Update KWS example.

examples/hey_snips/kws0/conf/mdtc.yaml

@@ -1,25 +1,33 @@
-data:
-  data_dir: '/PATH/TO/DATA/hey_snips_research_6k_en_train_eval_clean_ter'
+# https://yaml.org/type/float.html
+###########################################
+#                   Data                  #
+###########################################
 dataset: 'paddleaudio.datasets:HeySnips'
+data_dir: '/PATH/TO/DATA/hey_snips_research_6k_en_train_eval_clean_ter'
 
-model:
-  num_keywords: 1
+############################################
+#           Network Architecture           #
+############################################
 backbone: 'paddlespeech.kws.models:MDTC'
-  config:
+num_keywords: 1
 stack_num: 3
 stack_size: 4
 in_channels: 80
 res_channels: 32
 kernel_size: 5
 
-feature:
+###########################################
+#                Feature                  #
+###########################################
 feat_type: 'kaldi_fbank'
 sample_rate: 16000
 frame_shift: 10
 frame_length: 25
 n_mels: 80
 
-training:
+###########################################
+#                Training                 #
+###########################################
 epochs: 100
 num_workers: 16
 batch_size: 100
@@ -30,7 +38,9 @@ training:
 weight_decay: 0.00005
 grad_clip: 5.0
 
-scoring:
+###########################################
+#                Scoring                  #
+###########################################
 batch_size: 100
 num_workers: 16
 checkpoint: './checkpoint/epoch_100/model.pdparams'

examples/hey_snips/kws0/local/plot.sh

@@ -1,2 +1,25 @@
 #!/bin/bash
-python3 ${BIN_DIR}/plot_det_curve.py --cfg_path=$1 --keyword HeySnips
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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
+#
+# 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.
+
+if [ $# != 3 ];then
+    echo "usage: ${0} config_path checkpoint output_file"
+    exit -1
+fi
+
+keyword=$1
+stats_file=$2
+img_file=$3
+
+python3 ${BIN_DIR}/plot_det_curve.py --keyword_label ${keyword} --stats_file ${stats_file} --img_file ${img_file}

examples/hey_snips/kws0/local/score.sh

@@ -1,5 +1,27 @@
 #!/bin/bash
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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
+#
+# 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.
 
-python3 ${BIN_DIR}/score.py --cfg_path=$1
+if [ $# != 4 ];then
+    echo "usage: ${0} checkpoint score_file stats_file"
+    exit -1
+fi
 
-python3 ${BIN_DIR}/compute_det.py --cfg_path=$1
+cfg_path=$1
+ckpt=$2
+score_file=$3
+stats_file=$4
+
+python3 ${BIN_DIR}/score.py --config ${cfg_path} --ckpt ${ckpt} --score_file ${score_file} || exit -1
+python3 ${BIN_DIR}/compute_det.py --config ${cfg_path} --score_file ${score_file} --stats_file ${stats_file} || exit -1

examples/hey_snips/kws0/local/train.sh

@@ -1,13 +1,31 @@
 #!/bin/bash
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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
+#
+# 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.
+
+if [ $# != 2 ];then
+    echo "usage: ${0} num_gpus config_path"
+    exit -1
+fi
 
 ngpu=$1
 cfg_path=$2
 
 if [ ${ngpu} -gt 0 ]; then
     python3 -m paddle.distributed.launch --gpus $CUDA_VISIBLE_DEVICES ${BIN_DIR}/train.py \
-    --cfg_path ${cfg_path}
+    --config ${cfg_path}
 else
     echo "set CUDA_VISIBLE_DEVICES to enable multi-gpus trainning."
     python3 ${BIN_DIR}/train.py \
-    --cfg_path ${cfg_path}
+    --config ${cfg_path}
 fi

examples/hey_snips/kws0/run.sh

@@ -32,10 +32,16 @@ if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
     ./local/train.sh ${ngpu} ${cfg_path} || exit -1
 fi
 
+ckpt=./checkpoint/epoch_100/model.pdparams
+score_file=./scores.txt
+stats_file=./stats.0.txt
+img_file=./det.png
+
 if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
-    ./local/score.sh ${cfg_path} || exit -1
+    ./local/score.sh ${cfg_path} ${ckpt} ${score_file} ${stats_file} || exit -1
 fi
 
