Commit 3467349f authored by Sulfyderz's avatar Sulfyderz
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[Error Update]:Adding an initialization to tutorials/tuto_1_iv_model.ipynb.

parent 6dd3f052
%% Cell type:markdown id: tags:
Train model for Diarization
====
This script trains UBM, TV and PLDA models for a diarization system.
Initialization
---
%% Cell type:code id: tags:
``` python
import logging
%matplotlib inline
from s4d.diar import Diar
from s4d.utils import *
from sidekit import Mixture, FactorAnalyser, StatServer, IdMap
import numpy
import logging
import re
import sidekit
from sidekit.sidekit_io import *
try:
from sortedcontainers import SortedDict as dict
except ImportError:
pass
```
%% Cell type:code id: tags:
``` python
init_logging(level=logging.INFO)
num_thread = 4
audio_dir = '../data/train/{}.wav'
ubm_seg_fn = './data/seg/ubm_ester.seg'
nb_gauss = 1024
mfcc_ubm_fn = './data/mfcc/ubm.h5'
ubm_idmap_fn = './data/mfcc/ubm_idmap.txt'
ubm_fn = './data/model/ester_ubm_'+str(nb_gauss)+'.h5'
tv_seg_fn = './data/seg/train.tv.seg'
rank_tv = 300
it_max_tv = 10
mfcc_tv_fn = './data/mfcc/tv.h5'
tv_idmap_fn = './data/mfcc/tv_idmap.h5'
tv_stat_fn = './data/model/tv.stat.h5'
tv_fn = './data/model/tv_'+str(rank_tv)+'.h5'
plda_seg_fn = './data/seg/train.plda.seg'
rank_plda = 150
it_max_plda = 10
mfcc_plda_fn = './data/mfcc/norm_plda.h5'
plda_idmap_fn = './data/mfcc/plda_idmap.h5'
plda_fn = './data/model/plda_'+str(rank_tv)+'_'+str(rank_plda)+'.h5'
norm_stat_fn = './data/model/norm.stat.h5'
norm_fn = './data/model/norm.h5'
norm_iv_fn = './data/model/norm.iv.h5'
matrices_fn = './data/model/matrices.h5'
model_fn = './data/model/ester_model_{}_{}_{}.h5'.format(nb_gauss, rank_tv, rank_plda)
```
%% Cell type:markdown id: tags:
Step 1: UBM
---
Extract MFCC for the UBM
%% Cell type:code id: tags:
``` python
logging.info('Computing MFCC for UBM')
diar_ubm = Diar.read_seg(ubm_seg_fn, normalize_cluster=True)
fe = get_feature_extractor(audio_dir, 'sid')
ubm_idmap = fe.save_multispeakers(diar_ubm.id_map(), output_feature_filename=mfcc_ubm_fn, keep_all=False)
ubm_idmap.write_txt(ubm_idmap_fn)
```
%% Cell type:markdown id: tags:
Train the UBM by EM
%% Cell type:code id: tags:
``` python
ubm_idmap = IdMap.read_txt(ubm_idmap_fn)
fs = get_feature_server(mfcc_ubm_fn, 'sid')
spk_lst = ubm_idmap.rightids
ubm = Mixture()
ubm.EM_split(fs, spk_lst, nb_gauss,
iterations=(1, 2, 2, 4, 4, 4, 8, 8, 8, 8, 8, 8, 8), num_thread=num_thread,
llk_gain=0.01)
ubm.write(ubm_fn, prefix='ubm/')
```
%% Cell type:markdown id: tags:
Step 2: TV
---
Extract MFCC for TV
%% Cell type:code id: tags:
``` python
logging.info('Computing MFCC for TV')
diar_tv = Diar.read_seg(tv_seg_fn, normalize_cluster=True)
fe = get_feature_extractor(audio_dir, 'sid')
tv_idmap = fe.save_multispeakers(diar_tv.id_map(), output_feature_filename=mfcc_tv_fn, keep_all=False)
tv_idmap.write(tv_idmap_fn)
```
%% Cell type:markdown id: tags:
Train a Total Variability model using the FactorAnalyser class
%% Cell type:code id: tags:
``` python
tv_idmap = IdMap.read(tv_idmap_fn)
ubm = Mixture()
ubm.read(ubm_fn, prefix='ubm/')
fs = get_feature_server(mfcc_tv_fn, 'sid')
tv_idmap.leftids = numpy.copy(tv_idmap.rightids)
tv_stat = StatServer(tv_idmap, ubm.get_distrib_nb(), ubm.dim())
tv_stat.accumulate_stat(ubm=ubm, feature_server=fs, seg_indices=range(tv_stat.segset.shape[0]), num_thread=num_thread)
tv_stat.write(tv_stat_fn)
fa = FactorAnalyser()
