convert-ej-m-adapt1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nThis program makes learning ev-gmm.\\n'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "\n",
    "\"\"\"\n",
    "This program makes learning ev-gmm.\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# __future__ module make compatible python2 and python3\n",
    "from __future__ import division, print_function\n",
    "\n",
    "# basic modules\n",
    "import os\n",
    "import os.path\n",
    "import time\n",
    "\n",
    "# for warning ignore\n",
    "import warnings\n",
    "#warning.filterwarnings('ignore')\n",
    "\n",
    "# for file system manupulation \n",
    "from shutil import rmtree \n",
    "import glob\n",
    "import argparse\n",
    "\n",
    "# for save object\n",
    "import pickle\n",
    "\n",
    "# for make glaph\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_style(\"whitegrid\")\n",
    "plt.rcParams['figure.figsize'] = (16, 5)\n",
    "import librosa.display\n",
    "\n",
    "# for scientific computing\n",
    "import numpy as np\n",
    "from numpy.linalg import norm \n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.mixture import GMM # GMM class cannot use after sklearn 0.20.0\n",
    "import sklearn.mixture\n",
    "#from sklearn.mixture.gaussian_mixture import _compute_precision_cholesky\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import scipy.sparse\n",
    "from scipy.signal import firwin, lfilter\n",
    "\n",
    "# for display audio controler\n",
    "from IPython.display import Audio\n",
    "\n",
    "# for manuplate audio data\n",
    "import soundfile as sf\n",
    "import pyworld as pw\n",
    "import pysptk\n",
    "from dtw import dtw\n",
    "from fastdtw import fastdtw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class WORLD(object):\n",
    "    \"\"\"\n",
    "    WORLD based speech analyzer and synthezer.\n",
    "    \n",
    "    Ref : https://github.com/k2kobayashi/sprocket/\n",
    "    \"\"\"\n",
    "    def __init__(self, fs=16000, fftl=1024, shiftms=5.0, minf0=40.0, maxf0=500.0):\n",
    "        \"\"\"\n",
    "        Parameters\n",
    "        ----------\n",
    "        fs : int \n",
    "            Sampling frequency \n",
    "        fftl : int\n",
    "            FFT length\n",
    "        shiftms : float\n",
    "            Shift length [ms]\n",
    "        minf0 : float\n",
    "            Floor in F0 estimation\n",
    "        maxf0 : float\n",
    "            Seli in F0 estimation\n",
    "        \"\"\"\n",
    "        \n",
    "        self.fs = fs\n",
    "        self.fftl = fftl\n",
    "        self.shiftms = shiftms\n",
    "        self.minf0 = minf0\n",
    "        self.maxf0 = maxf0\n",
    "        \n",
    "    def analyze(self, x):\n",
    "        \"\"\"\n",
    "        Analyze acoustic featueres.\n",
    "        \n",
    "        Parameters\n",
    "        ----------\n",
    "        x : array, shape(`T`)\n",
    "            monoral speech signal in time domain\n",
    "        \n",
    "        Returns\n",
    "        ----------\n",
    "        f0 : array, shape(`T`)\n",
    "            F0 sequence\n",
    "        sp : array, shape(`T`, `fftl / 2 + 1`)\n",
    "            Spectral envelope sequence\n",
    "        ap : array, shape(`T`, `fftl / 2 + 1`)\n",
    "            aperiodicity sequence\n",
    "        \"\"\"\n",
    "        \n",
    "        f0, time_axis = pw.harvest(x, self.fs, f0_floor=self.minf0,\n",
    "                                   f0_ceil=self.maxf0, frame_period=self.shiftms)\n",
    "        sp = pw.cheaptrick(x, f0, time_axis, self.fs, fft_size=self.fftl)\n",
    "        ap = pw.d4c(x, f0, time_axis, self.fs, fft_size=self.fftl)\n",
    "        \n",
    "        assert sp.shape == ap.shape\n",
    "        \n",
    "        return f0, sp, ap\n",
    "    \n",
    "    def analyze_f0(self, x):\n",
    "        \"\"\"\n",
    "        Analyze f0.\n",
    "        \n",
    "        Parameters\n",
    "        ----------\n",
    "        x : array, shape(`T`)\n",
    "            monoral speech signal in time domain\n",
    "        \n",
    "        Returns\n",
    "        ----------\n",
    "        f0 : array, shape(`T`)\n",
    "            F0 sequence\n",
    "        \"\"\"\n",
    "\n",
    "        f0, time_axis = pw.harvest(x, self.fs, f0_floor=self.minf0,\n",
    "                                   f0_ceil=self.maxf0, frame_period=self.shiftms)\n",
    "        \n",
    "        assert f0.shape == x.shape()\n",
    "        \n",
    "        return f0\n",
    "    \n",
    "    def synthesis(self, f0, sp, ap):\n",
    "        \"\"\"\n",
    "        Re-synthesizes a speech waveform from acoustic featueres.\n",
    "        \n",
    "        Parameters\n",
    "        ----------\n",
    "        f0 : array, shape(`T`)\n",
    "            F0 sequence\n",
    "        sp : array, shape(`T`, `fftl / 2 + 1`)\n",
    "            Spectral envelope sequence\n",
    "        ap : array, shape(`T`, `fftl / 2 + 1`)\n",
    "            aperiodicity sequence\n",
    "        \"\"\"\n",
    "\n",
    "        return pw.synthesize(f0, sp, ap, self.fs, frame_period=self.shiftms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class FeatureExtractor(object):\n",
    "    \"\"\"\n",
    "    Analyze acoustic features from a waveform.\n",
    "    \n",
    "    This class may have several types of estimeter like WORLD or STRAIGHT.\n",
    "    Default type is WORLD.\n",
    "    \n",
    "    Ref : https://github.com/k2kobayashi/sprocket/\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, analyzer='world', fs=16000, fftl=1024, \n",
    "                 shiftms=5.0, minf0=50.0, maxf0=500.0):\n",
    "        \"\"\"\n",
    "        Parameters\n",
    "        ----------\n",
    "        analyzer : str\n",
    "            Analyzer\n",
    "        fs : int \n",
    "            Sampling frequency \n",
    "        fftl : int\n",
    "            FFT length\n",
    "        shiftms : float\n",
    "            Shift length [ms]\n",
    "        minf0 : float\n",
    "            Floor in F0 estimation\n",
    "        maxf0 : float\n",
    "            Seli in F0 estimation\n",
    "        \"\"\"\n",
    "        \n",
    "        self.analyzer = analyzer\n",
    "        self.fs = fs\n",
    "        self.fftl = fftl\n",
    "        self.shiftms = shiftms\n",
    "        self.minf0 = minf0\n",
    "        self.maxf0 = maxf0\n",
    "    \n",
    "        if self.analyzer == 'world':\n",
    "            self.analyzer = WORLD(fs=self.fs, fftl=self.fftl, \n",
    "                                  minf0=self.minf0, maxf0=self.maxf0, shiftms=self.shiftms)\n",
    "        else:\n",
    "            raise('Analyzer Error : not support type, see FeatureExtractor class.')\n",
    "        \n",
    "        self._f0 = None\n",
    "        self._sp = None\n",
    "        self._ap = None\n",
    "        \n",
    "    def analyze(self, x):\n",
    "        \"\"\"\n",
    "        Analyze acoustic featueres.\n",
    "        \n",
    "        Parameters\n",
    "        ----------\n",
    "        x : array, shape(`T`)\n",
    "            monoral speech signal in time domain\n",
    "        \n",
    "        Returns\n",
    "        ----------\n",
    "        f0 : array, shape(`T`)\n",
    "            F0 sequence\n",
    "        sp : array, shape(`T`, `fftl / 2 + 1`)\n",
    "            Spectral envelope sequence\n",
    "        ap : array, shape(`T`, `fftl / 2 + 1`)\n",
    "            aperiodicity sequence\n",
    "        \"\"\"\n",
    "        \n",
    "        self.x = np.array(x, dtype=np.float)\n",
    "        self._f0, self._sp, self._ap = self.analyzer.analyze(self.x)\n",
    "        \n",
    "        # check f0 < 0\n",
    "        self._f0[self._f0 < 0] = 0\n",
    "        \n",
    "        if np.sum(self._f0) == 0.0:\n",
    "            print(\"Warning : F0 values are all zero.\")\n",
    "        \n",
    "        return self._f0, self._sp, self._ap\n",
    "    \n",
    "    def analyze_f0(self, x):\n",
    "        \"\"\"\n",
    "        Analyze f0.\n",
    "        \n",
    "        Parameters\n",
    "        ----------\n",
    "        x : array, shape(`T`)\n",
    "            monoral speech signal in time domain\n",
    "        \n",
    "        Returns\n",
    "        ----------\n",
    "        f0 : array, shape(`T`)\n",
    "            F0 sequence\n",
    "        \"\"\"\n",
    "\n",
    "        self.x = np.array(x, dtype=np.float)\n",
    "        self._f0 = self.analyzer.analyze_f0(self.x)\n",
    "\n",
    "        # check f0 < 0\n",
    "        self._f0[self._f0 < 0] = 0\n",
    "        \n",
    "        if np.sum(self._f0) == 0.0:\n",
    "            print(\"Warning : F0 values are all zero.\")\n",
    "        \n",
    "        return self._f0\n",
    "    \n",
    "    def mcep(self, dim=24, alpha=0.42):\n",
    "        \"\"\"\n",
    "        Convert mel-cepstrum sequence from spectral envelope.\n",
    "        \n",
    "        Parameters\n",
    "        ----------\n",
    "        dim : int\n",
    "            mel-cepstrum dimension\n",
    "        alpha : float\n",
    "            parameter of all-path filter\n",
    "        \n",
    "        Returns\n",
    "        ----------\n",
    "        mcep : array, shape(`T`, `dim + 1`)\n",
    "            mel-cepstrum sequence\n",
    "        \"\"\"        \n",
    "        \n",
    "        self._analyzed_check()\n",
    "        \n",
    "        return pysptk.sp2mc(self._sp, dim, alpha)\n",
    "    \n",
    "    def codeap(self):\n",
    "        \"\"\"\n",