+keyword=HeySnips
 if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
-    ./local/plot.sh ${cfg_path} || exit -1
+    ./local/plot.sh ${keyword} ${stats_file} ${img_file} || exit -1
 fi

paddlespeech/kws/exps/mdtc/compute_det.py

@@ -12,24 +12,15 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # Modified from wekws(https://github.com/wenet-e2e/wekws)
-import argparse
 import os
 
 import paddle
-import yaml
 from tqdm import tqdm
+from yacs.config import CfgNode
 
+from paddlespeech.s2t.training.cli import default_argument_parser
 from paddlespeech.s2t.utils.dynamic_import import dynamic_import
 
-# yapf: disable
-parser = argparse.ArgumentParser(__doc__)
-parser.add_argument("--cfg_path", type=str, required=True)
-parser.add_argument('--keyword_index', type=int, default=0, help='keyword index')
-parser.add_argument('--step', type=float, default=0.01, help='threshold step of trigger score')
-parser.add_argument('--window_shift', type=int, default=50, help='window_shift is used to skip the frames after triggered')
-args = parser.parse_args()
-# yapf: enable
-
 
 def load_label_and_score(keyword_index: int,
                          ds: paddle.io.Dataset,
@@ -61,26 +52,52 @@ def load_label_and_score(keyword_index: int,
 
 
 if __name__ == '__main__':
-    args.cfg_path = os.path.abspath(os.path.expanduser(args.cfg_path))
-    with open(args.cfg_path, 'r') as f:
-        config = yaml.safe_load(f)
+    parser = default_argument_parser()
+    parser.add_argument(
+        '--keyword_index', type=int, default=0, help='keyword index')
+    parser.add_argument(
+        '--step',
+        type=float,
+        default=0.01,
+        help='threshold step of trigger score')
+    parser.add_argument(
+        '--window_shift',
+        type=int,
+        default=50,
+        help='window_shift is used to skip the frames after triggered')
+    parser.add_argument(
+        "--score_file",
+        type=str,
+        required=True,
+        help='output file of trigger scores')
+    parser.add_argument(
+        '--stats_file',
+        type=str,
+        default='./stats.0.txt',
+        help='output file of detection error tradeoff')
+    args = parser.parse_args()
 
-    data_conf = config['data']
-    feat_conf = config['feature']
-    scoring_conf = config['scoring']
+    # https://yaml.org/type/float.html
+    config = CfgNode(new_allowed=True)
+    if args.config:
+        config.merge_from_file(args.config)
 
     # Dataset
-    ds_class = dynamic_import(data_conf['dataset'])
-    test_ds = ds_class(data_dir=data_conf['data_dir'], mode='test', **feat_conf)
-
-    score_file = os.path.abspath(scoring_conf['score_file'])
-    stats_file = os.path.abspath(scoring_conf['stats_file'])
+    ds_class = dynamic_import(config['dataset'])
+    test_ds = ds_class(
+        data_dir=config['data_dir'],
+        mode='test',
+        feat_type=config['feat_type'],
+        sample_rate=config['sample_rate'],
+        frame_shift=config['frame_shift'],
+        frame_length=config['frame_length'],
+        n_mels=config['n_mels'], )
 
     keyword_table, filler_table, filler_duration = load_label_and_score(
-        args.keyword, test_ds, score_file)
+        args.keyword_index, test_ds, args.score_file)
     print('Filler total duration Hours: {}'.format(filler_duration / 3600.0))
     pbar = tqdm(total=int(1.0 / args.step))
-    with open(stats_file, 'w', encoding='utf8') as fout:
+    with open(args.stats_file, 'w', encoding='utf8') as fout:
         keyword_index = args.keyword_index
         threshold = 0.0
         while threshold <= 1.0:
@@ -113,4 +130,4 @@ if __name__ == '__main__':
             pbar.update(1)
 
     pbar.close()
-    print('DET saved to: {}'.format(stats_file))
+    print('DET saved to: {}'.format(args.stats_file))