fa.total_variability(tv_stat_fn, ubm, rank_tv, nb_iter=it_max_tv, batch_size=1000, num_thread=num_thread)
write_tv_hdf5([fa.F, fa.mean, fa.Sigma], tv_fn)
```
%% Cell type:markdown id: tags:
Step 3: PLDA
---
Extract the MFCC for the PLDA
%% Cell type:code id: tags:
``` python
logging.info('Computing MFCC for PLDA')
diar_plda = Diar.read_seg(plda_seg_fn, normalize_cluster=True)
fe = get_feature_extractor(audio_dir, 'sid')
plda_idmap = fe.save_multispeakers(diar_plda.id_map(), output_feature_filename=mfcc_plda_fn, keep_all=False)
plda_idmap.write(plda_idmap_fn)
```
%% Cell type:markdown id: tags:
Accumulate statistics
%% Cell type:code id: tags:
``` python
plda_idmap = IdMap.read(plda_idmap_fn)
ubm = Mixture()
ubm.read(ubm_fn, prefix='ubm/')
tv, tv_mean, tv_sigma = read_tv_hdf5(tv_fn)
fs = get_feature_server(mfcc_plda_fn, 'sid')
plda_norm_stat = StatServer(plda_idmap, ubm.get_distrib_nb(), ubm.dim())
plda_norm_stat.accumulate_stat(ubm=ubm, feature_server=fs,
seg_indices=range(plda_norm_stat.segset.shape[0]), num_thread=num_thread)
plda_norm_stat.write(norm_stat_fn)
```
%% Cell type:markdown id: tags:
Extract i-vectors and compute norm
%% Cell type:code id: tags:
``` python
fa = FactorAnalyser(F=tv, mean=tv_mean, Sigma=tv_sigma)
norm_iv = fa.extract_ivectors(ubm, norm_stat_fn, num_thread=num_thread)
norm_iv.write(norm_iv_fn)
norm_mean, norm_cov = norm_iv.estimate_spectral_norm_stat1(1, 'sphNorm')
write_norm_hdf5([norm_mean, norm_cov], norm_fn)
norm_iv.spectral_norm_stat1(norm_mean[:1], norm_cov[:1])
```
%% Cell type:markdown id: tags:
Train the PLDA model
%% Cell type:code id: tags:
``` python
fa = FactorAnalyser()
fa.plda(norm_iv, rank_plda, nb_iter=it_max_plda)
write_plda_hdf5([fa.mean, fa.F, numpy.zeros((rank_tv, 0)), fa.Sigma], plda_fn)
```
%% Cell type:markdown id: tags:
Step 4: Compute additional data (optional)
---
Adding matrices for additional scoring methods:
* Mahalonobis matrix
* Lower Choleski decomposition of the WCCN matrix
* Within- and Between-class Covariance matrices
%% Cell type:code id: tags:
``` python
iv = StatServer(norm_iv_fn)
matrix_dict = {}
logging.info('compute mahalanobis_matrix')
mahalanobis_matrix = iv.get_mahalanobis_matrix_stat1()
matrix_dict['mahalanobis_matrix'] = mahalanobis_matrix
logging.info('compute wccn_choleski')
wccn_choleski = iv.get_wccn_choleski_stat1()
matrix_dict['wccn_choleski'] = wccn_choleski
logging.info('compute two_covariance')
within_covariance = iv.get_within_covariance_stat1()
matrix_dict['two_covariance/within_covariance'] = within_covariance
between_covariance = iv.get_between_covariance_stat1()
matrix_dict['two_covariance/between_covariance'] = between_covariance
write_dict_hdf5(matrix_dict, matrices_fn)
```
%% Cell type:markdown id: tags:
Step 5: Merge in one model
---
%% Cell type:code id: tags:
``` python
with h5py.File(model_fn, 'w') as model:
for fn in [ubm_fn, tv_fn, norm_fn, plda_fn, matrices_fn]:
if not os.path.exists(fn):
continue
with h5py.File(fn, 'r') as fh:
for group in fh:
logging.info(group)
fh.copy(group, model)
```
......
%% Cell type:markdown id: tags:
Diarization for ASR
===================
This script performs a BIC diarization (ussally for ASR decoding)
The proposed diarization system was inspired by the
system [1] which won the RT'04 fall evaluation
and the ESTER1 evaluation. It was developed during the ESTER2
evaluation campaign for the transcription with the goal of minimizing
word error rate.
