https://github.com/archinetai/audio-diffusion-pytorch-trainer

Trainer for audio-diffusion-pytorch
https://github.com/archinetai/audio-diffusion-pytorch-trainer

artificial-intelligence audio-generation deep-learning denoising-diffusion

Last synced: 8 days ago
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Trainer for audio-diffusion-pytorch

• Host: GitHub
• URL: https://github.com/archinetai/audio-diffusion-pytorch-trainer
• Owner: archinetai
• License: mit
• Created: 2022-08-19T08:56:48.000Z (about 2 years ago)
• Default Branch: main
• Last Pushed: 2023-01-13T14:23:16.000Z (almost 2 years ago)
• Last Synced: 2024-08-05T06:04:48.866Z (3 months ago)
• Topics: artificial-intelligence, audio-generation, deep-learning, denoising-diffusion
• Language: Python
• Homepage:
• Size: 264 KB
• Stars: 126
• Watchers: 6
• Forks: 22
• Open Issues: 9
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Trainer for [`audio-diffusion-pytorch`](https://github.com/archinetai/audio-diffusion-pytorch)

audio-diffusion-pytorch-trainer notebook: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/archinetai/audio-diffusion-pytorch-trainer/blob/main/notebooks/audio_diffusion_pytorch_trainer_v0_2.ipynb)

## Setup

(Optional) Create virtual environment and activate it

```bash

python3 -m venv venv

source venv/bin/activate

```

Install requirements

```bash

pip install -r requirements.txt

```

Add environment variables, rename `.env.tmp` to `.env` and replace with your own variables (example values are random)

```bash

DIR_LOGS=/logs

DIR_DATA=/data

# Required if using wandb logger

WANDB_PROJECT=audioproject

WANDB_ENTITY=johndoe

WANDB_API_KEY=a21dzbqlybbzccqla4txa21dzbqlybbzccqla4tx

# Required if using Common Voice dataset

HUGGINGFACE_TOKEN=hf_NUNySPyUNsmRIb9sUC4FKR2hIeacJOr4Rm

```

## Run Experiments

Run test experiment, see the [`exp`](exp/) folder for other experiments (create your own `.yaml` file there to run a custom experiment!)

```bash

python train.py exp=base_test

```

Run on GPU(s)

```bash

python train.py exp=base_test trainer.gpus=1

```

Resume run from a checkpoint

```bash

python train.py exp=base_test +ckpt=/logs/ckpts/2022-08-17-01-22-18/'last.ckpt'

```

## FAQ

How do I use the CommonVoice dataset?

Before running an experiment on commonvoice dataset you have to:

1. Create a Huggingface account if you don't already have one [here](https://huggingface.co/join)

2. Accept the terms of the version of [common voice dataset](https://huggingface.co/mozilla-foundation) you will be using by clicking on it and selecting "Access repository".

3. Add your [access token](https://huggingface.co/settings/tokens) to the `.env` file, for example `HUGGINGFACE_TOKEN=hf_NUNySPyUNsmRIb9sUC4FKR2hIeacJOr4Rm`.

How do I load the model once I'm done training?

If you want to load the checkpoint to restore training with the trainer you can do `python train.py exp=my_experiment +ckpt=/logs/ckpts/2022-08-17-01-22-18/'last.ckpt'`.

Otherwise if you want to instantiate a model from the checkpoint:

```py

from main.mymodule import Model

model = Model.load_from_checkpoint(

    checkpoint_path='my_checkpoint.ckpt',

    learning_rate=1e-4,

    beta1=0.9,

    beta2=0.99,

    in_channels=1,

    patch_size=16,

    all_other_paratemeters_here...

)

```

to get only the PyTorch `.pt` checkpoint you can save the internal model weights as `torch.save(model.model.state_dict(), 'torchckpt.pt')`.

Why no checkpoint is created at the end of the epoch?

If the epoch is shorter than `log_every_n_steps` it doesn't save the checkpoint at the end of the epoch, but after the provided number of steps. If you want to checkpoint more frequently you can add `every_n_train_steps` to the ModelCheckpoint e.g.:

```yaml

model_checkpoint:

    _target_: pytorch_lightning.callbacks.ModelCheckpoint

    monitor: "valid_loss"   # name of the logged metric which determines when model is improving

    save_top_k: 1           # save k best models (determined by above metric)

    save_last: True         # additionaly always save model from last epoch

    mode: "min"             # can be "max" or "min"

    verbose: False

    dirpath: ${logs_dir}/ckpts/${now:%Y-%m-%d-%H-%M-%S}

    filename: '{epoch:02d}-{valid_loss:.3f}'

    every_n_train_steps: 10

```

Note that logging the checkpoint so frequently is not recommended in general, since it takes a bit of time to store the file.