Use pretrained LM API for decoder example (#2317)

Summary:
update example ASR pipeline to use the recently added pretrained LM API for decoding

Pull Request resolved: https://github.com/pytorch/audio/pull/2317

Reviewed By: mthrok

Differential Revision: D35361354

Pulled By: carolineechen

fbshipit-source-id: cac7cf55bd9f86417f319191c1405819fe2a7b46

examples/asr/librispeech_ctc_decoder/inference.py

@@ -4,37 +4,24 @@ from typing import Optional
 
 import torch
 import torchaudio
-from torchaudio.prototype.ctc_decoder import lexicon_decoder
+from torchaudio.prototype.ctc_decoder import lexicon_decoder, download_pretrained_files
 
 
 logger = logging.getLogger(__name__)
 
 
-def _download_files(lexicon_file, kenlm_file):
-    torch.hub.download_url_to_file(
-        "https://pytorch.s3.amazonaws.com/torchaudio/tutorial-assets/ctc-decoding/lexicon-librispeech.txt", lexicon_file
-    )
-    torch.hub.download_url_to_file(
-        "https://pytorch.s3.amazonaws.com/torchaudio/tutorial-assets/ctc-decoding/4-gram-librispeech.bin", kenlm_file
-    )
-
-
 def run_inference(args):
     # get pretrained wav2vec2.0 model
     bundle = getattr(torchaudio.pipelines, args.model)
     model = bundle.get_model()
-    tokens = [label.lower() for label in bundle.get_labels()]
 
     # get decoder files
-    hub_dir = torch.hub.get_dir()
-    lexicon_file = f"{hub_dir}/lexicon.txt"
-    kenlm_file = f"{hub_dir}/kenlm.bin"
-    _download_files(lexicon_file, kenlm_file)
+    files = download_pretrained_files("librispeech-4-gram")
 
     decoder = lexicon_decoder(
-        lexicon=lexicon_file,
-        tokens=tokens,
-        lm=kenlm_file,
+        lexicon=files.lexicon,
+        tokens=files.tokens,
+        lm=files.lm,
         nbest=args.nbest,
         beam_size=args.beam_size,
         beam_size_token=args.beam_size_token,