paddlespeech/kws/exps/mdtc/plot_det_curve.py

@@ -17,12 +17,12 @@ import os
 
 import matplotlib.pyplot as plt
 import numpy as np
-import yaml
 
 # yapf: disable
 parser = argparse.ArgumentParser(__doc__)
-parser.add_argument("--cfg_path", type=str, required=True)
-parser.add_argument("--keyword", type=str, required=True)
+parser.add_argument('--keyword_label', type=str, required=True, help='keyword string shown on image')
+parser.add_argument('--stats_file', type=str, required=True, help='output file of detection error tradeoff')
+parser.add_argument('--img_file', type=str, default='./det.png', help='output det image')
 args = parser.parse_args()
 # yapf: enable
 
@@ -61,14 +61,8 @@ def plot_det_curve(keywords, stats_file, figure_file, xlim, x_step, ylim,
 
 
 if __name__ == '__main__':
-    args.cfg_path = os.path.abspath(os.path.expanduser(args.cfg_path))
-    with open(args.cfg_path, 'r') as f:
-        config = yaml.safe_load(f)
-
-    scoring_conf = config['scoring']
-    img_file = os.path.abspath(scoring_conf['img_file'])
-    stats_file = os.path.abspath(scoring_conf['stats_file'])
-    keywords = [args.keyword]
-    plot_det_curve(keywords, stats_file, img_file, 10, 2, 10, 2)
+    img_file = os.path.abspath(args.img_file)
+    stats_file = os.path.abspath(args.stats_file)
+    plot_det_curve([args.keyword_label], stats_file, img_file, 10, 2, 10, 2)
 
     print('DET curve image saved to: {}'.format(img_file))

paddlespeech/kws/exps/mdtc/score.py

@@ -12,55 +12,67 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # Modified from wekws(https://github.com/wenet-e2e/wekws)
-import argparse
-import os
-
 import paddle
-import yaml
 from tqdm import tqdm
+from yacs.config import CfgNode
 
 from paddlespeech.kws.exps.mdtc.collate import collate_features
 from paddlespeech.kws.models.mdtc import KWSModel
+from paddlespeech.s2t.training.cli import default_argument_parser
 from paddlespeech.s2t.utils.dynamic_import import dynamic_import
 
-# yapf: disable
-parser = argparse.ArgumentParser(__doc__)
-parser.add_argument("--cfg_path", type=str, required=True)
-args = parser.parse_args()
-# yapf: enable
-
 if __name__ == '__main__':
-    args.cfg_path = os.path.abspath(os.path.expanduser(args.cfg_path))
-    with open(args.cfg_path, 'r') as f:
-        config = yaml.safe_load(f)
+    parser = default_argument_parser()
+    parser.add_argument(
+        "--ckpt",
+        type=str,
+        required=True,
+        help='model checkpoint for evaluation.')
+    parser.add_argument(
+        "--score_file",
+        type=str,
+        default='./scores.txt',
+        help='output file of trigger scores')
+    args = parser.parse_args()
 
-    model_conf = config['model']
-    data_conf = config['data']
-    feat_conf = config['feature']
-    scoring_conf = config['scoring']
+    # https://yaml.org/type/float.html
+    config = CfgNode(new_allowed=True)
+    if args.config:
+        config.merge_from_file(args.config)
 
     # Dataset
-    ds_class = dynamic_import(data_conf['dataset'])
-    test_ds = ds_class(data_dir=data_conf['data_dir'], mode='test', **feat_conf)
+    ds_class = dynamic_import(config['dataset'])
+    test_ds = ds_class(
+        data_dir=config['data_dir'],
+        mode='test',
+        feat_type=config['feat_type'],
+        sample_rate=config['sample_rate'],
+        frame_shift=config['frame_shift'],
+        frame_length=config['frame_length'],
+        n_mels=config['n_mels'], )
     test_sampler = paddle.io.BatchSampler(
-        test_ds, batch_size=scoring_conf['batch_size'], drop_last=False)
+        test_ds, batch_size=config['batch_size'], drop_last=False)
     test_loader = paddle.io.DataLoader(
         test_ds,
         batch_sampler=test_sampler,
-        num_workers=scoring_conf['num_workers'],
+        num_workers=config['num_workers'],
         return_list=True,
         use_buffer_reader=True,
         collate_fn=collate_features, )
 
