dc9ed88f78
* initial_commit Signed-off-by: nithinraok <nithinrao.koluguri@gmail.com> * init diarizer Signed-off-by: nithinraok <nithinrao.koluguri@gmail.com> * vad+speaker Signed-off-by: nithinraok <nithinrao.koluguri@gmail.com> * vad update Signed-off-by: nithinraok <nithinrao.koluguri@gmail.com> * speaker done Signed-off-by: nithinraok <nithinrao.koluguri@gmail.com> * initial working version Signed-off-by: nithinraok <nithinrao.koluguri@gmail.com> * compare outputs Signed-off-by: nithinraok <nithinrao.koluguri@gmail.com> * added uem support Signed-off-by: nithinraok <nithinrao.koluguri@gmail.com> * pyannote improvements Signed-off-by: nithinraok <nithinrao.koluguri@gmail.com> * updated config and script name Signed-off-by: nithinraok <nithinrao.koluguri@gmail.com> * style fix Signed-off-by: nithinraok <nithinrao.koluguri@gmail.com> * update Jenkins file Signed-off-by: nithinraok <nithinrao.koluguri@gmail.com> * jenkins fix Signed-off-by: nithinraok <nithinrao.koluguri@gmail.com> * jenkins fix Signed-off-by: nithinraok <nithinrao.koluguri@gmail.com> * update file path in jenkins Signed-off-by: nithinraok <nithinrao.koluguri@gmail.com> * update file path in jenkins Signed-off-by: nithinraok <nithinrao.koluguri@gmail.com> * update file path in jenkins Signed-off-by: nithinraok <nithinrao.koluguri@gmail.com> * jenkins quote fix Signed-off-by: nithinraok <nithinrao.koluguri@gmail.com> * update offline speaker diarization notebook Signed-off-by: nithinraok <nithinrao.koluguri@gmail.com> * intial working asr_with_diarization Signed-off-by: nithinraok <nithinrao.koluguri@gmail.com> * almost done, revist scoring part Signed-off-by: nithinraok <nithinrao.koluguri@gmail.com> * fixed eval in offline diarization with asr Signed-off-by: nithinraok <nithinrao.koluguri@gmail.com> * update write2manifest to consider only up to max audio duration Signed-off-by: nithinraok <nithinrao.koluguri@gmail.com> * asr with diarization notebook Signed-off-by: nithinraok <nithinrao.koluguri@gmail.com> * Fixed ASR_with_diarization tutorial.ipynb and diarization_utils and edited config yaml file Signed-off-by: Taejin Park <tango4j@gmail.com> * Fixed VAD parameters in Speaker_Diarization_Inference.ipynb Signed-off-by: Taejin Park <tango4j@gmail.com> * Added Jenkins test, doc strings and updated README Signed-off-by: nithinraok <nithinrao.koluguri@gmail.com> * update jenkins test Signed-off-by: nithinraok <nithinrao.koluguri@gmail.com> * Doc info in offline_diarization_with_asr Signed-off-by: nithinraok <nithinrao.koluguri@gmail.com> * Review comments Signed-off-by: nithinraok <nithinrao.koluguri@gmail.com> * update outdir paths Signed-off-by: nithinraok <nithinrao.koluguri@gmail.com> Co-authored-by: Taejin Park <tango4j@gmail.com>
584 lines
21 KiB
Python
584 lines
21 KiB
Python
# Copyright (c) 2021, NVIDIA CORPORATION. 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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# Copyright (c) 2007-2020 The scikit-learn developers.
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# BSD 3-Clause License
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
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# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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# This file is part of https://github.com/scikit-learn/scikit-learn/blob/114616d9f6ce9eba7c1aacd3d4a254f868010e25/sklearn/manifold/_spectral_embedding.py and
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# https://github.com/tango4j/Auto-Tuning-Spectral-Clustering.
