https://github.com/archinetai/cqt-pytorch

An invertible and differentiable implementation of the Constant-Q Transform (CQT).
https://github.com/archinetai/cqt-pytorch

audio cqt deep-learning pytorch

Last synced: about 2 months ago
JSON representation

An invertible and differentiable implementation of the Constant-Q Transform (CQT).

• Host: GitHub
• URL: https://github.com/archinetai/cqt-pytorch
• Owner: archinetai
• License: mit
• Created: 2022-11-08T09:29:12.000Z (about 2 years ago)
• Default Branch: main
• Last Pushed: 2022-12-09T09:11:49.000Z (about 2 years ago)
• Last Synced: 2024-10-06T17:08:50.511Z (3 months ago)
• Topics: audio, cqt, deep-learning, pytorch
• Language: Python
• Homepage:
• Size: 1.08 MB
• Stars: 52
• Watchers: 6
• Forks: 3
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        

# CQT - PyTorch

An invertible and differentiable implementation of the Constant-Q Transform (CQT) using Non-stationary Gabor Transform (NSGT), in PyTorch.

```bash

pip install cqt-pytorch

```

[![PyPI - Python Version](https://img.shields.io/pypi/v/cqt-pytorch?style=flat&colorA=black&colorB=black)](https://pypi.org/project/cqt-pytorch/)

## Usage

```python

from cqt_pytorch import CQT

transform = CQT(

    num_octaves = 8,

    num_bins_per_octave = 64,

    sample_rate = 48000,

    block_length = 2 ** 18

)

# (Random) audio waveform tensor x

x = torch.randn(1, 2, 2**18) # [1, 1, 262144] = [batch_size, channels, timesteps]

z = transform.encode(x) # [1, 2, 512, 2839] = [batch_size, channels, frequencies, time]

y = transform.decode(z) # [1, 1, 262144]

```

### Example CQT Magnitude Spectrogram (z)



## TODO

* [x] Power of 2 length (with `power_of_2_length` constructor arg).

* [x] Understand why/if inverse window is necessary (it is necessary for perfect inversion).

* [x] Allow variable audio lengths by chunking (now input can be a multiple of `block_length`)

## Appreciation

Special thanks to [Eloi Moliner](https://github.com/eloimoliner) for taking the time to help me understand how CQT works. Check out his own implementation with interesting features at [eloimoliner/CQT_pytorch](https://github.com/eloimoliner/CQT_pytorch).

## Citations

```bibtex

@article{1210.0084,

Author = {Nicki Holighaus and Monika Dörfler and Gino Angelo Velasco and Thomas Grill},

Title = {A framework for invertible, real-time constant-Q transforms},

Year = {2012},

Eprint = {arXiv:1210.0084},

Doi = {10.1109/TASL.2012.2234114},

}

```