Create app.py

app.py

@@ -0,0 +1,79 @@
+import torch
+from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
+from fastapi import FastAPI, UploadFile, File, HTTPException
+import uvicorn
+import os
+import shutil
+
+app = FastAPI()
+
+# --- CONFIGURATION ---
+# 🚨 POINTING TO YOUR REBRANDED MODEL
+MODEL_ID = "metanthropic/neural-voice-v1" 
+device = "cpu" # Free tier is CPU-only
+torch_dtype = torch.float32
+
+print(f"🔹 Loading Sovereign Model: {MODEL_ID}...")
+
+try:
+    # 1. Load Model
+    model = AutoModelForSpeechSeq2Seq.from_pretrained(
+        MODEL_ID, 
+        torch_dtype=torch_dtype, 
+        low_cpu_mem_usage=True, 
+        use_safetensors=True
+    )
+    model.to(device)
+
+    # 2. Load Processor
+    processor = AutoProcessor.from_pretrained(MODEL_ID)
+
+    # 3. Create Pipeline
+    pipe = pipeline(
+        "automatic-speech-recognition",
+        model=model,
+        tokenizer=processor.tokenizer,
+        feature_extractor=processor.feature_extractor,
+        max_new_tokens=128,
+        chunk_length_s=15,
+        batch_size=16,
+        torch_dtype=torch_dtype,
+        device=device,
+    )
+    print("✅ Model Loaded Successfully.")
+except Exception as e:
+    print(f"❌ Error loading model: {e}")
+    raise e
+
+@app.get("/")
+def home():
+    return {"status": "Metanthropic Neural Voice Node Online", "model": MODEL_ID}
+
+@app.post("/transcribe")
+async def transcribe(file: UploadFile = File(...)):
+    # Create a temporary file to store the upload
+    temp_filename = f"temp_{file.filename}"
+    
+    try:
+        # Save uploaded file
+        with open(temp_filename, "wb") as buffer:
+            shutil.copyfileobj(file.file, buffer)
+
+        # Run Inference (The Magic)
+        print(f"🎙️ Transcribing {temp_filename}...")
+        result = pipe(temp_filename)
+        text = result["text"]
+        
+        return {"text": text.strip()}
+
+    except Exception as e:
+        print(f"❌ Transcription Error: {e}")
+        return {"error": str(e), "text": ""} # Return empty text on error
+        
+    finally:
+        # Cleanup temp file
+        if os.path.exists(temp_filename):
+            os.remove(temp_filename)
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=7860)