commit 709870359b346f3cc66cc285c386f92c2749107f
parent b95be59f002acad89bea34525809a88cb164d326
Author: Steven Atkinson <steven@atkinson.mn>
Date: Sun, 4 Dec 2022 17:29:22 -0800
Update README.md
Diffstat:
| M | README.md | | | 36 | ++++++++++++++++++++++-------------- |
1 file changed, 22 insertions(+), 14 deletions(-)
diff --git a/README.md b/README.md
@@ -6,27 +6,34 @@ For the code to create the plugin with a trained model, see my
## 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!
+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 notebooks under `bin\train`.
-Simply go to [https://colab.research.google.com/github/sdatkinson/neural-amp-modeler/blob/main/bin/train/colab.ipynb](https://colab.research.google.com/github/sdatkinson/neural-amp-modeler/blob/main/bin/train/colab.ipynb) and follow the steps to upload your data and start training!
+For the very easiest experience, simply go to
+[https://colab.research.google.com/github/sdatkinson/neural-amp-modeler/blob/main/bin/train/easy_colab.ipynb](https://colab.research.google.com/github/sdatkinson/neural-amp-modeler/blob/main/bin/train/easy_colab.ipynb) and follow the
+steps!
+
+For a little more visibility under the hood, you can use [colab.ipynb instead](https://colab.research.google.com/github/sdatkinson/neural-amp-modeler/blob/main/bin/train/colab.ipynb) instead.
**Pros:**
-* No local installation required!
-* Decent GPUs are available if you don't have one on your computer.
+
+- No local installation required!
+- Decent GPUs are available if you don't have one on your computer.
**Cons:**
-* Uploading your data can take a long time.
-* The session will time out after an hour or so (for free accounts), so extended
- training runs aren't really feasible. I've tried to set you up with a good
- model that should train quickly!
+
+- Uploading your data can take a long time.
+- The session will time out after a few hours (for free accounts), so extended
+ training runs aren't really feasible. Also, there's a usage limit so you can't hang
+ out all day. I've tried to set you up with a good model that should train reasonably
+ quickly!
## How to use (Local)
Alternatively, you can clone this repo to your computer and use it locally.
### Installation
+
```bash
conda env create -f environment.yml
conda activate nam
@@ -34,6 +41,7 @@ pip install .
```
### Things you can do
+
Here are the primary ways this is meant to be used:
#### Train a model
@@ -84,14 +92,14 @@ path/to/checkpoints/epoch=123_val_loss=0.000010.ckpt \
path/to/exported_models/MyAmp
```
-You'll want the `HardCodedModel.h` to paste over into the plugin source (i.e. [here](https://github.com/sdatkinson/iPlug2/blob/5a0f533f7a9e4ee691da26adb2a38d87905e87fe/Examples/NAM/HardCodedModel.h)).
+Then point the plugin at the exported model directory and you're good to go!
## Advanced usage
-The model architectures and cofigurations in `bin/train/inputs/models` should work plenty well out of the box.
+The model architectures and cofigurations in `bin/train/inputs/models` should work plenty well out of the box.
However, feel free to play around with it; sometimes some tweaks can help improve performance.
Also, you can train for shorter or longer.
1000 epochs is typically overkill, but how little you can get away with depends on the model you're using.
-I recommend watching the checkpoints and keeping an eye out for when the ESR drops below 0.01--usually it'll
-sound pretty good by that point.
+I recommend watching the checkpoints and keeping an eye out for when the ESR drops below
+0.01--usually it'll sound pretty good by that point.
