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Add KWS example.

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KP 3 years ago
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commit b60b1dadde
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examples/hey_snips/README.md
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examples/hey_snips/kws0/RESULTS.md
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examples/hey_snips/kws0/path.sh
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#!/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.
export MAIN_ROOT=`realpath ${PWD}/../../../`
export PATH=${MAIN_ROOT}:${MAIN_ROOT}/utils:${PATH}
export LC_ALL=C
export PYTHONDONTWRITEBYTECODE=1
# Use UTF-8 in Python to avoid UnicodeDecodeError when LC_ALL=C
export PYTHONIOENCODING=UTF-8
export PYTHONPATH=${MAIN_ROOT}:${PYTHONPATH}
export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/usr/local/lib/
MODEL=mdtc
export BIN_DIR=${MAIN_ROOT}/paddlespeech/kws/exps/${MODEL}

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examples/hey_snips/kws0/run.sh
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#!/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.
. ./path.sh
set -e
stage=0
stop_stage=50
# data directory
# if we set the variable ${dir}, we will store the wav info to this directory
# otherwise, we will store the wav info to vox1 and vox2 directory respectively
# vox2 wav path, we must convert the m4a format to wav format
dir=data/ # data info directory
exp_dir=exp/ecapa-tdnn-vox12-big/ # experiment directory
conf_path=conf/mdtc.yaml
gpus=0,1,2,3
source ${MAIN_ROOT}/utils/parse_options.sh || exit 1;
mkdir -p ${exp_dir}
if [ $stage -le 0 ] && [ ${stop_stage} -ge 0 ]; then
# stage 0: data prepare for vox1 and vox2, vox2 must be converted from m4a to wav
bash ./local/data.sh ${dir} ${conf_path}|| exit -1;
fi
if [ $stage -le 1 ] && [ ${stop_stage} -ge 1 ]; then
CUDA_VISIBLE_DEVICES=${gpus} bash ./local/train.sh ${dir} ${exp_dir} ${conf_path}
fi
if [ $stage -le 2 ] && [ ${stop_stage} -ge 2 ]; then
CUDA_VISIBLE_DEVICES=0 bash ./local/test.sh ${dir} ${exp_dir} ${conf_path}
fi

