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import timm
import json
import torch
from torchaudio.functional import resample
import numpy as np
from torchaudio.compliance import kaldi
import torch.nn.functional as F
import requests
TAG = "gaunernst/vit_base_patch16_1024_128.audiomae_as2m_ft_as20k"
MODEL = timm.create_model(f"hf_hub:{TAG}", pretrained=True).eval()
LABEL_URL = "https://huggingface.co/datasets/huggingface/label-files/raw/main/audioset-id2label.json"
AUDIOSET_LABELS = list(json.loads(requests.get(LABEL_URL).content).values())
SAMPLING_RATE = 16_000
MEAN = -4.2677393
STD = 4.5689974
def preprocess(x: torch.Tensor):
x = x - x.mean()
melspec = kaldi.fbank(x.unsqueeze(0), htk_compat=True, window_type="hanning", num_mel_bins=128)
if melspec.shape[0] < 1024:
melspec = F.pad(melspec, (0, 0, 0, 1024 - melspec.shape[0]))
else:
melspec = melspec[:1024]
melspec = (melspec - MEAN) / (STD * 2)
return melspec
def predict_class(x: np.ndarray):
x = torch.from_numpy(x)
if x.ndim > 1:
x = x.mean(-1)
assert x.ndim == 1
x = preprocess(x)
with torch.inference_mode():
logits = MODEL(x.view(1, 1, 1024, 128)).squeeze(0)
topk_probs, topk_classes = logits.sigmoid().topk(10)
preds = [[AUDIOSET_LABELS[cls], prob.item()*100] for cls, prob in zip(topk_classes, topk_probs)]
return preds |