Update examples/esc50. (#1203)

docs/source/cls/custom_dataset.md

@@ -17,8 +17,6 @@ Here is an example to build your custom dataset in `custom_dataset.py`:
 from paddleaudio.datasets.dataset import AudioClassificationDataset
 
 class CustomDataset(AudioClassificationDataset):
-    # All *.wav file with same sample rate 16k/24k/32k/44k.
-    sample_rate = 16000
     meta_file = '/PATH/TO/META_FILE.txt'
     # List all the class labels
     label_list = [
@@ -54,8 +52,20 @@ from paddleaudio.features import LogMelSpectrogram
 
 from custom_dataset import CustomDataset
 
+# Feature config should be align with pretrained model
+feat_conf = {
+  'sr': 32000,
+  'n_fft': 1024,
+  'hop_length': 320,
+  'window': 'hann',
+  'win_length': 1024,
+  'f_min': 50.0,
+  'f_max': 14000.0,
+  'n_mels': 64,
+}
+
 train_ds = CustomDataset()
-feature_extractor = LogMelSpectrogram(sr=train_ds.sample_rate)
+feature_extractor = LogMelSpectrogram(**feat_conf)
 
 train_sampler = paddle.io.DistributedBatchSampler(
     train_ds, batch_size=4, shuffle=True, drop_last=False)

examples/esc50/README.md

@@ -17,21 +17,30 @@ PaddleAudio提供了PANNs的CNN14、CNN10和CNN6的预训练模型，可供用
 - CNN6: 该模型主要包含4个卷积层和2个全连接层，模型参数的数量为4.5M，embbedding维度是512。
 
 
+## 数据集
+
+[ESC-50: Dataset for Environmental Sound Classification](https://github.com/karolpiczak/ESC-50) 是一个包含有 2000 个带标签的环境声音样本，音频样本采样率为 44,100Hz 的单通道音频文件，所有样本根据标签被划分为 50 个类别，每个类别有 40 个样本。
+
+## 模型指标
+
+根据 `ESC-50` 提供的fold信息，对数据集进行 5-fold 的 fine-tune 训练和评估，平均准确率如下：
+
+|Model|Acc|
+|--|--|
+|CNN14| 0.950
+
 ## 快速开始
 
 ### 模型训练
 
-以环境声音分类数据集`ESC50`为示例，运行下面的命令，可在训练集上进行模型的finetune，支持单机的单卡训练和多卡训练。
+运行下面的命令，可在训练集上进行模型的finetune，支持单机的单卡训练和多卡训练。
 
 启动训练:
 ```shell
-$ CUDA_VISIBLE_DEVICES=0 ./run.sh 1
+$ CUDA_VISIBLE_DEVICES=0 ./run.sh 1 conf/panns.yaml
 ```
 
-`paddlespeech/cls/exps/panns/train.py` 脚本中可支持配置的参数：
-
-- `device`: 指定模型预测时使用的设备。
-- `feat_backend`: 选择提取特征的后端，可选`'numpy'`或`'paddle'`，默认为`'numpy'`。
+训练的参数可在 `conf/panns.yaml` 的 `training` 中配置，其中：
 - `epochs`: 训练轮次，默认为50。
 - `learning_rate`: Fine-tune的学习率；默认为5e-5。
 - `batch_size`: 批处理大小，请结合显存情况进行调整，若出现显存不足，请适当调低这一参数；默认为16。
@@ -40,36 +49,31 @@ $ CUDA_VISIBLE_DEVICES=0 ./run.sh 1
 - `save_freq`: 训练过程中的模型保存频率，默认为10。
 - `log_freq`: 训练过程中的信息打印频率，默认为10。
 