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from collections import Counter
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import numpy as np
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import torch
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from sklearn.cluster._kmeans import k_means
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from sklearn.metrics.pairwise import cosine_similarity
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from sklearn.preprocessing import MinMaxScaler
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from nemo.utils import logging
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scaler = MinMaxScaler(feature_range=(0, 1))
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try:
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from torch.linalg import eigh as eigh
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TORCH_EIGN = True
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except ImportError:
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TORCH_EIGN = False
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from scipy.linalg import eigh as eigh
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logging.warning("Using eigen decomposition from scipy, upgrade torch to 1.9 or higher for faster clustering")
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def isGraphFullyConnected(affinity_mat):
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return getTheLargestComponent(affinity_mat, 0).sum() == affinity_mat.shape[0]
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def getTheLargestComponent(affinity_mat, seg_index):
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"""
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Find the largest affinity_mat connected components for each given node.
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This is for checking whether the affinity_mat is fully connected.
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"""
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num_of_segments = affinity_mat.shape[0]
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connected_nodes = np.zeros(num_of_segments).astype(np.bool)
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nodes_to_explore = np.zeros(num_of_segments).astype(np.bool)
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nodes_to_explore[seg_index] = True
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for k in range(num_of_segments):
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last_num_component = connected_nodes.sum()
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np.logical_or(connected_nodes, nodes_to_explore, out=connected_nodes)
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if last_num_component >= connected_nodes.sum():
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break
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indices = np.where(nodes_to_explore)[0]
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nodes_to_explore.fill(False)
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for i in indices:
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neighbors = affinity_mat[i]
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np.logical_or(nodes_to_explore, neighbors, out=nodes_to_explore)
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return connected_nodes
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def getKneighborsConnections(affinity_mat, p_value):
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"""
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Binarizes top-p values for each row from the given affinity matrix.
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"""
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binarized_affinity_mat = np.zeros_like(affinity_mat)
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for i, line in enumerate(affinity_mat):
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sorted_idx = np.argsort(line)
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sorted_idx = sorted_idx[::-1]
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indices = sorted_idx[:p_value]
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binarized_affinity_mat[indices, i] = 1
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return binarized_affinity_mat
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def getAffinityGraphMat(affinity_mat_raw, p_value):
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"""
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Calculates a binarized graph matrix and
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symmetrize the binarized graph matrix.
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"""
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X = getKneighborsConnections(affinity_mat_raw, p_value)
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symm_affinity_mat = 0.5 * (X + X.T)
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return symm_affinity_mat
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def getMinimumConnection(mat, max_N, n_list):
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"""
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Generates connections until fully connect all the nodes in the graph.
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If graph is not fully connected, it might generate an inaccurate results.
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"""
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p_value, index = 1, 0
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affinity_mat = getAffinityGraphMat(mat, p_value)
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for i, p_value in enumerate(n_list):
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fully_connected = isGraphFullyConnected(affinity_mat)
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affinity_mat = getAffinityGraphMat(mat, p_value)
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if fully_connected or p_value > max_N:
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break
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return affinity_mat, p_value
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def addAnchorEmb(emb, anchor_sample_n, anchor_spk_n, sigma):
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"""
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Add randomly generated synthetic embeddings to make eigen analysis more stable.
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We refer to these embeddings as anchor embeddings.
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emb (float):
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The input embedding from the emebedding extractor.
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anchor_sample_n (int):
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The number of embedding samples per speaker.
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anchor_sample_n = 10 is recommended.
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anchor_spk_n (int):
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The number of speakers for synthetic embedding.
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anchor_spk_n = 3 is recommended.
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sigma (int):
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The amplitude of synthetic noise for each embedding vector.
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If sigma value is too small, under-counting could happen.
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If sigma value is too large, over-counting could happen.
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sigma = 50 is recommended.
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"""
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emb_dim = emb.shape[1]
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mean, std_org = np.mean(emb, axis=0), np.std(emb, axis=0)
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new_emb_list = []
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for _ in range(anchor_spk_n):
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emb_m = np.tile(np.random.randn(1, emb_dim), (anchor_sample_n, 1))
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emb_noise = np.random.randn(anchor_sample_n, emb_dim).T
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emb_noise = np.dot(np.diag(std_org), emb_noise / np.max(np.abs(emb_noise))).T
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emb_gen = emb_m + sigma * emb_noise
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new_emb_list.append(emb_gen)
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new_emb_list.append(emb)
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new_emb_np = np.vstack(new_emb_list)
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return new_emb_np, anchor_sample_n * anchor_spk_n
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def getEnhancedSpeakerCount(key, emb, cuda, random_test_count=5, anchor_spk_n=3, anchor_sample_n=10, sigma=50):
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"""
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Calculates the number of speakers using NME analysis with anchor embeddings.
