Update app.py

app.py

@@ -2,12 +2,12 @@ import torch
 from transformers import pipeline
 import gradio as gr
 
-# Configuración
 MODEL_NAME = "openai/whisper-small"
 BATCH_SIZE = 8
-device = 0 if torch.cuda.is_available() else "cpu"
 
-# Inicializar el pipeline
+# Forzar uso de CPU
+device = -1 
+
 pipe = pipeline(
     task="automatic-speech-recognition",
     model=MODEL_NAME,
@@ -30,57 +30,48 @@ def format_timestamp(seconds: float, always_include_hours: bool = False, decimal
 
 def transcribe(file, task, return_timestamps):
     if file is None:
-        return "Por favor, sube un archivo o usa el micrófono."
-    
-    outputs = pipe(
-        file, 
-        batch_size=BATCH_SIZE, 
-        generate_kwargs={"task": task}, 
-        return_timestamps=return_timestamps
-    )
-    
+        return "Error: No se proporcionó archivo de audio."
+        
+    outputs = pipe(file, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=return_timestamps)
     text = outputs["text"]
+    
     if return_timestamps:
-        chunks = outputs["chunks"]
-        timestamps_text = [
+        timestamps = outputs["chunks"]
+        timestamps = [
             f"[{format_timestamp(chunk['timestamp'][0])} -> {format_timestamp(chunk['timestamp'][1])}] {chunk['text']}"
-            for chunk in chunks
+            for chunk in timestamps
         ]
-        text = "\n".join(timestamps_text)
+        text = "\n".join(timestamps)
     return text
 
-# Interfaz de Micrófono
+# Configuración de interfaces con sintaxis moderna (Gradio 4+)
 mic_transcribe = gr.Interface(
     fn=transcribe,
     inputs=[
-        gr.Audio(sources="microphone", type="filepath", label="Micrófono"),
-        gr.Radio(["transcribe", "translate"], label="Tarea", value="transcribe"),
-        gr.Checkbox(value=False, label="Incluir marcas de tiempo"),
+        gr.Audio(sources="microphone", type="filepath"),
+        gr.Radio(["transcribe", "translate"], label="Task", value="transcribe"),
+        gr.Checkbox(label="Return timestamps", value=False),
     ],
     outputs="text",
-    title="Whisper Demo: Transcribir Micrófono",
-    description=f"Demo usando el modelo [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}).",
-    allow_flagging="never",
+    title="Whisper Demo: Transcribe Microphone",
+    flagging_mode="never", # 'allow_flagging' ahora es 'flagging_mode'
 )
 
-# Interfaz de Archivo
 file_transcribe = gr.Interface(
     fn=transcribe,
     inputs=[
-        gr.Audio(sources="upload", type="filepath", label="Subir Archivo"),
-        gr.Radio(["transcribe", "translate"], label="Tarea", value="transcribe"),
-        gr.Checkbox(value=False, label="Incluir marcas de tiempo"),
+        gr.Audio(sources="upload", type="filepath", label="Audio file"),
+        gr.Radio(["transcribe", "translate"], label="Task", value="transcribe"),
+        gr.Checkbox(label="Return timestamps", value=False),
     ],
     outputs="text",
-    title="Whisper Demo: Transcribir Archivo",
-    description=f"Sube un archivo de audio para transcribirlo con Whisper.",
-    allow_flagging="never",
+    title="Whisper Demo: Transcribe Audio File",
+    flagging_mode="never",
 )
 
-# Crear la aplicación con pestañas
 demo = gr.TabbedInterface(
     [mic_transcribe, file_transcribe], 
-    ["Transcribir Micrófono", "Transcribir Archivo Audio"]
+    ["Transcribe Microphone", "Transcribe Audio File"]
 )
 
 if __name__ == "__main__":