     # Model
-    backbone_class = dynamic_import(model_conf['backbone'])
-    backbone = backbone_class(**model_conf['config'])
-    model = KWSModel(backbone=backbone, num_keywords=model_conf['num_keywords'])
-    model.set_state_dict(paddle.load(scoring_conf['checkpoint']))
+    backbone_class = dynamic_import(config['backbone'])
+    backbone = backbone_class(
+        stack_num=config['stack_num'],
+        stack_size=config['stack_size'],
+        in_channels=config['in_channels'],
+        res_channels=config['res_channels'],
+        kernel_size=config['kernel_size'], )
+    model = KWSModel(backbone=backbone, num_keywords=config['num_keywords'])
+    model.set_state_dict(paddle.load(args.ckpt))
     model.eval()
 
-    with paddle.no_grad(), open(
-            scoring_conf['score_file'], 'w', encoding='utf8') as fout:
+    with paddle.no_grad(), open(args.score_file, 'w', encoding='utf8') as f:
         for batch_idx, batch in enumerate(
                 tqdm(test_loader, total=len(test_loader))):
             keys, feats, labels, lengths = batch
@@ -73,7 +85,6 @@ if __name__ == '__main__':
                     keyword_scores = score[:, keyword_i]
                     score_frames = ' '.join(
                         ['{:.6f}'.format(x) for x in keyword_scores.tolist()])
-                    fout.write(
-                        '{} {} {}\n'.format(key, keyword_i, score_frames))
+                    f.write('{} {} {}\n'.format(key, keyword_i, score_frames))
 
-    print('Result saved to: {}'.format(scoring_conf['score_file']))
+    print('Result saved to: {}'.format(args.score_file))

paddlespeech/kws/exps/mdtc/train.py

@@ -11,77 +11,88 @@
 # 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.
-import argparse
 import os
 
 import paddle
-import yaml
+from yacs.config import CfgNode
 
 from paddleaudio.utils import logger
 from paddleaudio.utils import Timer
 from paddlespeech.kws.exps.mdtc.collate import collate_features
 from paddlespeech.kws.models.loss import max_pooling_loss
 from paddlespeech.kws.models.mdtc import KWSModel
+from paddlespeech.s2t.training.cli import default_argument_parser
 from paddlespeech.s2t.utils.dynamic_import import dynamic_import
 
-# yapf: disable
-parser = argparse.ArgumentParser(__doc__)
-parser.add_argument("--cfg_path", type=str, required=True)
+if __name__ == '__main__':
+    parser = default_argument_parser()
     args = parser.parse_args()
-# yapf: enable
 
-if __name__ == '__main__':
+    # https://yaml.org/type/float.html
+    config = CfgNode(new_allowed=True)
+    if args.config:
+        config.merge_from_file(args.config)
+
     nranks = paddle.distributed.get_world_size()
     if paddle.distributed.get_world_size() > 1:
         paddle.distributed.init_parallel_env()
     local_rank = paddle.distributed.get_rank()
 
-    args.cfg_path = os.path.abspath(os.path.expanduser(args.cfg_path))
-    with open(args.cfg_path, 'r') as f:
-        config = yaml.safe_load(f)
-
-    model_conf = config['model']
-    data_conf = config['data']
-    feat_conf = config['feature']
-    training_conf = config['training']
-
     # Dataset
-    ds_class = dynamic_import(data_conf['dataset'])
+    ds_class = dynamic_import(config['dataset'])
     train_ds = ds_class(
-        data_dir=data_conf['data_dir'], mode='train', **feat_conf)
-    dev_ds = ds_class(data_dir=data_conf['data_dir'], mode='dev', **feat_conf)
+        data_dir=config['data_dir'],
+        mode='train',
+        feat_type=config['feat_type'],
+        sample_rate=config['sample_rate'],
+        frame_shift=config['frame_shift'],
+        frame_length=config['frame_length'],
+        n_mels=config['n_mels'], )
+    dev_ds = ds_class(
+        data_dir=config['data_dir'],
+        mode='dev',
+        feat_type=config['feat_type'],
+        sample_rate=config['sample_rate'],
+        frame_shift=config['frame_shift'],
+        frame_length=config['frame_length'],
+        n_mels=config['n_mels'], )
 
