[Update]:Updating the README.md file for Pypi.

95406297 · Sulfyderz · 3467349f · 95406297 · 95406297 · 95406297
Commit 95406297 authored Sep 28, 2018 by Sulfyderz
--- a/README.md
+++ b/README.md
@@ -11,24 +11,20 @@ PREREQUISITES
 =============
 *Sidekit for Diarization* requires the following software installed for your platform:
 1. [Python](http://www.python.org)
 2. [NumPy](http://www.numpy.org/)
 3. [Scipy](http://http://www.scipy.org/)
 4. [Pandas](http://http://www.pandas.org/)
 5. [GLPK](https://www.gnu.org/software/glpk/)
-6. If you want to build the documentation: [Sphinx 1.1.0 or newer](http://http://sphinx-doc.org/)
+6. [Sphinx 1.1.0 or newer](http://http://sphinx-doc.org/) to build the documentation
 INSTALLATION
 ============
 We recommend the use of a virtual environment (e.g. [Miniconda](https://conda.io/miniconda.html) or [Virtualenv](https://virtualenv.readthedocs.io/en/latest/)).
-After downloading the project, install the requirements with:
+TUTORIALS
-```
+=========
-pip install -r requirements.txt
-```
+Once your installation is complete, you can take a look at the [tutorials](https://git-lium.univ-lemans.fr/Meignier/s4d/tree/master/tutorials).
-Then proceed to install s4d:
\ No newline at end of file
-```
-./install.sh
-```
-Once done, you can take a look at the [tutorials](https://git-lium.univ-lemans.fr/Meignier/s4d/tree/master/tutorials).
\ No newline at end of file
--- a/tutorials/tuto_1_iv_model.ipynb
+++ b/tutorials/tuto_1_iv_model.ipynb
@@ -15,7 +15,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
@@ -38,7 +38,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
@@ -89,9 +89,34 @@
  },
  {
   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
   "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2018-09-26 11:50:10,393 - INFO - Computing MFCC for UBM\n"
+     ]
+    },
+    {
+     "ename": "FileNotFoundError",
+     "evalue": "[Errno 2] No such file or directory: '../data/train/19981207_0700_0800_inter_fm_dga.wav'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-11-b9765c5346e8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mdiar_ubm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDiar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_seg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mubm_seg_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnormalize_cluster\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mfe\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_feature_extractor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maudio_dir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'sid'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mubm_idmap\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave_multispeakers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdiar_ubm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mid_map\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput_feature_filename\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmfcc_ubm_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeep_all\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0mubm_idmap\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite_txt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mubm_idmap_fn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Desktop/Doctorat/Tools/Environments/miniconda/Python3/lib/python3.6/site-packages/sidekit/features_extractor.py\u001b[0m in \u001b[0;36msave_multispeakers\u001b[0;34m(self, idmap, channel, input_audio_filename, output_feature_filename, keep_all, skip_existing_file)\u001b[0m\n\u001b[1;32m    460\u001b[0m             \u001b[0;31m# logging.info('tmp file name: '+temp_file_name)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    461\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvad\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 462\u001b[0;31m             \u001b[0mh5f\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextract\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mchannel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_audio_filename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbacking_store\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    463\u001b[0m             \u001b[0menergy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mh5f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshow\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'/energy'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    464\u001b[0m             \u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mh5f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshow\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'/vad'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Desktop/Doctorat/Tools/Environments/miniconda/Python3/lib/python3.6/site-packages/sidekit/features_extractor.py\u001b[0m in \u001b[0;36mextract\u001b[0;34m(self, show, channel, input_audio_filename, output_feature_filename, backing_store)\u001b[0m\n\u001b[1;32m    215\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    216\u001b[0m         \u001b[0;31m# Open audio file, get the signal and possibly the sampling frequency\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 217\u001b[0;31m         \u001b[0msignal\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_rate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mread_audio\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maudio_filename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msampling_frequency\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    218\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0msignal\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    219\u001b[0m             \u001b[0msignal\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msignal\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnewaxis\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Desktop/Doctorat/Tools/Environments/miniconda/Python3/lib/python3.6/site-packages/sidekit/frontend/io.py\u001b[0m