parent
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#!/bin/bash
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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export MAIN_ROOT=`realpath ${PWD}/../../../`
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export PATH=${MAIN_ROOT}:${MAIN_ROOT}/utils:${PATH}
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export LC_ALL=C
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export PYTHONDONTWRITEBYTECODE=1
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# Use UTF-8 in Python to avoid UnicodeDecodeError when LC_ALL=C
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export PYTHONIOENCODING=UTF-8
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export PYTHONPATH=${MAIN_ROOT}:${PYTHONPATH}
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export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/usr/local/lib/
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MODEL=mdtc
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export BIN_DIR=${MAIN_ROOT}/paddlespeech/kws/exps/${MODEL}
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#!/bin/bash
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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. ./path.sh
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set -e
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stage=0
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stop_stage=50
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# data directory
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# if we set the variable ${dir}, we will store the wav info to this directory
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# otherwise, we will store the wav info to vox1 and vox2 directory respectively
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# vox2 wav path, we must convert the m4a format to wav format
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dir=data/ # data info directory
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exp_dir=exp/ecapa-tdnn-vox12-big/ # experiment directory
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conf_path=conf/mdtc.yaml
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gpus=0,1,2,3
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source ${MAIN_ROOT}/utils/parse_options.sh || exit 1;
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mkdir -p ${exp_dir}
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if [ $stage -le 0 ] && [ ${stop_stage} -ge 0 ]; then
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# stage 0: data prepare for vox1 and vox2, vox2 must be converted from m4a to wav
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bash ./local/data.sh ${dir} ${conf_path}|| exit -1;
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fi
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if [ $stage -le 1 ] && [ ${stop_stage} -ge 1 ]; then
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CUDA_VISIBLE_DEVICES=${gpus} bash ./local/train.sh ${dir} ${exp_dir} ${conf_path}
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fi
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if [ $stage -le 2 ] && [ ${stop_stage} -ge 2 ]; then
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CUDA_VISIBLE_DEVICES=0 bash ./local/test.sh ${dir} ${exp_dir} ${conf_path}
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fi
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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import json
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import os
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import sys
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from tqdm import tqdm
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def load_label_and_score(keyword, 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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for line in fin:
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arr = line.strip().split()
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key = arr[0]
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current_keyword = arr[1]
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str_list = arr[2:]
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if int(current_keyword) == keyword:
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scores = list(map(float, str_list))
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if key not in score_table:
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score_table.update({key: scores})
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keyword_table = {}
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filler_table = {}
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filler_duration = 0.0
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with open(label_file, 'r', encoding='utf8') as fin:
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for line in fin:
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obj = json.loads(line.strip())
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assert 'key' in obj
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assert 'txt' in obj
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assert 'duration' in obj
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key = obj['key']
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index = obj['txt']
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duration = obj['duration']
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assert key in score_table
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if index == keyword:
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keyword_table[key] = score_table[key]
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else:
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filler_table[key] = score_table[key]
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filler_duration += duration
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return keyword_table, filler_table, filler_duration
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class Args:
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def __init__(self):
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self.test_data = '/ssd3/chenxiaojie06/PaddleSpeech/DeepSpeech/paddlespeech/kws/models/data/test/data.list'
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self.keyword = 0
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self.score_file = os.path.join(
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os.path.abspath(sys.argv[1]), 'score.txt')
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self.stats_file = os.path.join(
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os.path.abspath(sys.argv[1]), 'stats.0.txt')
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self.step = 0.01
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self.window_shift = 50
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args = Args()
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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=int, default=0, help='keyword label')
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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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# parser.add_argument('--window_shift', type=int, default=50,
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# help='window_shift is used to skip the frames after triggered')
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# parser.add_argument('--stats_file',
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# required=True,
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# help='false reject/alarm stats file')
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# args = parser.parse_args()
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window_shift = args.window_shift
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keyword_table, filler_table, filler_duration = load_label_and_score(
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args.keyword, args.test_data, args.score_file)
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print('Filler total duration Hours: {}'.format(filler_duration / 3600.0))
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pbar = tqdm(total=int(1.0 / args.step))
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with open(args.stats_file, 'w', encoding='utf8') as fout:
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keyword_index = int(args.keyword)
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threshold = 0.0
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while threshold <= 1.0:
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num_false_reject = 0
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# transverse the all keyword_table
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for key, score_list in keyword_table.items():
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# computer positive test sample, use the max score of list.
