From 6737c14fff2a23cf4cc7d2ae6d5c3bf4a5d12c98 Mon Sep 17 00:00:00 2001
From: 嘉渊 <wangjiaming.wjm@alibaba-inc.com>
Date: 星期四, 11 五月 2023 14:31:01 +0800
Subject: [PATCH] update repo
---
egs/librispeech_100h/conformer/run.sh | 31 ++++++++++++++++---------------
egs/aishell/conformer/run.sh | 1 +
2 files changed, 17 insertions(+), 15 deletions(-)
diff --git a/egs/aishell/conformer/run.sh b/egs/aishell/conformer/run.sh
index 536d221..eb3e13c 100755
--- a/egs/aishell/conformer/run.sh
+++ b/egs/aishell/conformer/run.sh
@@ -177,6 +177,7 @@
--njob ${njob} \
--gpuid_list ${gpuid_list} \
--data_path_and_name_and_type "${_data}/${scp},speech,${type}" \
+ --cmvn_file ${feats_dir}/data/${train_set}/cmvn/cmvn.mvn \
--key_file "${_logdir}"/keys.JOB.scp \
--asr_train_config "${asr_exp}"/config.yaml \
--asr_model_file "${asr_exp}"/"${inference_asr_model}" \
diff --git a/egs/librispeech_100h/conformer/run.sh b/egs/librispeech_100h/conformer/run.sh
index a855daa..7d63125 100755
--- a/egs/librispeech_100h/conformer/run.sh
+++ b/egs/librispeech_100h/conformer/run.sh
@@ -19,8 +19,8 @@
token_type=bpe
type=sound
scp=wav.scp
-stage=2
-stop_stage=2
+stage=3
+stop_stage=4
# feature configuration
feats_dim=80
@@ -89,22 +89,21 @@
utils/compute_cmvn.sh --cmd "$train_cmd" --nj $nj --feats_dim ${feats_dim} ${feats_dir}/data/${train_set}
fi
-dict=${feats_dir}/data/lang_char/${train_set}_${bpemode}${nbpe}_units.txt
+token_list=${feats_dir}/data/lang_char/${train_set}_${bpemode}${nbpe}_units.txt
bpemodel=${feats_dir}/data/lang_char/${train_set}_${bpemode}${nbpe}
-echo "dictionary: ${dict}"
+echo "dictionary: ${token_list}"
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
### Task dependent. You have to check non-linguistic symbols used in the corpus.
echo "stage 2: Dictionary and Json Data Preparation"
mkdir -p ${feats_dir}/data/lang_char/
- echo "<blank>" > ${dict}
- echo "<s>" >> ${dict}
- echo "</s>" >> ${dict}
+ echo "<blank>" > ${token_list}
+ echo "<s>" >> ${token_list}
+ echo "</s>" >> ${token_list}
cut -f 2- -d" " ${feats_dir}/data/${train_set}/text > ${feats_dir}/data/lang_char/input.txt
local/spm_train.py --input=${feats_dir}/data/lang_char/input.txt --vocab_size=${nbpe} --model_type=${bpemode} --model_prefix=${bpemodel} --input_sentence_size=100000000
- local/spm_encode.py --model=${bpemodel}.model --output_format=piece < ${feats_dir}/data/lang_char/input.txt | tr ' ' '\n' | sort | uniq | awk '{print $0}' >> ${dict}
- echo "<unk>" >> ${dict}
+ local/spm_encode.py --model=${bpemodel}.model --output_format=piece < ${feats_dir}/data/lang_char/input.txt | tr ' ' '\n' | sort | uniq | awk '{print $0}' >> ${token_list}
+ echo "<unk>" >> ${token_list}
fi
-
# Training Stage
world_size=$gpu_num # run on one machine
@@ -123,16 +122,17 @@
rank=$i
local_rank=$i
gpu_id=$(echo $CUDA_VISIBLE_DEVICES | cut -d',' -f$[$i+1])
- asr_train.py \
+ train.py \
+ --task_name asr \
--gpu_id $gpu_id \
--use_preprocessor true \
--split_with_space false \
--bpemodel ${bpemodel}.model \
--token_type $token_type \
- --dataset_type $dataset_type \
- --token_list $dict \
- --train_data_file $feats_dir/$dumpdir/${train_set}/ark_txt.scp \
- --valid_data_file $feats_dir/$dumpdir/${valid_set}/ark_txt.scp \
+ --token_list $token_list \
+ --data_dir ${feats_dir}/data \
+ --train_set ${train_set} \
+ --valid_set ${valid_set} \
--resume true \
--output_dir ${exp_dir}/exp/${model_dir} \
--config $asr_config \
@@ -183,6 +183,7 @@
--njob ${njob} \
--gpuid_list ${gpuid_list} \
--data_path_and_name_and_type "${_data}/${scp},speech,${type}" \
+ --cmvn_file ${feats_dir}/data/${train_set}/cmvn/cmvn.mvn \
--key_file "${_logdir}"/keys.JOB.scp \
--asr_train_config "${asr_exp}"/config.yaml \
--asr_model_file "${asr_exp}"/"${inference_asr_model}" \
--
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