exec pre-commit in paddlespeech vector, test=doc

paddlespeech/vector/__init__.py

@@ -10,4 +10,4 @@
 # 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.
\ No newline at end of file
+# limitations under the License.

paddlespeech/vector/exps/ecapa_tdnn/extract_emb.py

@@ -13,9 +13,8 @@
 # limitations under the License.
 import argparse
 import os
-
 import time
-import numpy as np
+
 import paddle
 from yacs.config import CfgNode
 
@@ -40,7 +39,8 @@ def extract_audio_embedding(args, config):
     ecapa_tdnn = EcapaTdnn(**config.model)
 
     # stage4: build the speaker verification train instance with backbone model
-    model = SpeakerIdetification(backbone=ecapa_tdnn, num_class=config.num_speakers)
+    model = SpeakerIdetification(
+        backbone=ecapa_tdnn, num_class=config.num_speakers)
     # stage 2: load the pre-trained model
     args.load_checkpoint = os.path.abspath(
         os.path.expanduser(args.load_checkpoint))
@@ -62,17 +62,17 @@ def extract_audio_embedding(args, config):
     # we need convert the audio feat to one-batch shape [batch, dim, time], where the batch is one
     # so the final shape is [1, dim, time]
     start_time = time.time()
-    feat = melspectrogram(x=waveform, 
-                          sr=config.sr,
-                          n_mels=config.n_mels,
-                          window_size=config.window_size,
-                          hop_length=config.hop_size)
+    feat = melspectrogram(
+        x=waveform,
+        sr=config.sr,
+        n_mels=config.n_mels,
+        window_size=config.window_size,
+        hop_length=config.hop_size)
     feat = paddle.to_tensor(feat).unsqueeze(0)
 
     # in inference period, the lengths is all one without padding
     lengths = paddle.ones([1])
-    feat = feature_normalize(
-        feat, mean_norm=True, std_norm=False)
+    feat = feature_normalize(feat, mean_norm=True, std_norm=False)
 
     # model backbone network forward the feats and get the embedding
     embedding = model.backbone(
@@ -80,7 +80,6 @@ def extract_audio_embedding(args, config):
     elapsed_time = time.time() - start_time
     audio_length = waveform.shape[0] / sr
 
-
     # stage 5: do global norm with external mean and std
     rtf = elapsed_time / audio_length
     logger.info(f"{args.device} rft={rtf}")

paddlespeech/vector/exps/ecapa_tdnn/train.py

@@ -13,9 +13,9 @@
 # limitations under the License.
 import argparse
 import os
+import time
 
 import numpy as np
-import time
 import paddle
 from paddle.io import BatchSampler
 from paddle.io import DataLoader
@@ -27,6 +27,7 @@ from paddleaudio.datasets.voxceleb import VoxCeleb
 from paddlespeech.s2t.utils.log import Log
 from paddlespeech.vector.io.augment import build_augment_pipeline
 from paddlespeech.vector.io.augment import waveform_augment
+from paddlespeech.vector.io.batch import batch_pad_right
 from paddlespeech.vector.io.batch import feature_normalize
 from paddlespeech.vector.io.batch import waveform_collate_fn
 from paddlespeech.vector.models.ecapa_tdnn import EcapaTdnn
@@ -36,7 +37,6 @@ from paddlespeech.vector.modules.sid_model import SpeakerIdetification
 from paddlespeech.vector.training.scheduler import CyclicLRScheduler
 from paddlespeech.vector.training.seeding import seed_everything
 from paddlespeech.vector.utils.time import Timer
-from paddlespeech.vector.io.batch import batch_pad_right
 
 logger = Log(__name__).getlog()
 
