new code xvector

nnet/__init__.py

@@ -29,6 +29,8 @@ Copyright 2014-2019 Anthony Larcher and Sylvain Meignier
 
 from sidekit.nnet.feed_forward import FForwardNetwork
 from sidekit.nnet.feed_forward import kaldi_to_hdf5
+from sidekit.nnet.xsets import XvectorMultiDataset, XvectorDataset, StatDataset
+from sidekit.nnet.xvectors import Xtractor, xtrain, extract_idmap, extract_parallel
 
 
 __author__ = "Anthony Larcher and Sylvain Meignier"

nnet/xsets.py

+# -*- coding: utf-8 -*-
+#
+# This file is part of SIDEKIT.
+#
+# SIDEKIT is a python package for speaker verification.
+# Home page: http://www-lium.univ-lemans.fr/sidekit/
+#
+# SIDEKIT is a python package for speaker verification.
+# Home page: http://www-lium.univ-lemans.fr/sidekit/
+#
+# SIDEKIT is free software: you can redistribute it and/or modify
+# it under the terms of the GNU LLesser General Public License as
+# published by the Free Software Foundation, either version 3 of the License,
+# or (at your option) any later version.
+#
+# SIDEKIT is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with SIDEKIT.  If not, see <http://www.gnu.org/licenses/>.
+
+"""
+Copyright 2014-2019 Anthony Larcher
+
+
+The authors would like to thank the BUT Speech@FIT group (http://speech.fit.vutbr.cz) and Lukas BURGET
+for sharing the source code that strongly inspired this module. Thank you for your valuable contribution.
+"""
+
+import h5py
+import numpy
+import torch
+
+from sidekit.frontend.io import _read_dataset_percentile
+from sidekit.features_server import FeaturesServer
+
+
+__license__ = "LGPL"
+__author__ = "Anthony Larcher"
+__copyright__ = "Copyright 2015-2019 Anthony Larcher"
+__maintainer__ = "Anthony Larcher"
+__email__ = "anthony.larcher@univ-lemans.fr"
+__status__ = "Production"
+__docformat__ = 'reStructuredText'
+
+
+from collections import OrderedDict
+from torch.utils.data import Dataset
+
+
+def read_batch(batch_file):
+    with h5py.File(batch_file, 'r') as h5f:
+        data = _read_dataset_percentile(h5f, 'data')
+        label = h5f['label'].value
+
+        # Normalize and reshape
+        data = data.reshape((len(label), data.shape[0] // len(label), data.shape[1])).transpose(0, 2, 1)
+        for idx in range(data.shape[0]):
+            m = data[idx].mean(axis=0)
+            s = data[idx].std(axis=0)
+            data[idx] = (data[idx] - m) / s
+        return data, label
+
+
+class XvectorDataset(Dataset):
+    """
+    Object that takes a list of files from a file and initialize a Dataset
+    """
+    def __init__(self, batch_list, batch_path):
+        with open(batch_list, 'r') as fh:
+            self.batch_files = [batch_path + '/' + l.rstrip() for l in fh]
+        self.len = len(self.batch_files)
+
+    def __getitem__(self, index):
+        data, label = read_batch(self.batch_files[index])
+        return torch.from_numpy(data).type(torch.FloatTensor), torch.from_numpy(label.astype('long'))
+
+    def __len__(self):
+        return self.len
+
+
+class XvectorMultiDataset(Dataset):
+    """
+    Object that takes a list of files as a Python List and initialize a DataSet
+    """
+    def __init__(self, batch_list, batch_path):
+        self.batch_files = [batch_path + '/' + l for l in batch_list]
+        self.len = len(self.batch_files)
+
+    def __getitem__(self, index):
+        data, label = read_batch(self.batch_files[index])
+        return torch.from_numpy(data).type(torch.FloatTensor), torch.from_numpy(label.astype('long'))
+
+    def __len__(self):
+        return self.len
+
+
+class StatDataset(Dataset):
+    """
+    Object that initialize a Dataset from an sidekit.IdMap
+    """
+    def __init__(self, idmap, fs_param):
+        self.idmap = idmap
+        self.fs = FeaturesServer(**fs_param)
+        self.len = self.idmap.leftids.shape[0]
+
+    def __getitem__(self, index):
+        data, _ = self.fs.load(self.idmap.rightids[index])
+        data = (data - data.mean(0)) / data.std(0)
+        data = data.reshape((1, data.shape[0], data.shape[1])).transpose(0, 2, 1).astype(numpy.float32)
+        return self.idmap.leftids[index], self.idmap.rightids[index], torch.from_numpy(data).type(torch.FloatTensor)
+
+
+    def __len__(self):
+        return self.len
+
+