[vec][score] add plda model, test=doc fix #1667
qingen
2 years ago
parent
0f7ede11ef
commit
84576d6956
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# Copyright (c) 2022 SpeechBrain Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""A popular speaker recognition/diarization model (LDA and PLDA).
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Relevant Papers
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- This implementation of PLDA is based on the following papers.
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- PLDA model Training
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* Ye Jiang et. al, "PLDA Modeling in I-Vector and Supervector Space for Speaker Verification," in Interspeech, 2012.
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* Patrick Kenny et. al, "PLDA for speaker verification with utterances of arbitrary duration," in ICASSP, 2013.
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- PLDA scoring (fast scoring)
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* Daniel Garcia-Romero et. al, “Analysis of i-vector length normalization in speaker recognition systems,” in Interspeech, 2011.
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* Weiwei-LIN et. al, "Fast Scoring for PLDA with Uncertainty Propagation," in Odyssey, 2016.
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* Kong Aik Lee et. al, "Multi-session PLDA Scoring of I-vector for Partially Open-Set Speaker Detection," in Interspeech 2013.
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Credits
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This code is adapted from: https://git-lium.univ-lemans.fr/Larcher/sidekit
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"""
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import copy
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import pickle
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import numpy
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from scipy import linalg
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from paddlespeech.vector.cluster.diarization import EmbeddingMeta
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def ismember(list1, list2):
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c = [item in list2 for item in list1]
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return c
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class Ndx:
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"""
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A class that encodes trial index information. It has a list of
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model names and a list of test segment names and a matrix
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indicating which combinations of model and test segment are
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trials of interest.
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Arguments
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---------
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modelset : list
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List of unique models in a ndarray.
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segset : list
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List of unique test segments in a ndarray.
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trialmask : 2D ndarray of bool.
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Rows correspond to the models and columns to the test segments. True, if the trial is of interest.
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"""
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def __init__(self,
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ndx_file_name="",
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models=numpy.array([]),
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testsegs=numpy.array([])):
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"""
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Initialize a Ndx object by loading information from a file.
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Arguments
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---------
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ndx_file_name : str
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Name of the file to load.
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"""
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self.modelset = numpy.empty(0, dtype="|O")
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self.segset = numpy.empty(0, dtype="|O")
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self.trialmask = numpy.array([], dtype="bool")
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if ndx_file_name == "":
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# This is needed to make sizes same
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d = models.shape[0] - testsegs.shape[0]
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if d != 0:
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if d > 0:
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last = str(testsegs[-1])
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pad = numpy.array([last] * d)
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testsegs = numpy.hstack((testsegs, pad))
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# pad = testsegs[-d:]
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# testsegs = numpy.concatenate((testsegs, pad), axis=1)
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else:
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d = abs(d)
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last = str(models[-1])
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pad = numpy.array([last] * d)
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models = numpy.hstack((models, pad))
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# pad = models[-d:]
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# models = numpy.concatenate((models, pad), axis=1)
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modelset = numpy.unique(models)
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segset = numpy.unique(testsegs)
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trialmask = numpy.zeros(
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(modelset.shape[0], segset.shape[0]), dtype="bool")
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for m in range(modelset.shape[0]):
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segs = testsegs[numpy.array(ismember(models, modelset[m]))]
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trialmask[m, ] = ismember(segset, segs) # noqa E231
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self.modelset = modelset
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self.segset = segset
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self.trialmask = trialmask
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assert self.validate(), "Wrong Ndx format"
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else:
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ndx = Ndx.read(ndx_file_name)
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self.modelset = ndx.modelset
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self.segset = ndx.segset
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self.trialmask = ndx.trialmask
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def save_ndx_object(self, output_file_name):
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with open(output_file_name, "wb") as output:
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pickle.dump(self, output, pickle.HIGHEST_PROTOCOL)
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def filter(self, modlist, seglist, keep):
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"""
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Removes some of the information in an Ndx. Useful for creating a
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gender specific Ndx from a pooled gender Ndx. Depending on the
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value of \'keep\', the two input lists indicate the strings to
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retain or the strings to discard.
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Arguments
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---------
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modlist : array
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A cell array of strings which will be compared with the modelset of 'inndx'.
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seglist : array
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A cell array of strings which will be compared with the segset of 'inndx'.
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keep : bool
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Indicating whether modlist and seglist are the models to keep or discard.
