Update timit recipe

egs/timit/ASR/local/compile_hlg.py

@@ -54,7 +54,7 @@ def compile_HLG(lang_dir: str) -> k2.Fsa:
     """
     Args:
       lang_dir:
-        The language directory, e.g., data/lang_phone or data/lang_bpe_5000.
+        The language directory, e.g., data/lang_phone.
 
     Return:
       An FSA representing HLG.
@@ -63,18 +63,7 @@ def compile_HLG(lang_dir: str) -> k2.Fsa:
     max_token_id = max(lexicon.tokens)
     logging.info(f"Building ctc_topo. max_token_id: {max_token_id}")
     H = k2.ctc_topo(max_token_id)
-    
+    L = k2.Fsa.from_dict(torch.load(f"{lang_dir}/L_disambig.pt"))
-    if Path(lang_dir / "L_disambig.pt").is_file():
-        logging.info("Loading L_disambig.pt")
-        d = torch.load(Path(lang_dir/"L_disambig.pt"))
-        L = k2.Fsa.from_dict(d)
-    else:
-        logging.info("Loading L_disambig.fst.txt")
-        with open(Path(lang_dir/"L_disambig.fst.txt")) as f:
-            L = k2.Fsa.from_openfst(f.read(), acceptor=False)
-            torch.save(L_disambig.as_dict(), Path(lang_dir / "L_disambig.pt"))
-
-    #L = k2.Fsa.from_dict(torch.load(f"{lang_dir}/L_disambig.pt"))
 
     if Path("data/lm/G.pt").is_file():
         logging.info("Loading pre-compiled G")

egs/timit/ASR/local/prepare_lang.py

@@ -106,7 +106,7 @@ def get_tokens(lexicon: Lexicon) -> List[str]:
     ans = set()
     for _, tokens in lexicon:
         ans.update(tokens)
-    #sorted_ans = sorted(list(ans))
+
     sorted_ans = list(ans)
     return sorted_ans
 
@@ -276,17 +276,10 @@ def lexicon_to_fst(
     next_state = 1  # the next un-allocated state, will be incremented as we go.
     arcs = []
 
-    print('token2id ori: ', token2id)
-    print('word2id ori: ', word2id)
-
     assert token2id["<eps>"] == 0
     assert word2id["<eps>"] == 0
 
     eps = 0
-    print('token2id new: ', token2id)
-    print('word2id new: ', word2id)
-
-    print(lexicon)
     for word, tokens in lexicon:
         assert len(tokens) > 0, f"{word} has no pronunciations"
         cur_state = loop_state
@@ -306,7 +299,7 @@ def lexicon_to_fst(
         # the other one to the sil_state.
         i = len(tokens) - 1
         w = word if i == 0 else eps
-        tokens[i] = tokens[i] if i >=0 else eps
+        tokens[i] = tokens[i] if i >= 0 else eps
         arcs.append([cur_state, loop_state, tokens[i], w, score])
 
     if need_self_loops:
@@ -326,7 +319,6 @@ def lexicon_to_fst(
     arcs = [[str(i) for i in arc] for arc in arcs]
     arcs = [" ".join(arc) for arc in arcs]
     arcs = "\n".join(arcs)
-    print(arcs)
     fsa = k2.Fsa.from_str(arcs, acceptor=False)
     return fsa
 
@@ -334,7 +326,6 @@ def lexicon_to_fst(
 def main():
     args = get_args()
     lang_dir = Path(args.lang_dir)
-    #out_dir = Path("data/lang_phone")
     lexicon_filename = lang_dir / "lexicon.txt"
 
     lexicon = read_lexicon(lexicon_filename)
@@ -386,7 +377,7 @@ def main():
         L.aux_labels_sym = k2.SymbolTable.from_file(lang_dir / "words.txt")
         L_disambig.labels_sym = L.labels_sym
         L_disambig.aux_labels_sym = L.aux_labels_sym
-        L.draw(out_dir / "L.png", title="L")
+        L.draw(lang_dir / "L.png", title="L")
         L_disambig.draw(lang_dir / "L_disambig.png", title="L_disambig")
 