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"""
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est_num_of_spk_list = []
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for seed in range(random_test_count):
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np.random.seed(seed)
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emb_aug, anchor_length = addAnchorEmb(emb, anchor_sample_n, anchor_spk_n, sigma)
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mat = getCosAffinityMatrix(emb_aug)
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nmesc = NMESC(
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mat,
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max_num_speaker=emb.shape[0],
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max_rp_threshold=0.25,
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sparse_search=True,
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sparse_search_volume=30,
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fixed_thres=None,
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NME_mat_size=300,
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cuda=cuda,
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)
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est_num_of_spk, _, _ = nmesc.NMEanalysis()
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est_num_of_spk_list.append(est_num_of_spk)
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ctt = Counter(est_num_of_spk_list)
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oracle_num_speakers = max(ctt.most_common(1)[0][0] - anchor_spk_n, 1)
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return oracle_num_speakers
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def getCosAffinityMatrix(emb):
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"""
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Calculates cosine similarity values among speaker embeddings.
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"""
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sim_d = cosine_similarity(emb)
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scaler.fit(sim_d)
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sim_d = scaler.transform(sim_d)
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return sim_d
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def getLaplacian(X):
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"""
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Calculates a laplacian matrix from an affinity matrix X.
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"""
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X[np.diag_indices(X.shape[0])] = 0
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A = X
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D = np.sum(np.abs(A), axis=1)
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D = np.diag(D)
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L = D - A
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return L
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def eigDecompose(laplacian, cuda, device=None):
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if TORCH_EIGN:
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if cuda:
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if device is None:
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device = torch.cuda.current_device()
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laplacian = torch.from_numpy(laplacian).float().to(device)
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else:
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laplacian = torch.from_numpy(laplacian).float()
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lambdas, diffusion_map = eigh(laplacian)
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lambdas = lambdas.cpu().numpy()
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diffusion_map = diffusion_map.cpu().numpy()
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else:
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lambdas, diffusion_map = eigh(laplacian)
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return lambdas, diffusion_map
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def getLamdaGaplist(lambdas):
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lambdas = np.real(lambdas)
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return list(lambdas[1:] - lambdas[:-1])
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def estimateNumofSpeakers(affinity_mat, max_num_speaker, is_cuda=False):
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"""
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Estimates the number of speakers using eigen decompose on laplacian Matrix.
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affinity_mat: (array)
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NxN affitnity matrix
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max_num_speaker: (int)
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Maximum number of clusters to consider for each session
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is_cuda: (bool)
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if cuda availble eigh decomposition would be computed on GPUs
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"""
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laplacian = getLaplacian(affinity_mat)
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lambdas, _ = eigDecompose(laplacian, is_cuda)
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lambdas = np.sort(lambdas)
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lambda_gap_list = getLamdaGaplist(lambdas)
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num_of_spk = np.argmax(lambda_gap_list[: min(max_num_speaker, len(lambda_gap_list))]) + 1
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return num_of_spk, lambdas, lambda_gap_list
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class _SpectralClustering:
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def __init__(self, n_clusters=8, random_state=0, n_init=10, p_value=10, n_jobs=None, cuda=False):
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self.n_clusters = n_clusters
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self.random_state = random_state
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self.n_init = n_init
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self.p_value = p_value
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self.affinity_matrix_ = None
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self.cuda = cuda
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def predict(self, X):
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if X.shape[0] != X.shape[1]:
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raise ValueError("The affinity matrix is not a square matrix.")