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score = max(score_list)
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if float(score) < threshold:
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num_false_reject += 1
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num_false_alarm = 0
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# transverse the all filler_table
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for key, score_list in filler_table.items():
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i = 0
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while i < len(score_list):
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if score_list[i] >= threshold:
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num_false_alarm += 1
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i += window_shift
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else:
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i += 1
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if len(keyword_table) != 0:
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false_reject_rate = num_false_reject / len(keyword_table)
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num_false_alarm = max(num_false_alarm, 1e-6)
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if filler_duration != 0:
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false_alarm_per_hour = num_false_alarm / \
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(filler_duration / 3600.0)
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fout.write('{:.6f} {:.6f} {:.6f}\n'.format(
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threshold, false_alarm_per_hour, false_reject_rate))
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threshold += args.step
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pbar.update(1)
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pbar.close()
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print('DET saved to: {}'.format(args.stats_file))
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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import sys
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import matplotlib.pyplot as plt
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import numpy as np
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def load_stats_file(stats_file):
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values = []
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with open(stats_file, 'r', encoding='utf8') as fin:
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for line in fin:
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arr = line.strip().split()
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threshold, fa_per_hour, frr = arr
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values.append([float(fa_per_hour), float(frr) * 100])
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values.reverse()
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return np.array(values)
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def plot_det_curve(keywords, stats_dir, figure_file, xlim, x_step, ylim,
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y_step):
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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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plt.rcParams['font.size'] = 12
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for index, keyword in enumerate(keywords):
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stats_file = os.path.join(stats_dir, 'stats.' + str(index) + '.txt')
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values = load_stats_file(stats_file)
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plt.plot(values[:, 0], values[:, 1], label=keyword)
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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.grid(linestyle='--')
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plt.legend(loc='best', fontsize=16)
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plt.savefig(figure_file)
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if __name__ == '__main__':
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keywords = ['Hey_Snips']
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img_path = os.path.join(os.path.abspath(sys.argv[1]), 'det.png')
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plot_det_curve(keywords,
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os.path.abspath(sys.argv[1]), img_path, 10, 2, 10, 2)
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print('DET curve image saved to: {}'.format(img_path))
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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import sys
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import time
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import paddle
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from mdtc import KWSModel
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from mdtc import MDTC
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from tqdm import tqdm
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from paddleaudio.datasets import HeySnips
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def collate_features(batch):
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# (key, feat, label) in one sample
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collate_start = time.time()
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keys = []
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feats = []
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labels = []
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lengths = []
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for sample in batch:
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keys.append(sample[0])
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feats.append(sample[1])
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labels.append(sample[2])
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lengths.append(sample[1].shape[0])
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max_length = max(lengths)
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for i in range(len(feats)):
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feats[i] = paddle.nn.functional.pad(
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feats[i], [0, max_length - feats[i].shape[0], 0, 0],
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data_format='NLC')
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return keys, paddle.stack(feats), paddle.to_tensor(
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labels), paddle.to_tensor(lengths)