-示例代码中使用的预训练模型为`CNN14`，如果想更换为其他预训练模型，可通过以下方式执行：
-```python
-from paddleaudio.datasets import ESC50
-from paddlespeech.cls.models import SoundClassifier
-from paddlespeech.cls.models import cnn14, cnn10, cnn6
-
+示例代码中使用的预训练模型为`CNN14`，如果想更换为其他预训练模型，可通过修改 `conf/panns.yaml` 的 `model` 中配置：
+```yaml
 # CNN14
-backbone = cnn14(pretrained=True, extract_embedding=True)
-model = SoundClassifier(backbone, num_class=len(ESC50.label_list))
-
+model:
+  backbone: 'paddlespeech.cls.models:cnn14'
+```
+```yaml
 # CNN10
-backbone = cnn10(pretrained=True, extract_embedding=True)
-model = SoundClassifier(backbone, num_class=len(ESC50.label_list))
-
+model:
+  backbone: 'paddlespeech.cls.models:cnn10'
+```
+```yaml
 # CNN6
-backbone = cnn6(pretrained=True, extract_embedding=True)
-model = SoundClassifier(backbone, num_class=len(ESC50.label_list))
+model:
+  backbone: 'paddlespeech.cls.models:cnn6'
 ```
 
 ### 模型预测
 
 ```shell
-$ CUDA_VISIBLE_DEVICES=0 ./run.sh 2
+$ CUDA_VISIBLE_DEVICES=0 ./run.sh 2 conf/panns.yaml
 ```
 
-`paddlespeech/cls/exps/panns/predict.py` 脚本中可支持配置的参数：
-
-- `device`: 指定模型预测时使用的设备。
-- `wav`: 指定预测的音频文件。
-- `feat_backend`: 选择提取特征的后端，可选`'numpy'`或`'paddle'`，默认为`'numpy'`。
+训练的参数可在 `conf/panns.yaml` 的 `predicting` 中配置，其中：
+- `audio_file`: 指定预测的音频文件。
 - `top_k`: 预测显示的top k标签的得分，默认为1。
 - `checkpoint`: 模型参数checkpoint文件。
 
@@ -88,7 +92,7 @@ Cat: 6.579841738130199e-06
 模型训练结束后，可以将已保存的动态图参数导出成静态图的模型和参数，然后实施静态图的部署。
 
 ```shell
-$ CUDA_VISIBLE_DEVICES=0 ./run.sh 3
+$ CUDA_VISIBLE_DEVICES=0 ./run.sh 3 ./checkpoint/epoch_50/model.pdparams ./export
 ```
 
 `paddlespeech/cls/exps/panns/export_model.py` 脚本中可支持配置的参数：
@@ -109,7 +113,7 @@ export
 `paddlespeech/cls/exps/panns/deploy/predict.py` 脚本使用了`paddle.inference`模块下的api，提供了python端部署的示例：
 
