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https://github.com/k2-fsa/icefall.git
synced 2025-08-26 18:24:18 +00:00
Do some changes
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55f9bbdb15
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@ -70,7 +70,7 @@ def compute_fbank_timit():
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recordings=m["recordings"],
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supervisions=m["supervisions"],
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)
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if "TRAIN" in partition:
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if partition == "TRAIN":
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cut_set = (
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cut_set
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+ cut_set.perturb_speed(0.9)
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@ -58,7 +58,7 @@ def prepare_lexicon(manifests_dir: str, lang_dir: str):
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Return:
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The lexicon.txt file and the train.text in lang_dir.
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"""
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phones = set([])
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phones = set()
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supervisions_train = Path(manifests_dir) / "supervisions_TRAIN.json"
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lexicon = Path(lang_dir) / "lexicon.txt"
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@ -23,8 +23,8 @@ stop_stage=100
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# on 39 phones. About how to get these LM files, you can know it
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# from https://github.com/luomingshuang/Train_LM_with_kaldilm.
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#
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# - lm_3_gram_tgmed.arpa
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# - lm_4_gram_tgmed.arpa
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# - lm_3_gram.arpa
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# - lm_4_gram.arpa
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#
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# - $dl_dir/musan
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# This directory contains the following directories downloaded from
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@ -135,7 +135,7 @@ if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
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--read-symbol-table="data/lang_phone/words.txt" \
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--disambig-symbol='#0' \
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--max-order=3 \
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$dl_dir/lm/lm_3_gram_tgmed.arpa > data/lm/G_3_gram.fst.txt
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$dl_dir/lm/lm_3_gram.arpa > data/lm/G_3_gram.fst.txt
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fi
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if [ ! -f data/lm/G_4_gram.fst.txt ]; then
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@ -144,7 +144,7 @@ if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
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--read-symbol-table="data/lang_phone/words.txt" \
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--disambig-symbol='#0' \
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--max-order=4 \
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$dl_dir/lm/lm_4_gram_tgmed.arpa > data/lm/G_4_gram.fst.txt
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$dl_dir/lm/lm_4_gram.arpa > data/lm/G_4_gram.fst.txt
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fi
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fi
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@ -410,7 +410,6 @@ def main():
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if params.method in ["nbest-rescoring", "whole-lattice-rescoring"]:
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if not (params.lm_dir / "G_4_gram.pt").is_file():
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logging.info("Loading G_4_gram.fst.txt")
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logging.warning("It may take 8 minutes.")
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with open(params.lm_dir / "G_4_gram.fst.txt") as f:
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first_word_disambig_id = lexicon.word_table["#0"]
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@ -452,9 +452,8 @@ class LiGRU_Layer(torch.nn.Module):
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).data
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# Sampling the mask
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drop_mask = self.drop_masks[
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self.drop_mask_cnt:self.drop_mask_cnt + self.batch_size
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]
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right_boundary = self.drop_mask_cnt + self.batch_size
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drop_mask = self.drop_masks[self.drop_mask_cnt:right_boundary]
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self.drop_mask_cnt = self.drop_mask_cnt + self.batch_size
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else:
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@ -409,7 +409,6 @@ def main():
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if params.method in ["nbest-rescoring", "whole-lattice-rescoring"]:
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if not (params.lm_dir / "G_4_gram.pt").is_file():
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logging.info("Loading G_4_gram.fst.txt")
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logging.warning("It may take 8 minutes.")
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with open(params.lm_dir / "G_4_gram.fst.txt") as f:
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first_word_disambig_id = lexicon.word_table["#0"]
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