Update app.py

app.py

@@ -2,32 +2,56 @@ import gradio as gr
 import torch
 import soundfile as sf
 import tempfile
+import os
 from transformers import AutoTokenizer, VitsModel
 import numpy as np
 
+# Bedel username/repo-gaaga saxda ah
 repo_id = "jellecali8/Somali_tts_model"
 
+# Load tokenizer iyo model
 tokenizer = AutoTokenizer.from_pretrained(repo_id)
 model = VitsModel.from_pretrained(repo_id)
 model.eval()
 
+# Load custom speaker embedding (.npy) file path
 try:
-    custom_embedding_np = np.load("somali_speaker_embedding.npy")
+    custom_embedding_np = np.load("somali_speaker_embedding.npy")  # Ku dar faylka Space folder-ka
     custom_embedding = torch.tensor(custom_embedding_np, dtype=torch.float32).unsqueeze(0)
 except Exception:
+    # Haddii embedding file ma jiro, isticmaal random tensor (kaliya tijaabo)
     custom_embedding = torch.randn(1, 256)
 
 def tts(text):
     try:
         if not text.strip():
-            return None
-        inputs = tokenizer(text, return_tensors="pt").to(next(model.parameters()).device)
-        custom_embedding_ = custom_embedding.to(next(model.parameters()).device)
+            return None  # Qoraal madhan ha soo gelin
+        # U gudbi inputs iyo embedding device-ka model-ka (CPU/GPU)
+        device = next(model.parameters()).device
+        inputs = tokenizer(text, return_tensors="pt").to(device)
+        custom_embedding_ = custom_embedding.to(device)
         with torch.no_grad():
             outputs = model(**inputs, speaker_embeddings=custom_embedding_)
             waveform = outputs.waveform.squeeze().cpu().numpy()
+
+        # Normalize waveform si uu u dhex maro -1.0 ilaa 1.0
+        max_val = max(abs(waveform.max()), abs(waveform.min()))
+        if max_val > 0:
+            waveform = waveform / max_val
+        else:
+            # Haddii waveform dhan yahay eber, soo celi error ama None
+            print("Warning: Waveform is all zeros")
+            return None
+
+        # Kaydi waveform file .wav ah
         tmp = tempfile.NamedTemporaryFile(delete=False, suffix=".wav")
         sf.write(tmp.name, waveform, 16000)
+
+        # Hubi file size si loo xaqiijiyo inuu sax yahay
+        if os.path.getsize(tmp.name) == 0:
+            print("Warning: Generated WAV file is empty")
+            return None
+
         return tmp.name
     except Exception as e:
         return f"Error: {str(e)}"