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"""
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if keep:
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keepmods = modlist
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keepsegs = seglist
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else:
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keepmods = diff(self.modelset, modlist)
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keepsegs = diff(self.segset, seglist)
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keepmodidx = numpy.array(ismember(self.modelset, keepmods))
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keepsegidx = numpy.array(ismember(self.segset, keepsegs))
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outndx = Ndx()
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outndx.modelset = self.modelset[keepmodidx]
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outndx.segset = self.segset[keepsegidx]
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tmp = self.trialmask[numpy.array(keepmodidx), :]
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outndx.trialmask = tmp[:, numpy.array(keepsegidx)]
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assert outndx.validate, "Wrong Ndx format"
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if self.modelset.shape[0] > outndx.modelset.shape[0]:
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print(
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"Number of models reduced from %d to %d" %
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self.modelset.shape[0],
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outndx.modelset.shape[0], )
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if self.segset.shape[0] > outndx.segset.shape[0]:
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print(
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"Number of test segments reduced from %d to %d",
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self.segset.shape[0],
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outndx.segset.shape[0], )
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return outndx
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def validate(self):
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"""
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Checks that an object of type Ndx obeys certain rules that
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must always be true. Returns a boolean value indicating whether the object is valid
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"""
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ok = isinstance(self.modelset, numpy.ndarray)
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ok &= isinstance(self.segset, numpy.ndarray)
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ok &= isinstance(self.trialmask, numpy.ndarray)
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ok &= self.modelset.ndim == 1
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ok &= self.segset.ndim == 1
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ok &= self.trialmask.ndim == 2
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ok &= self.trialmask.shape == (self.modelset.shape[0],
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self.segset.shape[0], )
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return ok
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class Scores:
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"""
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A class for storing scores for trials. The modelset and segset
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fields are lists of model and test segment names respectively.
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The element i,j of scoremat and scoremask corresponds to the
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trial involving model i and test segment j.
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Arguments
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---------
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modelset : list
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List of unique models in a ndarray.
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segset : list
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List of unique test segments in a ndarray.
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scoremask : 2D ndarray of bool
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Indicates the trials of interest, i.e.,
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the entry i,j in scoremat should be ignored if scoremask[i,j] is False.
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scoremat : 2D ndarray
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Scores matrix.
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"""
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def __init__(self, scores_file_name=""):
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"""
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Initialize a Scores object by loading information from a file HDF5 format.
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Arguments
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---------
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scores_file_name : str
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Name of the file to load.
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"""
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self.modelset = numpy.empty(0, dtype="|O")
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self.segset = numpy.empty(0, dtype="|O")
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self.scoremask = numpy.array([], dtype="bool")
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self.scoremat = numpy.array([])
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if scores_file_name == "":
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pass
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else:
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tmp = Scores.read(scores_file_name)
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self.modelset = tmp.modelset
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self.segset = tmp.segset
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self.scoremask = tmp.scoremask
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self.scoremat = tmp.scoremat
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def __repr__(self):
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ch = "modelset:\n"
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ch += self.modelset + "\n"
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ch += "segset:\n"
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ch += self.segset + "\n"
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ch += "scoremask:\n"
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ch += self.scoremask.__repr__() + "\n"
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ch += "scoremat:\n"
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ch += self.scoremat.__repr__() + "\n"
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def fa_model_loop(
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batch_start,
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mini_batch_indices,
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factor_analyser,
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stat0,
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stats,
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e_h,
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e_hh, ):
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"""
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A function for PLDA estimation.
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Arguments
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---------
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batch_start : int
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Index to start at in the list.
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mini_batch_indices : list
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Indices of the elements in the list (should start at zero).
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factor_analyser : instance of PLDA class
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PLDA class object.
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stat0 : tensor
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Matrix of zero-order statistics.
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stats: tensor
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Matrix of first-order statistics.
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e_h : tensor
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An accumulator matrix.
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e_hh: tensor
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An accumulator matrix.
