update compute_det_ctc
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dujing
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@ -1,6 +1,6 @@
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#!/bin/bash
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# Copyright 2021 Binbin Zhang(binbzha@qq.com)
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# 2023 dujing(thuduj12@163.com)
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# 2023 Jing Du(thuduj12@163.com)
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. ./path.sh
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@ -29,9 +29,6 @@ trainbase=true
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trainbase_dir=data/base
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trainbase_config=conf/ds_tcn_ctc_base.yaml
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trainbase_exp=exp/base
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if $trainbase; then
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checkpoint=$trainbase_exp/final.pt
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fi
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if [ ${stage} -le -3 ] && [ ${stop_stage} -ge -3 ]; then
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echo "Download and extracte all datasets"
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@ -131,7 +128,8 @@ if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ] && [ $trainbase == true ]; then
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--num_keywords $num_keywords \
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--min_duration 50 \
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--seed 666 \
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$cmvn_opts
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$cmvn_opts \
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--checkpoint $trainbase_exp/23.pt
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fi
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if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
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@ -141,6 +139,18 @@ if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
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$norm_mean && cmvn_opts="--cmvn_file data/train/global_cmvn"
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$norm_var && cmvn_opts="$cmvn_opts --norm_var"
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num_gpus=$(echo $gpus | awk -F ',' '{print NF}')
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if $trainbase; then
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echo "Use the base model you trained as checkpoint: $trainbase_exp/final.pt"
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checkpoint=$trainbase_exp/final.pt
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else
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echo "Use the base model trained with WenetSpeech as checkpoint: mobvoi_kws_transcription/final.pt"
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if [ ! -d mobvoi_kws_transcription ] ;then
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git clone https://www.modelscope.cn/datasets/thuduj12/mobvoi_kws_transcription.git
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fi
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checkpoint=mobvoi_kws_transcription/final.pt
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fi
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torchrun --standalone --nnodes=1 --nproc_per_node=$num_gpus \
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wekws/bin/train.py --gpus $gpus \
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--config $config \
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@ -176,7 +186,7 @@ if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
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--lexicon_file data/lexicon.txt
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python wekws/bin/compute_det_ctc.py \
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--keyword 嗨小问,你好问问 \
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--keywords 嗨小问,你好问问 \
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--test_data data/test/data.list \
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--window_shift $window_shift \
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--step 0.001 \
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@ -16,10 +16,8 @@
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import argparse, logging, glob
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import json, re, os, numpy as np
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import matplotlib.font_manager as fm
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import matplotlib.pyplot as plt
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font = fm.FontProperties(size=15)
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import pypinyin
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def split_mixed_label(input_str):
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tokens = []
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@ -41,7 +39,6 @@ def space_mixed_label(input_str):
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return space_str.strip()
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def load_label_and_score(keywords_list, label_file, score_file):
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# score_table: {uttid: [keywordlist]}
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score_table = {}
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with open(score_file, 'r', encoding='utf8') as fin:
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# read score file and store in table
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@ -84,7 +81,6 @@ def load_label_and_score(keywords_list, label_file, score_file):
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assert 'duration' in obj
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key = obj['key']
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# wav_file = obj['wav']
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txt = "".join(obj['tok'])
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txt = space_mixed_label(txt)
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txt_regstr_lrblk = ' ' + txt + ' '
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@ -125,12 +121,8 @@ def load_stats_file(stats_file):
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values.reverse()
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return np.array(values)
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def plot_det(dets_dir, figure_file, det_title="DetCurve"):
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xlim = '[0,2]'
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# xstep = kwargs.get('xstep', '1')
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ylim = '[15,30]'
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# ystep = kwargs.get('ystep', '5')
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def plot_det(dets_dir, figure_file, xlim=5, x_step=1, ylim=35, y_step=5):
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det_title = "DetCurve"
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plt.figure(dpi=200)
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plt.rcParams['xtick.direction'] = 'in'
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plt.rcParams['ytick.direction'] = 'in'
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@ -138,33 +130,25 @@ def plot_det(dets_dir, figure_file, det_title="DetCurve"):
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for file in glob.glob(f'{dets_dir}/*stats*.txt'):
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logging.info(f'reading det data from {file}')
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label = os.path.basename(file).split('.')[0]
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label = os.path.basename(file).split('.')[1]
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label = "".join(pypinyin.lazy_pinyin(label))
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values = load_stats_file(file)
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plt.plot(values[:, 0], values[:, 1], label=label)
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xlim_splits = xlim.strip().replace('[', '').replace(']', '').split(',')
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assert len(xlim_splits) == 2
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ylim_splits = ylim.strip().replace('[', '').replace(']', '').split(',')
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assert len(ylim_splits) == 2
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plt.xlim(float(xlim_splits[0]), float(xlim_splits[1]))
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plt.ylim(float(ylim_splits[0]), float(ylim_splits[1]))
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# plt.xticks(range(0, xlim + x_step, x_step))
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# plt.yticks(range(0, ylim + y_step, y_step))
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plt.xlim([0, xlim])
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plt.ylim([0, ylim])
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plt.xticks(range(0, xlim + x_step, x_step))
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plt.yticks(range(0, ylim + y_step, y_step))
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plt.xlabel('False Alarm Per Hour')
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plt.ylabel('False Rejection Rate (\\%)')
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plt.title(det_title, fontproperties=font)
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plt.ylabel('False Rejection Rate (%)')
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plt.grid(linestyle='--')
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# plt.legend(loc='best', fontsize=6)
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plt.legend(loc='upper right', fontsize=5)
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# plt.show()
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plt.legend(loc='best', fontsize=6)
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plt.savefig(figure_file)
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='compute det curve')
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parser.add_argument('--test_data', required=True, help='label file')
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parser.add_argument('--keyword', type=str, default=None, help='keyword label')
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parser.add_argument('--keywords', type=str, default=None, help='keywords, split with comma(,)')
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parser.add_argument('--score_file', required=True, help='score file')
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parser.add_argument('--step', type=float, default=0.01,
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help='threshold step')
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@ -178,10 +162,22 @@ if __name__ == '__main__':
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required=False,
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default=None,
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help='det curve path, default is stats_dir/det.png')
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parser.add_argument(
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'--xlim',
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type=int,
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default=5,
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help='xlim：range of x-axis, x is false alarm per hour')
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parser.add_argument('--x_step', type=int, default=1, help='step on x-axis')
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parser.add_argument(
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'--ylim',
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type=int,
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default=75,
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help='ylim：range of y-axis, y is false rejection rate')
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parser.add_argument('--y_step', type=int, default=5, help='step on y-axis')
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args = parser.parse_args()
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window_shift = args.window_shift
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keywords_list = args.keyword.strip().split(',')
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keywords_list = args.keywords.strip().split(',')
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keyword_filler_table = load_label_and_score(keywords_list, args.test_data, args.score_file)
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for keyword in keywords_list:
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@ -238,4 +234,4 @@ if __name__ == '__main__':
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det_curve_path = args.det_curve_path
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else:
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det_curve_path = os.path.join(stats_dir, 'det.png')
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plot_det(stats_dir, det_curve_path)
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plot_det(stats_dir, det_curve_path, args.xlim, args.x_step, args.ylim, args.y_step)
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