Add KWS example.

audio/paddleaudio/datasets/__init__.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 from .esc50 import ESC50
 from .gtzan import GTZAN
+from .hey_snips import HeySnips
 from .rirs_noises import OpenRIRNoise
 from .tess import TESS
 from .urban_sound import UrbanSound8K

audio/paddleaudio/datasets/dataset.py

@@ -17,6 +17,8 @@ import numpy as np
 import paddle
 
 from ..backends import load as load_audio
+from ..compliance.kaldi import fbank as kaldi_fbank
+from ..compliance.kaldi import mfcc as kaldi_mfcc
 from ..compliance.librosa import melspectrogram
 from ..compliance.librosa import mfcc
 
@@ -24,6 +26,8 @@ feat_funcs = {
     'raw': None,
     'melspectrogram': melspectrogram,
     'mfcc': mfcc,
+    'kaldi_fbank': kaldi_fbank,
+    'kaldi_mfcc': kaldi_mfcc,
 }
 
 
@@ -73,16 +77,24 @@ class AudioClassificationDataset(paddle.io.Dataset):
         feat_func = feat_funcs[self.feat_type]
 
         record = {}
-        record['feat'] = feat_func(
+        if self.feat_type in ['kaldi_fbank', 'kaldi_mfcc']:
-            waveform, sample_rate,
+            waveform = paddle.to_tensor(waveform).unsqueeze(0)  # (C, T)
-            **self.feat_config) if feat_func else waveform
+            record['feat'] = feat_func(
+                waveform=waveform, sr=self.sample_rate, **self.feat_config)
+        else:
+            record['feat'] = feat_func(
+                waveform, sample_rate,
+                **self.feat_config) if feat_func else waveform
         record['label'] = label
         return record
 
     def __getitem__(self, idx):
         record = self._convert_to_record(idx)
-        return np.array(record['feat']).transpose(), np.array(
+        if self.feat_type in ['kaldi_fbank', 'kaldi_mfcc']:
-            record['label'], dtype=np.int64)
+            return self.keys[idx], record['feat'], record['label']
+        else:
+            return np.array(record['feat']).transpose(), np.array(
+                record['label'], dtype=np.int64)
 
     def __len__(self):
         return len(self.files)

audio/paddleaudio/datasets/hey_snips.py

@@ -0,0 +1,72 @@
+# 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.
+import collections
+import json
+import os
+from typing import List
+from typing import Tuple
+
+from .dataset import AudioClassificationDataset
+
+__all__ = ['HeySnips']
+
+
+class HeySnips(AudioClassificationDataset):
+    meta_info = collections.namedtuple('META_INFO',
+                                       ('key', 'label', 'duration', 'wav'))
+
+    def __init__(self,
+                 data_dir: os.PathLike,
+                 mode: str='train',
+                 feat_type: str='kaldi_fbank',
+                 sample_rate: int=16000,
+                 **kwargs):
+        self.data_dir = data_dir
+        files, labels = self._get_data(mode)
+        super(HeySnips, self).__init__(
+            files=files,
+            labels=labels,
+            feat_type=feat_type,
+            sample_rate=sample_rate,
+            **kwargs)
+
+    def _get_meta_info(self, mode) -> List[collections.namedtuple]:
+        ret = []
+        with open(os.path.join(self.data_dir, '{}.json'.format(mode)),
+                  'r') as f:
+            data = json.load(f)
+            for item in data:
+                sample = collections.OrderedDict()
+                if item['duration'] > 0:
+                    sample['key'] = item['id']
+                    sample['label'] = 0 if item['is_hotword'] == 1 else -1
+                    sample['duration'] = item['duration']
+                    sample['wav'] = os.path.join(self.data_dir,
+                                                 item['audio_file_path'])
+                    ret.append(self.meta_info(*sample.values()))
+        return ret
+
+    def _get_data(self, mode: str) -> Tuple[List[str], List[int]]:
+        meta_info = self._get_meta_info(mode)
+
+        files = []
+        labels = []
+        self.keys = []
+        for sample in meta_info:
+            key, target, _, wav = sample
+            files.append(wav)
+            labels.append(int(target))
+            self.keys.append(key)
+
+        return files, labels

paddlespeech/kws/__init__.py

@@ -11,3 +11,4 @@
 # 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 .models.mdtc import MDTC

paddlespeech/kws/models/mdtc.py

@@ -179,6 +179,7 @@ class MDTC(nn.Layer):
                          causal))
             self.receptive_fields += self.blocks[-1].receptive_fields
         self.half_receptive_fields = self.receptive_fields // 2
+        self.hidden_dim = res_channels
 
     def forward(self, x: paddle.Tensor):
         if self.causal:
@@ -216,3 +217,30 @@ class MDTC(nn.Layer):
             outputs += x
         outputs = outputs.transpose([0, 2, 1])
         return outputs, None
+
+
+class KWSModel(nn.Layer):
+    def __init__(self, backbone, num_keywords):
+        super(KWSModel, self).__init__()
+        self.backbone = backbone
+        self.linear = nn.Linear(self.backbone.hidden_dim, num_keywords)
+        self.activation = nn.Sigmoid()
+
+    def forward(self, x):
+        outputs = self.backbone(x)
+        outputs = self.linear(outputs)
+        return self.activation(outputs)
+
+
+if __name__ == '__main__':
+    paddle.set_device('cpu')
+    from paddleaudio.features import LogMelSpectrogram
+    mdtc = MDTC(3, 4, 80, 32, 5, causal=True)
+
+    x = paddle.randn(shape=(32, 16000 * 5))
+    feature_extractor = LogMelSpectrogram(sr=16000, n_fft=512, n_mels=80)
+    feats = feature_extractor(x).transpose([0, 2, 1])
+    print(feats.shape)
+
+    res, _ = mdtc(feats)
+    print(res.shape)