from transformers import AutoProcessor, MusicgenForConditionalGeneration
from peft import PeftModel
import torch, soundfile as sf

processor  = AutoProcessor.from_pretrained("YourUsername/saraga-dreambooth-musicgen")
base_model = MusicgenForConditionalGeneration.from_pretrained("facebook/musicgen-small")
model      = PeftModel.from_pretrained(base_model, "YourUsername/saraga-dreambooth-musicgen")
model      = model.to("cuda").eval()

inputs = processor(text=["sks0 Calm, Carnatic, Flute"], return_tensors="pt").to("cuda")

with torch.no_grad():
    audio = model.generate(**inputs, max_new_tokens=512, guidance_scale=5.0)

sf.write("output.wav", audio[0, 0].cpu().numpy(), samplerate=32000)

Training Details

Parameter	Value
Base model	facebook/musicgen-small
Method	Audio DreamBooth + LoRA
LoRA rank (r)	32
LoRA alpha	64
Target modules	q_proj, v_proj
Dataset	DevPanda004/saraga (90 clips)
Sample rate	32000 Hz
Clip length	~15 seconds
Epochs	50
Optimizer	AdamW + CosineAnnealingLR
Training loss	Instance loss + Prior loss

Prompting Tips

Bound timbre + style: sks0 Calm, Carnatic, Flute
Cross-style transfer: sks0 Hindustani — tests if timbre survives style change
Baseline comparison: omit the sks token to hear the generic base model output

Limitations

Trained on 90 clips — a small dataset; may not generalise to all styles
sks tokens are arbitrary — only meaningful with this specific adapter
Based on musicgen-small; larger base models may produce better quality

License

CC-BY-NC-4.0 — free for non-commercial use with attribution.

sathyavgc/saraga-dreambooth-musicgen

Saraga DreamBooth — MusicGen LoRA

Usage

Training Details

Prompting Tips

Limitations

License

sathyavgc/
saraga-dreambooth-musicgen