Update app.py

app.py

@@ -64,32 +64,8 @@ def get_transcripts(df, audio_path):
     filename = create_manifest(df,audio_path)
     model = EncDecRNNTBPEModel.from_pretrained(model_name="nvidia/stt_en_fastconformer_transducer_large").to(device)
     model.eval()
-    config = OmegaConf.create({"manifest_filepath": filename, 'batch_size': 2})
-    dataloader = model._setup_transcribe_dataloader(config)
+    return model.transcribe(filename, batch_size=2)
     
-    hypotheses = []
-    all_hypotheses = []
-
-    for test_batch in (dataloader):
-        encoded, encoded_len = model.forward(
-            input_signal=test_batch[0].to(device), input_signal_length=test_batch[1].to(device)
-            )
-        best_hyp, all_hyp = model.decoding.rnnt_decoder_predictions_tensor(
-            encoded,
-            encoded_len,
-            return_hypotheses=False,
-            partial_hypotheses=None,)
-
-        hypotheses += best_hyp
-        if all_hyp is not None:
-            all_hypotheses += all_hyp
-        else:
-            all_hypotheses += best_hyp
-
-        del encoded
-        del test_batch
-
-    return hypotheses
 
 article = (
     "<p style='text-align: center'>"