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self.affinity_matrix_ = X
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labels = self.clusterSpectralEmbeddings(self.affinity_matrix_, n_init=self.n_init, cuda=self.cuda)
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return labels
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def clusterSpectralEmbeddings(self, affinity, n_init=10, cuda=False):
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spectral_emb = self.getSpectralEmbeddings(affinity, n_spks=self.n_clusters, drop_first=False, cuda=cuda)
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_, labels, _ = k_means(spectral_emb, self.n_clusters, random_state=self.random_state, n_init=n_init)
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return labels
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def getSpectralEmbeddings(self, affinity_mat, n_spks=8, drop_first=True, cuda=False):
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n_nodes = affinity_mat.shape[0]
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if not isGraphFullyConnected(affinity_mat):
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logging.warning("Graph is not fully connected and the clustering result might not be accurate.")
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laplacian = getLaplacian(affinity_mat)
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lambdas_, diffusion_map_ = eigDecompose(laplacian, cuda)
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lambdas = lambdas_[:n_spks]
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diffusion_map = diffusion_map_[:, :n_spks]
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embedding = diffusion_map.T[n_spks::-1]
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return embedding[:n_spks].T
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class NMESC:
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"""
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Normalized Maximum Eigengap based Spectral Clustering (NME-SC)
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uses Eigengap analysis to get an estimated p-value for
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affinity binarization and an estimated number of speakers.
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p_value (also referred to as p_neighbors) is for taking
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top p number of affinity values and convert those to 1 while
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convert the rest of values to 0.
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p_value can be also tuned on a development set without performing
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NME-analysis.
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Reference: Auto-Tuning Spectral Clustering for Speaker Diarization
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Using Normalized Maximum Eigengap (https://arxiv.org/abs/2003.02405)
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Parameters:
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Please refer to def __init__()
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Methods:
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NMEanalysis():
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Performs NME-analysis to estimate p_value and the number of speakers.
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subsampleAffinityMat(NME_mat_size):
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Subsamples the number of speakers to reduce the computational load.
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getPvalueList():
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Generates a list contains p-values that need to be examined.
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getEigRatio(p_neighbors):
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calculates g_p, which is a ratio between p_neighbors and the maximum eigengap.
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getLamdaGaplist(lambdas):
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Calculates lambda gap values from an array contains ambda values.
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estimateNumofSpeakers(affinity_mat):
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Estimates the number of speakers using lambda gap list.
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"""
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def __init__(
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self,
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mat,
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max_num_speaker=10,
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max_rp_threshold=0.250,
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sparse_search=True,
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sparse_search_volume=30,
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use_subsampling_for_NME=True,
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fixed_thres=None,
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cuda=False,
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NME_mat_size=512,
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):
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"""
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Parameters:
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mat: (numpy.array)
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Cosine similarity matrix calculated from speaker embeddings.
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max_num_speaker: (int)
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Maximum number of speakers for estimating number of speakers.
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Shows stable performance under 20.
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max_rp_threshold: (float)
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Limits the range of parameter search.
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Clustering performance can vary depending on this range.
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Default is 0.25.
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sparse_search: (bool)
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To increase the speed of parameter estimation, sparse_search=True
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limits the number of p_values we search.
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sparse_search_volume: (int)
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The number of p_values we search during NME analysis.
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Default is 30. The lower the value, the faster NME-analysis becomes.
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Lower than 20 might cause a poor parameter estimation.
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use_subsampling_for_NME: (bool)
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Use subsampling to reduce the calculational complexity.
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Default is True.
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fixed_thres: (float or None)
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A fixed threshould can be used instead of estimating the
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threshold with NME analysis. If fixed_thres is float,
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it skips NME analysis part.
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cuda: (bool)
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Use cuda for Eigen decomposition if cuda=True.
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NME_mat_size: (int)
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Targeted size of matrix for NME analysis.
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"""
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self.max_num_speaker = max_num_speaker
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self.max_rp_threshold = max_rp_threshold
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self.use_subsampling_for_NME = use_subsampling_for_NME
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self.NME_mat_size = NME_mat_size
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self.sparse_search = sparse_search
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self.sparse_search_volume = sparse_search_volume
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self.fixed_thres = fixed_thres
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self.cuda = cuda
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self.eps = 1e-10
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self.max_N = None
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self.mat = mat
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self.p_value_list = []
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def NMEanalysis(self):
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"""
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Subsamples the input matrix to reduce the computational load.
