Create app.py

app.py

@@ -0,0 +1,73 @@
+import gradio as gr
+import torchaudio
+import torch
+import os
+from rave import RAVE  # Assuming rave.py or pip package is available
+from huggingface_hub import hf_hub_download
+
+# ✅ Available RAVE models (can expand dynamically from HF repo)
+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 a RAVE model from Hugging Face or cache."""
+    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 = RAVE.load(model_file)  # RAVE.load assumes wrapper for loading .ts file
+    model.eval()
+    MODEL_CACHE[model_name] = model
+    return model
+
+def apply_rave(audio, model_name):
+    """Apply selected RAVE style transfer model to uploaded audio."""
+    model = load_rave_model(model_name)
+
+    # Convert numpy audio (from Gradio) to torch tensor
+    audio_tensor = torch.tensor(audio[0]).unsqueeze(0)  # [1, samples]
+    sr = audio[1]
+
+    if sr != 48000:
+        audio_tensor = torchaudio.functional.resample(audio_tensor, sr, 48000)
+        sr = 48000
+
+    # Pass through model (encode -> decode)
+    with torch.no_grad():
+        z = model.encode(audio_tensor)
+        processed_audio = model.decode(z)
+
+    processed_audio = processed_audio.squeeze().cpu().numpy()
+    return (processed_audio, sr)
+
+# 🎛 Gradio Interface
+with gr.Blocks() as demo:
+    gr.Markdown("## 🎛 RAVE Style Transfer on Stems")
+    gr.Markdown("Upload audio, select 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")
+
+    # API + UI trigger
+    process_btn = gr.Button("Apply Style Transfer")
+    process_btn.click(fn=apply_rave, inputs=[audio_input, model_selector], outputs=output_audio)
+
+demo.launch()