sarayusapa/sam-carnatic

SAM-Audio: Carnatic Raga Classifier

A CNN + Segment Attention model for classifying Carnatic ragas from audio.

Model Details

• Architecture: SAM-Audio (CNN mel-spectrogram encoder + latent segmentation tokens + masked segment prediction + contrastive learning)
• Parameters: 2.6M
• Training data: sarayusapa/carnatic-ragas with 3x pitch-shift augmentation
• Best validation accuracy: 99.62%
• Best epoch: 17

Supported Ragas

ID	Raga
0	Amritavarshini
1	Hamsanaadam
2	Kalyani
3	Kharaharapriya
4	Mayamalavagoulai
5	Sindhubhairavi
6	Todi
7	Varali

Usage

import torch
import librosa
from safetensors.torch import load_file

# Load model
from train import SAMAudioModel

config = json.load(open("config.json"))
model = SAMAudioModel(
    encoder_config=config["encoder"],
    num_classes=config["num_classes"],
    num_segments=config["num_segments"],
)
state_dict = load_file("model.safetensors")
model.load_state_dict(state_dict)
model.eval()

# Load audio
y, sr = librosa.load("audio.mp3", sr=16000, mono=True)
waveform = torch.from_numpy(y[:320000]).float().unsqueeze(0)

# Predict
with torch.no_grad():
    outputs = model(input_audio=waveform)
    probs = torch.softmax(outputs["raga_logits"], dim=-1)
    pred = probs.argmax(dim=-1).item()
    print(f"Predicted: {config['id2label'][str(pred)]} ({probs[0][pred]:.1%})")

Training

• 3x pitch-shift augmentation (original + random up [1-4 semitones] + random down [1-4 semitones])
• Tanpura reference pitch shifts with audio, forcing the model to learn relative intervals
• BFloat16 mixed precision on RTX 4090
• Cosine annealing LR with warmup
• Early stopping with patience=5