From a568e374849fc1fac57bc189a58d38238dada40a Mon Sep 17 00:00:00 2001 From: dohe0342 Date: Tue, 14 Feb 2023 15:18:18 +0900 Subject: [PATCH] from local --- .../ASR/conformer_ctc3/.decode_multi.py.swp | Bin 57344 -> 57344 bytes .../ASR/conformer_ctc3/decode_multi.py | 14 ++++++++++---- 2 files changed, 10 insertions(+), 4 deletions(-) diff --git a/egs/tedlium2/ASR/conformer_ctc3/.decode_multi.py.swp b/egs/tedlium2/ASR/conformer_ctc3/.decode_multi.py.swp index 3276f37299a0804a4a846db58753c7bfe4d3d1f3..e39504d0cb5dfcae83159d0241a519f37dd7c1a3 100644 GIT binary patch delta 602 zcmXxh-%C?r7zgm@I45j6+Zg7Jx_9hio5+|5;g2R}N=<1HRJ#}=`*Ai8+BtSS>F{QY zQiCqfl@{~|l;V0ph$wc|Vz+taCM42TT@>AP6Zw5N_rT|Hp67ke!+GDgShf_)mY!7j zLG%tA3xx^cB)aYYk=`!#)SQyf{Ov4AgZz=p9B>@khzxtPWzckm!F& zx`_^8AD+QeP+%VBzzW}RQ5Eh*a*CP@DQZ>=tOe-;>FI~z delta 477 zcmXBQPbh)mSDWnR13RxYCKAmsmwVmbJeh9#o5DdeK~9i5z& ztY5h>c9GI#5hVx3X`}o(DT?pAJ@u)l=lAsV{GM1!jHN{DnA{)ekbG{L5GJCZ+SgD| z!un_u9PHB+6`Xw5%xs3Bj!363v^ber9_R}gh}xk6eoKfhU<KUf2-#AiB%A<)9aw}Q41opSalL!Eg9F%r vF6acefvTtk$qN>Eh7&l3O$hV7%$a^WV__fmVtySDi7fqOWtFQ2ao$D$`M+rW diff --git a/egs/tedlium2/ASR/conformer_ctc3/decode_multi.py b/egs/tedlium2/ASR/conformer_ctc3/decode_multi.py index 7a5275cfc..78d72f3bc 100755 --- a/egs/tedlium2/ASR/conformer_ctc3/decode_multi.py +++ b/egs/tedlium2/ASR/conformer_ctc3/decode_multi.py @@ -627,15 +627,16 @@ def save_results( print("settings\tWER", file=f) for key, val in test_set_wers: print("{}\t{}".format(key, val), file=f) - + + wer = None s = "\nFor {}, WER of different settings are:\n".format(test_set_name) note = "\tbest for {}".format(test_set_name) for key, val in test_set_wers: s += "{}\t{}{}\n".format(key, val, note) note = "" - print(key) - print(val) + wer = val logging.info(s) + return val @torch.no_grad() @@ -816,7 +817,12 @@ def main() -> None: eos_id=eos_id, ) - save_results(params=params, test_set_name=test_set, results_dict=results_dict) + wer = save_results(params=params, test_set_name=test_set, results_dict=results_dict) + wer_dict[epoch] = wer + + wer_dict = sorted(wer_dict.items(), key=lambda x:x[1]) + for k, v in wer_dict: + print(k, v) torch.set_num_threads(1) # when we import add_model_arguments from train.py