    "        \"\"\"\n",
    "        self._analyzed_check()\n",
    "        \n",
    "        return pw.code_aperiodicity(self._ap, self.fs)\n",
    "    \n",
    "    def npow(self):\n",
    "        \"\"\"\n",
    "        Normalized power sequence from spectral envelope.\n",
    "        \n",
    "        Returns\n",
    "        ----------\n",
    "        npow : vector, shape(`T`, `1`)\n",
    "            Normalized power sequence of the given waveform\n",
    "        \"\"\"\n",
    "        \n",
    "        self._analyzed_check()\n",
    "        \n",
    "        npow = np.apply_along_axis(self._spvec2pow, 1, self._sp)\n",
    "        \n",
    "        meanpow = np.mean(npow)\n",
    "        npow = 10.0 * np.log10(npow / meanpow)\n",
    "        \n",
    "        return npow\n",
    "    \n",
    "    def _spvec2pow(self, specvec):\n",
    "        \"\"\"\n",
    "        \"\"\"\n",
    "        fftl2 = len(specvec) - 1\n",
    "        fftl = fftl2 * 2\n",
    "        \n",
    "        power = specvec[0] + specvec[fftl2]\n",
    "        for k in range(1, fftl2):\n",
    "            power += 2.0 * specvec[k]\n",
    "        power /= fftl\n",
    "        \n",
    "        return power\n",
    "        \n",
    "    def _analyzed_check(self):\n",
    "        if self._f0 is None and self._sp is None and self._ap is None:\n",
    "            raise('Call FeatureExtractor.analyze() before this method.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Synthesizer(object):\n",
    "    \"\"\"\n",
    "    Synthesize a waveform from acoustic features.\n",
    "    \n",
    "    Ref : https://github.com/k2kobayashi/sprocket/\n",
    "    \"\"\"\n",
    "    def __init__(self, fs=16000, fftl=1024, shiftms=5.0):\n",
    "        \"\"\"\n",
    "        Parameters\n",
    "        ----------\n",
    "        fs : int \n",
    "            Sampling frequency \n",
    "        fftl : int\n",
    "            FFT length\n",
    "        shiftms : float\n",
    "            Shift length [ms]\n",
    "        \"\"\"\n",
    "        \n",
    "        self.fs = fs\n",
    "        self.fftl = fftl\n",
    "        self.shiftms = shiftms\n",
    "    \n",
    "    def synthesis(self, f0, mcep, ap, rmcep=None, alpha=0.42):\n",
    "        \"\"\"\n",
    "        Re-synthesizes a speech waveform from acoustic featueres.\n",
    "        \n",
    "        Parameters\n",
    "        ----------\n",
    "        f0 : array, shape(`T`)\n",
    "            F0 sequence\n",
    "        mcep : array, shape(`T`, `dim`)\n",
    "            mel-cepstrum sequence\n",
    "        ap : array, shape(`T`, `fftl / 2 + 1`)\n",
    "            aperiodicity sequence\n",
    "        rmcep : array, shape(`T`, `dim`)\n",
    "            array of reference mel-cepstrum sequence\n",
    "        alpha : float\n",
    "            parameter of all-path filter\n",
    "            \n",
    "        Returns\n",
    "        ----------\n",
    "        wav : array,\n",
    "            syntesized waveform\n",
    "        \"\"\"\n",
    "        \n",
    "        if rmcep is not None:\n",
    "            # power modification\n",
    "            mcep = mod_power(mcep, rmcep, alpha=alpha)\n",
    "        \n",
    "        sp = pysptk.mc2sp(mcep, alpha, self.fftl)\n",
    "        wav = pw.synthesize(f0, sp, ap, self.fs, frame_period=self.shiftms)\n",
    "        \n",
    "        return wav\n",
    "        \n",
    "    def synthesis_diff(self, x, diffmcep, rmcep=None, alpha=0.42):\n",
    "        \"\"\"\n",
    "        Re-synthesizes a speech waveform from acoustic featueres.\n",
    "        filtering with a differential mel-cepstrum.\n",
    "        \n",
    "        Parameters\n",
    "        ----------\n",
    "        x : array, shape(`samples`)\n",
    "            array of waveform sequence\n",
    "        diffmcep : array, shape(`T`, `dim`)\n",
    "            array of differential mel-cepstrum sequence\n",
    "        rmcep : array, shape(`T`, `dim`)\n",
    "            array of reference mel-cepstrum sequence\n",
    "        alpha : float\n",
    "            parameter of all-path filter\n",
    "            \n",
    "        Returns\n",
    "        ----------\n",
    "        wav : array,\n",
    "            syntesized waveform\n",
    "        \"\"\"      \n",
    "        \n",
    "        x = x.astype(np.float64)\n",
    "        dim = diffmcep.shape[1] - 1 \n",
    "        shiftl = int(self.fs / 1000 * self.shiftms)\n",
    "        \n",
    "        if rmcep is not None:\n",
    "            # power modification\n",
    "            diffmcep = mod_power(rmcep + diffmcep, rmcep, alpha=alpha) - rmcep        \n",
    "        \n",
    "        # mc2b = transform mel-cepstrum to MLSA digital filter coefficients.\n",
    "        b = np.apply_along_axis(pysptk.mc2b, 1, diffmcep, alpha)\n",
    "        \n",
    "        mlsa_fil = pysptk.synthesis.Synthesizer(pysptk.synthesis.MLSADF(dim, alpha=alpha),\n",
    "                                                shiftl)\n",
    "        wav = mlsa_fil.synthesis(x, b)\n",
    "        \n",
    "        return wav\n",
    "    \n",
    "    def synthesis_sp(self, f0, sp, ap):\n",
    "        \"\"\"\n",
    "        Re-synthesizes a speech waveform from acoustic featueres.\n",
    "        \n",
    "        Parameters\n",
    "        ----------\n",
    "        f0 : array, shape(`T`)\n",
    "            F0 sequence\n",
    "        spc : array, shape(`T`, `dim`)\n",
    "            mel-cepstrum sequence\n",
    "        ap : array, shape(`T`, `fftl / 2 + 1`)\n",
    "            aperiodicity sequence\n",
    "            \n",
    "        Returns\n",
    "        ----------\n",
    "        wav : array,\n",
    "            syntesized waveform\n",
    "        \"\"\"      \n",
    "        \n",
    "        wav = pw.synthesize(f0, sp, ap, self.fs, frame_period=self.shiftms)\n",
    "        \n",
    "        return wav\n",
    "    \n",
    "def mod_power(cvmcep, rmcep, alpha=0.42, irlen=256):\n",
    "    \"\"\"\n",
    "    power modification based on inpuulse responce\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    cvmcep : array, shape(`T`, `dim`)\n",
    "        array of converted mel-cepstrum\n",
    "    rmcep : arraym shape(`T`, `dim`)\n",
    "        array of reference mel-cepstrum\n",
    "    alpha : float\n",
    "        parameter of all-path filter\n",
    "    irlen : int\n",
    "        Length for IIR filter\n",
    "\n",
    "    Returns\n",
    "    ----------\n",
    "    modified_cvmcep : array, shape(`T`, `dim`)\n",
    "        array of power modified converted mel-cepstrum\n",
    "    \"\"\"\n",
    "\n",
    "    if rmcep.shape != cvmcep.shape:\n",
    "        raise ValueError(\n",
    "            \"The shape of the converted and reference mel-cepstrum are different : {} / {}.format(cvmcep.shape, rmcep.shape)\"\n",
    "        )\n",
    "\n",
    "    # mc2e = Compute energy from mel-cepstrum. e-option\n",
    "    cv_e = pysptk.mc2e(cvmcep, alpha=alpha, irlen=irlen)\n",
    "    r_e = pysptk.mc2e(rmcep, alpha=alpha, irlen=irlen)\n",
    "\n",
    "    dpow = np.log(r_e / cv_e) / 2\n",
    "\n",
    "    modified_cvmcep = np.copy(cvmcep)\n",
    "    modified_cvmcep[:, 0] += dpow\n",
    "\n",
    "    return modified_cvmcep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def util methods\n",
    "def melcd(array1, array2):\n",
    "    \"\"\"\n",
    "    calculate mel-cepstrum distortion\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    array1, array2 : array, shape(`T`, `dim`) or shape(`dim`)\n",
    "        Array of original and target.\n",
    "    \n",
    "    Returns\n",
    "    ----------\n",
    "    mcd : scala, number > 0\n",
    "        Scala of mel-cepstrum distoriton\n",
    "    \"\"\"\n",
    "    if array1.shape != array2.shape:\n",
    "        raise ValueError(\n",
    "            \"The shape of both array are different : {} / {}.format(array1.shape,array2.shape)\"\n",
    "        )    \n",
    "   \n",
    "    if array1.ndim == 2:\n",
    "        diff = array1 - array2\n",
    "        mcd = 10.0 / np.log(10) * np.mean(np.sqrt(2.0 * np.sum(diff ** 2, axis=1)))\n",
    "    elif array1.ndim == 1:\n",
    "        diff = array1 - array2\n",
    "        mcd = 10.0 / np.log(10) * np.sqrt(2.0 * np.sum(diff ** 2))\n",
    "    else:\n",
    "        raise ValueError(\"Dimension mismatch.\")\n",
    "        \n",
    "    return mcd\n",
    "\n",
    "def delta(data, win=[-1.0, 1.0, 0]):\n",
    "    \"\"\"\n",
    "    calculate delta component\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    data : array, shape(`T`, `dim`)\n",
    "        Array of static matrix sequence.\n",
    "    win : array, shape(`3`)\n",
    "        The shape of window matrix.\n",
    "    \n",
    "    Returns\n",
    "    ----------\n",
    "    delta : array, shape(`T`, `dim`)\n",
    "        Array of delta matrix sequence.\n",
    "    \"\"\"\n",
    "    \n",
    "    if data.ndim == 1:\n",
    "        # change vector into 1d-array\n",
    "        T = len(data)\n",
    "        dim = data.ndim\n",
    "        data = data.reshape(T, dim)\n",
    "    else:\n",
    "        T, dim = data.shape\n",
    "    \n",
    "    win = np.array(win, dtype=np.float64)\n",
    "    delta = np.zeros((T, dim))\n",
    "    \n",
    "    delta[0] = win[0] * data[0] + win[1] * data[1]\n",
    "    delta[-1] = win[0] * data[-2] + win[1] * data[-1]\n",
    "    \n",
    "    for i in range(len(win)):\n",
    "        delta[1:T - 1] += win[i] * delta[i:T - 2 + i]\n",
    "    \n",