in \u001b[0;36mread_audio\u001b[0;34m(input_file_name, framerate)\u001b[0m\n\u001b[1;32m    420\u001b[0m         \u001b[0msig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mread_framerate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msampwidth\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mread_sph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_file_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'p'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    421\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'.wav'\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'.wave'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 422\u001b[0;31m         \u001b[0msig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mread_framerate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msampwidth\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mread_wav\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_file_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    423\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'.pcm'\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'.raw'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    424\u001b[0m         \u001b[0msig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mread_framerate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msampwidth\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mread_pcm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_file_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Desktop/Doctorat/Tools/Environments/miniconda/Python3/lib/python3.6/site-packages/sidekit/frontend/io.py\u001b[0m in \u001b[0;36mread_wav\u001b[0;34m(input_file_name)\u001b[0m\n\u001b[1;32m    117\u001b[0m     \u001b[0;34m:\u001b[0m\u001b[0;32mreturn\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    118\u001b[0m     \"\"\"\n\u001b[0;32m--> 119\u001b[0;31m     \u001b[0;32mwith\u001b[0m \u001b[0mwave\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_file_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"r\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mwfh\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    120\u001b[0m         \u001b[0;34m(\u001b[0m\u001b[0mnchannels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msampwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mframerate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnframes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcomptype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcompname\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwfh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetparams\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    121\u001b[0m         \u001b[0mraw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwfh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreadframes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnframes\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mnchannels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Desktop/Doctorat/Tools/Environments/miniconda/Python3/lib/python3.6/wave.py\u001b[0m in \u001b[0;36mopen\u001b[0;34m(f, mode)\u001b[0m\n\u001b[1;32m    497\u001b[0m             \u001b[0mmode\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'rb'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    498\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mmode\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m'r'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rb'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 499\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mWave_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    500\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mmode\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m'w'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'wb'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    501\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mWave_write\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Desktop/Doctorat/Tools/Environments/miniconda/Python3/lib/python3.6/wave.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f)\u001b[0m\n\u001b[1;32m    157\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_i_opened_the_file\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    158\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 159\u001b[0;31m             \u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbuiltins\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rb'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    160\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_i_opened_the_file\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    161\u001b[0m         \u001b[0;31m# else, assume it is an open file object already\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '../data/train/19981207_0700_0800_inter_fm_dga.wav'"
+     ]
+    }
+   ],
   "source": [
    "logging.info('Computing MFCC for UBM')\n",
    "diar_ubm = Diar.read_seg(ubm_seg_fn, normalize_cluster=True)\n",

 %% 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
 %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)
 ```
+%% Output
+    2018-09-26 11:50:10,393 - INFO - Computing MFCC for UBM
+    ---------------------------------------------------------------------------
+    FileNotFoundError                         Traceback (most recent call last)
+    <ipython-input-11-b9765c5346e8> in <module>()
+          2 diar_ubm = Diar.read_seg(ubm_seg_fn, normalize_cluster=True)
+          3 fe = get_feature_extractor(audio_dir, 'sid')
+    ----> 4 ubm_idmap = fe.save_multispeakers(diar_ubm.id_map(), output_feature_filename=mfcc_ubm_fn, keep_all=False)
+          5 ubm_idmap.write_txt(ubm_idmap_fn)
+    ~/Desktop/Doctorat/Tools/Environments/miniconda/Python3/lib/python3.6/site-packages/sidekit/features_extractor.py in save_multispeakers(self, idmap, channel, input_audio_filename, output_feature_filename, keep_all, skip_existing_file)