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"""
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rank = factor_analyser.F.shape[1]
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if factor_analyser.Sigma.ndim == 2:
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A = factor_analyser.F.T.dot(factor_analyser.F)
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inv_lambda_unique = dict()
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for sess in numpy.unique(stat0[:, 0]):
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inv_lambda_unique[sess] = linalg.inv(sess * A + numpy.eye(A.shape[
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0]))
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tmp = numpy.zeros(
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(factor_analyser.F.shape[1], factor_analyser.F.shape[1]),
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dtype=numpy.float64, )
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for idx in mini_batch_indices:
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if factor_analyser.Sigma.ndim == 1:
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inv_lambda = linalg.inv(
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numpy.eye(rank) + (factor_analyser.F.T * stat0[
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idx + batch_start, :]).dot(factor_analyser.F))
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else:
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inv_lambda = inv_lambda_unique[stat0[idx + batch_start, 0]]
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aux = factor_analyser.F.T.dot(stats[idx + batch_start, :])
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numpy.dot(aux, inv_lambda, out=e_h[idx])
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e_hh[idx] = inv_lambda + numpy.outer(e_h[idx], e_h[idx], tmp)
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def _check_missing_model(enroll, test, ndx):
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# Remove missing models and test segments
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clean_ndx = ndx.filter(enroll.modelset, test.segset, True)
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# Align EmbeddingMeta to match the clean_ndx
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enroll.align_models(clean_ndx.modelset)
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test.align_segments(clean_ndx.segset)
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return clean_ndx
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def fast_PLDA_scoring(
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enroll,
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test,
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ndx,
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mu,
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F,
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Sigma,
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test_uncertainty=None,
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Vtrans=None,
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p_known=0.0,
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scaling_factor=1.0,
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check_missing=True, ):
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"""
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Compute the PLDA scores between to sets of vectors. The list of
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trials to perform is given in an Ndx object. PLDA matrices have to be
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pre-computed. i-vectors/x-vectors are supposed to be whitened before.
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Arguments
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---------
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enroll : speechbrain.utils.Xvector_PLDA_sp.StatObject_SB
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A StatServer in which stat1 are xvectors.
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test : speechbrain.utils.Xvector_PLDA_sp.StatObject_SB
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A StatServer in which stat1 are xvectors.
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ndx : speechbrain.utils.Xvector_PLDA_sp.Ndx
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An Ndx object defining the list of trials to perform.
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mu : double
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The mean vector of the PLDA gaussian.
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F : tensor
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The between-class co-variance matrix of the PLDA.
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Sigma: tensor
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The residual covariance matrix.
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p_known : float
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Probability of having a known speaker for open-set
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identification case (=1 for the verification task and =0 for the
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closed-set case).
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check_missing : bool
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If True, check that all models and segments exist.
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"""
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enroll_ctr = copy.deepcopy(enroll)
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test_ctr = copy.deepcopy(test)
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# Remove missing models and test segments
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if check_missing:
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clean_ndx = _check_missing_model(enroll_ctr, test_ctr, ndx)
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else:
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clean_ndx = ndx
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# Center the i-vectors around the PLDA mean
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enroll_ctr.center_stats(mu)
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test_ctr.center_stats(mu)
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# Compute constant component of the PLDA distribution
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invSigma = linalg.inv(Sigma)
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I_spk = numpy.eye(F.shape[1], dtype="float")
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K = F.T.dot(invSigma * scaling_factor).dot(F)
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K1 = linalg.inv(K + I_spk)
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K2 = linalg.inv(2 * K + I_spk)
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# Compute the Gaussian distribution constant
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alpha1 = numpy.linalg.slogdet(K1)[1]
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alpha2 = numpy.linalg.slogdet(K2)[1]
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plda_cst = alpha2 / 2.0 - alpha1
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# Compute intermediate matrices
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Sigma_ac = numpy.dot(F, F.T)
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Sigma_tot = Sigma_ac + Sigma
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Sigma_tot_inv = linalg.inv(Sigma_tot)
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Tmp = linalg.inv(Sigma_tot - Sigma_ac.dot(Sigma_tot_inv).dot(Sigma_ac))
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Phi = Sigma_tot_inv - Tmp
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Psi = Sigma_tot_inv.dot(Sigma_ac).dot(Tmp)
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# Compute the different parts of PLDA score
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model_part = 0.5 * numpy.einsum("ij, ji->i",
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enroll_ctr.stats.dot(Phi),
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enroll_ctr.stats.T)
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seg_part = 0.5 * numpy.einsum("ij, ji->i",
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test_ctr.stats.dot(Phi), test_ctr.stats.T)
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# Compute verification scores
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score = Scores() # noqa F821
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score.modelset = clean_ndx.modelset
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score.segset = clean_ndx.segset
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score.scoremask = clean_ndx.trialmask
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score.scoremat = model_part[:, numpy.newaxis] + seg_part + plda_cst
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score.scoremat += enroll_ctr.stats.dot(Psi).dot(test_ctr.stats.T)
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score.scoremat *= scaling_factor
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# Case of open-set identification, we compute the log-likelihood
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# by taking into account the probability of having a known impostor
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# or an out-of set class
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if p_known != 0:
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N = score.scoremat.shape[0]
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open_set_scores = numpy.empty(score.scoremat.shape)
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tmp = numpy.exp(score.scoremat)
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for ii in range(N):
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# open-set term
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open_set_scores[ii, :] = score.scoremat[ii, :] - numpy.log(
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p_known * tmp[~(numpy.arange(N) == ii)].sum(axis=0) / (
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N - 1) + (1 - p_known))
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score.scoremat = open_set_scores
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return score
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class PLDA:
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"""
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A class to train PLDA model from embeddings.