    "    return delta\n",
    "\n",
    "def static_delta(data, win=[-1.0, 1.0, 0]):\n",
    "    \"\"\"\n",
    "    calculate static and delta component\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    data : array, shape(`T`, `dim`)\n",
    "        Array of static matrix sequence.\n",
    "    win : array, shape(`3`)\n",
    "        The shape of window matrix.\n",
    "    \n",
    "    Returns\n",
    "    ----------\n",
    "    sddata : array, shape(`T`, `dim * 2`)\n",
    "        Array of static and delta matrix sequence.\n",
    "    \"\"\"\n",
    "    \n",
    "    sddata = np.c_[data, delta(data, win)]\n",
    "    \n",
    "    assert sddata.shape[1] == data.shape[1] * 2\n",
    "    \n",
    "    return sddata\n",
    "\n",
    "def construct_static_and_delta_matrix(T, D, win=[-1.0, 1.0, 0]):\n",
    "    \"\"\"\n",
    "    calculate static and delta transformation matrix\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    T : scala, `T`\n",
    "        Scala of time length\n",
    "    D : scala, `D`\n",
    "        Scala of the number of dimension.\n",
    "    win : array, shape(`3`)\n",
    "        The shape of window matrix.\n",
    "    \n",
    "    Returns\n",
    "    ----------\n",
    "    W : array, shape(`2 * D * T`, `D * T`)\n",
    "        Array of static and delta transformation matrix.\n",
    "    \"\"\"\n",
    "    \n",
    "    static = [0, 1, 0]\n",
    "    delta = win\n",
    "    assert len(static) == len(delta)\n",
    "    \n",
    "    # generate full W\n",
    "    DT = D * T\n",
    "    ones = np.ones(DT)\n",
    "    row = np.arange(2 * DT).reshape(2 * T, D) # generate serial numbers\n",
    "    static_row = row[::2] # [1,2,3,4,5] => [1,3,5]\n",
    "    delta_row = row[1::2] # [1,2,3,4,5] => [2,4]\n",
    "    col = np.arange(DT)\n",
    "    \n",
    "    data = np.array([ones * static[0], ones * static[1],\n",
    "                      ones * static[2], ones * delta[0],\n",
    "                      ones * delta[1], ones * delta[2]]).flatten()\n",
    "    row = np.array([[static_row] * 3, [delta_row] * 3]).flatten()\n",
    "    col = np.array([[col - D, col, col + D] * 2]).flatten()\n",
    "\n",
    "    # remove component at first and end frame\n",
    "    valid_idx = np.logical_not(np.logical_or(col < 0, col >= DT))\n",
    "    \n",
    "    W = scipy.sparse.csr_matrix(\n",
    "        (data[valid_idx], (row[valid_idx], col[valid_idx])), shape=(2 * DT, DT))\n",
    "    W.eliminate_zeros()\n",
    "    \n",
    "    return W\n",
    "    \n",
    "def extfrm(data, npow, power_threshold=-20):\n",
    "    \"\"\"\n",
    "    Extract frame over the power threshold\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    data : array, shape(`T`, `dim`)\n",
    "        array of input data\n",
    "    npow : array, shape(`T`)\n",
    "        vector of normalized power sequence\n",
    "    threshold : scala\n",
    "        scala of power threshold [dB]\n",
    "        \n",
    "    Returns\n",
    "    ----------\n",
    "    data : array, shape(`T_ext`, `dim`)\n",
    "        remaining data after extracting frame\n",
    "        `T_ext` <= `T`\n",
    "    \"\"\"\n",
    "    T = data.shape[0]\n",
    "    if T != len(npow):\n",
    "        raise(\"Length of two vectors is different.\")\n",
    "        \n",
    "    valid_index = np.where(npow > power_threshold)\n",
    "    extdata = data[valid_index]\n",
    "    assert extdata.shape[0] <= T\n",
    "    \n",
    "    return extdata\n",
    "\n",
    "def estimate_twf(orgdata, tardata, distance='melcd', fast=True, otflag=None):\n",
    "    \"\"\"\n",
    "    time warping function estimator\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    orgdata : array, shape(`T_org`, `dim`)\n",
    "        array of source feature\n",
    "    tardata : array, shape(`T_tar`, `dim`)\n",
    "        array of target feature\n",
    "    distance : str \n",
    "        distance function\n",
    "    fast : bool\n",
    "        use fastdtw instead of dtw\n",
    "    otflag : str\n",
    "        Alignment into the length of specification\n",
    "        'org' : alignment into original length\n",
    "        'tar' : alignment into target length   \n",
    "    \n",
    "    Returns\n",
    "    ----------\n",
    "    twf : array, shape(`2`, `T`)\n",
    "        time warping function between original and target\n",
    "    \"\"\"\n",
    "    \n",
    "    if distance == 'melcd':\n",
    "        def distance_func(x, y): return melcd(x, y)\n",
    "    else:\n",
    "        raise ValueError('this distance method is not support.')\n",
    "    \n",
    "    if fast:\n",
    "        _, path = fastdtw(orgdata, tardata, dist=distance_func)\n",
    "        twf = np.array(path).T\n",
    "    else:\n",
    "        _, _, _, twf = dtw(orgdata, tardata, distance_func) \n",
    "        \n",
    "    if otflag is not None:\n",
    "        twf = modify_twf(twf, otflag=otflag)\n",
    "    \n",
    "    return twf\n",
    "\n",
    "def align_data(org_data, tar_data, twf):\n",
    "    \"\"\"\n",
    "    get aligned joint feature vector\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    org_data : array, shape(`T_org`, `dim_org`)\n",
    "        Acoustic feature vector of original speaker\n",
    "    tar_data : array, shape(`T_tar`, `dim_tar`)\n",
    "        Acoustic feature vector of target speaker\n",
    "    twf : array, shape(`2`, `T`)\n",
    "        time warping function between original and target\n",
    "        \n",
    "    Returns\n",
    "    ----------\n",
    "    jdata : array, shape(`T_new`, `dim_org + dim_tar`)\n",
    "        Joint feature vector between source and target\n",
    "    \"\"\"\n",
    "    \n",
    "    jdata = np.c_[org_data[twf[0]], tar_data[twf[1]]]\n",
    "    return jdata\n",
    "\n",
    "def modify_twf(twf, otflag=None):\n",
    "    \"\"\"\n",
    "    align specified length\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    twf : array, shape(`2`, `T`)\n",
    "        time warping function between original and target\n",
    "    otflag : str\n",
    "        Alignment into the length of specification\n",
    "        'org' : alignment into original length\n",
    "        'tar' : alignment into target length   \n",
    "    \n",
    "    Returns\n",
    "    ----------\n",
    "    mod_twf : array, shape(`2`, `T_new`)\n",
    "        time warping function of modified alignment\n",
    "    \"\"\"\n",
    "    \n",
    "    if otflag == 'org':\n",
    "        of, indice = np.unique(twf[0], return_index=True)\n",
    "        mod_twf = np.c_[of, twf[1][indice]].T\n",
    "    elif otflag == 'tar':\n",
    "        tf, indice = np.unique(twf[1], return_index=True)\n",
    "        mod_twf = np.c_[twf[0][indice], tf].T \n",
    "    \n",
    "    return mod_twf\n",
    "\n",
    "def low_cut_filter(x, fs, cutoff=70):\n",
    "    \"\"\"\n",
    "    low cut filter\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    x : array, shape('samples')\n",
    "        waveform sequence\n",
    "    fs : array, int\n",
    "        Sampling frequency\n",
    "    cutoff : float\n",
    "        cutoff frequency of low cut filter\n",
    "    \n",
    "    Returns\n",
    "    ----------\n",
    "    lct_x : array, shape('samples')\n",
    "        Low cut filtered waveform sequence\n",
    "    \"\"\"\n",
    "    \n",
    "    nyquist = fs // 2\n",
    "    norm_cutoff = cutoff / nyquist\n",
    "    \n",
    "    # low cut filter\n",
    "    fil = firwin(255, norm_cutoff, pass_zero=False)\n",
    "    lct_x = lfilter(fil, 1, x)\n",
    "    \n",
    "    return lct_x\n",
    "\n",
    "def extsddata(data, npow, power_threshold=-20):\n",
    "    \"\"\"\n",
    "    get power extract static and delta feature vector\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    data : array, shape(`T`, `dim`)\n",
    "        acoustic feature vector\n",
    "    npow : array, shape(`T`)\n",
    "        normalized power vector\n",
    "    power_threshold : float\n",
    "        power threshold\n",
    "    \n",
    "    Returns\n",
    "    ----------\n",
    "    extsddata : array, shape(`T_new`, `dim * 2`)\n",
    "        silence remove static and delta feature vector\n",
    "    \"\"\"\n",
    "    \n",
    "    extsddata = extfrm(static_delta(data), npow, power_threshold=power_threshold)\n",
    "    return extsddata\n",
    "\n",
    "def transform_jnt(array_list):\n",
    "    num_files = len(array_list)\n",
    "    for i in range(num_files):\n",
    "        if i == 0:\n",
    "            jnt = array_list[i]\n",
    "        else:\n",
    "            jnt = np.r_[jnt, array_list[i]]\n",
    "    return jnt\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "class F0statistics(object):\n",
    "    \"\"\"\n",
    "    Estimate F0 statistics and convert F0\n",
    "    \"\"\"\n",
    "    def __init__(self):\n",
    "        pass\n",
    "    \n",
    "    def estimate(self, f0list):\n",
    "        \"\"\"\n",
    "        estimate F0 statistics from list of f0\n",
    "        \n",
    "        Parameters\n",
    "        ----------\n",
    "        f0list : list, shape(`f0num`)\n",
    "            List of several F0 sequence\n",
    "        \n",
    "        Returns\n",
    "        ----------\n",
    "        f0stats : array, shape(`[mean, std]`)\n",
    "            values of mean and standard deviation for log f0\n",
    "        \"\"\"\n",
    "        \n",
    "        n_files = len(f0list)\n",
    "        assert n_files != 0\n",
    "\n",
    "        for i in range(n_files):\n",
    "            f0 = f0list[i]\n",
