Update app.py

app.py

@@ -28,7 +28,7 @@ device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 print(f"Bruker enhet: {device}")
 
 @spaces.GPU(duration=60 * 2)
-def pipe(file, return_timestamps=False,lang="no"):
+def pipe(file, return_timestamps=False,lang):
     asr = pipeline(
         task="automatic-speech-recognition",
         model=MODEL_NAME,
@@ -52,7 +52,7 @@ def format_output(text):
     text = re.sub(r'(\.{3,}|[.!:?])', lambda m: m.group() + '<br>', text)
     return text
 
-def transcribe(file, return_timestamps=False,lang="no"):
+def transcribe(file, return_timestamps=False,lang_nn=False):
     
     waveform, sample_rate = torchaudio.load(file)
     audio_duration = waveform.size(1) / sample_rate  
@@ -69,12 +69,12 @@ def transcribe(file, return_timestamps=False,lang="no"):
         truncated = False
 
         
-    if lang=="no":    
+    if not lang_nn:    
         if not return_timestamps:
-            text = pipe(file_to_transcribe)["text"]
+            text = pipe(file_to_transcribe, return_timestams=False, lang="no")["text"]
             formatted_text = format_output(text)
         else:
-            chunks = pipe(file_to_transcribe, return_timestamps=True)["chunks"]
+            chunks = pipe(file_to_transcribe, return_timestamps=True, lang="no")["chunks"]
             text = []
             for chunk in chunks:
                 start_time = time.strftime('%H:%M:%S', time.gmtime(chunk["timestamp"][0])) if chunk["timestamp"][0] is not None else "??:??:??"