Fix interactive asr (#900)

* updated the build_generator call to include the models argument

* fixed RuntimeError: Input type (torch.FloatTensor) and weight type (torch.cuda.FloatTensor) should be the same

examples/interactive_asr/utils.py

@@ -55,8 +55,9 @@ def check_args(args):
 
 def process_predictions(args, hypos, sp, tgt_dict):
     res = []
+    device = torch.device("cuda:0" if torch.cuda.is_available() and not args.cpu else "cpu")
     for hypo in hypos[: min(len(hypos), args.nbest)]:
-        hyp_pieces = tgt_dict.string(hypo["tokens"].int().cpu())
+        hyp_pieces = tgt_dict.string(hypo["tokens"].int().to(device))
         hyp_words = sp.DecodePieces(hyp_pieces.split())
         res.append(hyp_words)
     return res
@@ -96,7 +97,8 @@ def calcMN(features):
 def transcribe(waveform, args, task, generator, models, sp, tgt_dict):
     num_features = 80
     output = torchaudio.compliance.kaldi.fbank(waveform, num_mel_bins=num_features)
-    output_cmvn = calcMN(output.cpu().detach())
+    device = torch.device("cuda:0" if torch.cuda.is_available() and not args.cpu else "cpu")
+    output_cmvn = calcMN(output.to(device).detach())
 
     # size (m, n)
     source = output_cmvn
@@ -151,7 +153,7 @@ def setup_asr(args, logger):
     optimize_models(args, use_cuda, models)
 
     # Initialize generator
-    generator = task.build_generator(args)
+    generator = task.build_generator(models, args)
 
     sp = spm.SentencePieceProcessor()
     sp.Load(os.path.join(args.data, "spm.model"))