    "            nonzero_indices = np.nonzero(f0)\n",
    "            if i == 0:\n",
    "                f0s = np.log(f0[nonzero_indices])\n",
    "            else:\n",
    "                f0s = np.r_[f0s, np.log(f0[nonzero_indices])]\n",
    "        \n",
    "        f0stats = np.array([np.mean(f0s), np.std(f0s)])\n",
    "        \n",
    "        return f0stats\n",
    "\n",
    "    def convert(self, f0, orgf0stats, tarf0stats):\n",
    "        \"\"\"\n",
    "        convert F0 based on F0 statistics\n",
    "        \n",
    "        Parameters\n",
    "        ----------\n",
    "        f0 : array, shape(`T`, `1`)\n",
    "            array of F0 sequence\n",
    "        orgf0stats : array, shape(`[mean, std]`)\n",
    "            vectors of mean and standard deviation of log f0 for original speaker\n",
    "        tarf0stats : array, shape(`[mean, std]`)\n",
    "            vectors of mean and standard deviation of log f0 for target speaker\n",
    "        \n",
    "        Returns\n",
    "        ----------\n",
    "        cvf0 : array, shape(`T`, `1`)\n",
    "            array of converted F0 sequence\n",
    "        \"\"\"\n",
    "        \n",
    "        # get length and dimension\n",
    "        T = len(f0)\n",
    "        \n",
    "        # perform f0 conversion\n",
    "        cvf0 = np.zeros(T)\n",
    "        \n",
    "        nonzero_indices  = f0 > 0\n",
    "        cvf0[nonzero_indices] = np.exp((tarf0stats[1] / orgf0stats[1]) * (np.log(f0[nonzero_indices]) - orgf0stats[0]) + tarf0stats[0])\n",
    "        \n",
    "        return cvf0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "class GV(object):\n",
    "    \"\"\"\n",
    "    Estimate statistics and perform postfilter based on the GV statistics.\n",
    "    \"\"\"\n",
    "    def __init__(self):\n",
    "        pass\n",
    "    \n",
    "    def estimate(self, datalist):\n",
    "        \"\"\"\n",
    "        estimate GV statistics from list of data\n",
    "        \n",
    "        Parameters\n",
    "        ----------\n",
    "        datalist : list, shape(`num_data`)\n",
    "            List of several data ([T, dim]) sequence\n",
    "        \n",
    "        Returns\n",
    "        ----------\n",
    "        gvstats : array, shape(`2`, `dim`)\n",
    "            array of mean and standard deviation for GV\n",
    "        \"\"\"\n",
    "        \n",
    "        n_files = len(datalist)\n",
    "        assert n_files != 0\n",
    "        \n",
    "        var = []\n",
    "        for i in range(n_files):\n",
    "            data = datalist[i]\n",
    "            var.append(np.var(data, axis=0))\n",
    "            \n",
    "        # calculate vm and vv\n",
    "        vm = np.mean(np.array(var), axis=0)\n",
    "        vv = np.var(np.array(var), axis=0)\n",
    "        gvstats = np.r_[vm, vv]\n",
    "        gvstats = gvstats.reshape(2, len(vm))\n",
    "        \n",
    "        return gvstats\n",
    "\n",
    "    def postfilter(self, data, gvstats, cvgvstats=None, alpha=1.0, startdim=1):\n",
    "        \"\"\"\n",
    "        perform postfilter based on GV statistics into data\n",
    "        \n",
    "        Parameters\n",
    "        ----------\n",
    "        data : array, shape(`T`, `dim`)\n",
    "            array of data sequence\n",
    "        gvstats : array, shape(`2`, `dim`)\n",
    "            array of mean and variance for target GV\n",
    "        cvgvstats : array, shape(`2`, `dim`)\n",
    "            array of mean and variance for converted GV\n",
    "        alpha : float\n",
    "            morphing coefficient between GV transformed data and data.\n",
    "            alpha * gvpf(data) + (1 - alpha) * data\n",
    "        startdim : int\n",
    "            start dimension to perform GV postfilter\n",
    "        \n",
    "        Returns\n",
    "        ----------\n",
    "        filtered_data : array, shape(`T`, `data`)\n",
    "            array of GV postfiltered data sequnece\n",
    "        \"\"\"\n",
    "        \n",
    "        # get length and dimension\n",
    "        T, dim = data.shape\n",
    "        assert gvstats is not None\n",
    "        assert dim == gvstats.shape[1]\n",
    "        \n",
    "        # calculate statics of input data\n",
    "        datamean = np.mean(data, axis=0)\n",
    "        \n",
    "        if cvgvstats is None:\n",
    "            # use variance of the given data\n",
    "            datavar = np.var(data, axis=0)\n",
    "        else:\n",
    "            # use variance of trained gv stats\n",
    "            datavar = cvgvstats[0]\n",
    "        \n",
    "        # perform GV postfilter\n",
    "        filterd = np.sqrt(gvstats[0, startdim:] / datavar[startdim:]) * (data[:, startdim:] - datamean[startdim:]) + datamean[startdim:]\n",
    "        \n",
    "        filterd_data = np.c_[data[:, :startdim], filterd]\n",
    "        \n",
    "        return alpha * filterd_data + (1 - alpha) * data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 0. config path\n",
    "__versions = \"pre-stored-ej\"\n",
    "__same_path = \"./utterance/\" + __versions + \"/\"\n",
    "prepare_path = __same_path + \"output/\"\n",
    "pre_stored_source_list = __same_path + 'pre-source/**/V01/T01/**/*.wav'\n",
    "pre_stored_list = __same_path + \"pre/**/V01/T01/**/*.wav\"\n",
    "output_path = \"./utterance/old-test/orl/ej-m/4/adapt1/\"\n",
    "\n",
    "# 1. estimate features\n",
    "feat = FeatureExtractor()\n",
    "synthesizer = Synthesizer()\n",
    "\n",
    "org_f0list = None\n",
    "org_splist = None\n",
    "org_mceplist = None\n",
    "org_aplist = None\n",
    "org_npowlist = None\n",
    "org_codeaplist = None\n",
    "\n",
    "if os.path.exists(prepare_path + \"_org_f0.pickle\") \\\n",
    "    and os.path.exists(prepare_path + \"_org_sp.pickle\") \\\n",
    "    and os.path.exists(prepare_path + \"_org_ap.pickle\") \\\n",
    "    and os.path.exists(prepare_path + \"_org_mcep.pickle\") \\\n",
    "    and os.path.exists(prepare_path + \"_org_npow.pickle\") \\\n",
    "    and os.path.exists(prepare_path + \"_org_codeap.pickle\"):\n",
    "        \n",
    "    with open(prepare_path + \"_org_f0.pickle\", 'rb') as f:    \n",
    "        org_f0list = pickle.load(f)\n",
    "    with open(prepare_path + \"_org_sp.pickle\", 'rb') as f:   \n",
    "        org_splist = pickle.load(f)\n",
    "    with open(prepare_path + \"_org_ap.pickle\", 'rb') as f:  \n",
    "        org_aplist = pickle.load(f)\n",
    "    with open(prepare_path + \"_org_mcep.pickle\", 'rb') as f:  \n",
    "        org_mceplist = pickle.load(f)\n",
    "    with open(prepare_path + \"_org_npow.pickle\", 'rb') as f:  \n",
    "        org_npowlist = pickle.load(f)\n",
    "    with open(prepare_path + \"_org_codeap.pickle\", 'rb') as f:  \n",
    "        org_codeaplist = pickle.load(f) \n",
    "else:\n",
    "    org_f0list = []\n",
    "    org_splist = []\n",
    "    org_mceplist = []\n",
    "    org_aplist = []\n",
    "    org_npowlist = []\n",
    "    org_codeaplist = []\n",
    "    ite = 0\n",
    "    for files in sorted(glob.iglob(pre_stored_source_list, recursive=True)):\n",
    "        wavf = files\n",
    "        x, fs = sf.read(wavf)\n",
    "        x = np.array(x, dtype=np.float)\n",
    "        x = low_cut_filter(x, fs, cutoff=70)\n",
    "        assert fs == 16000\n",
    "\n",
    "        print(\"extract acoustic featuers: \" + wavf)\n",
    "\n",
    "        f0, sp, ap = feat.analyze(x)\n",
    "        mcep = feat.mcep()\n",
    "        npow = feat.npow()\n",
    "        codeap = feat.codeap()\n",
    "        wav = synthesizer.synthesis_sp(f0, sp, ap)\n",
    "        wav = np.clip(wav, -32768, 32767)\n",
    "        sf.write(prepare_path + \"src_ansys_{}_.wav\".format(ite), wav, fs)\n",
    "\n",
    "        org_f0list.append(f0)\n",
    "        org_splist.append(sp)\n",
    "        org_mceplist.append(mcep)\n",
    "        org_aplist.append(ap)\n",
    "        org_npowlist.append(npow)\n",
    "        org_codeaplist.append(codeap)\n",
    "\n",
    "        wav = synthesizer.synthesis(f0, mcep, ap)\n",
    "        wav = np.clip(wav, -32768, 32767)\n",
    "        sf.write(prepare_path + \"src_mcep_{}_.wav\".format(ite), wav, fs)\n",
    "        ite = ite + 1\n",
    "\n",
    "    with open(prepare_path + \"_org_f0.pickle\", 'wb') as f:    \n",
    "        pickle.dump(org_f0list, f)\n",
    "    with open(prepare_path + \"_org_sp.pickle\", 'wb') as f:   \n",
    "        pickle.dump(org_splist, f)\n",
    "    with open(prepare_path + \"_org_npow.pickle\", 'wb') as f:   \n",
    "        pickle.dump(org_npowlist, f)\n",
    "    with open(prepare_path + \"_org_ap.pickle\", 'wb') as f:  \n",
    "        pickle.dump(org_aplist, f)\n",
    "    with open(prepare_path + \"_org_mcep.pickle\", 'wb') as f:  \n",
    "        pickle.dump(org_mceplist, f)\n",
    "    with open(prepare_path + \"_org_codeap.pickle\", 'wb') as f:  \n",
    "        pickle.dump(org_codeaplist, f) \n",
    "\n",
    "mid_f0list = None\n",
    "mid_mceplist = None\n",
    "mid_aplist = None\n",
    "mid_npowlist = None\n",
    "mid_splist = None\n",
    "mid_codeaplist = None        \n",
    "\n",
    "if os.path.exists(prepare_path + \"_mid_f0.pickle\") \\\n",
    "    and os.path.exists(prepare_path + \"_mid_sp_0_.pickle\") \\\n",
    "    and os.path.exists(prepare_path + \"_mid_ap_0_.pickle\") \\\n",
    "    and os.path.exists(prepare_path + \"_mid_mcep.pickle\") \\\n",
    "    and os.path.exists(prepare_path + \"_mid_npow.pickle\") \\\n",