@@ -145,7 +145,7 @@ def main(args, config):
         reader_start = time.time()
         for batch_idx, batch in enumerate(train_loader):
             train_reader_cost += time.time() - reader_start
-            
+
             # stage 9-1: batch data is audio sample points and speaker id label
             feat_start = time.time()
             waveforms, labels = batch['waveforms'], batch['labels']
@@ -165,11 +165,12 @@ def main(args, config):
             # stage 9-3: extract the audio feats,such fbank, mfcc, spectrogram
             feats = []
             for waveform in waveforms.numpy():
-                feat = melspectrogram(x=waveform, 
-                                      sr=config.sr, 
-                                      n_mels=config.n_mels, 
-                                      window_size=config.window_size, 
-                                      hop_length=config.hop_size)
+                feat = melspectrogram(
+                    x=waveform,
+                    sr=config.sr,
+                    n_mels=config.n_mels,
+                    window_size=config.window_size,
+                    hop_length=config.hop_size)
                 feats.append(feat)
             feats = paddle.to_tensor(np.asarray(feats))
 
@@ -202,7 +203,7 @@ def main(args, config):
             num_corrects += (preds == labels).numpy().sum()
             num_samples += feats.shape[0]
             timer.count()  # step plus one in timer
-            
+
             # stage 9-10: print the log information only on 0-rank per log-freq batchs
             if (batch_idx + 1) % config.log_interval == 0 and local_rank == 0:
                 lr = optimizer.get_lr()
@@ -213,9 +214,12 @@ def main(args, config):
                     epoch, config.epochs, batch_idx + 1, steps_per_epoch)
                 print_msg += ' loss={:.4f}'.format(avg_loss)
                 print_msg += ' acc={:.4f}'.format(avg_acc)
-                print_msg += ' avg_reader_cost: {:.5f} sec,'.format(train_reader_cost / config.log_interval)
-                print_msg += ' avg_feat_cost: {:.5f} sec,'.format(train_feat_cost / config.log_interval)
-                print_msg += ' avg_train_cost: {:.5f} sec,'.format(train_run_cost / config.log_interval)
+                print_msg += ' avg_reader_cost: {:.5f} sec,'.format(
+                    train_reader_cost / config.log_interval)
+                print_msg += ' avg_feat_cost: {:.5f} sec,'.format(
+                    train_feat_cost / config.log_interval)
+                print_msg += ' avg_train_cost: {:.5f} sec,'.format(
+                    train_run_cost / config.log_interval)
                 print_msg += ' lr={:.4E} step/sec={:.2f} | ETA {}'.format(
                     lr, timer.timing, timer.eta)
                 logger.info(print_msg)
@@ -262,11 +266,12 @@ def main(args, config):
 
                     feats = []
                     for waveform in waveforms.numpy():
-                        feat = melspectrogram(x=waveform, 
-                                              sr=config.sr,
-                                              n_mels=config.n_mels,
-                                              window_size=config.window_size,
-                                              hop_length=config.hop_size)
+                        feat = melspectrogram(
+                            x=waveform,
+                            sr=config.sr,
+                            n_mels=config.n_mels,
+                            window_size=config.window_size,
+                            hop_length=config.hop_size)
                         feats.append(feat)
 
                     feats = paddle.to_tensor(np.asarray(feats))
@@ -285,7 +290,8 @@ def main(args, config):
             # stage 9-14: Save model parameters
             save_dir = os.path.join(args.checkpoint_dir,
                                     'epoch_{}'.format(epoch))
-            last_saved_epoch = os.path.join('epoch_{}'.format(epoch), "model.pdparams")
+            last_saved_epoch = os.path.join('epoch_{}'.format(epoch),
+                                            "model.pdparams")
             logger.info('Saving model checkpoint to {}'.format(save_dir))
             paddle.save(model.state_dict(),
                         os.path.join(save_dir, 'model.pdparams'))
@@ -300,10 +306,13 @@ def main(args, config):
         final_model = os.path.join(args.checkpoint_dir, "model.pdparams")
         logger.info(f"we will create the final model: {final_model}")
         if os.path.islink(final_model):
-            logger.info(f"An {final_model} already exists, we will rm is and create it again")
+            logger.info(
+                f"An {final_model} already exists, we will rm is and create it again"
+            )
             os.unlink(final_model)
         os.symlink(last_saved_epoch, final_model)
 
+
 if __name__ == "__main__":
     # yapf: disable
     parser = argparse.ArgumentParser(__doc__)