Update app.py

app.py

@@ -1,47 +1,49 @@
-import os
-import gradio as gr
-from faster_whisper import WhisperModel
-from pytube import YouTube
-
-# Inicializar el modelo Whisper
-model = WhisperModel("base", device="cuda" if gr.device.is_available("cuda") else "cpu", compute_type="float16")
-
-def transcribe_audio(audio_path):
-    segments, _ = model.transcribe(audio_path, beam_size=5)
-    return " ".join([segment.text for segment in segments])
-
-def process_youtube(youtube_url):
-    try:
-        yt = YouTube(youtube_url)
-        audio_stream = yt.streams.filter(only_audio=True).first()
-        
-        if not os.path.exists("temp"):
-            os.makedirs("temp")
-        
-        output_path = audio_stream.download(output_path="temp")
-        return transcribe_audio(output_path)
-    except Exception as e:
-        return f"Error processing YouTube URL: {str(e)}"
-
-def transcribe(audio_file, youtube_url):
-    if audio_file:
-        return transcribe_audio(audio_file)
-    elif youtube_url:
-        return process_youtube(youtube_url)
-    else:
-        return "Please provide either an audio file or a YouTube URL."
-
-# Definir la interfaz de Gradio
-iface = gr.Interface(
-    fn=transcribe,
-    inputs=[
-        gr.Audio(type="filepath", label="Upload Audio File"),
-        gr.Textbox(label="Or Enter YouTube URL")
-    ],
-    outputs="text",
-    title="Whisper Transcription App",
-    description="Upload an audio file or provide a YouTube URL to transcribe."
-)
-
-# Lanzar la aplicación
+import os
+import gradio as gr
+from faster_whisper import WhisperModel
+from pytube import YouTube
+import torch
+
+# Inicializar el modelo Whisper
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model = WhisperModel("base", device=device, compute_type="float16")
+
+def transcribe_audio(audio_path):
+    segments, _ = model.transcribe(audio_path, beam_size=5)
+    return " ".join([segment.text for segment in segments])
+
+def process_youtube(youtube_url):
+    try:
+        yt = YouTube(youtube_url)
+        audio_stream = yt.streams.filter(only_audio=True).first()
+        
+        if not os.path.exists("temp"):
+            os.makedirs("temp")
+        
+        output_path = audio_stream.download(output_path="temp")
+        return transcribe_audio(output_path)
+    except Exception as e:
+        return f"Error processing YouTube URL: {str(e)}"
+
+def transcribe(audio_file, youtube_url):
+    if audio_file:
+        return transcribe_audio(audio_file)
+    elif youtube_url:
+        return process_youtube(youtube_url)
+    else:
+        return "Please provide either an audio file or a YouTube URL."
+
+# Definir la interfaz de Gradio
+iface = gr.Interface(
+    fn=transcribe,
+    inputs=[
+        gr.Audio(type="filepath", label="Upload Audio File"),
+        gr.Textbox(label="Or Enter YouTube URL")
+    ],
+    outputs="text",
+    title="Whisper Transcription App",
+    description="Upload an audio file or provide a YouTube URL to transcribe."
+)
+
+# Lanzar la aplicación
 iface.launch()