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"""
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if self.use_subsampling_for_NME:
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subsample_ratio = self.subsampleAffinityMat(self.NME_mat_size)
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# Scans p_values and find a p_value that generates
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# the smallest g_p value.
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eig_ratio_list, est_spk_n_dict = [], {}
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self.p_value_list = self.getPvalueList()
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for p_value in self.p_value_list:
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est_num_of_spk, g_p = self.getEigRatio(p_value)
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est_spk_n_dict[p_value] = est_num_of_spk
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eig_ratio_list.append(g_p)
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index_nn = np.argmin(eig_ratio_list)
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rp_p_value = self.p_value_list[index_nn]
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affinity_mat = getAffinityGraphMat(self.mat, rp_p_value)
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# Checks whether affinity graph is fully connected.
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# If not, it adds minimum number of connections to make it fully connected.
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if not isGraphFullyConnected(affinity_mat):
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affinity_mat, rp_p_value = getMinimumConnection(self.mat, self.max_N, self.p_value_list)
|
|
|
|
p_hat_value = int(subsample_ratio * rp_p_value)
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|
est_num_of_spk = est_spk_n_dict[rp_p_value]
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|
return est_num_of_spk, p_hat_value, eig_ratio_list[index_nn]
|
|
|
|
def subsampleAffinityMat(self, NME_mat_size):
|
|
"""
|
|
Performs Subsampling of affinity matrix.
|
|
This subsampling is for calculational complexity, not for performance.
|
|
The smaller NME_mat_size is,
|
|
- the bigger the chance of missing a speaker.
|
|
- the faster p-value estimation speed (based on eigen decomposition).
|
|
|
|
Recommended NME_mat_size is 250~750.
|
|
However, if there are speakers who speak for very short period of time in the recording,
|
|
this subsampling might make the system miss the underrepresented speaker.
|
|
Use this with caution.
|
|
|
|
Parameters:
|
|
NME_mat_size: (int)
|
|
Targeted matrix size
|
|
|
|
Returns:
|
|
subsample_ratio : (float)
|
|
The ratio between NME_mat_size and the original matrix size
|
|
|
|
"""
|
|
subsample_ratio = int(max(1, self.mat.shape[0] / NME_mat_size))
|
|
self.mat = self.mat[::subsample_ratio, ::subsample_ratio]
|
|
return subsample_ratio
|
|
|
|
def getEigRatio(self, p_neighbors):
|
|
"""
|
|
For a given p_neighbors value,
|
|
calculates g_p, which is a ratio
|
|
between p_neighbors and the maximum eigengap.
|
|
|
|
For more details: https://arxiv.org/abs/2003.02405
|
|
|
|
Parameters:
|
|
p_neighbors: (int)
|
|
Determines how many binary graph connections we want to keep for each row.
|
|
|
|
Returns:
|
|
est_num_of_spk: (int)
|
|
Estimated number of speakers
|
|
|
|
g_p: (float)
|
|
The ratio between p_neighbors value and the maximum eigen gap value.
|
|
"""
|
|
|
|
affinity_mat = getAffinityGraphMat(self.mat, p_neighbors)
|
|
est_num_of_spk, lambdas, lambda_gap_list = estimateNumofSpeakers(affinity_mat, self.max_num_speaker, self.cuda)
|
|
arg_sorted_idx = np.argsort(lambda_gap_list[: self.max_num_speaker])[::-1]
|
|
max_key = arg_sorted_idx[0]
|
|
max_eig_gap = lambda_gap_list[max_key] / (max(lambdas) + self.eps)
|
|
g_p = (p_neighbors / self.mat.shape[0]) / (max_eig_gap + self.eps)
|
|
|
|
return est_num_of_spk, g_p
|
|
|
|
def getPvalueList(self):
|
|
"""
|
|
Generates a p-value (p_neighbour) list for searching.