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if __name__ == '__main__':
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# Dataset
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feat_conf = {
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# 'n_mfcc': 80,
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'n_mels': 80,
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'frame_shift': 10,
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'frame_length': 25,
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# 'dither': 1.0,
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}
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test_ds = HeySnips(
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mode='test', feat_type='kaldi_fbank', sample_rate=16000, **feat_conf)
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test_sampler = paddle.io.BatchSampler(
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test_ds, batch_size=32, drop_last=False)
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test_loader = paddle.io.DataLoader(
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test_ds,
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batch_sampler=test_sampler,
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num_workers=16,
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return_list=True,
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use_buffer_reader=True,
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collate_fn=collate_features, )
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# Model
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backbone = MDTC(
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stack_num=3,
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stack_size=4,
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in_channels=80,
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res_channels=32,
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kernel_size=5,
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causal=True, )
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model = KWSModel(backbone=backbone, num_keywords=1)
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model = paddle.DataParallel(model)
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# kws_checkpoint = '/ssd3/chenxiaojie06/PaddleSpeech/DeepSpeech/paddlespeech/kws/models/checkpoint/epoch_10_0.8903940343290826/model.pdparams'
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kws_checkpoint = os.path.join(
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os.path.abspath(sys.argv[1]), 'model.pdparams')
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model.set_state_dict(paddle.load(kws_checkpoint))
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model.eval()
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score_abs_path = os.path.join(os.path.abspath(sys.argv[1]), 'score.txt')
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with paddle.no_grad(), open(score_abs_path, 'w', encoding='utf8') as fout:
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for batch_idx, batch in enumerate(
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tqdm(test_loader, total=len(test_loader))):
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keys, feats, labels, lengths = batch
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logits = model(feats)
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num_keywords = logits.shape[2]
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for i in range(len(keys)):
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key = keys[i]
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score = logits[i][:lengths[i]]
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for keyword_i in range(num_keywords):
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keyword_scores = score[:, keyword_i]
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score_frames = ' '.join(
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['{:.6f}'.format(x) for x in keyword_scores.tolist()])
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fout.write(
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'{} {} {}\n'.format(key, keyword_i, score_frames))
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print('Scores saved to: {}'.format(score_abs_path))
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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
|
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#
|
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# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
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#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
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import os
|
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import time
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import paddle
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from loss import max_pooling_loss
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from mdtc import KWSModel
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from mdtc import MDTC
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from paddleaudio.datasets import HeySnips
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from paddleaudio.utils import logger
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from paddleaudio.utils import Timer
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def collate_features(batch):
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# (key, feat, label)
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collate_start = time.time()
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keys = []
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feats = []
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labels = []
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lengths = []
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for sample in batch:
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keys.append(sample[0])
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feats.append(sample[1])
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labels.append(sample[2])
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lengths.append(sample[1].shape[0])
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max_length = max(lengths)
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for i in range(len(feats)):
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feats[i] = paddle.nn.functional.pad(
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feats[i], [0, max_length - feats[i].shape[0], 0, 0],
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data_format='NLC')
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return keys, paddle.stack(feats), paddle.to_tensor(