Automatic transcription requires accurate segment boundaries. Segment
boundaries have to be set within non-informative zones such as filler
words.
Speaker diarization needs to produce homogeneous speech segments;
however, purity and coverage of the speaker clusters are the main
objectives here. Errors such as having two distinct clusters (i.e.,
detected speakers) corresponding to the same real speaker, or
conversely, merging segments of two real speakers into only one cluster,
get heavier penalty in the NIST time-based diarization metric than
misplaced boundaries.
The system is composed of acoustic BIC segmentation followed with BIC
hierarchical clustering. Viterbi decoding is performed to adjust the
segment boundaries.
Music and jingle regions are not removed but a speech activity
diarization could be load before to segment and cluster the show.
Optionally, long segments are cut to be shorter than 20 seconds.
[1] C. Barras, X. Zhu, S. Meignier, and J. L. Gauvain, “Multistage speaker diarization of broadcast news,” IEEE Transactions on Audio, Speech, and Language Processing, vol. 14, no. 5, pp. 1505–1512, Sep. 2006.
%% Cell type:code id: tags:
``` python
%matplotlib inline
__license__ = "LGPL"
__author__ = "Sylvain Meignier"
__copyright__ = "Copyright 2015-2016 Sylvain Meignier"
__license__ = "LGPL"
__maintainer__ = "Sylvain Meignier"
__email__ = "sidekit@univ-lemans.fr"
__status__ = "Production"
__docformat__ = 'reStructuredText'
import argparse
import logging
import matplotlib
import copy
import os
from matplotlib import pyplot as plot
from s4d.utils import *
from s4d.diar import Diar
from s4d import viterbi, segmentation
from s4d.clustering import hac_bic
from sidekit.sidekit_io import init_logging
from s4d.gui.dendrogram import plot_dendrogram
```
%%%% Output: stream
Import theano
%%%% Output: stream
Can not use cuDNN on context None: Disabled by dnn.enabled flag
Mapped name None to device cuda: GeForce GTX TITAN X (0000:03:00.0)
/Users/Sulfyderz/Desktop/Doctorat/Tools/Environments/miniconda/Python3/lib/python3.6/site-packages/sidekit/bosaris/detplot.py:39: UserWarning: matplotlib.pyplot as already been imported, this call will have no effect.
matplotlib.use('PDF')
WARNING:root:WARNNG: libsvm is not installed, please refer to the documentation if you intend to use SVM classifiers
%% Cell type:markdown id: tags:
BIC diarization
===============
Arguments, variables and logger
-------------------------------
Set the logger
%% Cell type:code id: tags:
``` python
loglevel = logging.INFO
init_logging(level=loglevel)
```
%% Cell type:markdown id: tags:
Set the input audio or mfcc file and the speech activity detection file (optional).
%% Cell type:code id: tags:
``` python
data_dir = 'data'
show = '20041008_1800_1830_INFO_DGA'
input_show = os.path.join(data_dir, 'audio', show + '.wav')
input_sad = os.path.join(data_dir, 'sad', show + '.sad.seg')
#input_sad = None
```
%% Cell type:markdown id: tags:
Size of left or right windows (step 2)
%% Cell type:code id: tags:
``` python
win_size=250
```
%% Cell type:markdown id: tags:
Threshold for:
* Linear segmentation (step 3)
* BIC HAC (step 4)
* Viterbi (step 5)
%% Cell type:code id: tags:
``` python
thr_l = 2
thr_h = 3
thr_vit = -250
```
%% Cell type:markdown id: tags:
If ``save_all`` is ``True`` then all produced diarization are saved
%% Cell type:code id: tags:
``` python
save_all = True
```
%% Cell type:markdown id: tags:
Prepare various variables
%% Cell type:code id: tags:
``` python
wdir = os.path.join('out', show)
if not os.path.exists(wdir):
os.makedirs(wdir)
```
%% Cell type:markdown id: tags:
Step 1: MFCC
-------------
Extract and load the MFCC
%% Cell type:code id: tags:
``` python
logging.info('Make MFCC')
if save_all:
fe = get_feature_extractor(input_show, type_feature_extractor='basic')
mfcc_filename = os.path.join(wdir, show + '.mfcc.h5')
fe.save(show, output_feature_filename=mfcc_filename)
fs = get_feature_server(mfcc_filename, feature_server_type='basic')
