Update app.py (#1)

- Update app.py (c89f5fe69fe38927219be0c29b98e2f317f2a788)

app.py

@@ -1,67 +1,41 @@
-import gradio as gr
+import os
+import tempfile
 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")  # 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  # 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
+        
+        # Hubi waveform-ka: min, max, shape
+        print(f"Waveform shape: {waveform.shape}, min: {waveform.min()}, max: {waveform.max()}")
+        
+        # Normalize waveform
         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
+        
+        # Hubi faylka la abuuray: path iyo size
+        print(f"Generated WAV file path: {tmp.name}")
+        print(f"WAV file size: {os.path.getsize(tmp.name)} bytes")
+        
         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)}"
-
-demo = gr.Interface(
-    fn=tts,
-    inputs=gr.Textbox(lines=2, placeholder="Gali qoraalkaaga halkan..."),
-    outputs=gr.Audio(type="filepath"),
-    title="Somali TTS with VITS",
-    description="Qor qoraal Soomaali ah, riix 'Submit', oo dhageyso codka Somali native."
-)
-
-demo.launch()