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labels), paddle.to_tensor(lengths)
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if __name__ == '__main__':
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# Dataset
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feat_conf = {
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# 'n_mfcc': 80,
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'n_mels': 80,
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'frame_shift': 10,
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'frame_length': 25,
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# 'dither': 1.0,
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}
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data_dir = '/ssd1/chenxiaojie06/datasets/hey_snips/hey_snips_research_6k_en_train_eval_clean_ter'
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train_ds = HeySnips(
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data_dir=data_dir,
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mode='train',
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feat_type='kaldi_fbank',
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sample_rate=16000,
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**feat_conf)
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dev_ds = HeySnips(
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data_dir=data_dir,
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mode='dev',
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feat_type='kaldi_fbank',
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sample_rate=16000,
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**feat_conf)
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training_conf = {
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'epochs': 100,
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'learning_rate': 0.001,
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'weight_decay': 0.00005,
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'num_workers': 16,
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'batch_size': 100,
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'checkpoint_dir': './checkpoint',
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'save_freq': 10,
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'log_freq': 10,
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}
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train_sampler = paddle.io.BatchSampler(
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train_ds,
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batch_size=training_conf['batch_size'],
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shuffle=True,
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drop_last=False)
|
||||
train_loader = paddle.io.DataLoader(
|
||||
train_ds,
|
||||
batch_sampler=train_sampler,
|
||||
num_workers=training_conf['num_workers'],
|
||||
return_list=True,
|
||||
use_buffer_reader=True,
|
||||
collate_fn=collate_features, )
|
||||
|
||||
# Model
|
||||
backbone = MDTC(
|
||||
stack_num=3,
|
||||
stack_size=4,
|
||||
in_channels=80,
|
||||
res_channels=32,
|
||||
kernel_size=5,
|
||||
causal=True, )
|
||||
model = KWSModel(backbone=backbone, num_keywords=1)
|
||||
model = paddle.DataParallel(model)
|
||||
clip = paddle.nn.ClipGradByGlobalNorm(5.0)
|
||||
optimizer = paddle.optimizer.Adam(
|
||||
learning_rate=training_conf['learning_rate'],
|
||||
weight_decay=training_conf['weight_decay'],
|
||||
parameters=model.parameters(),
|
||||
grad_clip=clip)
|
||||
criterion = max_pooling_loss
|
||||
|
||||
steps_per_epoch = len(train_sampler)
|
||||
timer = Timer(steps_per_epoch * training_conf['epochs'])
|
||||
timer.start()
|
||||
|
||||
for epoch in range(1, training_conf['epochs'] + 1):
|
||||
model.train()
|
||||
|
||||
avg_loss = 0
|
||||
num_corrects = 0
|
||||
num_samples = 0
|
||||
batch_start = time.time()
|
||||
for batch_idx, batch in enumerate(train_loader):
|
||||
# print('Fetch one batch: {:.4f}'.format(time.time()-batch_start))
|
||||
keys, feats, labels, lengths = batch
|
||||
logits = model(feats)
|
||||
loss, corrects, acc = criterion(logits, labels, lengths)
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
if isinstance(optimizer._learning_rate,
|
||||
paddle.optimizer.lr.LRScheduler):
|
||||
optimizer._learning_rate.step()
|
||||
optimizer.clear_grad()
|
||||
|
||||
# Calculate loss
|
||||
avg_loss += loss.numpy()[0]
|
||||
|
||||
# Calculate metrics
|
||||
num_corrects += corrects
|
||||
num_samples += feats.shape[0]
|
||||
|
||||
timer.count()
|
||||
|
||||
if (batch_idx + 1) % training_conf['log_freq'] == 0:
|
||||
lr = optimizer.get_lr()
|
||||
avg_loss /= training_conf['log_freq']
|
||||
avg_acc = num_corrects / num_samples
|
||||
|
||||
print_msg = 'Epoch={}/{}, Step={}/{}'.format(
|
||||
epoch, training_conf['epochs'], batch_idx + 1,
|
||||
steps_per_epoch)
|
||||
print_msg += ' loss={:.4f}'.format(avg_loss)
|
||||
print_msg += ' acc={:.4f}'.format(avg_acc)
|
||||
print_msg += ' lr={:.6f} step/sec={:.2f} | ETA {}'.format(
|
||||
lr, timer.timing, timer.eta)
|
||||
logger.train(print_msg)
|
||||
|
||||
avg_loss = 0
|
||||
num_corrects = 0
|
||||
num_samples = 0
|
||||
batch_start = time.time()
|
||||
|
||||
if epoch % training_conf[
|
||||
'save_freq'] == 0 and batch_idx + 1 == steps_per_epoch:
|
||||
dev_sampler = paddle.io.BatchSampler(
|
||||
dev_ds,
|
||||
batch_size=training_conf['batch_size'],
|
||||
shuffle=False,
|
||||
drop_last=False)
|
||||
dev_loader = paddle.io.DataLoader(
|
||||
dev_ds,
|
||||
batch_sampler=dev_sampler,
|
||||
num_workers=training_conf['num_workers'],
|
||||
return_list=True,
|
||||
use_buffer_reader=True,
|
||||
collate_fn=collate_features, )
|
||||
|
||||
model.eval()
|
||||
num_corrects = 0
|
||||
num_samples = 0
|
||||
with logger.processing('Evaluation on validation dataset'):
|
||||
for batch_idx, batch in enumerate(dev_loader):
|
||||
keys, feats, labels, lengths = batch
|
||||
logits = model(feats)
|
||||
loss, corrects, acc = criterion(logits, labels, lengths)
|
||||
num_corrects += corrects
|
||||
num_samples += feats.shape[0]
|
||||
|
||||
eval_acc = num_corrects / num_samples
|
||||
print_msg = '[Evaluation result]'
|
||||
print_msg += ' dev_acc={:.4f}'.format(eval_acc)
|
||||
|
||||
logger.eval(print_msg)
|
||||
|
||||
# Save model
|
||||
save_dir = os.path.join(training_conf['checkpoint_dir'],
|
||||
'epoch_{}_{:.4f}'.format(epoch, eval_acc))
|
||||
logger.info('Saving model checkpoint to {}'.format(save_dir))
|
||||
paddle.save(model.state_dict(),
|
||||
os.path.join(save_dir, 'model.pdparams'))
|
||||
paddle.save(optimizer.state_dict(),
|
||||
os.path.join(save_dir, 'model.pdopt'))
|
Loading…
Reference in new issue