add different tess feature config

examples/tess/README.md

+# 背景
+
+模型任务与模型间接请参见 examples/esc50, 本目录是为了校验和测试 paddle.audio 的feature, backend等相关模块而建立.
+
+## 数据集
+
+[TESS: Toronto emotional speech set](https://tspace.library.utoronto.ca/handle/1807/24487) 是一个包含有 200 个目标词的时长为 2 ~ 3 秒的音频,七种情绪的数据集。由两个女演员录制(24岁和64岁),其中情绪分别是愤怒,恶心,害怕,高兴,惊喜,伤心,平淡.
+
+## 模型指标
+
+根据 `TESS` 提供的fold信息，对数据集进行 5-fold 的 fine-tune 2 epoch 训练和评估，dev准确率如下：
+
+|Model|feat_type|Acc|
+|--|--|--|
+|CNN14| mfcc | 0.8304 |
+|CNN14| logmelspectrogram | 0.9893 |
+|CNN14| spectrogram| 0.1304 | 
+|CNN14| melspectrogram| 0.1339 | 
+
+因为是功能验证,所以只config中训练了 2 个epoch.
+log_melspectrogram feature 在迭代 3 个epoch后, acc可以达到0.9983%.
+mfcc feature 在迭代3个epoch后, acc可以达到0.9983%.
+spectrogram feature 在迭代11个epoch后,acc可达0.95%.
+melspectrogram feature 在迭代17个epoch后,acc可到0.9375%.
+
+### 模型训练
+
+启动训练:
+```shell
+$ CUDA_VISIBLE_DEVICES=0 ./run.sh 1 conf/panns_mfcc.yaml
+$ CUDA_VISIBLE_DEVICES=0 ./run.sh 1 conf/panns_logmelspectrogram.yaml
+$ CUDA_VISIBLE_DEVICES=0 ./run.sh 1 conf/panns_melspectrogram.yaml
+$ CUDA_VISIBLE_DEVICES=0 ./run.sh 1 conf/panns_pectrogram.yaml
+```

examples/tess/cls0/conf/panns_logmelspectrogram.yaml

+data:
+  dataset: 'paddle.audio.datasets:TESS'
+  num_classes: 7
+  train:
+    mode: 'train'
+    split: 1
+    feat_type: 'logmelspectrogram'
+  dev:
+    mode: 'dev'
+    split: 1
+    feat_type: 'logmelspectrogram'
+
+model:
+  backbone: 'paddlespeech.cls.models:cnn14'
+
+feature:
+  n_fft: 1024
+  hop_length: 320
+  window: 'hann'
+  win_length: 1024
+  f_min: 50.0
+  f_max: 14000.0
+  n_mels: 64
+
+training:
+  epochs: 2
+  learning_rate: 0.0005
+  num_workers: 2
+  batch_size: 128
+  checkpoint_dir: './checkpoint_logmelspectrogram'
+  save_freq: 1
+  log_freq: 1

examples/tess/cls0/conf/panns_melspectrogram.yaml

+data:
+  dataset: 'paddle.audio.datasets:TESS'
+  num_classes: 7
+  train:
+    mode: 'train'
+    split: 1
+    feat_type: 'melspectrogram'
+  dev:
+    mode: 'dev'
+    split: 1
+    feat_type: 'melspectrogram'
+
+model:
+  backbone: 'paddlespeech.cls.models:cnn14'
+
+feature:
+  n_fft: 1024
+  hop_length: 320
+  window: 'hann'
+  win_length: 1024
+  f_min: 50.0
+  f_max: 14000.0
+  n_mels: 64
+
+training:
+  epochs: 2
+  learning_rate: 0.0005
+  num_workers: 2
+  batch_size: 128
+  checkpoint_dir: './checkpoint_melspectrogram'
+  save_freq: 1
+  log_freq: 1