 ```shell
-$ CUDA_VISIBLE_DEVICES=0 ./run.sh 4
+$ CUDA_VISIBLE_DEVICES=0 ./run.sh 4 cpu ./export /audio/dog.wav
 ```
 
 `paddlespeech/cls/exps/panns/deploy/predict.py` 脚本中可支持配置的主要参数：

examples/esc50/cls0/conf/panns.yaml

@@ -0,0 +1,36 @@
+data:
+  dataset: 'paddleaudio.datasets:ESC50'
+  num_classes: 50
+  train:
+    mode: 'train'
+    split: 1
+  dev:
+    mode: 'dev'
+    split: 1
+
+model:
+  backbone: 'paddlespeech.cls.models:cnn14'
+
+feature:
+  sr: 32000
+  n_fft: 1024
+  hop_length: 320
+  window: 'hann'
+  win_length: 1024
+  f_min: 50.0
+  f_max: 14000.0
+  n_mels: 64
+
+training:
+  epochs: 50
+  learning_rate: 0.00005
+  num_workers: 2
+  batch_size: 16
+  checkpoint_dir: './checkpoint'
+  save_freq: 10
+  log_freq: 10
+
+predicting:
+  audio_file: '/audio/dog.wav'
+  top_k: 10
+  checkpoint: './checkpoint/epoch_50/model.pdparams'

examples/esc50/cls0/local/export.sh

@@ -1,8 +1,8 @@
 #!/bin/bash
 
-ckpt_dir=$1
+ckpt=$1
 output_dir=$2
 
 python3 ${BIN_DIR}/export_model.py \
---checkpoint ${ckpt_dir}/model.pdparams \
+--checkpoint ${ckpt} \
 --output_dir ${output_dir}

examples/esc50/cls0/local/infer.sh

@@ -1,11 +1,4 @@
 #!/bin/bash
 
-audio_file=$1
-ckpt_dir=$2
-feat_backend=$3
-
 python3 ${BIN_DIR}/predict.py \
---wav ${audio_file} \
---feat_backend ${feat_backend} \
---top_k 10 \
---checkpoint ${ckpt_dir}/model.pdparams
+--cfg_path=$1

examples/esc50/cls0/local/train.sh

@@ -1,25 +1,12 @@
 #!/bin/bash
 
 ngpu=$1
-feat_backend=$2
-
-num_epochs=50
-batch_size=16
-ckpt_dir=./checkpoint
-save_freq=10
+cfg_path=$2
 
 if [ ${ngpu} -gt 0 ]; then
     python3 -m paddle.distributed.launch --gpus $CUDA_VISIBLE_DEVICES ${BIN_DIR}/train.py \
-    --epochs ${num_epochs} \
-    --feat_backend ${feat_backend} \
-    --batch_size ${batch_size} \
-    --checkpoint_dir ${ckpt_dir} \
-    --save_freq ${save_freq}
+    --cfg_path ${cfg_path}
 else
     python3 ${BIN_DIR}/train.py \
-    --epochs ${num_epochs} \
-    --feat_backend ${feat_backend} \
-    --batch_size ${batch_size} \
-    --checkpoint_dir ${ckpt_dir} \
-    --save_freq ${save_freq}
+    --cfg_path ${cfg_path}
 fi

examples/esc50/cls0/run.sh

@@ -6,28 +6,30 @@ ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
 
 stage=$1
 stop_stage=100
-feat_backend=numpy
-audio_file=~/cat.wav
-ckpt_dir=./checkpoint/epoch_50
-output_dir=./export
-infer_device=cpu
 
 if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
-    ./local/train.sh ${ngpu} ${feat_backend} || exit -1
+    cfg_path=$2
+    ./local/train.sh ${ngpu} ${cfg_path} || exit -1
     exit 0
 fi
 
 if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
-    ./local/infer.sh ${audio_file} ${ckpt_dir} ${feat_backend} || exit -1
+    cfg_path=$2
+    ./local/infer.sh ${cfg_path} || exit -1
     exit 0
 fi
 
 if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
-    ./local/export.sh ${ckpt_dir} ${output_dir} || exit -1
+    ckpt=$2
+    output_dir=$3
+    ./local/export.sh ${ckpt} ${output_dir} || exit -1
     exit 0
 fi
 
 if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
-    ./local/static_model_infer.sh ${infer_device} ${output_dir} ${audio_file} || exit -1
+    infer_device=$2
+    graph_dir=$3
+    audio_file=$4
+    ./local/static_model_infer.sh ${infer_device} ${graph_dir} ${audio_file} || exit -1
     exit 0
 fi

paddleaudio/datasets/dataset.py

@@ -36,6 +36,7 @@ class AudioClassificationDataset(paddle.io.Dataset):
                  files: List[str],
                  labels: List[int],
                  feat_type: str='raw',
+                 sample_rate: int=None,
                  **kwargs):
         """
         Ags:
@@ -55,6 +56,7 @@ class AudioClassificationDataset(paddle.io.Dataset):
         self.labels = labels
 
