Improve speed on CPU only

app.py

@@ -48,12 +48,14 @@ if torch.cuda.is_available():
     torch_dtype = torch.float16
     speech_model_id = "openai/whisper-large-v3-turbo"
     batch_size = 16
+    stride_length_s_tuple = (4, 2)
 else:
     model_device = "cpu"
     pipeline_device = -1  # CPU for pipeline.
     torch_dtype = torch.float32
     speech_model_id = "openai/whisper-tiny"
     batch_size = 2
+    stride_length_s_tuple = None
 
 try:
     model = AutoModelForSpeechSeq2Seq.from_pretrained(
@@ -85,7 +87,7 @@ def transcribe_audio(audio_path: str, batch_size: int) -> Dict[str, Any]:
         result = pipe(
             audio_path,
             chunk_length_s=10,
-            stride_length_s=(4, 2),
+            stride_length_s=stride_length_s_tuple,
             batch_size=batch_size,
             return_timestamps=True,
         )
@@ -198,8 +200,12 @@ def process_video(video_url: str):
     srt_text = convert_to_srt(transcribed_text)
     
     # Generate chapters from the SRT.
-    chapters = generate_chapters(srt_text)
-    
+    response = generate_chapters(srt_text)
+
+    # Extract only the chapters part and add a footer
+    cleaned_text = response.split("Chapters:")[1] if "Chapters:" in response else response
+    chapters = f"{cleaned_text.strip()}\n\nGenerated using free 'GenAI ChapterCraft' tool."
+
     return srt_text, chapters
 
 # -------------------------------------------