repair the code according to the part comment, test=doc

examples/voxceleb/sv0/local/speaker_verification_cosine.py

@@ -11,21 +11,21 @@
 # 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 argparse
 import ast
 import os
 
 import numpy as np
 import paddle
+import paddle.nn.functional as F
 from paddle.io import BatchSampler
 from paddle.io import DataLoader
-import paddle.nn.functional as F
-from paddlespeech.vector.training.metrics import compute_eer
+from tqdm import tqdm
+
 from paddleaudio.datasets.voxceleb import VoxCeleb1
 from paddlespeech.vector.models.ecapa_tdnn import EcapaTdnn
-from paddlespeech.vector.training.sid_model import SpeakerIdetification
-from tqdm import tqdm
+from paddlespeech.vector.modules.sid_model import SpeakerIdetification
+from paddlespeech.vector.training.metrics import compute_eer
 
 
 def pad_right_2d(x, target_length, axis=-1, mode='constant', **kwargs):
@@ -44,7 +44,7 @@ def pad_right_2d(x, target_length, axis=-1, mode='constant', **kwargs):
     return np.pad(x, pad_width, mode=mode, **kwargs)
 
 
-def feature_normalize(batch, mean_norm: bool = True, std_norm: bool = True):
+def feature_normalize(batch, mean_norm: bool=True, std_norm: bool=True):
     ids = [item['id'] for item in batch]
     lengths = np.asarray([item['feat'].shape[1] for item in batch])
     feats = list(
@@ -58,8 +58,8 @@ def feature_normalize(batch, mean_norm: bool = True, std_norm: bool = True):
         mean = feat.mean(axis=-1, keepdims=True) if mean_norm else 0
         std = feat.std(axis=-1, keepdims=True) if std_norm else 1
         feats[i][:, :lengths[i]] = (feat - mean) / std
-        assert feats[i][:, lengths[i]:].sum(
-        ) == 0  # Padding valus should all be 0.
+        assert feats[i][:, lengths[
+            i]:].sum() == 0  # Padding valus should all be 0.
 
     # Converts into ratios.
     lengths = (lengths / lengths.max()).astype(np.float32)
@@ -98,15 +98,15 @@ def main(args):
     print(f'Checkpoint loaded from {args.load_checkpoint}')
 
     # stage4: construct the enroll and test dataloader
-    enrol_ds = VoxCeleb1(subset='enrol',
+    enrol_ds = VoxCeleb1(
+        subset='enrol',
         feat_type='melspectrogram',
         random_chunk=False,
         n_mels=80,
         window_size=400,
         hop_length=160)
     enrol_sampler = BatchSampler(
-                    enrol_ds, 
-                    batch_size=args.batch_size,
+        enrol_ds, batch_size=args.batch_size,
         shuffle=True)  # Shuffle to make embedding normalization more robust.
     enrol_loader = DataLoader(enrol_ds,
                     batch_sampler=enrol_sampler,
@@ -115,16 +115,16 @@ def main(args):
                     num_workers=args.num_workers,
                     return_list=True,)
 
-    test_ds = VoxCeleb1(subset='test',
+    test_ds = VoxCeleb1(
+        subset='test',
         feat_type='melspectrogram',
         random_chunk=False,
         n_mels=80,
         window_size=400,
         hop_length=160)
 
-    test_sampler = BatchSampler(test_ds, 
-                                batch_size=args.batch_size, 
-                                shuffle=True)
+    test_sampler = BatchSampler(
+        test_ds, batch_size=args.batch_size, shuffle=True)
     test_loader = DataLoader(test_ds,
                             batch_sampler=test_sampler,
                             collate_fn=lambda x: feature_normalize(
@@ -169,12 +169,13 @@ def main(args):
                             embedding_mean, embedding_std = mean, std
                         else:
                             weight = 1 / batch_count  # Weight decay by batches.
-                            embedding_mean = (
-                                1 - weight) * embedding_mean + weight * mean
-                            embedding_std = (
-                                1 - weight) * embedding_std + weight * std
+                            embedding_mean = (1 - weight
+                                              ) * embedding_mean + weight * mean
+                            embedding_std = (1 - weight
+                                             ) * embedding_std + weight * std
                         # Apply global embedding normalization.
-                        embeddings = (embeddings - embedding_mean) / embedding_std
+                        embeddings = (
+                            embeddings - embedding_mean) / embedding_std
 
                     # Update embedding dict.
                     id2embedding.update(dict(zip(ids, embeddings)))
@@ -201,6 +202,7 @@ def main(args):
         f'EER of verification test: {EER*100:.4f}%, score threshold: {threshold:.5f}'
     )
 
+
 if __name__ == "__main__":
     # yapf: disable
     parser = argparse.ArgumentParser(__doc__)

examples/voxceleb/sv0/local/train.py

@@ -22,22 +22,23 @@ from paddle.io import DistributedBatchSampler
 
 from paddleaudio.datasets.voxceleb import VoxCeleb1
 from paddleaudio.features.core import melspectrogram
-from paddlespeech.vector.training.time import Timer
-from paddlespeech.vector.datasets.batch import feature_normalize
-from paddlespeech.vector.datasets.batch import waveform_collate_fn
-from paddlespeech.vector.layers.loss import AdditiveAngularMargin
-from paddlespeech.vector.layers.loss import LogSoftmaxWrapper
-from paddlespeech.vector.layers.lr import CyclicLRScheduler
+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
-from paddlespeech.vector.training.sid_model import SpeakerIdetification
+from paddlespeech.vector.modules.loss import AdditiveAngularMargin
+from paddlespeech.vector.modules.loss import LogSoftmaxWrapper
+from paddlespeech.vector.modules.lr import CyclicLRScheduler
+from paddlespeech.vector.modules.sid_model import SpeakerIdetification
+from paddlespeech.vector.utils.time import Timer
 
