diff --git a/README.md b/README.md
index e07cc6c..04b0f56 100644
--- a/README.md
+++ b/README.md
@@ -54,6 +54,15 @@ We plan to support a variaty of hardwares and platforms, including:
* Android
* Raspberry Pi
+## Discussion
+
+For Chinese users, you can scan the QR code on the left to follow our offical account of WeNet.
+We also created a WeChat group for better discussion and quicker response.
+Please scan the QR code on the right to join the chat group.
+
+| 
| 
|
+| ---- | ---- |
+
## Reference
* Mining Effective Negative Training Samples for Keyword Spotting
diff --git a/examples/hi_xiaowen/s0/README.md b/examples/hi_xiaowen/s0/README.md
index b88ad49..87ff5c3 100644
--- a/examples/hi_xiaowen/s0/README.md
+++ b/examples/hi_xiaowen/s0/README.md
@@ -4,9 +4,7 @@ FRRs with FAR fixed at once per hour:
|------------------|-----------|-----------|------------|--------------|
| GRU | 203 | 80(avg30) | 0.088901 | 0.083827 |
| TCN | 134 | 80(avg30) | 0.023494 | 0.029884 |
-| DS_TCN | 21 | 60 | 0.011559 | 0.014190 |
-| DS_TCN | 21 | 80 | 0.010807 | 0.014754 |
-| DS_TCN | 21 | 80(avg30) | 0.009867 | 0.014472 |
-| DS_TCN(spec_aug) | 21 | 80(avg30) | 0.029039 | 0.022648 |
+| DS_TCN | 21 | 80(avg30) | 0.019641 | 0.018325 |
+| DS_TCN(spec_aug) | 21 | 80(avg30) | 0.029509 | 0.008928 |
| MDTC | 156 | 80(avg10) | 0.007142 | 0.005920 |
| MDTC_Small | 31 | 80(avg10) | 0.005357 | 0.005920 |
diff --git a/examples/hi_xiaowen/s0/run.sh b/examples/hi_xiaowen/s0/run.sh
index 7c97999..3f7967b 100755
--- a/examples/hi_xiaowen/s0/run.sh
+++ b/examples/hi_xiaowen/s0/run.sh
@@ -9,15 +9,15 @@ stage=0
stop_stage=4
num_keywords=2
-config=conf/mdtc_small.yaml
-norm_mean=false
-norm_var=false
+config=conf/ds_tcn.yaml
+norm_mean=true
+norm_var=true
gpu_id=0
checkpoint=
-dir=exp/mdtc_small
+dir=exp/ds_tcn
-num_average=10
+num_average=30
score_checkpoint=$dir/avg_${num_average}.pt
download_dir=./data/local # your data dir
@@ -82,6 +82,7 @@ if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
--num_workers 8 \
--num_keywords $num_keywords \
--min_duration 50 \
+ --seed 666 \
$cmvn_opts \
${checkpoint:+--checkpoint $checkpoint}
fi
@@ -97,7 +98,7 @@ if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
# Compute posterior score
result_dir=$dir/test_$(basename $score_checkpoint)
mkdir -p $result_dir
- python kws/bin/score.py --gpu 1 \
+ python kws/bin/score.py --gpu $gpu_id \
--config $dir/config.yaml \
--test_data data/test/data.list \
--batch_size 256 \
diff --git a/examples/speechcommand_v1/s0/run.sh b/examples/speechcommand_v1/s0/run.sh
index d843209..b18a856 100755
--- a/examples/speechcommand_v1/s0/run.sh
+++ b/examples/speechcommand_v1/s0/run.sh
@@ -13,7 +13,7 @@ num_keywords=11
config=conf/mdtc.yaml
norm_mean=false
norm_var=false
-gpu_id=4
+gpu_id=0
checkpoint=
dir=exp/mdtc
@@ -79,3 +79,30 @@ if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
$cmvn_opts \
${checkpoint:+--checkpoint $checkpoint}
fi
+
+if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
+ # Do model average
+ python kws/bin/average_model.py \
+ --dst_model $score_checkpoint \
+ --src_path $dir \
+ --num ${num_average} \
+ --val_best
+
+ # Testing
+ result_dir=$dir/test_$(basename $score_checkpoint)
+ mkdir -p $result_dir
+ python kws/bin/compute_accuracy.py --gpu 3 \
+ --config $dir/config.yaml \
+ --test_data data/test/data.list \
+ --batch_size 256 \
+ --num_workers 8 \
+ --checkpoint $score_checkpoint
+fi
+
+
+if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
+ python kws/bin/export_jit.py --config $dir/config.yaml \
+ --checkpoint $score_checkpoint \
+ --output_file $dir/final.zip \
+ --output_quant_file $dir/final.quant.zip
+fi
diff --git a/kws/bin/compute_accuracy.py b/kws/bin/compute_accuracy.py
new file mode 100644
index 0000000..a6cde27
--- /dev/null
+++ b/kws/bin/compute_accuracy.py
@@ -0,0 +1,102 @@
+# Copyright (c) 2021 Binbin Zhang(binbzha@qq.com)
+#
+# 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.
