diff --git a/api/src/inference/base.py b/api/src/inference/base.py
index 93fcf69..5cb84dc 100644
--- a/api/src/inference/base.py
+++ b/api/src/inference/base.py
@@ -11,7 +11,7 @@ class AudioChunk:
     
     def __init__(self,
                  audio: np.ndarray,
-                 word_timestamps: Optional[List]=None
+                 word_timestamps: Optional[List]=[]
                  ):
         self.audio=audio
         self.word_timestamps=word_timestamps
diff --git a/api/src/services/audio.py b/api/src/services/audio.py
index 8bc99bb..f8b765a 100644
--- a/api/src/services/audio.py
+++ b/api/src/services/audio.py
@@ -57,7 +57,6 @@ class AudioNormalizer:
         non_silent_index_start, non_silent_index_end = None,None 
 
         for X in range(0,len(audio_data)):
-            #print(audio_data[X])
             if audio_data[X] > amplitude_threshold:
                 non_silent_index_start=X
                 break
@@ -149,11 +148,9 @@ class AudioService:
             if normalizer is None:
                 normalizer = AudioNormalizer()
             
-            print("1")
             audio_chunk.audio = await normalizer.normalize(audio_chunk.audio)
-            print("2")
             audio_chunk = AudioService.trim_audio(audio_chunk,chunk_text,speed,is_last_chunk,normalizer)
-            print("3")
+
             # Get or create format-specific writer
             writer_key = f"{output_format}_{sample_rate}"
             if is_first_chunk or writer_key not in AudioService._writers:
@@ -161,18 +158,16 @@ class AudioService:
                     output_format, sample_rate
                 )
             writer = AudioService._writers[writer_key]
-            print("4")
+            
             # Write audio data first
             if len(audio_chunk.audio) > 0:
                 chunk_data = writer.write_chunk(audio_chunk.audio)
-            print("5")
+
             # Then finalize if this is the last chunk
             if is_last_chunk:
-                print("6")
                 final_data = writer.write_chunk(finalize=True)
-                print("7")
                 del AudioService._writers[writer_key]
-                return final_data if final_data else b""
+                return final_data if final_data else b"", audio_chunk
 
             return chunk_data if chunk_data else b"", audio_chunk
 
@@ -206,8 +201,10 @@ class AudioService:
         start_index,end_index=normalizer.find_first_last_non_silent(audio_chunk.audio,chunk_text,speed,is_last_chunk=is_last_chunk)
         
         audio_chunk.audio=audio_chunk.audio[start_index:end_index]
-        for timestamp in audio_chunk.word_timestamps:
-            timestamp["start_time"]-=start_index * 24000
-            timestamp["end_time"]-=start_index * 24000
+        
+        if audio_chunk.word_timestamps is not None:
+            for timestamp in audio_chunk.word_timestamps:
+                timestamp["start_time"]-=start_index / 24000
+                timestamp["end_time"]-=start_index / 24000
         return audio_chunk
     
\ No newline at end of file
diff --git a/api/src/services/tts_service.py b/api/src/services/tts_service.py
index 67e2641..0fcaac7 100644
--- a/api/src/services/tts_service.py
+++ b/api/src/services/tts_service.py
@@ -6,6 +6,7 @@ import tempfile
 import time
 from typing import AsyncGenerator, List, Optional, Tuple, Union
 
+from ..inference.base import AudioChunk
 import numpy as np
 import torch
 from kokoro import KPipeline
@@ -62,9 +63,8 @@ class TTSService:
                     if not output_format:
                         yield np.array([], dtype=np.float32)
                         return
-
-                    result = await AudioService.convert_audio(
-                        np.array([0], dtype=np.float32),  # Dummy data for type checking
+                    result, _ = await AudioService.convert_audio(
+                        AudioChunk(np.array([0], dtype=np.float32)),  # Dummy data for type checking
                         24000,
                         output_format,
                         speed,
@@ -119,7 +119,7 @@ class TTSService:
                             print(chunk_data.word_timestamps)
                             yield chunk_data.audio
                 else:
-                    print("old backend")
+
                     # For legacy backends, load voice tensor
                     voice_tensor = await self._voice_manager.load_voice(
                         voice_name, device=backend.device
@@ -315,7 +315,8 @@ class TTSService:
 
         except Exception as e:
             logger.error(f"Error in phoneme audio generation: {str(e)}")
-            raise
+            raise e
+        
 
     async def generate_audio(
         self,