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The input is in paddlespeech.vector.cluster.diarization.EmbeddingMeta format.
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Trains a simplified PLDA model no within-class covariance matrix but full residual covariance matrix.
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Arguments
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---------
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mean : tensor
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Mean of the vectors.
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F : tensor
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Eigenvoice matrix.
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Sigma : tensor
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Residual matrix.
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"""
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def __init__(
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self,
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mean=None,
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F=None,
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Sigma=None,
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rank_f=100,
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nb_iter=10,
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scaling_factor=1.0, ):
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self.mean = None
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self.F = None
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self.Sigma = None
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self.rank_f = rank_f
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self.nb_iter = nb_iter
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self.scaling_factor = scaling_factor
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if mean is not None:
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self.mean = mean
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if F is not None:
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self.F = F
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if Sigma is not None:
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self.Sigma = Sigma
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def plda(
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self,
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emb_meta=None,
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output_file_name=None, ):
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"""
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Trains PLDA model with no within class covariance matrix but full residual covariance matrix.
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Arguments
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---------
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emb_meta : paddlespeech.vector.cluster.diarization.EmbeddingMeta
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Contains vectors and meta-information to perform PLDA
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rank_f : int
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Rank of the between-class covariance matrix.
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nb_iter : int
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Number of iterations to run.
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scaling_factor : float
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Scaling factor to downscale statistics (value between 0 and 1).
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output_file_name : str
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Name of the output file where to store PLDA model.
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"""
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# Dimension of the vector (x-vectors stored in stats)
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vect_size = emb_meta.stats.shape[1]
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# Initialize mean and residual covariance from the training data
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self.mean = emb_meta.get_mean_stats()
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self.Sigma = emb_meta.get_total_covariance_stats()
