Update app.py

app.py

@@ -54,9 +54,9 @@ This Gradio demo showcases **IndicConformer**, a speech recognition model for **
 hf_token = os.getenv("HF_TOKEN")
 device = "cuda:0" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
 torch_dtype = torch.bfloat16 if device != "cpu" else torch.float32
-model_name_or_path = "ai4bharat/indicconformer_stt_bn_hybrid_ctc_rnnt_large"
-# model = nemo_asr.models.EncDecCTCModel.from_pretrained(model_name_or_path).to(device)
-model = nemo_asr.models.EncDecCTCModel.restore_from("indicconformer_stt_bn_hybrid_rnnt_large.nemo").to(device)
+model_name_or_path = "ai4bharat/IndicConformer"
+model = nemo_asr.models.EncDecCTCModel.from_pretrained(model_name_or_path).to(device)
+# model = nemo_asr.models.EncDecCTCModel.restore_from("indicconformer_stt_bn_hybrid_rnnt_large.nemo").to(device)
 model.eval()
 
 CACHE_EXAMPLES = os.getenv("CACHE_EXAMPLES") == "1" and torch.cuda.is_available()