Create app.py

app.py

@@ -0,0 +1,37 @@
+import os
+import io
+from fastapi import FastAPI
+from pydantic import BaseModel
+from transformers import AutoProcessor, VitsForConditionalGeneration
+import torch
+from fastapi.responses import StreamingResponse
+
+# Use /tmp for cache to avoid permission errors
+os.environ["HF_HOME"] = "/tmp"
+
+app = FastAPI()
+
+# Load processor and model once on startup
+model_name = "Somali-tts/somali_tts_model"
+processor = AutoProcessor.from_pretrained(model_name)
+model = VitsForConditionalGeneration.from_pretrained(model_name)
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+
+class TextInput(BaseModel):
+    inputs: str
+
+@app.post("/synthesize")
+async def synthesize_tts(data: TextInput):
+    inputs = processor(data.inputs, return_tensors="pt").to(device)
+    with torch.no_grad():
+        audio = model.generate(**inputs)
+    audio = audio.squeeze().cpu().numpy()
+
+    # Convert to WAV bytes in-memory
+    import soundfile as sf
+    buf = io.BytesIO()
+    sf.write(buf, audio, samplerate=22050, format="WAV")
+    buf.seek(0)
+
+    return StreamingResponse(buf, media_type="audio/wav")