|
||||
|
||||
# Sum stat0 and stat1 for each speaker model
|
||||
model_shifted_stat, session_per_model = emb_meta.sum_stat_per_model()
|
||||
|
||||
# Number of speakers (classes) in training set
|
||||
class_nb = model_shifted_stat.modelset.shape[0]
|
||||
|
||||
# Multiply statistics by scaling_factor
|
||||
model_shifted_stat.stat0 *= self.scaling_factor
|
||||
model_shifted_stat.stats *= self.scaling_factor
|
||||
session_per_model *= self.scaling_factor
|
||||
|
||||
# Covariance for stats
|
||||
sigma_obs = emb_meta.get_total_covariance_stats()
|
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evals, evecs = linalg.eigh(sigma_obs)
|
||||
|
||||
# Initial F (eigen voice matrix) from rank
|
||||
idx = numpy.argsort(evals)[::-1]
|
||||
evecs = evecs.real[:, idx[:self.rank_f]]
|
||||
self.F = evecs[:, :self.rank_f]
|
||||
|
||||
# Estimate PLDA model by iterating the EM algorithm
|
||||
for it in range(self.nb_iter):
|
||||
|
||||
# E-step
|
||||
|
||||
# Copy stats as they will be whitened with a different Sigma for each iteration
|
||||
local_stat = copy.deepcopy(model_shifted_stat)
|
||||
|
||||
# Whiten statistics (with the new mean and Sigma)
|
||||
local_stat.whiten_stats(self.mean, self.Sigma)
|
||||
|
||||
# Whiten the EigenVoice matrix
|
||||
eigen_values, eigen_vectors = linalg.eigh(self.Sigma)
|
||||
ind = eigen_values.real.argsort()[::-1]
|
||||
eigen_values = eigen_values.real[ind]
|
||||
eigen_vectors = eigen_vectors.real[:, ind]
|
||||
sqr_inv_eval_sigma = 1 / numpy.sqrt(eigen_values.real)
|
||||
sqr_inv_sigma = numpy.dot(eigen_vectors,
|
||||
numpy.diag(sqr_inv_eval_sigma))
|
||||
self.F = sqr_inv_sigma.T.dot(self.F)
|
||||
|
||||
# Replicate self.stat0
|
||||
index_map = numpy.zeros(vect_size, dtype=int)
|
||||
_stat0 = local_stat.stat0[:, index_map]
|
||||
|
||||
e_h = numpy.zeros((class_nb, self.rank_f))
|
||||
e_hh = numpy.zeros((class_nb, self.rank_f, self.rank_f))
|
||||
|
||||
# loop on model id's
|
||||
fa_model_loop(
|
||||
batch_start=0,
|
||||
mini_batch_indices=numpy.arange(class_nb),
|
||||
factor_analyser=self,
|
||||
stat0=_stat0,
|
||||
stats=local_stat.stats,
|
||||
e_h=e_h,
|
||||
e_hh=e_hh, )
|
||||
|
||||
# Accumulate for minimum divergence step
|
||||
_R = numpy.sum(e_hh, axis=0) / session_per_model.shape[0]
|
||||
|
||||
_C = e_h.T.dot(local_stat.stats).dot(linalg.inv(sqr_inv_sigma))
|
||||
_A = numpy.einsum("ijk,i->jk", e_hh, local_stat.stat0.squeeze())
|
||||
|
||||
# M-step
|
||||
self.F = linalg.solve(_A, _C).T
|
||||
|
||||
# Update the residual covariance
|
||||
self.Sigma = sigma_obs - self.F.dot(_C) / session_per_model.sum()
|
||||
|
||||
# Minimum Divergence step
|
||||
self.F = self.F.dot(linalg.cholesky(_R))
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
import random
|
||||
|
||||
dim, N, n_spkrs = 10, 100, 10
|
||||
train_xv = numpy.random.rand(N, dim)
|
||||
md = ['md' + str(random.randrange(1, n_spkrs, 1)) for i in range(N)] # spk
|
||||
modelset = numpy.array(md, dtype="|O")
|
||||
sg = ['sg' + str(i) for i in range(N)] # utt
|
||||
segset = numpy.array(sg, dtype="|O")
|
||||
stat0 = numpy.array([[1.0]] * N)
|
||||
xvectors_stat = EmbeddingMeta(
|
||||
modelset=modelset, segset=segset, stats=train_xv)
|
||||
# Training PLDA model: M ~ (mean, F, Sigma)
|
||||
plda = PLDA(rank_f=5)
|
||||
plda.plda(xvectors_stat)
|
||||
print(plda.mean.shape) #(10,)
|
||||
print(plda.F.shape) #(10, 5)
|
||||
print(plda.Sigma.shape) #(10, 10)
|
||||
# Enrollment (20 utts),
|
||||
en_N = 20
|
||||
en_xv = numpy.random.rand(en_N, dim)
|
||||
en_sgs = ['en' + str(i) for i in range(en_N)]
|
||||
en_sets = numpy.array(en_sgs, dtype="|O")
|
||||
en_stat = EmbeddingMeta(modelset=en_sets, segset=en_sets, stats=en_xv)
|
||||
# Test (30 utts)
|
||||
te_N = 30
|
||||
te_xv = numpy.random.rand(te_N, dim)
|
||||
te_sgs = ['te' + str(i) for i in range(te_N)]
|
||||
te_sets = numpy.array(te_sgs, dtype="|O")
|
||||
te_stat = EmbeddingMeta(modelset=te_sets, segset=te_sets, stats=te_xv)
|
||||
ndx = Ndx(models=en_sets, testsegs=te_sets)
|
||||
# PLDA Scoring
|
||||
scores_plda = fast_PLDA_scoring(en_stat, te_stat, ndx, plda.mean, plda.F,
|
||||
plda.Sigma)
|
||||
print(scores_plda.scoremat.shape) #(20, 30)
|
||||
Loading…
Reference in new issue