         self.feat_type = feat_type
+        self.sample_rate = sample_rate
         self.feat_config = kwargs  # Pass keyword arguments to customize feature config
 
     def _get_data(self, input_file: str):
@@ -63,7 +65,11 @@ class AudioClassificationDataset(paddle.io.Dataset):
     def _convert_to_record(self, idx):
         file, label = self.files[idx], self.labels[idx]
 
+        if self.sample_rate is None:
             waveform, sample_rate = load_audio(file)
+        else:
+            waveform, sample_rate = load_audio(file, sr=self.sample_rate)
+
         feat_func = feat_funcs[self.feat_type]
 
         record = {}

paddlespeech/cls/exps/panns/predict.py

@@ -12,38 +12,29 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import argparse
+import os
 
-import numpy as np
 import paddle
 import paddle.nn.functional as F
+import yaml
 
 from paddleaudio.backends import load as load_audio
-from paddleaudio.datasets import ESC50
 from paddleaudio.features import LogMelSpectrogram
-from paddleaudio.features import melspectrogram
-from paddlespeech.cls.models import cnn14
+from paddleaudio.utils import logger
 from paddlespeech.cls.models import SoundClassifier
+from paddlespeech.s2t.utils.dynamic_import import dynamic_import
 
 # yapf: disable
 parser = argparse.ArgumentParser(__doc__)
-parser.add_argument("--wav", type=str, required=True, help="Audio file to infer.")
-parser.add_argument("--feat_backend", type=str, choices=['numpy', 'paddle'], default='numpy', help="Choose backend to extract features from audio files.")
-parser.add_argument("--top_k", type=int, default=1, help="Show top k predicted results")
-parser.add_argument("--checkpoint", type=str, required=True, help="Checkpoint of model.")
+parser.add_argument("--cfg_path", type=str, required=True)
 args = parser.parse_args()
 # yapf: enable
 
 
-def extract_features(file: str, feat_backend: str='numpy',
-                     **kwargs) -> paddle.Tensor:
-    waveform, sr = load_audio(file, sr=None)
-
-    if args.feat_backend == 'numpy':
-        feat = melspectrogram(waveform, sr, **kwargs).transpose()
-        feat = np.expand_dims(feat, 0)
-        feat = paddle.to_tensor(feat)
-    else:
-        feature_extractor = LogMelSpectrogram(sr=sr, **kwargs)
+def extract_features(file: str, **feat_conf) -> paddle.Tensor:
+    file = os.path.abspath(os.path.expanduser(file))
+    waveform, _ = load_audio(file, sr=feat_conf['sr'])
+    feature_extractor = LogMelSpectrogram(**feat_conf)
     feat = feature_extractor(paddle.to_tensor(waveform).unsqueeze(0))
     feat = paddle.transpose(feat, [0, 2, 1])
     return feat
@@ -51,19 +42,31 @@ def extract_features(file: str, feat_backend: str='numpy',
 
 if __name__ == '__main__':
 
+    args.cfg_path = os.path.abspath(os.path.expanduser(args.cfg_path))
+    with open(args.cfg_path, 'r') as f:
+        config = yaml.safe_load(f)
+
+    model_conf = config['model']
+    data_conf = config['data']
+    feat_conf = config['feature']
+    predicting_conf = config['predicting']
+
+    ds_class = dynamic_import(data_conf['dataset'])
+    backbone_class = dynamic_import(model_conf['backbone'])
+
     model = SoundClassifier(
-        backbone=cnn14(pretrained=False, extract_embedding=True),
-        num_class=len(ESC50.label_list))
-    model.set_state_dict(paddle.load(args.checkpoint))
+        backbone=backbone_class(pretrained=False, extract_embedding=True),
+        num_class=len(ds_class.label_list))
+    model.set_state_dict(paddle.load(predicting_conf['checkpoint']))
     model.eval()
 
-    feat = extract_features(args.wav, args.feat_backend)
+    feat = extract_features(predicting_conf['audio_file'], **feat_conf)
     logits = model(feat)
     probs = F.softmax(logits, axis=1).numpy()
 
