Update app.py

app.py

@@ -1,28 +1,30 @@
-from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
-import torch
-import librosa
-import gradio as gr
-
-# Cargamos el modelo de guaraní
-model_name = "ivangtorre/wav2vec2-xlsr-300m-guarani"
-processor = Wav2Vec2Processor.from_pretrained(model_name)
-model = Wav2Vec2ForCTC.from_pretrained(model_name)
-
-# Transcripción
-def transcribir(audio):
-    audio_data, _ = librosa.load(audio, sr=16000)
-    inputs = processor(audio_data, sampling_rate=16000, return_tensors="pt", padding=True)
-    with torch.no_grad():
-        logits = model(**inputs).logits
-    predicted_ids = torch.argmax(logits, dim=-1)
-    transcription = processor.batch_decode(predicted_ids)[0]
-    return transcription.lower()
-
-# Interfaz de Gradio
-demo = gr.Interface(
-    fn=transcribir,
-    inputs=gr.Audio(type="filepath"),
-    outputs="text",
-    title="Transcriptor Guaraní",
-    description="Subí un audio en guaraní (.ogg, .wav) y obtené la transcripción"
-)
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
+import torch
+import librosa
+import gradio as gr
+
+# Cargamos el modelo de guaraní
+model_name = "ivangtorre/wav2vec2-xlsr-300m-guarani"
+processor = Wav2Vec2Processor.from_pretrained(model_name)
+model = Wav2Vec2ForCTC.from_pretrained(model_name)
+
+# Transcripción
+def transcribir(audio):
+    audio_data, _ = librosa.load(audio, sr=16000)
+    inputs = processor(audio_data, sampling_rate=16000, return_tensors="pt", padding=True)
+    with torch.no_grad():
+        logits = model(**inputs).logits
+    predicted_ids = torch.argmax(logits, dim=-1)
+    transcription = processor.batch_decode(predicted_ids)[0]
+    return transcription.lower()
+
+# Interfaz de Gradio
+demo = gr.Interface(
+    fn=transcribir,
+    inputs=gr.Audio(type="filepath"),
+    outputs="text",
+    title="Transcriptor Guaraní",
+    description="Subí un audio en guaraní (.ogg, .wav) y obtené la transcripción"
+)
+
+demo.launch()