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import gradio as gr
import torchaudio
import torch
import numpy as np
from huggingface_hub import hf_hub_download
# ✅ Map of model names to files on Hugging Face
RAVE_MODELS = {
"Guitar": "guitar_iil_b2048_r48000_z16.ts",
"Soprano Sax": "sax_soprano_franziskaschroeder_b2048_r48000_z20.ts",
"Organ (Archive)": "organ_archive_b2048_r48000_z16.ts",
"Organ (Bach)": "organ_bach_b2048_r48000_z16.ts",
"Voice Multivoice": "voice-multi-b2048-r48000-z11.ts",
"Birds Dawn Chorus": "birds_dawnchorus_b2048_r48000_z8.ts",
"Magnets": "magnets_b2048_r48000_z8.ts",
"Whale Songs": "humpbacks_pondbrain_b2048_r48000_z20.ts"
}
MODEL_CACHE = {}
def load_rave_model(model_name):
"""Load TorchScript RAVE model from Hugging Face Hub."""
if model_name in MODEL_CACHE:
return MODEL_CACHE[model_name]
model_file = hf_hub_download(
repo_id="Intelligent-Instruments-Lab/rave-models",
filename=RAVE_MODELS[model_name]
)
model = torch.jit.load(model_file, map_location="cpu")
model.eval()
MODEL_CACHE[model_name] = model
return model
def apply_rave(audio, model_name):
"""Apply selected RAVE model to uploaded audio."""
model = load_rave_model(model_name)
# Convert numpy audio to torch tensor
audio_tensor = torch.tensor(audio[0]).unsqueeze(0) # [1, samples]
sr = audio[1]
# ✅ Resample if needed (most RAVE models expect 48kHz)
if sr != 48000:
audio_tensor = torchaudio.functional.resample(audio_tensor, sr, 48000)
sr = 48000
with torch.no_grad():
# ✅ TorchScript models have encode & decode methods
z = model.encode(audio_tensor)
processed_audio = model.decode(z)
return (processed_audio.squeeze().cpu().numpy(), sr)
# 🎛 Gradio UI
with gr.Blocks() as demo:
gr.Markdown("## 🎛 RAVE Style Transfer on Stems")
gr.Markdown("Upload audio, pick a RAVE model, and get a transformed version.")
with gr.Row():
audio_input = gr.Audio(type="numpy", label="Upload Audio", sources=["upload", "microphone"])
model_selector = gr.Dropdown(list(RAVE_MODELS.keys()), label="Select Style", value="Guitar")
with gr.Row():
output_audio = gr.Audio(type="numpy", label="Transformed Audio")
process_btn = gr.Button("Apply Style Transfer")
process_btn.click(fn=apply_rave, inputs=[audio_input, model_selector], outputs=output_audio)
demo.launch()
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