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.gitattributes

@@ -33,3 +33,9 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+examples/blues.wav filter=lfs diff=lfs merge=lfs -text
+examples/classical.wav filter=lfs diff=lfs merge=lfs -text
+examples/hiphop.wav filter=lfs diff=lfs merge=lfs -text
+examples/metal.wav filter=lfs diff=lfs merge=lfs -text
+examples/pop.wav filter=lfs diff=lfs merge=lfs -text
+examples/reggae.wav filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,21 @@
+import gradio as gr
+import numpy as np
+from transformers import pipeline
+import os
+
+example_list = [["examples/" + example] for example in os.listdir("examples")]
+
+classifier = pipeline('audio-classification', model='SamuelM0422/distilhubert-finetuned-gtzan')
+
+title = 'Music Classification 🎙️'
+description = 'A distilhubert model finetuned at gtzan dataset to classify music genres'
+
+def predict(example):
+  #print(type(example))
+  example = {'array': np.array(example[1], dtype=np.float32), 'sampling_rate': example[0]}
+  pred = classifier(example)
+  return {p['label']: p['score'] for p in pred}
+
+demo = gr.Interface(fn=predict, title=title, description=description,
+                    inputs="audio", outputs="label", examples=example_list, flagging_mode='never')
+demo.launch()

examples/blues.wav

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examples/classical.wav

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examples/hiphop.wav

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examples/metal.wav

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examples/pop.wav

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examples/reggae.wav

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requirements.txt

@@ -0,0 +1,4 @@
+gradio
+transformers
+datasets
+torchaudio