model inferencing

.gitignore

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+.env

handler.py

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+import base64, io
+import numpy as np
+import soundfile as sf
+
+class EndpointHandler:
+    def __init__(self, path=""):
+        # Later: load ACE-Step pipeline/model here once.
+        self.ready = True
+
+    def __call__(self, data):
+        inputs = data.get("inputs", data)
+        duration = int(inputs.get("duration_sec", 6))
+        sr = int(inputs.get("sample_rate", 44100))
+        seed = int(inputs.get("seed", 42))
+
+        rng = np.random.default_rng(seed)
+        t = np.linspace(0, duration, int(sr * duration), endpoint=False)
+        audio = (0.07*np.sin(2*np.pi*440*t) + 0.01*rng.standard_normal(len(t))).astype(np.float32)
+
+        buf = io.BytesIO()
+        sf.write(buf, audio, sr, format="WAV")
+        return {
+            "audio_base64_wav": base64.b64encode(buf.getvalue()).decode("utf-8"),
+            "sample_rate": sr,
+            "duration_sec": duration
+        }