full-code commited

app.py

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+ 
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torchaudio
+import torchaudio.transforms as T
+import torchvision.models as models
+import gradio as gr
+import numpy as np
+import os
+ 
+SAMPLE_RATE = 22050
+CROP_SEC = 6.0
+CROP_LEN = int(SAMPLE_RATE * CROP_SEC)
+N_MELS = 128
+N_FFT = 2048
+HOP_LENGTH = 512
+
+GENRES = sorted(["blues", "classical", "country", "disco", "hiphop",
+                 "jazz", "metal", "pop", "reggae", "rock"])
+GENRE2ID = {g: i for i, g in enumerate(GENRES)}
+ID2GENRE = {i: g for i, g in enumerate(GENRES)}
+
+DEVICE = torch.device("cpu")  
+class PretrainedEfficientNet(nn.Module):
+    def __init__(self, num_classes=10):
+        super().__init__()
+        self.net = models.efficientnet_b0(weights=None)   
+        old = self.net.features[0][0]
+        self.net.features[0][0] = nn.Conv2d(
+            1, old.out_channels, kernel_size=old.kernel_size,
+            stride=old.stride, padding=old.padding, bias=False)
+        self.net.classifier[1] = nn.Linear(
+            self.net.classifier[1].in_features, num_classes)
+
+    def forward(self, x):
+        return self.net(x)
+ 
+model = PretrainedEfficientNet(num_classes=10)
+weights_path = os.path.join(os.path.dirname(__file__), "best_effnet.pth")
+state_dict = torch.load(weights_path, map_location=DEVICE, weights_only=True)
+model.load_state_dict(state_dict)
+model.eval()
+model.to(DEVICE)
+ 
+mel_transform = T.MelSpectrogram(
+    sample_rate=SAMPLE_RATE, n_fft=N_FFT,
+    hop_length=HOP_LENGTH, n_mels=N_MELS)
+db_transform = T.AmplitudeToDB()
+
+
+def preprocess_audio(audio_tuple): 
+    sr, waveform_np = audio_tuple
+ 
+    waveform = torch.tensor(waveform_np, dtype=torch.float32)
+ 
+    if waveform.dim() == 2: 
+        waveform = waveform.mean(dim=-1)
+    waveform = waveform.unsqueeze(0)   
+ 
+    if waveform.abs().max() > 2.0:
+        waveform = waveform / 32768.0
+ 
+    if sr != SAMPLE_RATE:
+        waveform = torchaudio.functional.resample(waveform, sr, SAMPLE_RATE)
+
+    return waveform
+
+
+def crop_or_pad(waveform, length): 
+    if waveform.shape[1] >= length:
+        start = (waveform.shape[1] - length) // 2  
+        return waveform[:, start:start + length]
+    return F.pad(waveform, (0, length - waveform.shape[1]))
+
+
+def get_tta_crops(waveform, crop_len): 
+    crops = []
+    total = waveform.shape[1]
+    if total <= crop_len:
+        padded = F.pad(waveform, (0, crop_len - total))
+        return [padded]
+
+    crops.append(waveform[:, :crop_len])                           
+    mid = (total - crop_len) // 2
+    crops.append(waveform[:, mid:mid + crop_len])                  
+    crops.append(waveform[:, -crop_len:])                           
+    return crops
+
+
+def wave_to_mel(wave): 
+    mel = mel_transform(wave)
+    mel_db = db_transform(mel)
+    mel_db = (mel_db - mel_db.mean()) / (mel_db.std() + 1e-6)
+    return mel_db
+ 
+@torch.no_grad()
+def predict_genre(audio): 
+    if audio is None:
+        return {g: 0.0 for g in GENRES}
+
+    waveform = preprocess_audio(audio)
+    crops = get_tta_crops(waveform, CROP_LEN)
+ 
+    avg_probs = torch.zeros(10)
+    for crop in crops:
+        mel = wave_to_mel(crop).unsqueeze(0).to(DEVICE)  
+        logits = model(mel)
+        probs = torch.softmax(logits, dim=1).squeeze(0).cpu()
+        avg_probs += probs
+    avg_probs /= len(crops)
+
+    result = {GENRES[i]: float(avg_probs[i]) for i in range(10)}
+    return result
+ 
+DESCRIPTION = """
+## Messy Mashup — Music Genre Classifier
+
+Upload a music clip or record from your microphone and the AI will 
+identify the genre from 10 categories: **Blues, Classical, Country, Disco, 
+HipHop, Jazz, Metal, Pop, Reggae, Rock**.
+
+### How it works
+- **Model:** EfficientNet-B0 fine-tuned on 10,000+ synthetic mashups
+- **Test-Time Augmentation:** 3 crops (start, middle, end) averaged for robustness
+- **Training Score:** 0.90 Macro F1
+
+*Built for BSDA2001P: Introduction to DL and GenAI — IIT Madras*
+"""
+
+demo = gr.Interface(
+    fn=predict_genre,
+    inputs=gr.Audio(
+        label="Upload or Record Audio",
+        type="numpy"
+    ),
+    outputs=gr.Label(
+        num_top_classes=10,
+        label="Genre Prediction"
+    ),
+    title="Messy Mashup Genre Classifier",
+    description=DESCRIPTION,
+    theme=gr.themes.Soft(
+        primary_hue="violet",
+        secondary_hue="blue",
+    ),
+    allow_flagging="never",
+    analytics_enabled=False,
+)
+
+if __name__ == "__main__":
+    demo.launch()

best_effnet.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:fc8bc0ba0e496a4d9a0100954ed71b43f5935bbfccd84b825a40d7d98d9ca305
+size 16388071

requirements.txt

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+torch
+torchaudio
+torchvision
+gradio>=4.0
+numpy