update logging information

egs/himia/wuw/ctc_tdnn/decode.py

@@ -21,12 +21,16 @@ import copy
 import logging
 from concurrent.futures import ProcessPoolExecutor
 from typing import Tuple
+from pathlib import Path
 
 import numpy as np
 from lhotse.features.io import NumpyHdf5Reader
 from tqdm import tqdm
 
-from icefall.utils import AttributeDict
+from icefall.utils import (
+    AttributeDict,
+    setup_logger,
+)
 
 from train import get_params
 from graph import ctc_trivial_decoding_graph
@@ -242,26 +246,33 @@ def get_parser():
         description="A simple FST decoder for the wake word detection\n"
     )
     parser.add_argument(
-        "--decoding-graph", help="decoding graph", default="himia_ctc_graph.txt"
+        "--post-h5",
+        type=str,
+        help="model output in h5 format",
+    )
+    parser.add_argument(
+        "--score-file",
+        type=str,
+        help="file to save scores of each utterance",
     )
-    parser.add_argument("--post-h5", help="model output in h5 format")
-    parser.add_argument("--score-file", help="file to save scores of each utterance")
     return parser
 
 
 def main():
-    logging.basicConfig(
-        level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s"
-    )
     parser = get_parser()
     args = parser.parse_args()
     params = get_params()
     params.update(vars(args))
+    post_dir = Path(params.post_h5).parent
+    test_set = Path(params.post_h5).stem
+    setup_logger(f"{post_dir}/log/log-decode-{test_set}")
 
-    keys = NumpyHdf5Reader(params.post_h5).hdf.keys()
     graph = FiniteStateTransducer(ctc_trivial_decoding_graph(params.wakeup_word_tokens))
+
     logging.info(f"Graph used:\n{graph.to_str()}")
-    logging.info("About to load data to decoder.")
+
+    logging.info(f"About to load {test_set}.")
+    keys = NumpyHdf5Reader(params.post_h5).hdf.keys()
     with ProcessPoolExecutor() as executor, open(
         params.score_file, "w", encoding="utf8"
     ) as fout:
@@ -269,11 +280,13 @@ def main():
             executor.submit(decode_utt, params, key, params.post_h5, graph)
             for key in tqdm(keys)
         ]
-        logging.info("Decoding.")
+        logging.info(f"Decoding {test_set}.")
         for future in tqdm(futures):
             k, v = future.result()
             fout.write(str(k) + " " + str(v) + "\n")
 
+        logging.info(f"Finish decoding {test_set}.")
+
 
 if __name__ == "__main__":
     main()

egs/himia/wuw/ctc_tdnn/inference.py

@@ -140,7 +140,7 @@ def main():
     out_dir = f"{params.exp_dir}/post/epoch_{params.epoch}-avg_{params.avg}/"
     Path(out_dir).mkdir(parents=True, exist_ok=True)
     params.out_dir = out_dir
-    setup_logger(f"{out_dir}/log-decode")
+    setup_logger(f"{out_dir}/log/log-inference")
     logging.info("Decoding started")
     logging.info(params)
 
@@ -186,6 +186,7 @@ def main():
     test_dls = [aishell_test_dl, test_dl, cw_test_dl]
 
     for test_set, test_dl in zip(test_sets, test_dls):
+        logging.info(f"About to inference {test_set}")
         inference_dataset(
             dl=test_dl,
             params=params,
@@ -193,6 +194,8 @@ def main():
             test_set=test_set,
         )
 
+        logging.info(f"finish inferencing {test_set}")
+
     logging.info("Done!")
 
 

egs/himia/wuw/ctc_tdnn/tokenizer.py

@@ -64,7 +64,9 @@ class WakeupWordTokenizer(object):
         self.negative_word_tokens = [1]
         self.negative_number_tokens = 1
 
-    def texts_to_token_ids(self, texts: List[str]) -> Tuple[torch.Tensor, torch.Tensor, int]:
+    def texts_to_token_ids(
+        self, texts: List[str]
+    ) -> Tuple[torch.Tensor, torch.Tensor, int]:
         """Convert a list of texts to a list of k2.Fsa based texts.
 
         Args:

egs/himia/wuw/ctc_tdnn/train.py

@@ -564,7 +564,7 @@ def run(rank, world_size, args):
         params=params,
     )
 
-    for epoch in range(params.start_epoch, params.num_epochs):
+    for epoch in range(params.start_epoch, params.num_epochs + 1):
         fix_random_seed(params.seed + epoch)
         train_dl.sampler.set_epoch(epoch)
 

egs/himia/wuw/local/auc.py

@@ -25,16 +25,29 @@ import numpy as np
 from pathlib import Path
 from sklearn.metrics import roc_curve, auc
 
+from icefall.utils import setup_logger
+
 
 def get_args():
     parser = argparse.ArgumentParser()
     parser.add_argument(
-        "--positive-score-file", required=True, help="score file of positive data"
+        "--positive-score-file",
+        type=str,
+        required=True,
+        help="score file of positive data",
     )
     parser.add_argument(
-        "--negative-score-file", required=True, help="score file of negative data"
+        "--negative-score-file",
+        type=str,
+        required=True,
+        help="score file of negative data",
+    )
+    parser.add_argument(
+        "--legend",
+        type=str,
+        required=True,
+        help="legend of ROC curve picture.",
     )
-    parser.add_argument("--legend", required=True, help="utt2dur file of negative data")
     return parser.parse_args()
 
 
@@ -88,10 +101,13 @@ def get_roc_and_auc(
 
 
 def main():
-    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
 
     args = get_args()
-    logging.basicConfig(format=formatter, level=logging.INFO)
+
+    score_dir = Path(args.positive_score_file).parent
+    setup_logger(f"{score_dir}/log/log-auc-{args.legend}")
+    logging.info(f"About to compute AUC of {args.legend}")
+
     pos_dict = load_score(args.positive_score_file)
     neg_dict = load_score(args.negative_score_file)
     fpr, tpr, roc_auc = get_roc_and_auc(pos_dict, neg_dict)

egs/himia/wuw/run_ctc_tdnn.sh

@@ -39,7 +39,6 @@ if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
   log "Stage 2: Decode and compute area under curve(AUC)"
   for test_set in test aishell_test cw_test; do
     python ctc_tdnn/decode.py \
-      --decoding-graph ./data/LG.int \
       --post-h5 ${post_dir}/${test_set}.h5 \
       --score-file ${post_dir}/fst_${test_set}_pos_h5.txt
   done