README.md

---
title: Audio Diffusion
emoji: 🎵
colorFrom: pink
colorTo: blue
sdk: gradio
sdk_version: 3.1.4
app_file: app.py
pinned: false
license: gpl-3.0
---
# audio-diffusion [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/teticio/audio-diffusion/blob/master/notebooks/gradio_app.ipynb)

### Apply [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239) using the new Hugging Face [diffusers](https://github.com/huggingface/diffusers) package to synthesize music instead of images.

---

**UPDATES**: 

4/10/2022
It is now possible to mask parts of the input audio during generation which means you can stitch several samples together (think "out-painting").

27/9/2022
You can now generate an audio based on a previous one. You can use this to generate variations of the same audio or even to "remix" a track (via a sort of "style transfer"). You can find examples of how to do this in the [`test_model.ipynb`](https://colab.research.google.com/github/teticio/audio-diffusion/blob/master/notebooks/test_model.ipynb) notebook.

---

![mel spectrogram](mel.png)

---

Audio can be represented as images by transforming to a [mel spectrogram](https://en.wikipedia.org/wiki/Mel-frequency_cepstrum), such as the one shown above. The class `Mel` in `mel.py` can convert a slice of audio into a mel spectrogram of `x_res` x `y_res` and vice versa. The higher the resolution, the less audio information will be lost. You can see how this works in the [`test_mel.ipynb`](https://github.com/teticio/audio-diffusion/blob/main/notebooks/test_mel.ipynb) notebook.

A DDPM model is trained on a set of mel spectrograms that have been generated from a directory of audio files. It is then used to synthesize similar mel spectrograms, which are then converted back into audio.

You can play around with some pretrained models on [Google Colab](https://colab.research.google.com/github/teticio/audio-diffusion/blob/master/notebooks/test_model.ipynb) or [Hugging Face spaces](https://huggingface.co/spaces/teticio/audio-diffusion). Check out some automatically generated loops [here](https://soundcloud.com/teticio2/sets/audio-diffusion-loops).


| Model | Dataset | Description |
|-------|---------|-------------|
| [teticio/audio-diffusion-256](https://huggingface.co/teticio/audio-diffusion-256) | [teticio/audio-diffusion-256](https://huggingface.co/datasets/teticio/audio-diffusion-256) | My "liked" Spotify playlist |
| [teticio/audio-diffusion-breaks-256](https://huggingface.co/teticio/audio-diffusion-breaks-256) | [teticio/audio-diffusion-breaks-256](https://huggingface.co/datasets/teticio/audio-diffusion-breaks-256) | Samples that have been used in music, sourced from [WhoSampled](https://whosampled.com) and [YouTube](https://youtube.com) |
| [teticio/audio-diffusion-instrumental-hiphop-256](https://huggingface.co/teticio/audio-diffusion-instrumental-hiphop-256) | [teticio/audio-diffusion-instrumental-hiphop-256](https://huggingface.co/datasets/teticio/audio-diffusion-instrumental-hiphop-256) | Instrumental Hip Hop music |

---

## Generate Mel spectrogram dataset from directory of audio files
#### Training can be run with Mel spectrograms of resolution 64x64 on a single commercial grade GPU (e.g. RTX 2080 Ti). The `hop_length` should be set to 1024 for better results.

```bash
python audio_to_images.py \
  --resolution 64 \
  --hop_length 1024 \
  --input_dir path-to-audio-files \
  --output_dir data-test
```

#### Generate dataset of 256x256 Mel spectrograms and push to hub (you will need to be authenticated with `huggingface-cli login`).

```bash
python audio_to_images.py \
  --resolution 256 \
  --input_dir path-to-audio-files \
  --output_dir data-256 \
  --push_to_hub teticio/audio-diffusion-256
```
## Train model
#### Run training on local machine.

```bash
accelerate launch --config_file accelerate_local.yaml \
  train_unconditional.py \
  --dataset_name data-64 \
  --resolution 64 \
  --hop_length 1024 \
  --output_dir ddpm-ema-audio-64 \
  --train_batch_size 16 \
  --num_epochs 100 \
  --gradient_accumulation_steps 1 \
  --learning_rate 1e-4 \
  --lr_warmup_steps 500 \
  --mixed_precision no
```

#### Run training on local machine with `batch_size` of 2 and `gradient_accumulation_steps` 8 to compensate, so that 256x256 resolution model fits on commercial grade GPU and push to hub.

```bash
accelerate launch --config_file accelerate_local.yaml \
  train_unconditional.py \
  --dataset_name teticio/audio-diffusion-256 \
  --resolution 256 \
  --output_dir latent-audio-diffusion-256 \
  --num_epochs 100 \
  --train_batch_size 2 \
  --eval_batch_size 2 \
  --gradient_accumulation_steps 8 \
  --learning_rate 1e-4 \
  --lr_warmup_steps 500 \
  --mixed_precision no \
  --push_to_hub True \
  --hub_model_id latent-audio-diffusion-256 \
  --hub_token $(cat $HOME/.huggingface/token)
```

#### Run training on SageMaker.

```bash
accelerate launch --config_file accelerate_sagemaker.yaml \
  strain_unconditional.py \
  --dataset_name teticio/audio-diffusion-256 \
  --resolution 256 \
  --output_dir ddpm-ema-audio-256 \
  --train_batch_size 16 \
  --num_epochs 100 \
  --gradient_accumulation_steps 1 \
  --learning_rate 1e-4 \
  --lr_warmup_steps 500 \
  --mixed_precision no
```