Create app.py

app.py

@@ -0,0 +1,51 @@
+import gradio as gr
+import torch
+from transformers import pipeline
+
+username = "ardneebwar"  ## Complete your username
+model_id = f"{username}/distilhubert-finetuned-gtzan"
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+pipe = pipeline("audio-classification", model=model_id, device=device)
+
+# def predict_trunc(filepath):
+#     preprocessed = pipe.preprocess(filepath)
+#     truncated = pipe.feature_extractor.pad(preprocessed,truncation=True, max_length = 16_000*30)
+#     model_outputs = pipe.forward(truncated)
+#     outputs = pipe.postprocess(model_outputs)
+
+#     return outputs
+
+
+def classify_audio(filepath):
+    """
+      Goes from
+      [{'score': 0.8339303731918335, 'label': 'country'},
+    {'score': 0.11914275586605072, 'label': 'rock'},]
+     to
+     {"country":  0.8339303731918335, "rock":0.11914275586605072}
+    """
+    preds = pipe(filepath)
+    # preds = predict_trunc(filepath)
+    outputs = {}
+    for p in preds:
+        outputs[p["label"]] = p["score"]
+    return outputs
+
+
+title = "🎵 Music Genre Classifier"
+description = """
+demo to showcase the music
+classification model that we just trained on the [GTZAN](https://huggingface.co/datasets/marsyas/gtzan)
+"""
+
+filenames = ['blues.00098.wav', "disco.00020.wav", "metal.00014.wav", "reggae.00021.wav", "rock.00058.wav"]
+filenames = [[f"./{f}"] for f in filenames]
+demo = gr.Interface(
+    fn=classify_audio,
+    inputs=gr.Audio(type="filepath"),
+    outputs=gr.outputs.Label(),
+    title=title,
+    description=description,
+    examples=filenames,
+)
+demo.launch()