Updated app.py

app.py

@@ -1,19 +1,21 @@
 from fastai.vision.all import *
 import librosa
 import gradio as gr
+import numpy as np
 
 learn = load_learner('audio_mnist_classifier_v1.pkl')
 categories = ('0', '1', '2', '3', '4', '5', '6', '7', '8', '9')
 
 def mel_spectrogram_tfm(file):
     y, sr = librosa.load(file)
+    y, _ = librosa.effects.trim(y)
     spec = librosa.feature.melspectrogram(y, sr=sr, n_fft=2048, hop_length=512)
     spec_db = librosa.amplitude_to_db(spec, ref=np.max)
     return spec_db
 
 def classify(audio):
-    img = mel_spectrogram_tfm(audio)
-    img = PILImage.resize((225,225))
+    spec_db = mel_spectrogram_tfm(audio)
+    img = PILImage.create(spec_db).resize((225,225))
     pred, idx, prob = learn.predict(img)
     return dict(zip(categories, map(float, prob)))