+
+from __future__ import print_function
+
+import argparse
+import copy
+import logging
+import os
+
+import torch
+import yaml
+from torch.utils.data import DataLoader
+
+from kws.dataset.dataset import Dataset
+from kws.model.kws_model import init_model
+from kws.utils.checkpoint import load_checkpoint
+from kws.utils.executor import Executor
+
+
+def get_args():
+ parser = argparse.ArgumentParser(description='recognize with your model')
+ parser.add_argument('--config', required=True, help='config file')
+ parser.add_argument('--test_data', required=True, help='test data file')
+ parser.add_argument('--gpu',
+ type=int,
+ default=-1,
+ help='gpu id for this rank, -1 for cpu')
+ parser.add_argument('--checkpoint', required=True, help='checkpoint model')
+ parser.add_argument('--batch_size',
+ default=16,
+ type=int,
+ help='batch size for inference')
+ parser.add_argument('--num_workers',
+ default=0,
+ type=int,
+ help='num of subprocess workers for reading')
+ parser.add_argument('--pin_memory',
+ action='store_true',
+ default=False,
+ help='Use pinned memory buffers used for reading')
+ parser.add_argument('--prefetch',
+ default=100,
+ type=int,
+ help='prefetch number')
+ args = parser.parse_args()
+ return args
+
+
+def main():
+ args = get_args()
+ logging.basicConfig(level=logging.DEBUG,
+ format='%(asctime)s %(levelname)s %(message)s')
+ os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu)
+
+ with open(args.config, 'r') as fin:
+ configs = yaml.load(fin, Loader=yaml.FullLoader)
+
+ test_conf = copy.deepcopy(configs['dataset_conf'])
+ test_conf['filter_conf']['max_length'] = 102400
+ test_conf['filter_conf']['min_length'] = 0
+ test_conf['speed_perturb'] = False
+ test_conf['spec_aug'] = False
+ test_conf['shuffle'] = False
+ test_conf['feature_extraction_conf']['dither'] = 0.0
+ test_conf['batch_conf']['batch_size'] = args.batch_size
+
+ test_dataset = Dataset(args.test_data, test_conf)
+ test_data_loader = DataLoader(test_dataset,
+ batch_size=None,
+ pin_memory=args.pin_memory,
+ num_workers=args.num_workers)
+
+ # Init asr model from configs
+ model = init_model(configs['model'])
+
+ load_checkpoint(model, args.checkpoint)
+ use_cuda = args.gpu >= 0 and torch.cuda.is_available()
+ device = torch.device('cuda' if use_cuda else 'cpu')
+ model = model.to(device)
+ executor = Executor()
+ model.eval()
+ training_config = configs['training_config']
+ with torch.no_grad():
+ test_loss, test_acc = executor.test(model, test_data_loader, device,
+ training_config)
+ logging.info('Test Loss {} Acc {}'.format(test_loss, test_acc))
+
+
+if __name__ == '__main__':
+ main()
diff --git a/kws/model/tcn.py b/kws/model/tcn.py
index 6a002b1..958a951 100644
--- a/kws/model/tcn.py
+++ b/kws/model/tcn.py
@@ -20,21 +20,14 @@ import torch.nn as nn
