refactor: improve ASR pipeline initialization and transcription handling

src/speech_to_text.py

@@ -4,33 +4,28 @@ import os
 
 # Initialize the ASR pipeline with a specific model
 # Using a smaller Whisper model for quicker setup, but larger models offer better accuracy
-asr_pipeline = pipeline("automatic-speech-recognition", model="openai/whisper-tiny.en")
+asr_pipeline = pipeline(
+    "automatic-speech-recognition",
+    model="openai/whisper-tiny.en",
+)
 
 def transcribe_audio(audio_filepath):
     """
     Transcribes an audio file using the Hugging Face ASR pipeline.
     """
     try:
-        transcription = asr_pipeline(audio_filepath)
+        transcription = asr_pipeline(audio_filepath, return_timestamps=True)
         return transcription["text"]
     except Exception as e:
         return f"Error during transcription: {e}"
 
 # Example usage:
 if __name__ == "__main__":
-    audio_file = "./downloaded_files/99c9cc74-fdc8-46c6-8f8d-3ce2d3bfeea3.mp3"
+    audio_file = "./downloaded_files/1f975693-876d-457b-a649-393859e79bf3.mp3"
 
     if os.path.exists(audio_file): # Check if the (placeholder or real) file exists
         print(f"Attempting to transcribe: {audio_file}")
         transcribed_text = transcribe_audio(audio_file)
-        print(f"Transcription: {transcribed_text}")
+        print(f"Transcription:\n{transcribed_text}")
     else:
-        print(f"File not found: {audio_file}. Please provide a valid audio file.")
-
-    # To integrate this into your FileIdentifier or a subsequent processing step:
-    # identified_file_info = identifier.identify_file("path/to/your/audio.mp3")
-    # if identified_file_info.get("determined_type") == "audio":
-    #     text_from_audio = transcribe_audio(identified_file_info["filepath"])
-    #     print(f"Processed audio: {text_from_audio}")
-    # else:
-    #     print("Not an audio file or error in identification.")
+        print(f"File not found: {audio_file}. Please provide a valid audio file.")