Update utils_hf.py

utils_hf.py

@@ -19,8 +19,6 @@ load_dotenv()
 hf_token = os.environ.get("HF_DIARIZATION_TOKEN")
 api_key = os.environ.get("OPENAI_API_KEY")
 
-client = Client(api_key=api_key)
-
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 st.info(f"Usando dispositivo: {device}")
 
@@ -150,20 +148,6 @@ def transcribe_audio_whisper_lib(audio_path):
         st.stop()
 
 
-def transcribe_audio_openai(audio_path):
-    try:
-        with open(audio_path, "rb") as audio_file:
-            transcript = client.audio.transcriptions.create(
-                model="whisper-1",
-                file=audio_file,
-                response_format="json"
-            )
-        return transcript.text
-    except Exception as e:
-        st.error(f"Error al transcribir el audio: {e}")
-        st.stop()
-
-
 def download_youtube_audio(url, progress_bar):
     # No incluyas la extensión '.wav' en el 'outtmpl'
     output_path = os.path.join('temp_audio')
@@ -242,47 +226,4 @@ class StreamlitProgressHook(ProgressHook):
             progress_message = f"{step_name:<20} ━ Progress data unavailable"
         self.progress_text.text(progress_message)
 
-# def diarize_in_segments(audio_path, segment_duration=300):
-#     try:
-#         audio_duration = get_audio_duration(audio_path)
-#         diarization_segments = []
-#         diarization_text = ""
-#         speakers = set()
-#
-#         # Procesar cada segmento por separado
-#         for start_time in range(0, int(audio_duration), segment_duration):
-#             end_time = min(start_time + segment_duration, audio_duration)
-#             st.info(f"Procesando segmento desde {start_time} hasta {end_time} segundos...")
-#
-#             with StreamlitProgressHook() as hook:
-#                 diarization = pyannote_pipeline({
-#                     'audio': audio_path,
-#                     'start': start_time,
-#                     'end': end_time
-#                 }, hook=hook)
-#
-#             for segment, _, speaker in diarization.itertracks(yield_label=True):
-#                 diarization_segments.append((segment.start, segment.end, speaker))
-#                 diarization_text += f"{segment.start:.2f} - {segment.end:.2f}: {speaker}\n"
-#                 speakers.add(speaker)
-#
-#         return diarization_segments, diarization_text, speakers
-#     except Exception as e:
-#         st.error(f"Error al realizar la diarización de un segmento: {e}")
-#         return None, None, 0
-
-# def transcribe_audio_whisper_transformers(audio_path):
-#     try:
-#         # Leer el archivo de audio y convertirlo en un numpy ndarray
-#         audio_data, _ = sf.read(audio_path)
-#
-#         # Convertir a un solo canal si es necesario
-#         if len(audio_data.shape) > 1:
-#             audio_data = np.mean(audio_data, axis=1)
-#
-#         # Transcribir el audio usando el modelo Whisper
-#         transcript = whisper_pipeline(audio_data, return_timestamps=True)
-#         return transcript['text']
-#     except Exception as e:
-#         st.error(f"Error al transcribir el audio: {e}")
-#         st.stop()
+