import torch.nn.functional as F
-class CnnBlock(nn.Module):
+class Block(nn.Module):
def __init__(self,
channel: int,
kernel_size: int,
dilation: int,
dropout: float = 0.1):
super().__init__()
- # The CNN used here is causal convolution
self.padding = (kernel_size - 1) * dilation
- self.cnn = nn.Conv1d(channel,
- channel,
- kernel_size,
- stride=1,
- dilation=dilation)
- self.dropout = nn.Dropout(dropout)
def forward(self, x: torch.Tensor, cache: Optional[torch.Tensor] = None):
"""
@@ -43,6 +36,7 @@ class CnnBlock(nn.Module):
Returns:
torch.Tensor(B, D, T)
"""
+ # The CNN used here is causal convolution
if cache is None:
y = F.pad(x, (self.padding, 0), value=0.0)
else:
@@ -50,14 +44,32 @@ class CnnBlock(nn.Module):
assert y.size(2) > self.padding
new_cache = y[:, :, -self.padding:]
+ # self.cnn is defined in the subclass of Block
y = self.cnn(y)
- y = F.relu(y)
- y = self.dropout(y)
y = y + x # residual connection
return y, new_cache
-class DsCnnBlock(nn.Module):
+class CnnBlock(Block):
+ def __init__(self,
+ channel: int,
+ kernel_size: int,
+ dilation: int,
+ dropout: float = 0.1):
+ super().__init__(channel, kernel_size, dilation, dropout)
+ self.cnn = nn.Sequential(
+ nn.Conv1d(channel,
+ channel,
+ kernel_size,
+ stride=1,
+ dilation=dilation),
+ nn.BatchNorm1d(channel),
+ nn.ReLU(),
+ nn.Dropout(dropout),
+ )
+
+
+class DsCnnBlock(Block):
""" Depthwise Separable Convolution
"""
def __init__(self,
@@ -65,41 +77,21 @@ class DsCnnBlock(nn.Module):
kernel_size: int,
dilation: int,
dropout: float = 0.1):
- super().__init__()
- # The CNN used here is causal convolution
- self.padding = (kernel_size - 1) * dilation
- self.depthwise_cnn = nn.Conv1d(channel,
- channel,
- kernel_size,
- stride=1,
- dilation=dilation,
- groups=channel)
- self.pointwise_cnn = nn.Conv1d(channel,
- channel,
- kernel_size=1,
- stride=1)
- self.dropout = nn.Dropout(dropout)
-
- def forward(self, x: torch.Tensor, cache: Optional[torch.Tensor] = None):
- """
- Args:
- x(torch.Tensor): Input tensor (B, D, T)
- Returns:
- torch.Tensor(B, D, T)
- """
- if cache is None:
- y = F.pad(x, (self.padding, 0), value=0.0)
- else:
- y = torch.cat((cache, x), dim=2)
- assert y.size(2) > self.padding
- new_cache = y[:, :, -self.padding:]
-
- y = self.depthwise_cnn(y)
- y = self.pointwise_cnn(y)
- y = F.relu(y)
- y = self.dropout(y)
- y = y + x # residual connection
- return y, new_cache
+ super().__init__(channel, kernel_size, dilation, dropout)
+ self.cnn = nn.Sequential(
+ nn.Conv1d(channel,
+ channel,
+ kernel_size,
+ stride=1,
+ dilation=dilation,
+ groups=channel),
+ nn.BatchNorm1d(channel),
+ nn.ReLU(),
+ nn.Conv1d(channel, channel, kernel_size=1, stride=1),
+ nn.BatchNorm1d(channel),
+ nn.ReLU(),
+ nn.Dropout(dropout),
+ )
class TCN(nn.Module):