    "    and os.path.exists(prepare_path + \"_mid_codeap.pickle\"):\n",
    "        \n",
    "    with open(prepare_path + \"_mid_f0.pickle\", 'rb') as f:    \n",
    "        mid_f0list = pickle.load(f)\n",
    "    for i in range(0, len(org_splist)*21, len(org_splist)):  \n",
    "        with open(prepare_path + \"_mid_sp_{}_.pickle\".format(i), 'rb') as f:\n",
    "            temp_splist = pickle.load(f)\n",
    "            if mid_splist is None:\n",
    "                mid_splist = temp_splist\n",
    "            else:\n",
    "                mid_splist = mid_splist + temp_splist\n",
    "    for i in range(0, len(org_aplist)*21, len(org_aplist)):  \n",
    "        with open(prepare_path + \"_mid_ap_{}_.pickle\".format(i), 'rb') as f:\n",
    "            temp_aplist = pickle.load(f)\n",
    "            if mid_aplist is None:\n",
    "                mid_aplist = temp_aplist\n",
    "            else:\n",
    "                mid_aplist = mid_aplist + temp_aplist    \n",
    "    with open(prepare_path + \"_mid_mcep.pickle\", 'rb') as f:  \n",
    "        mid_mceplist = pickle.load(f)\n",
    "    with open(prepare_path + \"_mid_npow.pickle\", 'rb') as f:  \n",
    "        mid_npowlist = pickle.load(f)\n",
    "    with open(prepare_path + \"_mid_codeap.pickle\", 'rb') as f:  \n",
    "        mid_codeaplist = pickle.load(f) \n",
    "else:        \n",
    "    mid_f0list = []\n",
    "    mid_mceplist = []\n",
    "    mid_aplist = []\n",
    "    mid_npowlist = []\n",
    "    mid_splist = []\n",
    "    mid_codeaplist = []\n",
    "    ite = 0\n",
    "    for files in sorted(glob.iglob(pre_stored_list, recursive=True)):\n",
    "        wavf = files\n",
    "        x, fs = sf.read(wavf)\n",
    "        x = np.array(x, dtype=np.float)\n",
    "        x = low_cut_filter(x, fs, cutoff=70)\n",
    "        assert fs == 16000\n",
    "\n",
    "        print(\"extract acoustic featuers: \" + wavf)\n",
    "\n",
    "        f0, sp, ap = feat.analyze(x)\n",
    "        mcep = feat.mcep()\n",
    "        npow = feat.npow()\n",
    "        codeap = feat.codeap()\n",
    "        name, ext = os.path.splitext(wavf)\n",
    "        wav = synthesizer.synthesis_sp(f0, sp, ap)\n",
    "        wav = np.clip(wav, -32768, 32767)\n",
    "        sf.write(prepare_path + \"mid_ansys_{}_.wav\".format(ite), wav, fs)\n",
    "\n",
    "        mid_f0list.append(f0)\n",
    "        mid_splist.append(sp)\n",
    "        mid_mceplist.append(mcep)\n",
    "        mid_aplist.append(ap)\n",
    "        mid_npowlist.append(npow)\n",
    "        mid_codeaplist.append(codeap)\n",
    "\n",
    "        wav = synthesizer.synthesis(f0, mcep, ap)\n",
    "        wav = np.clip(wav, -32768, 32767)\n",
    "        sf.write(prepare_path + \"mid_mcep_{}_.wav\".format(ite), wav, fs)\n",
    "        ite = ite + 1\n",
    "        \n",
    "    with open(prepare_path + \"_mid_f0.pickle\", 'wb') as f:\n",
    "        print(f)\n",
    "        pickle.dump(mid_f0list, f)\n",
    "    with open(prepare_path + \"_mid_npow.pickle\", 'wb') as f:\n",
    "        print(f)\n",
    "        pickle.dump(mid_npowlist, f)\n",
    "    for i in range(0, len(mid_splist), len(org_splist)):\n",
    "        with open(prepare_path + \"_mid_sp_{}_.pickle\".format(i), 'wb') as f:   \n",
    "            print(f)\n",
    "            pickle.dump(mid_splist[i:i+len(org_splist)], f)\n",
    "    for i in range(0, len(mid_aplist), len(org_aplist)):\n",
    "        with open(prepare_path + \"_mid_ap_{}_.pickle\".format(i), 'wb') as f:\n",
    "            print(f)\n",
    "            pickle.dump(mid_aplist[i:i+len(org_aplist)], f)\n",
    "    with open(prepare_path + \"_mid_mcep.pickle\", 'wb') as f:\n",
    "        print(f)\n",
    "        pickle.dump(mid_mceplist, f)\n",
    "    with open(prepare_path + \"_mid_codeap.pickle\", 'wb') as f:\n",
    "        print(f)\n",
    "        pickle.dump(mid_codeaplist, f) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "class GMMTrainer(object):\n",
    "    \"\"\"\n",
    "    this class offers the training of GMM with several types of covariance matrix.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    n_mix : int \n",
    "        the number of mixture components of the GMM\n",
    "    n_iter : int\n",
    "        the number of iteration for EM algorithm\n",
    "    covtype : str\n",
    "        the type of covariance matrix of the GMM\n",
    "        'full': full-covariance matrix\n",
    "    \n",
    "    Attributes\n",
    "    ---------\n",
    "    param : \n",
    "        sklearn-based model parameters of the GMM\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, n_mix=64, n_iter=100, covtype='full', params='wmc'):\n",
    "        self.n_mix = n_mix\n",
    "        self.n_iter = n_iter\n",
    "        self.covtype = covtype\n",
    "        self.params = params\n",
    "        self.param = sklearn.mixture.GMM(n_components=self.n_mix,\n",
    "                                                     covariance_type=self.covtype,\n",
    "                                                     n_iter=self.n_iter, params=self.params)\n",
    "        \n",
    "    def train(self, jnt):\n",
    "        \"\"\"\n",
    "        fit GMM parameter from given joint feature vector\n",
    "        \n",
    "        Parametes\n",
    "        ---------\n",
    "        jnt : array, shape(`T`, `jnt.shape[0]`)\n",
    "            joint feature vector of original and target feature vector consisting of static and delta components\n",
    "        \"\"\"\n",
    "        \n",
    "        if self.covtype == 'full':\n",
    "            self.param.fit(jnt)\n",
    "            \n",
    "        return\n",
    "    \n",
    "class GMMConvertor(object):\n",
    "    \"\"\"\n",
    "    this class offers the several conversion techniques such as Maximum Likelihood Parameter Generation (MLPG)\n",
    "    and Minimum Mean Square Error (MMSE).\n",
    "    \n",
    "    Parametes\n",
    "    ---------\n",
    "    n_mix : int\n",
    "        the number of mixture components of the GMM\n",
    "    covtype : str\n",
    "        the type of covariance matrix of the GMM\n",
    "        'full': full-covariance matrix\n",
    "    gmmmode : str\n",
    "        the type of the GMM for opening\n",
    "        `None` : Normal Joint Density - GMM (JD-GMM)\n",
    "    \n",
    "    Attributes\n",
    "    ---------\n",
    "    param : \n",
    "        sklearn-based model parameters of the GMM\n",
    "    w : shape(`n_mix`)\n",
    "        vector of mixture component weight of the GMM\n",
    "    jmean : shape(`n_mix`, `jnt.shape[0]`)\n",
    "        Array of joint mean vector of the GMM\n",
    "    jcov : shape(`n_mix`, `jnt.shape[0]`, `jnt.shape[0]`)\n",
    "        array of joint covariance matrix of the GMM\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, n_mix=64, covtype='full', gmmmode=None):\n",
    "        self.n_mix = n_mix\n",
    "        self.covtype = covtype\n",
    "        self.gmmmode = gmmmode\n",
    "        \n",
    "    def open_from_param(self, param):\n",
    "        \"\"\"\n",
    "        open GMM from GMMTrainer\n",
    "        \n",
    "        Parameters\n",
    "        ----------\n",
    "        param : GMMTrainer\n",
    "            GMMTrainer class\n",
    "        \"\"\"\n",
    "        \n",
    "        self.param = param\n",
    "        self._deploy_parameters()\n",
    "        \n",
    "        return\n",
    "    \n",
    "    def convert(self, data, cvtype='mlpg'):\n",
    "        \"\"\"\n",
    "        convert data based on conditional probability density function\n",
    "        \n",
    "        Parametes\n",
    "        ---------\n",
    "        data : array, shape(`T`, `dim`)\n",
    "            original data will be converted\n",
    "        cvtype : str\n",
    "            type of conversion technique\n",
    "            `mlpg` : maximum likelihood parameter generation\n",
    "            \n",
    "        Returns\n",
    "        ----------\n",
    "        odata : array, shape(`T`, `dim`)\n",
    "            converted data\n",
    "        \"\"\"\n",
    "        \n",
    "        # estimate parameter sequence\n",
    "        cseq, wseq, mseq, covseq = self._gmmmap(data)\n",
    "        \n",
    "        if cvtype == 'mlpg':\n",
    "            odata = self._mlpg(mseq, covseq)\n",
    "        else:\n",
    "            raise ValueError('please choose conversion mode in `mlpg`.')\n",
    "        \n",
    "        return odata\n",
    "    \n",
    "    def _gmmmap(self, sddata):\n",
    "        # paramete for sequencial data\n",
    "        T, sddim = sddata.shape\n",
    "        \n",
    "        # estimate posterior sequence\n",
    "        wseq = self.pX.predict_proba(sddata)\n",
    "        \n",
    "        # estimate mixture sequence\n",
    "        cseq = np.argmax(wseq, axis=1)\n",
    "        \n",
    "        mseq = np.zeros((T, sddim))\n",
    "        covseq = np.zeros((T, sddim, sddim))\n",
    "        for t in range(T):\n",
    "            # read maximum likelihood mixture component in frame t\n",
    "            m = cseq[t]\n",
    "            \n",
    "            # conditional mean vector sequence\n",
    "            mseq[t] = self.meanY[m] + self.A[m] @ (sddata[t] - self.meanX[m])\n",
    "            \n",
    "            # conditional covariance sequence\n",
    "            covseq[t] = self.cond_cov_inv[m]\n",
    "        \n",
    "        return cseq, wseq, mseq, covseq\n",
    "    \n",
    "    def _mlpg(self, mseq, covseq):\n",
    "        # parameter for sequencial data\n",