examples/tess/cls0/conf/panns_mfcc.yaml

+data:
+  dataset: 'paddle.audio.datasets:TESS'
+  num_classes: 7
+  train:
+    mode: 'train'
+    split: 1
+    feat_type: 'mfcc'
+  dev:
+    mode: 'dev'
+    split: 1
+    feat_type: 'mfcc'
+
+model:
+  backbone: 'paddlespeech.cls.models:cnn14'
+
+feature:
+  n_fft: 1024
+  hop_length: 320
+  window: 'hann'
+  win_length: 1024
+  f_min: 50.0
+  f_max: 14000.0
+  n_mfcc: 64
+  n_mels: 64
+
+training:
+  epochs: 2
+  learning_rate: 0.0005
+  num_workers: 2
+  batch_size: 128
+  checkpoint_dir: './checkpoint_mfcc'
+  save_freq: 1
+  log_freq: 1

examples/tess/cls0/conf/panns_spectrogram.yaml

+data:
+  dataset: 'paddle.audio.datasets:TESS'
+  num_classes: 7
+  train:
+    mode: 'train'
+    split: 1
+    feat_type: 'spectrogram'
+  dev:
+    mode: 'dev'
+    split: 1
+    feat_type: 'spectrogram'
+
+model:
+  backbone: 'paddlespeech.cls.models:cnn14'
+
+feature:
+  n_fft: 126
+  hop_length: 320
+  window: 'hann'
+
+training:
+  epochs: 2
+  learning_rate: 0.0005
+  num_workers: 2
+  batch_size: 128
+  checkpoint_dir: './checkpoint_spectrogram'
+  save_freq: 1
+  log_freq: 1

examples/tess/cls0/local/train.py

@@ -22,6 +22,7 @@ from paddleaudio.utils import Timer
 from paddlespeech.cls.models import SoundClassifier
 from paddlespeech.utils.dynamic_import import dynamic_import
 
+
 # yapf: disable
 parser = argparse.ArgumentParser(__doc__)
 parser.add_argument("--cfg_path", type=str, required=True)
@@ -61,12 +62,17 @@ if __name__ == "__main__":
     model_conf = config['model']
     data_conf = config['data']
     feat_conf = config['feature']
+    feat_type = data_conf['train']['feat_type']
     training_conf = config['training']
 
     # Dataset
+
+    # set audio backend, make sure paddleaudio >= 1.0.2 installed.
+    paddle.audio.backends.set_backend('soundfile')
+
     ds_class = dynamic_import(data_conf['dataset'])
-    train_ds = ds_class(**data_conf['train'])
-    dev_ds = ds_class(**data_conf['dev'])
+    train_ds = ds_class(**data_conf['train'], **feat_conf)
+    dev_ds = ds_class(**data_conf['dev'], **feat_conf)
     train_sampler = paddle.io.DistributedBatchSampler(
         train_ds,
         batch_size=training_conf['batch_size'],
@@ -101,7 +107,7 @@ if __name__ == "__main__":
         num_corrects = 0
         num_samples = 0
         for batch_idx, batch in enumerate(train_loader):
-            feats, labels, length = batch # feats(N, length, n_mels)
+            feats, labels, length = batch # feats-->(N, length, n_mels)
 
             logits = model(feats)
 
@@ -129,7 +135,7 @@ if __name__ == "__main__":
                 avg_loss /= training_conf['log_freq']
                 avg_acc = num_corrects / num_samples
 
-                print_msg = 'Epoch={}/{}, Step={}/{}'.format(
+                print_msg = feat_type + ' Epoch={}/{}, Step={}/{}'.format(
                     epoch, training_conf['epochs'], batch_idx + 1,
                     steps_per_epoch)
                 print_msg += ' loss={:.4f}'.format(avg_loss)
@@ -153,23 +159,23 @@ if __name__ == "__main__":
                 dev_ds,
                 batch_sampler=dev_sampler,
                 num_workers=training_conf['num_workers'],
-                return_list=True, )
+                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):
-                    waveforms, labels = batch
-                    feats = feature_extractor(waveforms)
-
+                    feats, labels, length = batch
                     logits = model(feats)
 
                     preds = paddle.argmax(logits, axis=1)
                     num_corrects += (preds == labels).numpy().sum()
                     num_samples += feats.shape[0]
 
-            print_msg = '[Evaluation result]'
+            print_msg = '[Evaluation result] ' + str(feat_type)
             print_msg += ' dev_acc={:.4f}'.format(num_corrects / num_samples)
 
             logger.eval(print_msg)