Update DocVoice.py

DocVoice.py

@@ -1,16 +1,54 @@
-import pyttsx3
-
-def text_to_speech(text: str):
-    # Initialize engine
-    engine = pyttsx3.init()
-
-    # Use default voice
-    engine.setProperty('voice', engine.getProperty('voices')[0].id)
-
-    # Speak the text
-    engine.say(text)
-    engine.runAndWait()
-
-# Example usage
-if __name__ == "__main__":
-    text_to_speech("Hello Abdul Moiz! This is your Riaya Tech project speaking.")
+# DocVoice.py
+import torch
+from transformers import pipeline
+
+# -------------------
+# 1️⃣ Detect GPU
+# -------------------
+use_cuda = torch.cuda.is_available()
+device_index = 0 if use_cuda else -1
+device_str = "cuda" if use_cuda else "cpu"
+dtype = torch.float16 if use_cuda else torch.float32
+
+# -------------------
+# 2️⃣ Load TTS model from Hugging Face
+# -------------------
+tts_model_id = "espnet/kan-bayashi_ljspeech_vits"  # Example TTS model, English voice
+
+tts_pipe = pipeline(
+    "text-to-speech",
+    model=tts_model_id,
+    device=device_index,
+    torch_dtype=dtype
+)
+
+print("🔊 TTS pipeline ready using Hugging Face.")
+
+# -------------------
+# 3️⃣ TTS Helper Function
+# -------------------
+def text_to_speech(text: str, filename="assistant_response.wav"):
+    """
+    Generate speech from text and save as WAV file.
+    """
+    if not text.strip():
+        return
+    
+    print(f"📝 Generating audio for: {text}")
+    
+    # Generate audio
+    speech_array = tts_pipe(text)["audio"]
+    
+    # Convert to int16 and save as WAV
+    import numpy as np
+    import scipy.io.wavfile as wav
+
+    wav.write(filename, 22050, (speech_array * 32767).astype(np.int16))
+    print(f"✅ Audio saved as {filename}")
+    
+    # Optional: play audio automatically (requires sounddevice)
+    try:
+        import sounddevice as sd
+        sd.play(speech_array, samplerate=22050)
+    except Exception as e:
+        print(f"⚠️ Could not play audio automatically: {e}")