|
|
"""
|
|
if self.fixed_thres:
|
|
p_value_list = [int(self.mat.shape[0] * self.fixed_thres)]
|
|
self.max_N = p_value_list[0]
|
|
else:
|
|
self.max_N = int(self.mat.shape[0] * self.max_rp_threshold)
|
|
if self.sparse_search:
|
|
N = min(self.max_N, self.sparse_search_volume)
|
|
p_value_list = list(np.linspace(1, self.max_N, N, endpoint=True).astype(int))
|
|
else:
|
|
p_value_list = list(range(1, self.max_N))
|
|
|
|
return p_value_list
|
|
|
|
|
|
def COSclustering(
|
|
key,
|
|
emb,
|
|
oracle_num_speakers=None,
|
|
max_num_speaker=8,
|
|
min_samples_for_NMESC=6,
|
|
enhanced_count_thres=80,
|
|
max_rp_threshold=0.25,
|
|
sparse_search_volume=30,
|
|
fixed_thres=None,
|
|
cuda=False,
|
|
):
|
|
"""
|
|
Clustering method for speaker diarization based on cosine similarity.
|
|
|
|
Parameters:
|
|
key: (str)
|
|
A unique ID for each speaker
|
|
|
|
emb: (numpy array)
|
|
Speaker embedding extracted from an embedding extractor
|
|
|
|
oracle_num_speaker: (int or None)
|
|
Oracle number of speakers if known else None
|
|
|
|
max_num_speaker: (int)
|
|
Maximum number of clusters to consider for each session
|
|
|
|
min_samples_for_NMESC: (int)
|
|
Minimum number of samples required for NME clustering, this avoids
|
|
zero p_neighbour_lists. If the input has fewer segments than min_samples,
|
|
it is directed to the enhanced speaker counting mode.
|
|
|
|
enhanced_count_thres: (int)
|
|
For short audio recordings under 60 seconds, clustering algorithm cannot
|
|
accumulate enough amount of speaker profile for each cluster.
|
|
Thus, getEnhancedSpeakerCount() employs anchor embeddings (dummy representations)
|
|
to mitigate the effect of cluster sparsity.
|
|
enhanced_count_thres = 80 is recommended.
|
|
|
|
max_rp_threshold: (float)
|
|
Limits the range of parameter search.
|
|
Clustering performance can vary depending on this range.
|
|
Default is 0.25.
|
|
|
|
sparse_search_volume: (int)
|
|
The number of p_values we search during NME analysis.
|
|
Default is 30. The lower the value, the faster NME-analysis becomes.
|
|
Lower than 20 might cause a poor parameter estimation.
|
|
|
|
Returns:
|
|
Y: (List[int])
|
|
Speaker label for each segment.
|
|
"""
|
|
if emb.shape[0] == 1:
|
|
return np.array([0])
|
|
elif emb.shape[0] <= max(enhanced_count_thres, min_samples_for_NMESC) and oracle_num_speakers is None:
|
|
est_num_of_spk_enhanced = getEnhancedSpeakerCount(key, emb, cuda)
|
|
else:
|
|
est_num_of_spk_enhanced = None
|
|
|
|
if oracle_num_speakers:
|
|
max_num_speaker = oracle_num_speakers
|
|
|
|
mat = getCosAffinityMatrix(emb)
|
|
nmesc = NMESC(
|
|
mat,
|
|
max_num_speaker=max_num_speaker,
|
|
max_rp_threshold=max_rp_threshold,
|
|
sparse_search=True,
|
|
sparse_search_volume=sparse_search_volume,
|
|
fixed_thres=fixed_thres,
|
|
NME_mat_size=300,
|
|
cuda=cuda,
|
|
)
|
|
|
|
if emb.shape[0] > min_samples_for_NMESC:
|
|
est_num_of_spk, p_hat_value, best_g_p_value = nmesc.NMEanalysis()
|
|
affinity_mat = getAffinityGraphMat(mat, p_hat_value)
|
|
else:
|
|
affinity_mat = mat
|
|
|
|
if oracle_num_speakers:
|
|
est_num_of_spk = oracle_num_speakers
|
|
elif est_num_of_spk_enhanced:
|
|
est_num_of_spk = est_num_of_spk_enhanced
|
|
|
|
spectral_model = _SpectralClustering(n_clusters=est_num_of_spk, cuda=cuda)
|
|
Y = spectral_model.predict(affinity_mat)
|
|
|
|
return Y
|