Update app.py

app.py

@@ -12,15 +12,15 @@ from transformers import pipeline # le framework de huggingface
 
 app = FastAPI()
 
-#pipe_flan = pipeline("text2text-generation", model="google/flan-t5-small")
+#deepneurones = pipeline("text2text-generation", model="google/flan-t5-small")
 #deepneurones = pipeline("automatic-speech-recognition")# la liste des pipelines de huggingface est disponible ici :https://huggingface.co/docs/transformers/quicktour. pipeline() telecharge dans un cache local le modele deeplearning
-deepneurones= pipeline("automatic-speech-recognition", model="facebook/wav2vec2-base-960h")
-
+#deepneurones= pipeline("automatic-speech-recognition", model="facebook/wav2vec2-base-960h") # il faut choisir un modele
+deepneurones = Pipeline.from_pretrained("pyannote/speaker-diarization")
 @app.get("/healthcheck")
 def healthcheck():
     
-    #output = pipe_flan(input)
-    #pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization")
+    #output = deepneurones(input)
+
     #pipeline("file.wav")
     return {"output":"OK"}
 @app.post("/stt")