    "        T, sddim = mseq.shape\n",
    "        \n",
    "        # prepare W\n",
    "        W = construct_static_and_delta_matrix(T, sddim // 2)\n",
    "        \n",
    "        # prepare D\n",
    "        D = get_diagonal_precision_matrix(T, sddim, covseq)\n",
    "        \n",
    "        # calculate W'D\n",
    "        WD = W.T @ D\n",
    "        \n",
    "        # W'DW\n",
    "        WDW = WD @ W\n",
    "        \n",
    "        # W'Dm\n",
    "        WDM = WD @ mseq.flatten()\n",
    "        \n",
    "        # estimate y = (W'DW)^-1 * W'Dm\n",
    "        odata = scipy.sparse.linalg.spsolve(WDW, WDM, use_umfpack=False).reshape(T, sddim // 2)\n",
    "        \n",
    "        return odata\n",
    "    \n",
    "    def _deploy_parameters(self):\n",
    "        # read JD-GMM parameters from self.param\n",
    "        self.W = self.param.weights_\n",
    "        self.jmean = self.param.means_\n",
    "        self.jcov = self.param.covars_\n",
    "        \n",
    "        # devide GMM parameters into source and target parameters\n",
    "        sddim = self.jmean.shape[1] // 2\n",
    "        self.meanX = self.jmean[:, 0:sddim]\n",
    "        self.meanY = self.jmean[:, sddim:]\n",
    "        self.covXX = self.jcov[:, :sddim, :sddim]\n",
    "        self.covXY = self.jcov[:, :sddim, sddim:]\n",
    "        self.covYX = self.jcov[:, sddim:, :sddim]\n",
    "        self.covYY = self.jcov[:, sddim:, sddim:]\n",
    "        \n",
    "        # change model parameter of GMM into that of gmmmode\n",
    "        if self.gmmmode is None:\n",
    "            pass\n",
    "        else:\n",
    "            raise ValueError('please choose GMM mode in [None]')\n",
    "            \n",
    "        # estimate parameters for conversion\n",
    "        self._set_Ab()\n",
    "        self._set_pX()\n",
    "        \n",
    "        return\n",
    "    \n",
    "    def _set_Ab(self):\n",
    "        # calculate A and b from self.jmean, self.jcov\n",
    "        sddim = self.jmean.shape[1] // 2\n",
    "        \n",
    "        # calculate inverse covariance for covariance XX in each mixture\n",
    "        self.covXXinv = np.zeros((self.n_mix, sddim, sddim))\n",
    "        for m in range(self.n_mix):\n",
    "            self.covXXinv[m] = np.linalg.inv(self.covXX[m])\n",
    "            \n",
    "        # calculate A, b, and conditional covariance given X\n",
    "        self.A = np.zeros((self.n_mix, sddim, sddim))\n",
    "        self.b = np.zeros((self.n_mix, sddim))\n",
    "        self.cond_cov_inv = np.zeros((self.n_mix, sddim, sddim))\n",
    "        for m in range(self.n_mix):\n",
    "            # calculate A (A = yxcov_m * xxcov_m^-1)\n",
    "            self.A[m] = self.covYX[m] @ self.covXXinv[m]\n",
    "            \n",
    "            # calculate b (b = mean^Y - A * mean^X)\n",
    "            self.b[m] = self.meanY[m] - self.A[m] @ self.meanX[m]\n",
    "            \n",
    "            # calculate conditional covariance (cov^(Y|X)^-1 = (yycov - A * xycov)^-1)\n",
    "            self.cond_cov_inv[m] = np.linalg.inv(self.covYY[m] - self.A[m] @ self.covXY[m])\n",
    "            \n",
    "        return\n",
    "    \n",
    "    def _set_pX(self):\n",
    "        # probability density function of X \n",
    "        self.pX = sklearn.mixture.GMM(n_components=self.n_mix, covariance_type=self.covtype)\n",
    "        self.pX.weights_ = self.W\n",
    "        self.pX.means_ = self.meanX\n",
    "        self.pX.covars_ = self.covXX\n",
    "        \n",
    "        # following function is required to estimate porsterior\n",
    "        # p(x | \\lambda^(X))\n",
    "        #self.pX.precisions_cholesky_ = _compute_precision_cholesky(self.covXX, self.covtype)\n",
    "        \n",
    "        return\n",
    "    \n",
    "def get_diagonal_precision_matrix(T, D, covseq):\n",
    "    return scipy.sparse.block_diag(covseq, format='csr')        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_alignment(odata, onpow, tdata, tnpow, opow=-20, tpow=-20, sd=0, cvdata=None, given_twf=None, otflag=None, distance='melcd'):\n",
    "    \"\"\"\n",
    "    get alignment between original and target.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    odata : array, shape(`T`, `dim`)\n",
    "        acoustic feature vector of original\n",
    "    onpow : array, shape(`T`)\n",
    "        Normalized power vector of original\n",
    "    tdata : array, shape(`T`, `dim`)\n",
    "        acoustic feature vector of target\n",
    "    tnpow : array, shape(`T`)\n",
    "        Normalized power vector of target\n",
    "    opow : float\n",
    "        power threshold of original\n",
    "    tpow : float\n",
    "        power threshold of target\n",
    "    sd : int\n",
    "        start dimension to be used for alignment\n",
    "    cvdata : array, shape(`T`, `dim`)\n",
    "        converted original data\n",
    "    given_twf : array, shape(`T_new`, `dim * 2`)\n",
    "        Alignment given twf\n",
    "    otflag : str\n",
    "        Alignment into the length of specification\n",
    "        'org' : alignment into original length\n",
    "        'tar' : alignment into target length\n",
    "    distance : str\n",
    "        Distance function to be used\n",
    "    \n",
    "    Returns\n",
    "    ----------\n",
    "    jdata : array, shape(`T_new`, `dim * 2`)\n",
    "        joint static and delta feature vector\n",
    "    twf : array, shape(`T_new`, `dim * 2`)\n",
    "        Time warping function\n",
    "    mcd : float\n",
    "        Mel-cepstrum distortion between arrays\n",
    "    \"\"\"\n",
    "    \n",
    "    oexdata = extsddata(odata[:, sd:], onpow, power_threshold=opow)\n",
    "    texdata = extsddata(tdata[:, sd:], tnpow, power_threshold=tpow)\n",
    "    \n",
    "    if cvdata is None:\n",
    "        align_odata = oexdata\n",
    "    else:\n",
    "        cvexdata = extsddata(cvdata, onpow, power_threshold=opow)\n",
    "        align_odata = cvexdata\n",
    "    \n",
    "    if given_twf is None: \n",
    "        twf = estimate_twf(align_odata, texdata, distance=distance, fast=False, otflag=otflag)\n",
    "    else:\n",
    "        twf = given_twf\n",
    "    \n",
    "    jdata = align_data(oexdata, texdata, twf)\n",
    "    mcd = melcd(align_odata[twf[0]], texdata[twf[1]])\n",
    "    \n",
    "    return jdata, twf, mcd\n",
    "\n",
    "def align_feature_vectors(odata, onpows, tdata, tnpows, opow=-100, tpow=-100, itnum=3, sd=0, given_twfs=None, otflag=None):\n",
    "    \"\"\"\n",
    "    get alignment to create joint feature vector\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    odata : list, (`num_files`)\n",
    "        List of original feature vectors\n",
    "    onpow : list, (`num_files`)\n",
    "        List of original npows\n",
    "    tdata : list, (`num_files`)\n",
    "        List of target feature vectors\n",
    "    tnpow : list, (`num_files`)\n",
    "        List of target npows\n",
    "    opow : float\n",
    "        power threshold of original\n",
    "    tpow : float\n",
    "        power threshold of target\n",
    "    itnum : int\n",
    "        the number of iteration\n",
    "    sd : int\n",
    "        start dimension of feature vector to be used for alignment\n",
    "    given_twf : array, shape(`T_new`, `dim * 2`)\n",
    "        use given alignment while 1st iteration\n",
    "    otflag : str\n",
    "        Alignment into the length of specification\n",
    "        'org' : alignment into original length\n",
    "        'tar' : alignment into target length\n",
    "    distance : str\n",
    "        Distance function to be used\n",
    "    \n",
    "    Returns\n",
    "    ----------\n",
    "    jdata : array, shape(`T_new`, `dim * 2`)\n",
    "        joint static and delta feature vector\n",
    "    twf : array, shape(`T_new`, `dim * 2`)\n",
    "        Time warping function\n",
    "    mcd : float\n",
    "        Mel-cepstrum distortion between arrays\n",
    "    \"\"\"\n",
    "    \n",
    "    it = 1\n",
    "    num_files = len(odata)\n",
    "    cvgmm, cvdata = None, None\n",
    "    for it in range(1, itnum+1):\n",
    "        print('{}-th joint feature extraction starts.'.format(it))\n",
    "        \n",
    "        # alignment\n",
    "        twfs, jfvs = [], []\n",
    "        for i in range(num_files):\n",
    "            if it == 1 and given_twfs is not None:\n",
    "                gtwf = given_twfs[i]\n",
    "            else:\n",
    "                gtwf = None\n",
    "            \n",
    "            if it > 1:\n",
    "                cvdata = cvgmm.convert(static_delta(odata[i][:, sd:]))\n",
    "            \n",
    "            jdata, twf, mcd = get_alignment(odata[i], onpows[i], tdata[i], tnpows[i], opow=opow, tpow=tpow,\n",
    "                                            sd=sd, cvdata=cvdata, given_twf=gtwf, otflag=otflag)\n",
    "            twfs.append(twf)\n",
    "            jfvs.append(jdata)\n",
    "            print('distortion [dB] for {}-th file: {}'.format(i+1, mcd))\n",
    "            \n",
    "        jnt_data = transform_jnt(jfvs)\n",
    "        \n",
    "        if it != itnum:\n",
    "            # train GMM, if not final iteration\n",
    "            datagmm = GMMTrainer()\n",
    "            datagmm.train(jnt_data)\n",
    "            cvgmm = GMMConvertor()\n",
    "            cvgmm.open_from_param(datagmm.param)\n",
    "        it += 1\n",
    "    return jfvs, twfs    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2. estimate twf and jnt\n",
