README.md

PaSST package for HEAR 2021 NeurIPS Challenge Holistic Evaluation of Audio Representations

This is an implementation for Efficient Training of Audio Transformers with Patchout for HEAR 2021 NeurIPS Challenge Holistic Evaluation of Audio Representations

CUDA version

This is an implementation is tested with CUDA version 11.1, and torch installed:

pip3 install torch==1.8.1+cu111  torchaudio==0.8.1 -f https://download.pytorch.org/whl/lts/1.8/torch_lts.html

Installation

Install the latest version of this repo:

pip install -e 'git+https://github.com/kkoutini/[email protected]#egg=hear21passt' 

The models follow the common API of HEAR 21 :

hear-validator --model hear21passt.base.pt hear21passt.base
hear-validator --model noweights.txt hear21passt.base2level
hear-validator --model noweights.txt hear21passt.base2levelmel

There are three modules available hear21passt.base,hear21passt.base2level, hear21passt.base2levelmel :

import torch

from hear21passt.base import load_model, get_scene_embeddings, get_timestamp_embeddings

model = load_model().cuda()
seconds = 15
audio = torch.ones((3, 32000 * seconds))*0.5
embed, time_stamps = get_timestamp_embeddings(audio, model)
print(embed.shape)
embed = get_scene_embeddings(audio, model)
print(embed.shape)

Getting the Logits/Class Labels

You can get the logits (before the sigmoid activation) for the 527 classes of audioset:

from hear21passt.base import load_model

model = load_model(mode="logits").cuda()
logits = model(wave_signal)

The class labels indices can be found here

Supporting longer clips

In case of an input longer than 10 seconds, the get_scene_embeddings method compute the average of the embedding of a 10-second overlapping windows. Depending on the application, it may be useful to use a pre-trained that can extract embeddings from 20 or 30 seconds without averaging. These variant has pre-trained time positional encoding or 20/30 seconds:

# from version 0.0.18, it's possible to use:
from hear21passt.base20sec import load_model # up to 20 seconds of audio.
# or 
from hear21passt.base30sec import load_model # up to 30 seconds of audio.

model = load_model(mode="logits").cuda()
logits = model(wave_signal)