Upload tutorial for model training

tutorials/tuto_1_iv_model.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Train model for Diarization\n",
+    "====\n",
+    "\n",
+    "This script trains UBM, TV and PLDA models for a diarization system.\n",
+    "\n",
+    "Initialization\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "from s4d.diar import Diar\n",
+    "from s4d.utils import *\n",
+    "\n",
+    "from sidekit import Mixture, FactorAnalyser, StatServer, IdMap\n",
+    "import numpy\n",
+    "import logging\n",
+    "import re\n",
+    "import sidekit\n",
+    "from sidekit.sidekit_io import *\n",
+    "try:\n",
+    "    from sortedcontainers import SortedDict as dict\n",
+    "except ImportError:\n",
+    "    pass"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "init_logging(level=logging.INFO)\n",
+    "num_thread = 4\n",
+    "audio_dir = '../data/train/{}.wav'\n",
+    "\n",
+    "\n",
+    "ubm_seg_fn = './data/seg/ubm_ester.seg'\n",
+    "nb_gauss = 1024\n",
+    "mfcc_ubm_fn = './data/mfcc/ubm.h5'\n",
+    "ubm_idmap_fn = './data/mfcc/ubm_idmap.txt'\n",
+    "ubm_fn = './data/model/ester_ubm_'+str(nb_gauss)+'.h5'\n",
+    "\n",
+    "\n",
+    "tv_seg_fn = './data/seg/train.tv.seg'\n",
+    "rank_tv = 300\n",
+    "it_max_tv = 10\n",
+    "mfcc_tv_fn = './data/mfcc/tv.h5'\n",
+    "tv_idmap_fn = './data/mfcc/tv_idmap.h5'\n",
+    "tv_stat_fn  = './data/model/tv.stat.h5'\n",
+    "tv_fn = './data/model/tv_'+str(rank_tv)+'.h5'\n",
+    "\n",
+    "\n",
+    "plda_seg_fn = './data/seg/train.plda.seg'\n",
+    "rank_plda = 150\n",
+    "it_max_plda = 10\n",
+    "mfcc_plda_fn = './data/mfcc/norm_plda.h5'\n",
+    "plda_idmap_fn = './data/mfcc/plda_idmap.h5'\n",
+    "plda_fn = './data/model/plda_'+str(rank_tv)+'_'+str(rank_plda)+'.h5'\n",
+    "norm_stat_fn = './data/model/norm.stat.h5'\n",
+    "norm_fn = './data/model/norm.h5'\n",
+    "norm_iv_fn = './data/model/norm.iv.h5'\n",
+    "\n",
+    "\n",
+    "matrices_fn = './data/model/matrices.h5'\n",
+    "model_fn = './data/model/ester_model_{}_{}_{}.h5'.format(nb_gauss, rank_tv, rank_plda)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Step 1: UBM\n",
+    "---\n",
+    "Extract MFCC for the UBM"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "logging.info('Computing MFCC for UBM')\n",
+    "diar_ubm = Diar.read_seg(ubm_seg_fn, normalize_cluster=True)\n",
+    "fe = get_feature_extractor(audio_dir, 'sid')\n",
+    "ubm_idmap = fe.save_multispeakers(diar_ubm.id_map(), output_feature_filename=mfcc_ubm_fn, keep_all=False)\n",
+    "ubm_idmap.write_txt(ubm_idmap_fn)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Train the UBM by EM"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ubm_idmap = IdMap.read_txt(ubm_idmap_fn)\n",
+    "\n",
+    "fs = get_feature_server(mfcc_ubm_fn, 'sid')\n",
+    "\n",
+    "spk_lst = ubm_idmap.rightids\n",
+    "ubm = Mixture()\n",
+    "ubm.EM_split(fs, spk_lst, nb_gauss,\n",
+    "             iterations=(1, 2, 2, 4, 4, 4, 8, 8, 8, 8, 8, 8, 8), num_thread=num_thread,\n",
+    "             llk_gain=0.01)\n",
+    "ubm.write(ubm_fn, prefix='ubm/')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Step 2: TV\n",
+    "---\n",
+    "Extract MFCC for TV"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "logging.info('Computing MFCC for TV')\n",
+    "diar_tv = Diar.read_seg(tv_seg_fn, normalize_cluster=True)\n",
+    "fe = get_feature_extractor(audio_dir, 'sid')\n",
+    "tv_idmap = fe.save_multispeakers(diar_tv.id_map(), output_feature_filename=mfcc_tv_fn, keep_all=False)\n",
+    "tv_idmap.write(tv_idmap_fn)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Train a Total Variability model using the FactorAnalyser class"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tv_idmap = IdMap.read(tv_idmap_fn)\n",
+    "\n",
+    "ubm = Mixture()\n",
+    "ubm.read(ubm_fn, prefix='ubm/')\n",
+    "\n",
+    "fs = get_feature_server(mfcc_tv_fn, 'sid')\n",
+    "\n",
+    "tv_idmap.leftids = numpy.copy(tv_idmap.rightids)\n",
+    "\n",
+    "tv_stat = StatServer(tv_idmap, ubm.get_distrib_nb(), ubm.dim())\n",