     train_sampler = paddle.io.DistributedBatchSampler(
         train_ds,
-        batch_size=training_conf['batch_size'],
+        batch_size=config['batch_size'],
         shuffle=True,
         drop_last=False)
     train_loader = paddle.io.DataLoader(
         train_ds,
         batch_sampler=train_sampler,
-        num_workers=training_conf['num_workers'],
+        num_workers=config['num_workers'],
         return_list=True,
         use_buffer_reader=True,
         collate_fn=collate_features, )
 
     # Model
-    backbone_class = dynamic_import(model_conf['backbone'])
-    backbone = backbone_class(**model_conf['config'])
-    model = KWSModel(backbone=backbone, num_keywords=model_conf['num_keywords'])
+    backbone_class = dynamic_import(config['backbone'])
+    backbone = backbone_class(
+        stack_num=config['stack_num'],
+        stack_size=config['stack_size'],
+        in_channels=config['in_channels'],
+        res_channels=config['res_channels'],
+        kernel_size=config['kernel_size'], )
+    model = KWSModel(backbone=backbone, num_keywords=config['num_keywords'])
     model = paddle.DataParallel(model)
-    clip = paddle.nn.ClipGradByGlobalNorm(training_conf['grad_clip'])
+    clip = paddle.nn.ClipGradByGlobalNorm(config['grad_clip'])
     optimizer = paddle.optimizer.Adam(
-        learning_rate=training_conf['learning_rate'],
-        weight_decay=training_conf['weight_decay'],
+        learning_rate=config['learning_rate'],
+        weight_decay=config['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 = Timer(steps_per_epoch * config['epochs'])
     timer.start()
 
-    for epoch in range(1, training_conf['epochs'] + 1):
+    for epoch in range(1, config['epochs'] + 1):
         model.train()
 
         avg_loss = 0
@@ -107,15 +118,13 @@ if __name__ == '__main__':
 
             timer.count()
 
-            if (batch_idx + 1
-                ) % training_conf['log_freq'] == 0 and local_rank == 0:
+            if (batch_idx + 1) % config['log_freq'] == 0 and local_rank == 0:
                 lr = optimizer.get_lr()
-                avg_loss /= training_conf['log_freq']
+                avg_loss /= config['log_freq']
                 avg_acc = num_corrects / num_samples
 
                 print_msg = 'Epoch={}/{}, Step={}/{}'.format(
-                    epoch, training_conf['epochs'], batch_idx + 1,
-                    steps_per_epoch)
+                    epoch, config['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(
@@ -126,17 +135,17 @@ if __name__ == '__main__':
                 num_corrects = 0
                 num_samples = 0
 
-        if epoch % training_conf[
+        if epoch % config[
                 'save_freq'] == 0 and batch_idx + 1 == steps_per_epoch and local_rank == 0:
             dev_sampler = paddle.io.BatchSampler(
                 dev_ds,
-                batch_size=training_conf['batch_size'],
+                batch_size=config['batch_size'],
                 shuffle=False,
                 drop_last=False)
             dev_loader = paddle.io.DataLoader(
                 dev_ds,
                 batch_sampler=dev_sampler,
-                num_workers=training_conf['num_workers'],
+                num_workers=config['num_workers'],
                 return_list=True,
                 use_buffer_reader=True,
                 collate_fn=collate_features, )
@@ -159,7 +168,7 @@ if __name__ == '__main__':
             logger.eval(print_msg)
 
             # Save model
-            save_dir = os.path.join(training_conf['checkpoint_dir'],
+            save_dir = os.path.join(config['checkpoint_dir'],
                                     'epoch_{}'.format(epoch))
             logger.info('Saving model checkpoint to {}'.format(save_dir))
             paddle.save(model.state_dict(),