File size: 1,039 Bytes
1c045db | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | import torch, numpy as np, librosa
from transformers import ASTForAudioClassification, ASTFeatureExtractor
SR=16000; SEG_LEN=SR*14; HOP_LEN=SR*7
def rms_normalize(x):
return x*(0.15/(np.sqrt((x**2).mean())+1e-8))
def get_segments(y):
segs=[]
for s in range(0,len(y)-SEG_LEN+1,HOP_LEN):
segs.append(y[s:s+SEG_LEN])
if not segs: segs=[y[:SEG_LEN]]
return segs
class Model:
def __init__(self, path):
self.fe = ASTFeatureExtractor.from_pretrained(path)
self.model = ASTForAudioClassification.from_pretrained(path)
self.model.eval()
def __call__(self, fp):
y,_=librosa.load(fp,sr=SR)
if len(y)<SEG_LEN:
y=np.tile(y,int(np.ceil(SEG_LEN/len(y))))
probs=[]
for seg in get_segments(y):
seg=rms_normalize(seg)
x=self.fe(seg,sampling_rate=SR,return_tensors="pt")["input_values"]
p=torch.softmax(self.model(x).logits,dim=1).detach().numpy()[0]
probs.append(p)
return np.median(probs,0)
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