     sorted_indices = (-probs[0]).argsort()
 
-    msg = f'[{args.wav}]\n'
-    for idx in sorted_indices[:args.top_k]:
-        msg += f'{ESC50.label_list[idx]}: {probs[0][idx]}\n'
-    print(msg)
+    msg = f"[{predicting_conf['audio_file']}]\n"
+    for idx in sorted_indices[:predicting_conf['top_k']]:
+        msg += f'{ds_class.label_list[idx]}: {probs[0][idx]}\n'
+    logger.info(msg)

paddlespeech/cls/exps/panns/train.py

@@ -15,24 +15,17 @@ import argparse
 import os
 
 import paddle
+import yaml
 
-from paddleaudio.datasets import ESC50
 from paddleaudio.features import LogMelSpectrogram
 from paddleaudio.utils import logger
 from paddleaudio.utils import Timer
-from paddlespeech.cls.models import cnn14
 from paddlespeech.cls.models import SoundClassifier
+from paddlespeech.s2t.utils.dynamic_import import dynamic_import
 
 # yapf: disable
 parser = argparse.ArgumentParser(__doc__)
-parser.add_argument("--epochs", type=int, default=50, help="Number of epoches for fine-tuning.")
-parser.add_argument("--feat_backend", type=str, choices=['numpy', 'paddle'], default='numpy', help="Choose backend to extract features from audio files.")
-parser.add_argument("--learning_rate", type=float, default=5e-5, help="Learning rate used to train with warmup.")
-parser.add_argument("--batch_size", type=int, default=16, help="Total examples' number in batch for training.")
-parser.add_argument("--num_workers", type=int, default=0, help="Number of workers in dataloader.")
-parser.add_argument("--checkpoint_dir", type=str, default='./checkpoint', help="Directory to save model checkpoints.")
-parser.add_argument("--save_freq", type=int, default=10, help="Save checkpoint every n epoch.")
-parser.add_argument("--log_freq", type=int, default=10, help="Log the training infomation every n steps.")
+parser.add_argument("--cfg_path", type=str, required=True)
 args = parser.parse_args()
 # yapf: enable
 
@@ -42,50 +35,60 @@ if __name__ == "__main__":
         paddle.distributed.init_parallel_env()
     local_rank = paddle.distributed.get_rank()
 
-    backbone = cnn14(pretrained=True, extract_embedding=True)
-    model = SoundClassifier(backbone, num_class=len(ESC50.label_list))
-    model = paddle.DataParallel(model)
-    optimizer = paddle.optimizer.Adam(
-        learning_rate=args.learning_rate, parameters=model.parameters())
-    criterion = paddle.nn.loss.CrossEntropyLoss()
+    args.cfg_path = os.path.abspath(os.path.expanduser(args.cfg_path))
+    with open(args.cfg_path, 'r') as f:
+        config = yaml.safe_load(f)
 
-    if args.feat_backend == 'numpy':
-        train_ds = ESC50(mode='train', feat_type='melspectrogram')
-        dev_ds = ESC50(mode='dev', feat_type='melspectrogram')
-    else:
-        train_ds = ESC50(mode='train')
-        dev_ds = ESC50(mode='dev')
-        feature_extractor = LogMelSpectrogram(sr=16000)
+    model_conf = config['model']
+    data_conf = config['data']
+    feat_conf = config['feature']
+    training_conf = config['training']
 
+    # Dataset
+    ds_class = dynamic_import(data_conf['dataset'])
+    train_ds = ds_class(**data_conf['train'])
+    dev_ds = ds_class(**data_conf['dev'])
     train_sampler = paddle.io.DistributedBatchSampler(
-        train_ds, batch_size=args.batch_size, shuffle=True, drop_last=False)
+        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=args.num_workers,
+        num_workers=training_conf['num_workers'],
         return_list=True,
         use_buffer_reader=True, )
 
+    # Feature
+    feature_extractor = LogMelSpectrogram(**feat_conf)
+
+    # Model
+    backbone_class = dynamic_import(model_conf['backbone'])
+    backbone = backbone_class(pretrained=True, extract_embedding=True)