else:
fs = get_feature_server(input_show, feature_server_type='basic')
cep, _ = fs.load(show)
```
%%%% Output: stream
2018-06-11 10:46:17,143 - INFO - Make MFCC
2018-06-11 10:46:17,144 - INFO - data/audio ## 20041008_1800_1830_INFO_DGA ## .wav
2018-06-11 10:46:17,145 - INFO - --------------------
2018-06-11 10:46:17,145 - INFO - show: empty keep_all_features: True
audio_filename_structure: data/audio/20041008_1800_1830_INFO_DGA.wav
feature_filename_structure: {}
pre-emphasis: 0.97
lower_frequency: 133.3333 higher_frequency: 6855.4976
sampling_frequency: 16000
filter bank: 40 filters of type log
ceps_number: 13
window_size: 0.025 shift: 0.01
vad: None snr: None
2018-06-11 10:46:17,146 - INFO - --------------------
2018-06-11 10:46:17,147 - INFO - show: empty
input_feature_filename: empty
feature_filename_structure: {}
Post processing options:
mask: None
feat_norm: None
dct_pca: False, dct_pca_config: (12, 12, None)
sdc: False, sdc_config: (1, 3, 7)
delta: False, double_delta: False, delta_filter: [ 0.25 0.5 0.25 0. -0.25 -0.5 -0.25]
rasta: False
keep_all_features: True
2018-06-11 10:46:21,768 - INFO - process part : 0.000000 1822.912125 1822.912125
2018-06-11 10:46:24,513 - INFO - no vad
2018-06-11 10:46:24,518 - INFO - !! size of signal cep: 0.000050 len 13 type size 4
2018-06-11 10:46:24,602 - INFO - [ True True True ..., True True True]
%% Cell type:code id: tags:
``` python
cep.shape
```
%%%% Output: execute_result
(182289, 14)
%% Cell type:markdown id: tags:
Step 2: Initialization
------
The initial diarization is loaded from a speech activity detection
diarization (SAD) or a segment is created from the first to the last
MFCC feature.
%% Cell type:code id: tags:
``` python
logging.info('Check initial segmentation')
if input_sad is not None:
init_diar = Diar.read_seg(input_sad)
init_diar.pack(50)
else:
init_diar = segmentation.init_seg(cep, show)
if save_all:
init_filename = os.path.join(wdir, show + '.i.seg')
Diar.write_seg(init_filename, init_diar)
```
%%%% Output: stream
2018-06-11 10:46:30,818 - INFO - Check initial segmentation
%% Cell type:markdown id: tags:
Step 3: Gaussian Divergence segmentation
----------------------------------------
First segmentation: Segment each segment of ``init_diar`` using the
Gaussian Divergence method
%% Cell type:code id: tags:
``` python
logging.info('Gaussian Divergence segmentation')
seg_diar = segmentation.segmentation(cep, init_diar, win_size)
if save_all:
seg_filename = os.path.join(wdir, show + '.s.seg')
Diar.write_seg(seg_filename, seg_diar)
```
%%%% Output: stream
2018-06-11 10:46:30,828 - INFO - Gaussian Divergence segmentation
%% Cell type:markdown id: tags:
Step 4: linear BIC segmentation
-------------------------------
This segmentation over the signal fuses consecutive segments of the same
speaker from the start to the end of the record. The measure employs the
$\Delta BIC$ based on Bayesian Information Criterion , using full
covariance Gaussians (see class ``gauss.GaussFull``).
%% Cell type:code id: tags:
``` python
logging.info('Linear BIC, alpha: %f', thr_l)
bicl_diar = segmentation.bic_linear(cep, seg_diar, thr_l, sr=False)
if save_all:
bicl_filename = os.path.join(wdir, show + '.l.seg')
Diar.write_seg(bicl_filename, bicl_diar)
```
%%%% Output: stream
2018-06-11 10:46:31,246 - INFO - Linear BIC, alpha: 2.000000
%% Cell type:markdown id: tags:
Step 5: BIC HAC
---------------
Perform a BIC HAC
%% Cell type:code id: tags:
``` python
logging.info('BIC HAC, alpha: %f', thr_h)
bic = hac_bic.HAC_BIC(cep, bicl_diar, thr_h, sr=False)
bich_diar = bic.perform(to_the_end=True)
if save_all:
bichac_filename = os.path.join(wdir, show + '.h.seg')
Diar.write_seg(bichac_filename, bich_diar)
link, data = plot_dendrogram(bic.merge, 0, size=(25,6), log=True)
```
%%%% Output: stream
2018-06-11 10:46:31,374 - INFO - BIC HAC, alpha: 3.000000
%%%% Output: display_data
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