment = "...This probably won't sound great :("
+ else:
+ esr_comment = "...Something seems to have gone wrong."
+ print(f"Error-signal ratio = {esr:.3f}")
+ print(esr_comment)
+
+ plt.figure(figsize=(16, 5))
+ plt.plot(output[window_start:window_end], label="Prediction")
+ plt.plot(ds.y[window_start:window_end], linestyle="--", label="Target")
+ plt.title(f"ESR={esr:.3f}")
+ plt.legend()
+
+
+def _get_valid_export_directory():
+ def get_path(version):
+ return Path("exported_models", f"version_{version}")
+
+ version = 1
+ while get_path(version).exists():
+ version += 1
+ return get_path(version)
+
+
+def run(epochs=100, stage_1_channels=16, stage_2_channels=8):
+ _check_for_files()
+ delay = _calibrate_delay()
+ data_config, model_config, learning_config = _get_configs(
+ delay, epochs, stage_1_channels, stage_2_channels
+ )
+
+ print("Starting training. Let's rock!")
+ model = Model.init_from_config(model_config)
+ data_config["common"]["nx"] = model.net.receptive_field
+ dataset_train = init_dataset(data_config, Split.TRAIN)
+ dataset_validation = init_dataset(data_config, Split.VALIDATION)
+ train_dataloader = DataLoader(dataset_train, **learning_config["train_dataloader"])
+ val_dataloader = DataLoader(dataset_validation, **learning_config["val_dataloader"])
+
+ trainer = pl.Trainer(
+ callbacks=[
+ pl.callbacks.model_checkpoint.ModelCheckpoint(
+ filename="checkpoint_best_{epoch:04d}_{step}_{ESR:.4f}_{MSE:.3e}",
+ save_top_k=3,
+ monitor="val_loss",
+ every_n_epochs=1,
+ ),
+ pl.callbacks.model_checkpoint.ModelCheckpoint(
+ filename="checkpoint_last_{epoch:04d}_{step}", every_n_epochs=1
+ ),
+ ],
+ **learning_config["trainer"],
+ )
+ trainer.fit(model, train_dataloader, val_dataloader)
+
+ # Go to best checkpoint
+ best_checkpoint = trainer.checkpoint_callback.best_model_path
+ if best_checkpoint != "":
+ model = Model.load_from_checkpoint(
+ trainer.checkpoint_callback.best_model_path,
+ **Model.parse_config(model_config),
+ )
+ model.eval()
+
+ _plot(
+ model,
+ dataset_validation,
+ window_start=100_000, # Start of the plotting window, in samples
+ window_end=101_000, # End of the plotting window, in samples
+ )
+
+ print("Exporting your model...")
+ model_export_outdir = _get_valid_export_directory()
+ model_export_outdir.mkdir(parents=True, exist_ok=False)
+ model.net.export(model_export_outdir)
+ print(f"Model exported to {model_export_outdir}. Enjoy!")
