pipe

app.py

@@ -1,16 +1,18 @@
 from huggingsound import SpeechRecognitionModel
+from transformers import pipeline
 import gradio as gr
 
 
-model = SpeechRecognitionModel("patrickvonplaten/wav2vec2-large-xlsr-53-spanish-with-lm")
+#model = SpeechRecognitionModel("patrickvonplaten/wav2vec2-large-xlsr-53-spanish-with-lm")
+pipe = pipeline("automatic-speech-recognition", "patrickvonplaten/wav2vec2-large-xlsr-53-spanish-with-lm")
 
 
 def transcribe(audio, state=""):    
-    transcriptions_es = model.transcribe([audio])[0]    
+    #transcriptions_es = model.transcribe([audio])[0]        
+    transcriptions_es = pipe(audio)["text"]
     # Algoritmo here
-
     recomendacion = "definir variable"
-    return transcriptions_es['transcription'], recomendacion
+    return transcriptions_es, recomendacion
 
 inputs = gr.inputs.Audio(label="Dar click para escuchar tu voz", type="filepath", source="microphone")
 output1 = gr.outputs.Textbox(label="Asi se ve tu código")

requirements.txt

@@ -1,2 +1,3 @@
 gradio
-huggingsound
+huggingsound
+transformers