Update handler.py

handler.py

@@ -5,7 +5,18 @@ import torch
 
 SAMPLE_RATE = 16000
 
-pipe.model.config.forced_decoder_ids = pipe.tokenizer.get_decoder_prompt_ids(language=dk, task="transcribe")
+MODEL_NAME = "openai/whisper-large" #this always needs to stay in line 8 :D sorry for the hackiness
+lang = "dk"
+
+device = 0 if torch.cuda.is_available() else "cpu"
+pipe = pipeline(
+    task="automatic-speech-recognition",
+    model=MODEL_NAME,
+    chunk_length_s=30,
+    device=device,
+)
+
+pipe.model.config.forced_decoder_ids = pipe.tokenizer.get_decoder_prompt_ids(language=lang, task="transcribe")
 
 class EndpointHandler():
     def __init__(self, path=""):
@@ -30,4 +41,4 @@ class EndpointHandler():
         result = self.model.transcribe(audio_nparray)
 
         # postprocess the prediction
-        return {"text": result["text"]}
+        return {"tekst": result["text"]}