torchopenl3

pytorch port of the openl3 audio embedding (from the marl implementation)

Installation

Clone this repo and run cd torchopenl3 && pip install -e .

Usage

OpenL3 comes in a couple of flavors. We can choose from:

• input representations: mel128 or mel256. linear coming soon
• content types: music or env. The music model variant was trained on music, while the env was trained on environmental sounds.
• embedding size: output embedding size. Either 512 or 6144.

Let's load a model! We choose the mel128, music, 512 variant.

import torchopenl3
import torch
import numpy as np

DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu' # use GPU if we can!

# dummy audio
SAMPLE_RATE = 48000
audio = np.random.randn(1, SAMPLE_RATE).astype(np.float32) # 1 second of audio at 48kHz

model = torchopenl3.OpenL3Embedding(input_repr='mel128', 
                                    embedding_size=512, 
                                    content_type='music')

embedding = torchopenl3.embed(model=model, 
                            audio=audio, # shape sould be (channels, samples)
                            sample_rate=SAMPLE_RATE, # sample rate of input file
                            hop_size=1, 
                            device=DEVICE) # use gpu?

Tests

Tests are written using pytest. Run pip install pytest to install pytest. run pytest.