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paddlespeech/kws/exps/mdtc/compute_det.py
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# Copyright (c) 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.
import json
import os
import sys
from tqdm import tqdm
def load_label_and_score(keyword, label_file, score_file):
# score_table: {uttid: [keywordlist]}
score_table = {}
with open(score_file, 'r', encoding='utf8') as fin:
for line in fin:
arr = line.strip().split()
key = arr[0]
current_keyword = arr[1]
str_list = arr[2:]
if int(current_keyword) == keyword:
scores = list(map(float, str_list))
if key not in score_table:
score_table.update({key: scores})
keyword_table = {}
filler_table = {}
filler_duration = 0.0
with open(label_file, 'r', encoding='utf8') as fin:
for line in fin:
obj = json.loads(line.strip())
assert 'key' in obj
assert 'txt' in obj
assert 'duration' in obj
key = obj['key']
index = obj['txt']
duration = obj['duration']
assert key in score_table
if index == keyword:
keyword_table[key] = score_table[key]
else:
filler_table[key] = score_table[key]
filler_duration += duration
return keyword_table, filler_table, filler_duration
class Args:
def __init__(self):
self.test_data = '/ssd3/chenxiaojie06/PaddleSpeech/DeepSpeech/paddlespeech/kws/models/data/test/data.list'
self.keyword = 0
self.score_file = os.path.join(
os.path.abspath(sys.argv[1]), 'score.txt')
self.stats_file = os.path.join(
os.path.abspath(sys.argv[1]), 'stats.0.txt')
self.step = 0.01
self.window_shift = 50
args = Args()
if __name__ == '__main__':
# parser = argparse.ArgumentParser(description='compute det curve')
# parser.add_argument('--test_data', required=True, help='label file')
# parser.add_argument('--keyword', type=int, default=0, help='keyword label')
# parser.add_argument('--score_file', required=True, help='score file')
# parser.add_argument('--step', type=float, default=0.01,
# help='threshold step')
# parser.add_argument('--window_shift', type=int, default=50,
# help='window_shift is used to skip the frames after triggered')
# parser.add_argument('--stats_file',
# required=True,
# help='false reject/alarm stats file')
# args = parser.parse_args()
window_shift = args.window_shift
keyword_table, filler_table, filler_duration = load_label_and_score(
args.keyword, args.test_data, args.score_file)
print('Filler total duration Hours: {}'.format(filler_duration / 3600.0))
pbar = tqdm(total=int(1.0 / args.step))
with open(args.stats_file, 'w', encoding='utf8') as fout:
keyword_index = int(args.keyword)
threshold = 0.0
while threshold <= 1.0:
num_false_reject = 0
# transverse the all keyword_table
for key, score_list in keyword_table.items():
# computer positive test sample, use the max score of list.
score = max(score_list)
if float(score) < threshold:
num_false_reject += 1
num_false_alarm = 0
# transverse the all filler_table
for key, score_list in filler_table.items():
i = 0
while i < len(score_list):
if score_list[i] >= threshold:
num_false_alarm += 1
i += window_shift
else:
i += 1
if len(keyword_table) != 0:
false_reject_rate = num_false_reject / len(keyword_table)
num_false_alarm = max(num_false_alarm, 1e-6)
if filler_duration != 0:
false_alarm_per_hour = num_false_alarm / \
(filler_duration / 3600.0)
fout.write('{:.6f} {:.6f} {:.6f}\n'.format(
threshold, false_alarm_per_hour, false_reject_rate))
threshold += args.step
pbar.update(1)
pbar.close()
print('DET saved to: {}'.format(args.stats_file))

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paddlespeech/kws/exps/mdtc/plot_det_curve.py
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# Copyright (c) 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.
import os
import sys
import matplotlib.pyplot as plt
import numpy as np
def load_stats_file(stats_file):
values = []
with open(stats_file, 'r', encoding='utf8') as fin:
for line in fin:
arr = line.strip().split()
threshold, fa_per_hour, frr = arr
values.append([float(fa_per_hour), float(frr) * 100])
values.reverse()
return np.array(values)
def plot_det_curve(keywords, stats_dir, figure_file, xlim, x_step, ylim,
y_step):
plt.figure(dpi=200)
plt.rcParams['xtick.direction'] = 'in'
plt.rcParams['ytick.direction'] = 'in'
plt.rcParams['font.size'] = 12
for index, keyword in enumerate(keywords):
stats_file = os.path.join(stats_dir, 'stats.' + str(index) + '.txt')
values = load_stats_file(stats_file)
plt.plot(values[:, 0], values[:, 1], label=keyword)
plt.xlim([0, xlim])
plt.ylim([0, ylim])
plt.xticks(range(0, xlim + x_step, x_step))
plt.yticks(range(0, ylim + y_step, y_step))
plt.xlabel('False Alarm Per Hour')
plt.ylabel('False Rejection Rate (\\%)')
plt.grid(linestyle='--')
plt.legend(loc='best', fontsize=16)
plt.savefig(figure_file)
if __name__ == '__main__':
keywords = ['Hey_Snips']
img_path = os.path.join(os.path.abspath(sys.argv[1]), 'det.png')
plot_det_curve(keywords,
os.path.abspath(sys.argv[1]), img_path, 10, 2, 10, 2)
print('DET curve image saved to: {}'.format(img_path))