 # feat configuration
 cpu_feat_conf = {
     'n_mels': 80,
-    'window_size': 400,
-    'hop_length': 160,
+    'window_size': 400,  #ms
+    'hop_length': 160,  #ms
 }
 
+
 def main(args):
     # stage0: set the training device, cpu or gpu
     paddle.set_device(args.device)

paddleaudio/datasets/voxceleb.py

@@ -76,6 +76,9 @@ class VoxCeleb1(Dataset):
         'META_INFO', ('id', 'duration', 'wav', 'start', 'stop', 'spk_id'))
     base_path = os.path.join(DATA_HOME, 'vox1')
     wav_path = os.path.join(base_path, 'wav')
+    meta_path = os.path.join(base_path, 'meta')
+    veri_test_file = os.path.join(meta_path, 'veri_test2.txt')
+    csv_path = os.path.join(base_path, 'csv')
     subsets = ['train', 'dev', 'enrol', 'test']
 
     def __init__(

paddleaudio/utils/download.py

@@ -22,30 +22,22 @@ from .log import logger
 
 download.logger = logger
 
+__all__ = [
+    'decompress',
+    'download_and_decompress',
+    'load_state_dict_from_url',
+]
 
-def decompress(file: str, path: str=os.PathLike):
+
+def decompress(file: str):
     """
-    Extracts all files from a compressed file to specific path.
+    Extracts all files from a compressed file.
     """
     assert os.path.isfile(file), "File: {} not exists.".format(file)
-
-    if path is None:
-        print("decompress the data: {}".format(file))
     download._decompress(file)
-    else:
-        print("decompress the data: {} to {}".format(file, path))
-        if not os.path.isdir(path):
-            os.makedirs(path)
-
-        tmp_file = os.path.join(path, os.path.basename(file))
-        os.rename(file, tmp_file)
-        download._decompress(tmp_file)
-        os.rename(tmp_file, file)
 
 
-def download_and_decompress(archives: List[Dict[str, str]],
-                            path: str,
-                            decompress: bool=True):
+def download_and_decompress(archives: List[Dict[str, str]], path: str):
     """
     Download archieves and decompress to specific path.
     """
@@ -55,8 +47,8 @@ def download_and_decompress(archives: List[Dict[str, str]],
     for archive in archives:
         assert 'url' in archive and 'md5' in archive, \
             'Dictionary keys of "url" and "md5" are required in the archive, but got: {list(archieve.keys())}'
-        download.get_path_from_url(
-            archive['url'], path, archive['md5'], decompress=decompress)
+
+        download.get_path_from_url(archive['url'], path, archive['md5'])
 
 
 def load_state_dict_from_url(url: str, path: str, md5: str=None):

paddlespeech/vector/layers/loss.py → paddlespeech/vector/modules/loss.py

@@ -11,7 +11,6 @@
 # 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 math
 
 import paddle

paddlespeech/vector/training/metrics.py

+# 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.
+from typing import List
+
+import numpy as np
+from sklearn.metrics import roc_curve
+
+
+def compute_eer(labels: np.ndarray, scores: np.ndarray) -> List[float]:
+    '''
+    Compute EER and return score threshold.
+    '''
+    fpr, tpr, threshold = roc_curve(y_true=labels, y_score=scores)
+    fnr = 1 - tpr
+    eer_threshold = threshold[np.nanargmin(np.absolute((fnr - fpr)))]
+    eer = fpr[np.nanargmin(np.absolute((fnr - fpr)))]
+    return eer, eer_threshold

paddlespeech/vector/utils/download.py

+# 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 os
+from typing import Dict
+from typing import List
+
+from paddle.framework import load as load_state_dict
+from paddle.utils import download
+
+__all__ = [
+    'decompress',
+    'download_and_decompress',
+    'load_state_dict_from_url',
+]
+
+
+def decompress(file: str, path: str=os.PathLike):
+    """
+    Extracts all files from a compressed file to specific path.
+    """
+    assert os.path.isfile(file), "File: {} not exists.".format(file)
+
+    if path is None:
+        print("decompress the data: {}".format(file))
+        download._decompress(file)
+    else:
+        print("decompress the data: {} to {}".format(file, path))
+        if not os.path.isdir(path):
+            os.makedirs(path)
+
+        tmp_file = os.path.join(path, os.path.basename(file))
+        os.rename(file, tmp_file)
+        download._decompress(tmp_file)
+        os.rename(tmp_file, file)
+
+
+def download_and_decompress(archives: List[Dict[str, str]],
+                            path: str,
+                            decompress: bool=True):
+    """
+    Download archieves and decompress to specific path.
+    """
+    if not os.path.isdir(path):
+        os.makedirs(path)
+
+    for archive in archives:
+        assert 'url' in archive and 'md5' in archive, \
+            'Dictionary keys of "url" and "md5" are required in the archive, but got: {list(archieve.keys())}'
+        download.get_path_from_url(
+            archive['url'], path, archive['md5'], decompress=decompress)
+
+
+def load_state_dict_from_url(url: str, path: str, md5: str=None):
+    """
+    Download and load a state dict from url
+    """
+    if not os.path.isdir(path):
+        os.makedirs(path)
+
+    download.get_path_from_url(url, path, md5)
+    return load_state_dict(os.path.join(path, os.path.basename(url)))