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app.py

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+import gradio as gr
+import torch
+import librosa
+import numpy as np
+from transformers import ASTFeatureExtractor, ASTForAudioClassification
+
+# ── CONFIG ──────────────────────────────────────────────────────────────────
+HF_REPO    = "kashishvijayvergiya/music-genre-ast"   # your HF repo
+SAMPLE_RATE = 16000
+DURATION    = 20
+MAX_LENGTH  = SAMPLE_RATE * DURATION
+N_TTA       = 5
+
+GENRES   = ["blues", "classical", "country", "disco", "hiphop",
+            "jazz", "metal", "pop", "reggae", "rock"]
+id2label = {i: g for i, g in enumerate(GENRES)}
+
+GENRE_EMOJI = {
+    "blues": "🎸", "classical": "🎻", "country": "🤠", "disco": "🪩",
+    "hiphop": "🎤", "jazz": "🎺", "metal": "🤘", "pop": "🎵",
+    "reggae": "🌴", "rock": "🔥"
+}
+
+# ── LOAD MODEL (once at startup) ────────────────────────────────────────────
+print("Loading model...")
+feature_extractor = ASTFeatureExtractor.from_pretrained(HF_REPO)
+model = ASTForAudioClassification.from_pretrained(HF_REPO)
+model.eval()
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+model.to(DEVICE)
+print(f"Model ready on {DEVICE}!")
+
+# ── AUDIO HELPERS ───────────────────────────────────────────────────────────
+def load_audio(path):
+    y, _ = librosa.load(path, sr=SAMPLE_RATE, mono=True)
+    return y.astype(np.float32)
+
+def normalize(y):
+    return y / (np.max(np.abs(y)) + 1e-6)
+
+def random_crop(y):
+    if len(y) >= MAX_LENGTH:
+        start = np.random.randint(0, len(y) - MAX_LENGTH)
+        return y[start:start + MAX_LENGTH]
+    return np.pad(y, (0, MAX_LENGTH - len(y)))
+
+def center_crop(y):
+    if len(y) >= MAX_LENGTH:
+        start = (len(y) - MAX_LENGTH) // 2
+        return y[start:start + MAX_LENGTH]
+    return np.pad(y, (0, MAX_LENGTH - len(y)))
+
+# ── PREDICTION WITH TTA ─────────────────────────────────────────────────────
+def predict(audio_path):
+    if audio_path is None:
+        return "Please upload an audio file.", None
+
+    try:
+        audio = load_audio(audio_path)
+    except Exception as e:
+        return f"Error loading audio: {e}", None
+
+    # TTA: center crop + N_TTA-1 random crops
+    crops = [center_crop(audio)]
+    for _ in range(N_TTA - 1):
+        crops.append(random_crop(audio))
+
+    all_probs = []
+    for crop in crops:
+        crop = normalize(crop)
+        inputs = feature_extractor(
+            crop, sampling_rate=SAMPLE_RATE, return_tensors="pt"
+        )
+        input_values = inputs["input_values"].to(DEVICE)
+        with torch.no_grad():
+            logits = model(input_values=input_values).logits
+            probs  = torch.softmax(logits, dim=1).cpu().numpy()
+        all_probs.append(probs)
+
+    avg_probs   = np.mean(all_probs, axis=0)[0]          # shape: (10,)
+    pred_idx    = int(np.argmax(avg_probs))
+    pred_genre  = id2label[pred_idx]
+    confidence  = float(avg_probs[pred_idx]) * 100
+
+    # Build label dict for Gradio bar chart
+    label_probs = {
+        f"{GENRE_EMOJI.get(id2label[i], '')} {id2label[i].capitalize()}": float(avg_probs[i])
+        for i in range(len(GENRES))
+    }
+
+    result = f"## {GENRE_EMOJI.get(pred_genre, '')} {pred_genre.capitalize()}\n**Confidence: {confidence:.1f}%**"
+    return result, label_probs
+
+# ── GRADIO UI ────────────────────────────────────────────────────────────────
+with gr.Blocks(title="🎵 Music Genre Classifier") as demo:
+    gr.Markdown(
+        """
+        # 🎵 Music Genre Classifier
+        Upload any music file and the model will predict its genre.
+        Supports: blues, classical, country, disco, hip-hop, jazz, metal, pop, reggae, rock.
+
+        *Model: Fine-tuned Audio Spectrogram Transformer (AST) · TTA x5*
+        """
+    )
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            audio_input = gr.Audio(
+                label="Upload Audio",
+                type="filepath",
+                sources=["upload", "microphone"]
+            )
+            predict_btn = gr.Button("🎯 Predict Genre", variant="primary")
+
+        with gr.Column(scale=1):
+            result_md   = gr.Markdown(label="Prediction")
+            prob_chart  = gr.Label(label="Genre Probabilities", num_top_classes=10)
+
+    predict_btn.click(
+        fn      = predict,
+        inputs  = [audio_input],
+        outputs = [result_md, prob_chart]
+    )
+
+    gr.Examples(
+        examples    = [],          # add example audio paths here if you have them
+        inputs      = [audio_input],
+        label       = "Examples"
+    )
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+gradio>=4.0.0
+transformers>=4.36.0
+torch>=2.0.0
+torchaudio>=2.0.0
+librosa>=0.10.0
+numpy>=1.24.0