 

egs/timit/ASR/local/prepare_lexicon.py

@@ -64,25 +64,22 @@ def prepare_lexicon(manifests_dir: str, lang_dir: str):
     lexicon = Path(lang_dir) / "lexicon.txt"
 
     logging.info(f"Loading {supervisions_train}!")
-    with open(supervisions_train, 'r') as load_f:
+    with open(supervisions_train, "r") as load_f:
         load_dicts = json.load(load_f)
         for load_dict in load_dicts:
-            idx = load_dict['id']
+            text = load_dict["text"]
-            text = load_dict['text']
+            phones_list = list(filter(None, text.split(" ")))
-            phones_list = list(filter(None, text.split(' ')))
 
             for phone in phones_list:
                 if phone not in phones:
                     phones.append(phone)
 
-    with open(lexicon, 'w') as f:
+    with open(lexicon, "w") as f:
         for phone in sorted(phones):
             f.write(str(phone) + "  " + str(phone))
-            f.write('\n')
+            f.write("\n")
         f.write("<UNK>  <UNK>")
-        f.write('\n')
+        f.write("\n")
-
-    return lexicon
 
 
 def main():
@@ -90,9 +87,8 @@ def main():
     manifests_dir = Path(args.manifests_dir)
     lang_dir = Path(args.lang_dir)
 
-    logging.info(f"Generating lexicon.txt and train.text")
+    logging.info("Generating lexicon.txt")
-
+    prepare_lexicon(manifests_dir, lang_dir)
-    lexicon_file = prepare_lexicon(manifests_dir, lang_dir)
 
 
 if __name__ == "__main__":
@@ -103,4 +99,3 @@ if __name__ == "__main__":
     logging.basicConfig(format=formatter, level=logging.INFO)
 
     main()
-

egs/timit/ASR/tdnn_lstm_ctc/asr_datamodule.py

@@ -311,26 +311,20 @@ class TimitAsrDataModule(DataModule):
     @lru_cache()
     def train_cuts(self) -> CutSet:
         logging.info("About to get train cuts")
-        cuts_train = load_manifest(
+        cuts_train = load_manifest(self.args.feature_dir / "cuts_TRAIN.json.gz")
-            self.args.feature_dir / "cuts_TRAIN.json.gz"
-        )
 
         return cuts_train
 
     @lru_cache()
     def valid_cuts(self) -> CutSet:
         logging.info("About to get dev cuts")
-        cuts_valid = load_manifest(
+        cuts_valid = load_manifest(self.args.feature_dir / "cuts_DEV.json.gz")
-            self.args.feature_dir / "cuts_DEV.json.gz"
-        )
 
         return cuts_valid
 
     @lru_cache()
     def test_cuts(self) -> CutSet:
         logging.debug("About to get test cuts")
-        cuts_test = load_manifest(
+        cuts_test = load_manifest(self.args.feature_dir / "cuts_TEST.json.gz")
-            self.args.feature_dir / "cuts_TEST.json.gz"
-        )
 
         return cuts_test

egs/timit/ASR/tdnn_lstm_ctc/decode.py

@@ -449,7 +449,6 @@ def main():
     )
     if params.avg == 1:
         load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model)
-        #load_checkpoint(f"tmp/icefall_asr_librispeech_tdnn-lstm_ctc/exp/pretrained.pt", model)
     else:
         start = params.epoch - params.avg + 1
         filenames = []
@@ -470,18 +469,6 @@ def main():
     model.eval()
 
     timit = TimitAsrDataModule(args)
-    # CAUTION: `test_sets` is for displaying only.
-    # If you want to skip test-clean, you have to skip
-    # it inside the for loop. That is, use
-    #
-    #   if test_set == 'test-clean': continue
-    #
-    #test_sets = ["test-clean", "test-other"]
-    #test_sets = ["test-other"]
-    #for test_set, test_dl in zip(test_sets, librispeech.test_dataloaders()):
-        #if test_set == "test-clean": continue
-        #if test_set == "test-other": break
-    test_set = "TEST"
     test_dl = timit.test_dataloaders()
     results_dict = decode_dataset(
         dl=test_dl,
@@ -491,7 +478,7 @@ def main():
         lexicon=lexicon,
         G=G,
     )
-
+    test_set = "TEST"
     save_results(
         params=params, test_set_name=test_set, results_dict=results_dict
     )