commit 369e05ab7cced60292351bd5e63521ba2c9edc49
parent 7fa0fec58aa6d5600c1a104695e690353fbfa8de
Author: Caglayan Dicle <[email protected]>
Date: Fri, 24 Mar 2023 11:10:22 -0400
Issue 126 update readme turn key training (#150)
* Add demo json files
* Update readme
Diffstat:
3 files changed, 94 insertions(+), 16 deletions(-)
diff --git a/README.md b/README.md
@@ -65,28 +65,48 @@ from the terminal.
### Train models (Python script)
For users looking to get more fine-grained control over the modeling process,
-NAM includes a training script that can be run from the terminal, e.g.:
+NAM includes a training script that can be run from the terminal. In order to run it
+#### Download audio files
+Download the [v1_1_1.wav](https://drive.google.com/file/d/1v2xFXeQ9W2Ks05XrqsMCs2viQcKPAwBk/view?usp=share_link) and [overdrive.wav](https://drive.google.com/file/d/14w2utgL16NozmESzAJO_I0_VCt-5Wgpv/view?usp=share_link) to a folder of your choice
+
+#### Update data configuration
+Edit `bin/train/data/single_pair.json` to point to relevant audio files
+```json
+ "common": {
+ "x_path": "C:\\path\\to\\v1_1_1.wav",
+ "y_path": "C:\\path\\to\\overdrive.wav",
+ "delay": 0
+ }
+```
+
+#### Run training script
+Open up a terminal. Activate your nam environment and call the training with
+```bash
+python bin/train/main.py \
+bin/train/inputs/data/single_pair.json \
+bin/train/inputs/models/demonet.json \
+bin/train/inputs/learning/demo.json \
+bin/train/outputs/MyAmp
+```
+
+`data/single_pair.json` contains the information about the data you're training
+on
+`models/demonet.json` contains information about the model architecture that
+is being trained. The example used here uses a `feather` configured `wavenet`.
+`learning/demo.json` contains information about the training run itself (e.g. number of epochs).
+
+The configuration above runs a short (demo) training. For a real training you may prefer to run something like,
```bash
python bin/train/main.py \
-bin/train/inputs/config_data.json \
-bin/train/inputs/config_model.json \
-bin/train/inputs/config_learning.json \
+bin/train/inputs/data/single_pair.json \
+bin/train/inputs/models/wavenet.json \
+bin/train/inputs/learning/default.json \
bin/train/outputs/MyAmp
```
-where `config_data.json` contains the information about the data you're training
-on, `config_model.json` contains information about the model architecture that
-is being trained, and `config_learning.json` contains information about the
-training run itself (e.g. number of epochs).
-You'll need to configure the data JSON to the specifics of the data you're
-training on. The others may work for your needs out-of-the-box with no
-modification.
-
-Since NAM uses [PyTorch Lightning](https://lightning.ai/pages/open-source/)
-under the hood as a modeling framework, many of the configuration options that
-are passed to its componenets can be configured from the data/model/learning
-JSONs.
+As a side note, NAM uses [PyTorch Lightning](https://lightning.ai/pages/open-source/)
+under the hood as a modeling framework, and you can control many of the Pytorch Lightning configuration options from `bin/train/inputs/learning/default.json`
#### Export a model (to use with [the plugin](https://github.com/sdatkinson/NeuralAmpModelerPlugin))
Exporting the trained model to a `.nam` file for use with the plugin can be done
diff --git a/bin/train/inputs/learning/demo.json b/bin/train/inputs/learning/demo.json
@@ -0,0 +1,15 @@
+{
+ "train_dataloader": {
+ "batch_size": 16,
+ "shuffle": true,
+ "pin_memory": true,
+ "drop_last": true,
+ "num_workers": 0
+ },
+ "val_dataloader": {},
+ "trainer": {
+ "gpus": 0,
+ "max_epochs": 10
+ },
+ "trainer_fit_kwargs": {}
+}
+\ No newline at end of file
diff --git a/bin/train/inputs/models/demonet.json b/bin/train/inputs/models/demonet.json
@@ -0,0 +1,41 @@
+{
+ "net": {
+ "name": "WaveNet",
+ "config": {
+ "layers_configs": [
+ {
+ "input_size": 1,
+ "condition_size": 1,
+ "channels": 8,
+ "head_size": 4,
+ "kernel_size": 3,
+ "dilations": [1, 2, 4, 8, 16, 32, 64],
+ "activation": "Tanh",
+ "gated": false,
+ "head_bias": false
+ },
+ {
+ "condition_size": 1,
+ "input_size": 8,
+ "channels": 4,
+ "head_size": 1,
+ "kernel_size": 3,
+ "dilations": [128, 256, 512, 1, 2, 4, 8, 16, 32, 64, 128, 256, 512],
+ "activation": "Tanh",
+ "gated": false,
+ "head_bias": true
+ }
+ ],
+ "head_scale": 0.02
+ }
+ },
+ "optimizer": {
+ "lr": 0.004
+ },
+ "lr_scheduler": {
+ "class": "ExponentialLR",
+ "kwargs": {
+ "gamma": 0.993
+ }
+ }
+}
+\ No newline at end of file
