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PaddleSpeech/paddlespeech/kws/exps/mdtc/score.py

93 lines
3.5 KiB

# Copyright (c) 2021 Binbin Zhang(binbzha@qq.com)
# 2022 Shaoqing Yu(954793264@qq.com)
# 2022 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.
# Modified from wekws(https://github.com/wenet-e2e/wekws)
import paddle
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
if __name__ == '__main__':
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()
# 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(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=config['batch_size'], drop_last=False)
test_loader = paddle.io.DataLoader(
test_ds,
batch_sampler=test_sampler,
num_workers=config['num_workers'],
return_list=True,
use_buffer_reader=True,
collate_fn=collate_features, )
# Model
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(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
logits = model(feats)
num_keywords = logits.shape[2]
for i in range(len(keys)):
key = keys[i]
score = logits[i][:lengths[i]]
for keyword_i in range(num_keywords):
keyword_scores = score[:, keyword_i]
score_frames = ' '.join(
['{:.6f}'.format(x) for x in keyword_scores.tolist()])
f.write('{} {} {}\n'.format(key, keyword_i, score_frames))
print('Result saved to: {}'.format(args.score_file))