Update app.py

app.py

@@ -12,7 +12,7 @@ nltk.download("punkt")
 #model_name = "moro23/wav2vec-large-xls-r-300-ha-colab_4"
 #tokenizer = Wav2Vec2Tokenizer.from_pretrained(model_name, use_auth_token=token_value)
 #tokenizer = Wav2Vec2Processor.from_pretrained(model_name, use_auth_token=token_value)
-#model = Wav2Vec2ForCTC.from_pretrained(model_name, use_auth_token=token_value)
+#model = Wav2Vec2ForCTC.from_pretrained(model_name, use_auth_token=token_value).to("cuda")
 
 def load_data(input_file):
 
@@ -37,7 +37,8 @@ def asr_transcript(input_file):
   #Tokenize
   input_dict = tokenizer(speech, return_tensors="pt", padding=True)
   #Take logits
-  logits = model(input_dict.input_values.to("cuda").logits
+  logits = model(input_dict.input_values.to("cuda")).logits
+  
   #Take argmax
   predicted_ids = torch.argmax(logits, dim=-1)[0]
   #Get the words from predicted word ids