+        460             # logging.info('tmp file name: '+temp_file_name)
+        461             self.vad = None
+    --> 462             h5f = self.extract(show, channel, input_audio_filename, backing_store=False)
+        463             energy = h5f.get(show + '/energy').value
+        464             label = h5f.get(show + '/vad').value
+    ~/Desktop/Doctorat/Tools/Environments/miniconda/Python3/lib/python3.6/site-packages/sidekit/features_extractor.py in extract(self, show, channel, input_audio_filename, output_feature_filename, backing_store)
+        215
+        216         # Open audio file, get the signal and possibly the sampling frequency
+    --> 217         signal, sample_rate = read_audio(audio_filename, self.sampling_frequency)
+        218         if signal.ndim == 1:
+        219             signal = signal[:, numpy.newaxis]
+    ~/Desktop/Doctorat/Tools/Environments/miniconda/Python3/lib/python3.6/site-packages/sidekit/frontend/io.py in read_audio(input_file_name, framerate)
+        420         sig, read_framerate, sampwidth = read_sph(input_file_name, 'p')
+        421     elif ext.lower() == '.wav' or ext.lower() == '.wave':
+    --> 422         sig, read_framerate, sampwidth = read_wav(input_file_name)
+        423     elif ext.lower() == '.pcm' or ext.lower() == '.raw':
+        424         sig, read_framerate, sampwidth = read_pcm(input_file_name)
+    ~/Desktop/Doctorat/Tools/Environments/miniconda/Python3/lib/python3.6/site-packages/sidekit/frontend/io.py in read_wav(input_file_name)
+        117     :return:
+        118     """
+    --> 119     with wave.open(input_file_name, "r") as wfh:
+        120         (nchannels, sampwidth, framerate, nframes, comptype, compname) = wfh.getparams()
+        121         raw = wfh.readframes(nframes * nchannels)
+    ~/Desktop/Doctorat/Tools/Environments/miniconda/Python3/lib/python3.6/wave.py in open(f, mode)
+        497             mode = 'rb'
+        498     if mode in ('r', 'rb'):
+    --> 499         return Wave_read(f)
+        500     elif mode in ('w', 'wb'):
+        501         return Wave_write(f)
+    ~/Desktop/Doctorat/Tools/Environments/miniconda/Python3/lib/python3.6/wave.py in __init__(self, f)
+        157         self._i_opened_the_file = None
+        158         if isinstance(f, str):
+    --> 159             f = builtins.open(f, 'rb')
+        160             self._i_opened_the_file = f
+        161         # else, assume it is an open file object already
+    FileNotFoundError: [Errno 2] No such file or directory: '../data/train/19981207_0700_0800_inter_fm_dga.wav'
 %% 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)
 ```

--- a/tutorials/tuto_2_diar_bic.ipynb
+++ b/tutorials/tuto_2_diar_bic.ipynb
@@ -41,19 +41,9 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
   "metadata": {},
-   "outputs": [
+   "outputs": [],
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/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.\n",
-      "  matplotlib.use('PDF')\n",
-      "WARNING:root:WARNNG: libsvm is not installed, please refer to the documentation if you intend to use SVM classifiers\n"
-     ]
-    }
-   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
@@ -96,7 +86,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
@@ -113,7 +103,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
@@ -133,7 +123,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [

 %% 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
-    /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
    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
    (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
    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
    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
    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
    2018-06-11 10:46:31,374 - INFO - BIC HAC, alpha: 3.000000
 %% Cell type:markdown id: tags:
 Step 5: re-segmentation
 -----------------------
 Viterbi decoding
 * HMM is trained: one GMM per speaker, GMM has 8 component with diagonal covariance matrix. Only a penalty between state
 is fixed.
 * Emission is computed: likelyhood for each feature
 * a Viterbi decoding is performed
 %% Cell type:code id: tags:
 ``` python
 logging.info('Viterbi decoding, penalties: %f', thr_vit)
 vit_diar = viterbi.viterbi_decoding(cep, bich_diar, thr_vit)
 if save_all:
    vit_filename = os.path.join(wdir, show + '.d.seg')
    Diar.write_seg(vit_filename, vit_diar)
 ```
 %% Output
    2018-06-11 10:46:32,506 - INFO - Viterbi decoding, penalties: -250.000000
 %% Cell type:markdown id: tags:
 Compute the diarization error rate
 ----------------------------------
 %% Cell type:code id: tags:
 ``` python
 from s4d import scoring
 from tabulate import tabulate
 from s4d.gui.viewer import PlotDiar
 from s4d.gui.viewer_utils import *
 ref = Diar.read_mdtm(os.path.join(data_dir, 'seg', 'ester1.tst.mdtm'))
 uem = Diar.read_uem(os.path.join(data_dir, 'seg', 'ester1.tst.uem'))
 uem_show = uem.make_index(['show'])
 ref_show = ref.make_index(['show'])
 der = scoring.compute_der(vit_diar, ref_show[show], uem=uem_show[show], collar=25, no_overlap=False)
 tab = scoring.get_header()
 tab += der.get_table(show, time=False)
 print(tabulate(tab, tablefmt='psql', floatfmt='.2f', headers='firstrow'))
 diff_diar = diar_diff(vit_diar, ref_show[show], match=True)
 p = PlotDiar(diff_diar, size=(25, 6))
 p.draw()
 ```
 %% Output
    2018-06-11 10:46:41,697 - INFO - append collar
    +-----------------------------+--------+------+--------+-------+------+--------+--------+-----------+
    | show                        | type   |   fa |   miss |   sns |   fa |   miss |   conf |   speaker |
    |-----------------------------+--------+------+--------+-------+------+--------+--------+-----------|
    | 20041008_1800_1830_INFO_DGA | rate   | 0.19 |   0.45 |  0.64 | 0.19 |   1.01 |  12.12 |     13.33 |
    +-----------------------------+--------+------+--------+-------+------+--------+--------+-----------+
    uem from ref
    100