A 1D convolutional ResNet that classifies the raag (melodic framework) of a Hindustani classical music audio clip, across 50 raags.

The backbone is a 10-block 1D residual network operating on raw audio (resampled to 8kHz, 2-channel). Its architecture and pretrained weights originate from a ResNet trained by jeevster for Carnatic raga classification; we ported the backbone into this self-contained transformers-compatible model (no dependency on the original training code) and added a small classification head (Linear(300→64) → BatchNorm1d → ReLU → Dropout(0.2) → Linear(64→50)).

We first trained the new head from scratch on frozen-backbone embeddings (a small hyperparameter sweep over depth/width/batchnorm/dropout). Starting from that trained head, we then unfroze only the backbone's last residual block and fine-tuned it jointly with the head, end-to-end on raw audio (5-second random crops, batch size 8, discriminative learning rates), keeping the rest of the backbone frozen. Training used the 1161-clip train split of neerajaabhyankar/hindustani-raag-small (50 raags), with an 85/15 held-out validation split for early stopping.

On the dataset's 92-clip test split: accuracy 0.163, macro-F1 0.132 (chance level for 50 classes is 0.02).