+    "tv_stat.accumulate_stat(ubm=ubm, feature_server=fs, seg_indices=range(tv_stat.segset.shape[0]), num_thread=num_thread)\n",
+    "tv_stat.write(tv_stat_fn)\n",
+    "fa = FactorAnalyser()\n",
+    "fa.total_variability(tv_stat_fn, ubm, rank_tv, nb_iter=it_max_tv, batch_size=1000, num_thread=num_thread)\n",
+    "\n",
+    "write_tv_hdf5([fa.F, fa.mean, fa.Sigma], tv_fn)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Step 3: PLDA\n",
+    "---\n",
+    "Extract the MFCC for the PLDA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "logging.info('Computing MFCC for PLDA')\n",
+    "diar_plda = Diar.read_seg(plda_seg_fn, normalize_cluster=True)\n",
+    "fe = get_feature_extractor(audio_dir, 'sid')\n",
+    "plda_idmap = fe.save_multispeakers(diar_plda.id_map(), output_feature_filename=mfcc_plda_fn, keep_all=False)\n",
+    "plda_idmap.write(plda_idmap_fn)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Accumulate statistics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plda_idmap = IdMap.read(plda_idmap_fn)\n",
+    "\n",
+    "ubm = Mixture()\n",
+    "ubm.read(ubm_fn, prefix='ubm/')\n",
+    "tv, tv_mean, tv_sigma = read_tv_hdf5(tv_fn)\n",
+    "\n",
+    "fs = get_feature_server(mfcc_plda_fn, 'sid')\n",
+    "\n",
+    "plda_norm_stat = StatServer(plda_idmap, ubm.get_distrib_nb(), ubm.dim())\n",
+    "plda_norm_stat.accumulate_stat(ubm=ubm, feature_server=fs, \n",
+    "                               seg_indices=range(plda_norm_stat.segset.shape[0]), num_thread=num_thread)\n",
+    "plda_norm_stat.write(norm_stat_fn)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Extract i-vectors and compute norm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fa = FactorAnalyser(F=tv, mean=tv_mean, Sigma=tv_sigma)\n",
+    "norm_iv = fa.extract_ivectors(ubm, norm_stat_fn, num_thread=num_thread)\n",
+    "norm_iv.write(norm_iv_fn)\n",
+    "\n",
+    "norm_mean, norm_cov = norm_iv.estimate_spectral_norm_stat1(1, 'sphNorm')\n",
+    "\n",
+    "write_norm_hdf5([norm_mean, norm_cov], norm_fn)\n",
+    "\n",
+    "norm_iv.spectral_norm_stat1(norm_mean[:1], norm_cov[:1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Train the PLDA model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fa = FactorAnalyser()\n",
+    "fa.plda(norm_iv, rank_plda, nb_iter=it_max_plda)\n",
+    "write_plda_hdf5([fa.mean, fa.F, numpy.zeros((rank_tv, 0)), fa.Sigma], plda_fn)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Step 4: Compute additional data (optional)\n",
+    "---\n",
+    "Adding matrices for additional scoring methods: \n",
+    "* Mahalonobis matrix\n",
+    "* Lower Choleski decomposition of the WCCN matrix\n",
+    "* Within- and Between-class Covariance matrices"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iv = StatServer(norm_iv_fn)\n",
+    "matrix_dict = {}\n",
+    "\n",
+    "logging.info('compute mahalanobis_matrix')\n",
+    "mahalanobis_matrix = iv.get_mahalanobis_matrix_stat1()\n",
+    "matrix_dict['mahalanobis_matrix'] = mahalanobis_matrix\n",
+    "\n",
+    "logging.info('compute wccn_choleski')\n",
+    "wccn_choleski = iv.get_wccn_choleski_stat1()\n",
+    "matrix_dict['wccn_choleski'] = wccn_choleski\n",
+    "\n",
+    "logging.info('compute two_covariance')\n",
+    "within_covariance = iv.get_within_covariance_stat1()\n",
+    "matrix_dict['two_covariance/within_covariance'] = within_covariance\n",
+    "between_covariance = iv.get_between_covariance_stat1()\n",
+    "matrix_dict['two_covariance/between_covariance'] = between_covariance\n",
+    "\n",
+    "write_dict_hdf5(matrix_dict, matrices_fn)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Step 5: Merge in one model\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with h5py.File(model_fn, 'w') as model:\n",
+    "    for fn in [ubm_fn, tv_fn, norm_fn, plda_fn, matrices_fn]:\n",
+    "        if not os.path.exists(fn):\n",
+    "            continue\n",
+    "        with h5py.File(fn, 'r') as fh:\n",
+    "            for group in fh:\n",
+    "                logging.info(group)\n",
+    "                fh.copy(group, model)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}