Create app.py

app.py

@@ -0,0 +1,70 @@
+import os
+import torch
+import torchaudio
+import librosa
+import numpy as np
+import io
+import tempfile
+from flask import Flask, request, send_file, jsonify
+from flask_cors import CORS
+from audiocraft.models import MusicGen
+
+app = Flask(__name__)
+CORS(app)
+
+class FusionEngine:
+    def __init__(self):
+        self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
+        self.model = MusicGen.get_pretrained('facebook/musicgen-small')
+
+    def process(self, melody_bytes, style_bytes):
+        with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as m_file:
+            m_file.write(melody_bytes)
+            m_path = m_file.name
+        with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as s_file:
+            s_file.write(style_bytes)
+            s_path = s_file.name
+        try:
+            y2, sr2 = librosa.load(s_path, duration=10)
+            tempo_val, _ = librosa.beat.beat_track(y=y2, sr=sr2)
+            tempo = float(tempo_val[0]) if isinstance(tempo_val, (np.ndarray, list)) else float(tempo_val)
+            spec_centroid = np.mean(librosa.feature.spectral_centroid(y=y2, sr=sr2))
+            vibe = "electronic" if spec_centroid > 2500 else "organic"
+            accurate_prompt = f"A {vibe} version, {int(tempo)} BPM, studio quality."
+            self.model.set_generation_params(duration=15, use_sampling=True, top_k=250, temperature=0.7)
+            m_wav, sr = torchaudio.load(m_path)
+            if m_wav.shape[0] > 1: m_wav = m_wav.mean(dim=0, keepdim=True)
+            if sr != 32000:
+                resampler = torchaudio.transforms.Resample(sr, 32000)
+                m_wav = resampler(m_wav)
+                sr = 32000
+            result = self.model.generate_with_chroma(descriptions=[accurate_prompt], melody_wavs=m_wav[None, ...].to(self.device), melody_sample_rate=sr)
+            return result[0].cpu(), self.model.sample_rate
+        finally:
+            if os.path.exists(m_path): os.remove(m_path)
+            if os.path.exists(s_path): os.remove(s_path)
+
+engine = None
+
+@app.route('/', methods=['GET'])
+def health():
+    return jsonify({"status": "ready"}), 200
+
+@app.route('/fuse', methods=['POST'])
+def fuse():
+    global engine
+    if engine is None: engine = FusionEngine()
+    try:
+        m = request.files['melody'].read()
+        s = request.files['style'].read()
+        out_wav, sr = engine.process(m, s)
+        buffer = io.BytesIO()
+        torchaudio.save(buffer, out_wav, sr, format="wav")
+        buffer.seek(0)
+        return send_file(buffer, mimetype='audio/wav')
+    except Exception as e:
+        return jsonify({"error": str(e)}), 500
+
+if __name__ == "__main__":
+    port = int(os.environ.get("PORT", 7860))
+    app.run(host='0.0.0.0', port=port)