    "if os.path.exists(prepare_path + \"_jnt_mcep_0_.pickle\"):\n",
    "    pass\n",
    "else:\n",
    "    for i in range(0, len(mid_mceplist), len(org_mceplist)):\n",
    "        org_mceps = org_mceplist\n",
    "        org_npows = org_npowlist\n",
    "        mid_mceps = mid_mceplist[i:i+len(org_mceps)]\n",
    "        mid_npows = mid_npowlist[i:i+len(org_npows)]\n",
    "        assert len(org_mceps) == len(mid_mceps)\n",
    "        assert len(org_npows) == len(mid_npows)\n",
    "        assert len(org_mceps) == len(org_npows)\n",
    "\n",
    "        # dtw between original and target 0-th and silence\n",
    "        print(\"## alignment mcep 0-th and silence ##\")\n",
    "        jmceps, twfs = align_feature_vectors(org_mceps, org_npows, mid_mceps, mid_npows, opow=-15, tpow=-15, sd=1)\n",
    "        jnt_mcep = transform_jnt(jmceps)\n",
    "\n",
    "        # save joint feature vectors\n",
    "        with open(prepare_path + \"_jnt_mcep_{}_.pickle\".format(i), 'wb') as f:   \n",
    "            print(f)\n",
    "            pickle.dump(jnt_mcep, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<_io.BufferedReader name='./utterance/pre-stored-ej/output/initgmm.pickle'>\n"
     ]
    }
   ],
   "source": [
    "# 3. make EV-GMM\n",
    "initgmm, initgmm_codeap = None, None\n",
    "if os.path.exists(prepare_path + \"initgmm.pickle\"):\n",
    "    with open(prepare_path + \"initgmm.pickle\".format(i), 'rb') as f:   \n",
    "        print(f)\n",
    "        initgmm = pickle.load(f)\n",
    "else:\n",
    "    jnt, jnt_codeap = None, []\n",
    "    for i in range(0, len(mid_mceplist), len(org_mceplist)):\n",
    "        with open(prepare_path + \"_jnt_mcep_{}_.pickle\".format(i), 'rb') as f:  \n",
    "            temp_jnt = pickle.load(f)\n",
    "            if jnt is None:\n",
    "                jnt = temp_jnt\n",
    "            else:\n",
    "                jnt = np.r_[jnt, temp_jnt]\n",
    "\n",
    "    # train initial gmm\n",
    "    initgmm = GMMTrainer()\n",
    "    initgmm.train(jnt)\n",
    "\n",
    "    with open(prepare_path + \"initgmm.pickle\", 'wb') as f:   \n",
    "        print(f)\n",
    "        pickle.dump(initgmm, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get initial gmm params\n",
    "init_W = initgmm.param.weights_\n",
    "init_jmean = initgmm.param.means_\n",
    "init_jcov = initgmm.param.covars_\n",
    "sddim = init_jmean.shape[1] // 2\n",
    "init_meanX = init_jmean[:, :sddim]\n",
    "init_meanY = init_jmean[:, sddim:]\n",
    "init_covXX = init_jcov[:, :sddim, :sddim]\n",
    "init_covXY = init_jcov[:, :sddim, sddim:]\n",
    "init_covYX = init_jcov[:, sddim:, :sddim]\n",
    "init_covYY = init_jcov[:, sddim:, sddim:]\n",
    "fitted_source = init_meanX\n",
    "fitted_target = init_meanY\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "sv = None\n",
    "if os.path.exists(prepare_path + \"_sv.npy\"):\n",
    "    sv = np.array(sv)\n",
    "    sv = np.load(prepare_path + '_sv.npy')\n",
    "else:\n",
    "    depengmm, depenjnt = None, None\n",
    "    sv = []\n",
    "    for i in range(0, len(mid_mceplist), len(org_mceplist)):\n",
    "        with open(prepare_path + \"_jnt_mcep_{}_.pickle\".format(i), 'rb') as f:  \n",
    "            depenjnt = pickle.load(f)\n",
    "            depengmm = GMMTrainer(params='m')\n",
    "            depengmm.param.weights_ = init_W\n",
    "            depengmm.param.means_ = init_jmean\n",
    "            depengmm.param.covars_ = init_jcov\n",
    "            depengmm.train(depenjnt)\n",
    "            sv.append(depengmm.param.means_)\n",
    "    sv = np.array(sv)\n",
    "    np.save(prepare_path + \"_sv\", sv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_mix = 64\n",
    "S = int(len(mid_mceplist) / len(org_mceplist))\n",
    "assert S == 22\n",
    "\n",
    "source_pca = sklearn.decomposition.PCA()\n",
    "source_pca.fit(sv[:,:,:sddim].reshape((S, n_mix*sddim)))\n",
    "\n",
    "target_pca = sklearn.decomposition.PCA()\n",
    "target_pca.fit(sv[:,:,sddim:].reshape((S, n_mix*sddim)))\n",
    "\n",
    "eigenvectors = source_pca.components_.reshape((n_mix, sddim, S)), target_pca.components_.reshape((n_mix, sddim, S)) \n",
    "biasvectors = source_pca.mean_.reshape((n_mix, sddim)), target_pca.mean_.reshape((n_mix, sddim))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "extract acoustic featuers: ./utterance/pre-stored-ej/input/EJM10/V01/T01/TIMIT/000/A11.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/input/EJM10/V01/T01/TIMIT/000/A14.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/input/EJM10/V01/T01/TIMIT/000/A17.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/input/EJM10/V01/T01/TIMIT/000/A18.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/input/EJM10/V01/T01/TIMIT/000/A19.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/input/EJM10/V01/T01/TIMIT/000/A20.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/input/EJM10/V01/T01/TIMIT/000/A21.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/input/EJM10/V01/T01/TIMIT/000/A22.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/input/EJM10/V01/T01/TIMIT/000/A23.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/input/EJM10/V01/T01/TIMIT/000/A24.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/input/EJM10/V01/T01/TIMIT/000/A25.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/input/EJM10/V01/T01/TIMIT/000/A26.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/input/EJM10/V01/T01/TIMIT/000/A27.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/input/EJM10/V01/T01/TIMIT/000/A28.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/input/EJM10/V01/T01/TIMIT/000/A29.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/input/EJM10/V01/T01/TIMIT/000/A30.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/input/EJM10/V01/T01/TIMIT/000/A31.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/input/EJM10/V01/T01/TIMIT/000/A32.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/input/EJM10/V01/T01/TIMIT/000/A33.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/input/EJM10/V01/T01/TIMIT/000/A34.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/input/EJM10/V01/T01/TIMIT/000/A35.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/input/EJM10/V01/T01/TIMIT/000/A36.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/input/EJM10/V01/T01/TIMIT/000/A37.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/input/EJM10/V01/T01/TIMIT/000/A38.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/input/EJM10/V01/T01/TIMIT/000/A39.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/input/EJM10/V01/T01/TIMIT/000/A40.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/input/EJM10/V01/T01/TIMIT/000/A41.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/input/EJM10/V01/T01/TIMIT/000/A42.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/input/EJM10/V01/T01/TIMIT/000/A43.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/input/EJM10/V01/T01/TIMIT/000/A44.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/input/EJM10/V01/T01/TIMIT/000/A45.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/input/EJM10/V01/T01/TIMIT/000/A46.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/input/EJM10/V01/T01/TIMIT/000/A47.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/input/EJM10/V01/T01/TIMIT/000/A48.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/input/EJM10/V01/T01/TIMIT/000/A49.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/input/EJM10/V01/T01/TIMIT/000/A50.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/input/EJM10/V01/T01/TIMIT/000/A51.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/input/EJM10/V01/T01/TIMIT/000/A52.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/input/EJM10/V01/T01/TIMIT/000/A53.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/input/EJM10/V01/T01/TIMIT/000/A54.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/input/EJM10/V01/T01/TIMIT/000/A55.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/input/EJM10/V01/T01/TIMIT/000/A56.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/input/EJM10/V01/T01/TIMIT/000/A57.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/input/EJM10/V01/T01/TIMIT/000/A58.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/input/EJM10/V01/T01/TIMIT/000/A59.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/adaptation/EJM04/V01/T01/ATR503/A/A01.wav\n"
     ]
    }
   ],
   "source": [
    "# estimate statistic features\n",
    "for_convert_source = __same_path + 'input/EJM10/V01/T01/TIMIT/000/*.wav'\n",
    "for_convert_target = __same_path + 'adaptation/EJM04/V01/T01/ATR503/A/*.wav'\n",
    "\n",
    "src_f0list = []\n",
    "src_splist = []\n",
    "src_mceplist = []\n",
    "src_aplist = []\n",
    "src_npowlist = []\n",
    "src_codeaplist = []\n",
    "ite = 0\n",
    "if os.path.exists(__same_path + 'input/EJM10/V01/T01/TIMIT/000/A11.wav'):\n",
    "    for files in sorted(glob.iglob(for_convert_source, recursive=True)):\n",
    "        wavf = files\n",
    "        x, fs = sf.read(wavf)\n",
    "        x = np.array(x, dtype=np.float)\n",
    "        x = low_cut_filter(x, fs, cutoff=70)\n",
    "        assert fs == 16000\n",
    "\n",
    "        print(\"extract acoustic featuers: \" + wavf)\n",
    "\n",
    "        f0, sp, ap = feat.analyze(x)\n",
    "        mcep = feat.mcep()\n",
    "        npow = feat.npow()\n",
    "        codeap = feat.codeap()\n",
    "        wav = synthesizer.synthesis_sp(f0, sp, ap)\n",
    "        wav = np.clip(wav, -32768, 32767)\n",
