metadata

license: mit
tags:
  - audio tagging
  - audio events
  - audio embeddings
  - convnext-audio
  - audioset
inference: false

ConvNeXt-Tiny-AT is an audio tagging CNN model, trained on AudioSet (balanced+unbalanced subsets). It reached 0.471 mAP on the test set (Paper).

The model expects as input audio files of duration 10 seconds, and sample rate 32kHz. It provides logits and probabilities for the 527 audio event tags of AudioSet (see http://research.google.com/audioset/index.html). Two methods can also be used to get scene embeddings (a single vector per file) and frame-level embeddings, see below. The scene embedding is obtained from the frame-level embeddings, on which mean pooling is applied onto the frequency dim, followed by mean pooling + max pooling onto the time dim.

Install

This code is based on our repo: https://github.com/topel/audioset-convnext-inf

pip install git+https://github.com/topel/audioset-convnext-inf@pip-install

Usage

Below is an example of how to instantiate our model convnext_tiny_471mAP.pth

import os
import numpy as np
import torch
import torchaudio

from audioset_convnext_inf.pytorch.convnext import ConvNeXt
from audioset_convnext_inf.utils.utilities import read_audioset_label_tags

model = ConvNeXt.from_pretrained("topel/ConvNeXt-Tiny-AT", map_location='cpu')

print(
    "# params:",
    sum(param.numel() for param in model.parameters() if param.requires_grad),
)
if torch.cuda.is_available():
    device = torch.device("cuda")
else:
    device = torch.device("cpu")

if "cuda" in str(device):
    model = model.to(device)

Output:

# params: 28222767

Inference: get logits and probabilities

sample_rate = 32000
audio_target_length = 10 * sample_rate  # 10 s

AUDIO_FNAME = "f62-S-v2swA_200000_210000.wav"
AUDIO_FPATH = os.path.join("/path/to/audio", AUDIO_FNAME)

waveform, sample_rate_ = torchaudio.load(AUDIO_FPATH)
if sample_rate_ != sample_rate:
    print("ERROR: sampling rate not 32k Hz", sample_rate_)

waveform = waveform.to(device)

print("\nInference on " + AUDIO_FNAME + "\n")

with torch.no_grad():
    model.eval()
    output = model(waveform)

logits = output["clipwise_logits"]
print("logits size:", logits.size())

probs = output["clipwise_output"]
# Equivalent: probs = torch.sigmoid(logits)
print("probs size:", probs.size())

current_dir=os.getcwd()
lb_to_ix, ix_to_lb, id_to_ix, ix_to_id = read_audioset_label_tags(os.path.join(current_dir, "class_labels_indices.csv"))

threshold = 0.25
sample_labels = np.where(probs[0].clone().detach().cpu() > threshold)[0]
print("\nPredicted labels using activity threshold 0.25:\n")
# print(sample_labels)
for l in sample_labels:
    print("%s: %.3f"%(ix_to_lb[l], probs[0,l]))

Output:

logits size: torch.Size([1, 527])
probs size: torch.Size([1, 527])

Predicted labels using activity threshold 0.25:

Speech: 0.626
Music: 0.842
Musical instrument: 0.362
Plucked string instrument: 0.307
Ukulele: 0.703
Inside, small room: 0.305

Get audio scene embeddings

with torch.no_grad():
    model.eval()
    output = model.forward_scene_embeddings(waveform)

print("\nScene embedding, shape:", output.size())

Output:

Scene embedding, shape: torch.Size([1, 768])

Get frame-level embeddings

with torch.no_grad():
    model.eval()
    output = model.forward_frame_embeddings(waveform)

print("\nFrame-level embeddings, shape:", output.size())

Output:

Frame-level embeddings, shape: torch.Size([1, 768, 31, 7])

Zenodo

The checkpoint is also available on Zenodo: https://zenodo.org/record/8020843/files/convnext_tiny_471mAP.pth?download=1

Together with a second checkpoint: convnext_tiny_465mAP_BL_AC_70kit.pth

The second model is useful to perform audio captioning on the AudioCaps dataset without training data biases. It was trained the same way as the current model, for audio tagging on AudioSet, but the files from AudioCaps were removed from the AudioSet development set.

Citation

Paper available

Cite as: Pellegrini, T., Khalfaoui-Hassani, I., Labbé, E., Masquelier, T. (2023) Adapting a ConvNeXt Model to Audio Classification on AudioSet. Proc. INTERSPEECH 2023, 4169-4173, doi: 10.21437/Interspeech.2023-1564

@inproceedings{pellegrini23_interspeech,
  author={Thomas Pellegrini and Ismail Khalfaoui-Hassani and Etienne Labb\'e and Timoth\'ee Masquelier},
  title={{Adapting a ConvNeXt Model to Audio Classification on AudioSet}},
  year=2023,
  booktitle={Proc. INTERSPEECH 2023},
  pages={4169--4173},
  doi={10.21437/Interspeech.2023-1564}
}