fixing error and including example file

README.md

@@ -45,6 +45,8 @@ The user will logging using a password and user specified by me. That user and p
 - [ ] Obtain txt with Diarization.
 - [ ] Obtain plain txt with segments. 
 - [ ] Introduce POS.
+- [ ] Optional Preprocessing
 
 
-Introduce Tab for analysis including POS. Maybe it would be great to have a visualizer with the timestamps and other features in Streamlit. Quizas correcciones. 
+Introduce Tab for analysis including POS. Maybe it would be great to have a visualizer with the timestamps and other features in Streamlit. Quizas correcciones. 
+

app.py

@@ -78,7 +78,7 @@ def transcribeWhisperX(audiofile, model, language, patiente,
     file_path = Path(nombre_archivo)
     writter_args = {"highlight_words": None, "max_line_count": None, "max_line_width": None}
     srt_writer = get_writer("srt", Path("."))
-    srt_writer(result_aligned["segments"], str(file_path.stem), writter_args)
+    srt_writer(result_aligned, str(file_path.stem), writter_args)
 
     # with open(
     #    nombre_archivo,
@@ -111,7 +111,7 @@ transcribeI = gr.Interface(
         "Esta página realiza una transcripción de audio utilizando Whisper. Además añade varias mejoras y utilidades: a) Preprocesamiento del audio y limpieza de ruido ambiental, b) Conversión de los archivos de audio a un formato compatible con Whisper, c) Cálculo de la marca temporal palabra por palabra, d) Cálculo del nivel de seguridad de la transcripción, e) Conversión del resultado a .csv, .srt y ass.\n"
     ),
     allow_flagging="never",
-    #examples=[[None, "COSER-4004-01-00_5m.wav", "large-v2"]]
+    examples=[[None, "Espana 04 - Video 01 - extracto 2 min.wav", "large-v2"]]
 
 )