+    model = SoundClassifier(backbone, num_class=data_conf['num_classes'])
+    model = paddle.DataParallel(model)
+    optimizer = paddle.optimizer.Adam(
+        learning_rate=training_conf['learning_rate'],
+        parameters=model.parameters())
+    criterion = paddle.nn.loss.CrossEntropyLoss()
+
     steps_per_epoch = len(train_sampler)
-    timer = Timer(steps_per_epoch * args.epochs)
+    timer = Timer(steps_per_epoch * training_conf['epochs'])
     timer.start()
 
-    for epoch in range(1, args.epochs + 1):
+    for epoch in range(1, training_conf['epochs'] + 1):
         model.train()
 
         avg_loss = 0
         num_corrects = 0
         num_samples = 0
         for batch_idx, batch in enumerate(train_loader):
-            if args.feat_backend == 'numpy':
-                feats, labels = batch
-            else:
             waveforms, labels = batch
             feats = feature_extractor(
                 waveforms
             )  # Need a padding when lengths of waveforms differ in a batch.
-                feats = paddle.transpose(feats,
-                                         [0, 2, 1])  # To [N, length, n_mels]
+            feats = paddle.transpose(feats, [0, 2, 1])  # To [N, length, n_mels]
 
             logits = model(feats)
 
@@ -107,13 +110,15 @@ if __name__ == "__main__":
 
             timer.count()
 
-            if (batch_idx + 1) % args.log_freq == 0 and local_rank == 0:
+            if (batch_idx + 1
+                ) % training_conf['log_freq'] == 0 and local_rank == 0:
                 lr = optimizer.get_lr()
-                avg_loss /= args.log_freq
+                avg_loss /= training_conf['log_freq']
                 avg_acc = num_corrects / num_samples
 
                 print_msg = 'Epoch={}/{}, Step={}/{}'.format(
-                    epoch, args.epochs, batch_idx + 1, steps_per_epoch)
+                    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(
@@ -124,16 +129,17 @@ if __name__ == "__main__":
                 num_corrects = 0
                 num_samples = 0
 
-        if epoch % args.save_freq == 0 and batch_idx + 1 == steps_per_epoch and local_rank == 0:
+        if epoch % training_conf[
+                'save_freq'] == 0 and batch_idx + 1 == steps_per_epoch and local_rank == 0:
             dev_sampler = paddle.io.BatchSampler(
                 dev_ds,
-                batch_size=args.batch_size,
+                batch_size=training_conf['batch_size'],
                 shuffle=False,
                 drop_last=False)
             dev_loader = paddle.io.DataLoader(
                 dev_ds,
                 batch_sampler=dev_sampler,
-                num_workers=args.num_workers,
+                num_workers=training_conf['num_workers'],
                 return_list=True, )
 
             model.eval()
@@ -141,9 +147,6 @@ if __name__ == "__main__":
             num_samples = 0
             with logger.processing('Evaluation on validation dataset'):
                 for batch_idx, batch in enumerate(dev_loader):
-                    if args.feat_backend == 'numpy':
-                        feats, labels = batch
-                    else:
                     waveforms, labels = batch
                     feats = feature_extractor(waveforms)
                     feats = paddle.transpose(feats, [0, 2, 1])
@@ -160,7 +163,7 @@ if __name__ == "__main__":
             logger.eval(print_msg)
 
             # Save model
-            save_dir = os.path.join(args.checkpoint_dir,
+            save_dir = os.path.join(training_conf['checkpoint_dir'],
                                     'epoch_{}'.format(epoch))
             logger.info('Saving model checkpoint to {}'.format(save_dir))
             paddle.save(model.state_dict(),