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paddlespeech/kws/exps/mdtc/score.py
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# Copyright (c) 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.
import os
import sys
import time
import paddle
from mdtc import KWSModel
from mdtc import MDTC
from tqdm import tqdm
from paddleaudio.datasets import HeySnips
def collate_features(batch):
# (key, feat, label) in one sample
collate_start = time.time()
keys = []
feats = []
labels = []
lengths = []
for sample in batch:
keys.append(sample[0])
feats.append(sample[1])
labels.append(sample[2])
lengths.append(sample[1].shape[0])
max_length = max(lengths)
for i in range(len(feats)):
feats[i] = paddle.nn.functional.pad(
feats[i], [0, max_length - feats[i].shape[0], 0, 0],
data_format='NLC')
return keys, paddle.stack(feats), paddle.to_tensor(
labels), paddle.to_tensor(lengths)
if __name__ == '__main__':
# Dataset
feat_conf = {
# 'n_mfcc': 80,
'n_mels': 80,
'frame_shift': 10,
'frame_length': 25,
# 'dither': 1.0,
}
test_ds = HeySnips(
mode='test', feat_type='kaldi_fbank', sample_rate=16000, **feat_conf)
test_sampler = paddle.io.BatchSampler(
test_ds, batch_size=32, drop_last=False)
test_loader = paddle.io.DataLoader(
test_ds,
batch_sampler=test_sampler,
num_workers=16,
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)
# kws_checkpoint = '/ssd3/chenxiaojie06/PaddleSpeech/DeepSpeech/paddlespeech/kws/models/checkpoint/epoch_10_0.8903940343290826/model.pdparams'
kws_checkpoint = os.path.join(
os.path.abspath(sys.argv[1]), 'model.pdparams')
model.set_state_dict(paddle.load(kws_checkpoint))
model.eval()
score_abs_path = os.path.join(os.path.abspath(sys.argv[1]), 'score.txt')
with paddle.no_grad(), open(score_abs_path, 'w', encoding='utf8') as fout:
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()])
fout.write(
'{} {} {}\n'.format(key, keyword_i, score_frames))
print('Scores saved to: {}'.format(score_abs_path))

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paddlespeech/kws/exps/mdtc/train.py
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# Copyright (c) 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.
import os
import time
import paddle
from loss import max_pooling_loss
from mdtc import KWSModel
from mdtc import MDTC
from paddleaudio.datasets import HeySnips
from paddleaudio.utils import logger
from paddleaudio.utils import Timer
def collate_features(batch):
# (key, feat, label)
collate_start = time.time()
keys = []
feats = []
labels = []
lengths = []
for sample in batch:
keys.append(sample[0])
feats.append(sample[1])
labels.append(sample[2])
lengths.append(sample[1].shape[0])
max_length = max(lengths)
for i in range(len(feats)):
feats[i] = paddle.nn.functional.pad(
feats[i], [0, max_length - feats[i].shape[0], 0, 0],
data_format='NLC')
return keys, paddle.stack(feats), paddle.to_tensor(
labels), paddle.to_tensor(lengths)
if __name__ == '__main__':
# Dataset
feat_conf = {
# 'n_mfcc': 80,
'n_mels': 80,
'frame_shift': 10,
'frame_length': 25,
# 'dither': 1.0,
}
data_dir = '/ssd1/chenxiaojie06/datasets/hey_snips/hey_snips_research_6k_en_train_eval_clean_ter'
train_ds = HeySnips(
data_dir=data_dir,
mode='train',
feat_type='kaldi_fbank',
sample_rate=16000,
**feat_conf)
dev_ds = HeySnips(
data_dir=data_dir,
mode='dev',
feat_type='kaldi_fbank',
sample_rate=16000,
**feat_conf)
training_conf = {
'epochs': 100,
'learning_rate': 0.001,
'weight_decay': 0.00005,
'num_workers': 16,
'batch_size': 100,
'checkpoint_dir': './checkpoint',
'save_freq': 10,
'log_freq': 10,
}
train_sampler = paddle.io.BatchSampler(
train_ds,
batch_size=training_conf['batch_size'],
shuffle=True,
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'))
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