    "        sf.write(output_path + \"input_ansys_{}_.wav\".format(ite), wav, fs)\n",
    "\n",
    "        src_f0list.append(f0)\n",
    "        src_splist.append(sp)\n",
    "        src_mceplist.append(mcep)\n",
    "        src_aplist.append(ap)\n",
    "        src_npowlist.append(npow)\n",
    "        src_codeaplist.append(codeap)\n",
    "\n",
    "        wav = synthesizer.synthesis(f0, mcep, ap)\n",
    "        wav = np.clip(wav, -32768, 32767)\n",
    "        sf.write(output_path + \"input_mcep_{}_.wav\".format(ite), wav, fs)\n",
    "        ite = ite + 1\n",
    "\n",
    "else:\n",
    "    raise ValueError(\"No such files.\")\n",
    "tar_f0list = []\n",
    "tar_mceplist = []\n",
    "tar_aplist = []\n",
    "tar_npowlist = []\n",
    "tar_splist = []\n",
    "tar_codeaplist = []\n",
    "ite = 0\n",
    "\n",
    "if os.path.exists(__same_path + 'adaptation/EJM04/V01/T01/ATR503/A/A01.wav'):\n",
    "    for files in sorted(glob.iglob(for_convert_target, recursive=True)):\n",
    "        wavf = files\n",
    "        x, fs = sf.read(wavf)\n",
    "        x = np.array(x, dtype=np.float)\n",
    "        x = low_cut_filter(x, fs, cutoff=70)\n",
    "        assert fs == 16000\n",
    "\n",
    "        print(\"extract acoustic featuers: \" + wavf)\n",
    "\n",
    "        f0, sp, ap = feat.analyze(x)\n",
    "        mcep = feat.mcep()\n",
    "        npow = feat.npow()\n",
    "        codeap = feat.codeap()\n",
    "        name, ext = os.path.splitext(wavf)\n",
    "        wav = synthesizer.synthesis_sp(f0, sp, ap)\n",
    "        wav = np.clip(wav, -32768, 32767)\n",
    "        sf.write(output_path + \"target_ansys_{}_.wav\".format(ite), wav, fs)\n",
    "\n",
    "        tar_f0list.append(f0)\n",
    "        tar_splist.append(sp)\n",
    "        tar_mceplist.append(mcep)\n",
    "        tar_aplist.append(ap)\n",
    "        tar_npowlist.append(npow)\n",
    "        tar_codeaplist.append(codeap)\n",
    "\n",
    "        wav = synthesizer.synthesis(f0, mcep, ap)\n",
    "        wav = np.clip(wav, -32768, 32767)\n",
    "        sf.write(output_path + \"target_mcep_{}_.wav\".format(ite), wav, fs)\n",
    "        ite = ite + 1\n",
    "else:\n",
    "    raise ValueError(\"No such files.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "f0statis = F0statistics()\n",
    "tarf0stats = f0statis.estimate(tar_f0list)\n",
    "srcf0stats = f0statis.estimate(org_f0list)\n",
    "\n",
    "gv = GV()\n",
    "srcgvstats = gv.estimate(org_mceplist)\n",
    "targvstats = gv.estimate(tar_mceplist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:57: DeprecationWarning: Class GMM is deprecated; The class GMM is deprecated in 0.18 and will be  removed in 0.20. Use class GaussianMixture instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "adapt:  1 / 1\n",
      "epoch =  0\n",
      "epoch =  1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  7\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  11\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  13\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  14\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  15\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  16\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  17\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  18\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  19\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  21\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  22\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  23\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  24\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  25\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  26\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  27\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  28\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  29\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  30\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  31\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  32\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  33\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  34\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  35\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  36\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  37\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  38\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  39\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  40\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  41\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  42\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  43\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  44\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  45\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  46\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  47\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  48\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  49\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  51\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  52\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  53\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  54\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  55\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  56\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  57\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  58\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  59\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  60\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  61\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  62\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  63\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  65\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  66\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  67\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  68\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  69\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  70\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  71\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  72\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  73\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  74\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  75\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  76\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  77\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  78\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  79\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  80\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  81\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  82\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  83\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  84\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  85\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  86\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  87\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  88\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  89\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  90\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  91\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  92\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  93\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  94\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  95\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  96\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  97\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  98\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  99\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "# 5. fitting target\n",
    "epoch = 100\n",
    "\n",
    "fitgmm = sklearn.mixture.GMM(n_components=n_mix,\n",
    "                             covariance_type='full',\n",
    "                             n_iter=100)\n",
    "fitgmm.weights_ = init_W\n",
    "fitgmm.means_ = init_meanY\n",
    "fitgmm.covars_ = init_covYY\n",
    "\n",
    "for i in range(len(tar_mceplist)):\n",
    "    print(\"adapt: \", i+1, \"/\", len(tar_mceplist))\n",
    "    target = tar_mceplist[i]\n",
    "    target_pow = target[:, 0]\n",
    "    target = target[:, 1:]\n",
    "    for x in range(epoch):\n",
    "        print(\"epoch = \", x)\n",
    "        predict = fitgmm.predict_proba(np.atleast_2d(static_delta(target)))\n",
    "        \n",
    "        y = np.sum([predict[:, k:k+1] * (static_delta(target) - biasvectors[1][k]) for k in range(n_mix)], axis=1)\n",
    "        gamma = np.sum(predict, axis=0)\n",
    "        \n",
    "        left = np.sum([gamma[k] * np.dot(eigenvectors[1][k].T,\n",
    "                                         np.linalg.solve(fitgmm.covars_, eigenvectors[1])[k])\n",
    "                                                        for k in range(n_mix)], axis=0)\n",
    "        right = np.sum([np.dot(eigenvectors[1][k].T,\n",
    "                               np.linalg.solve(fitgmm.covars_, y)[k])\n",
    "                        for k in range(n_mix)], axis=0)\n",
    "        weight = np.linalg.solve(left, right)\n",
    "        \n",
    "        fitted_target = np.dot(eigenvectors[1], weight) + biasvectors[1]\n",
    "        fitgmm.means_ = fitted_target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mcepconvert(source, weights, jmean, meanX, covarXX, covarXY, covarYX, covarYY,\n",
    "                fitted_source, fitted_target):\n",
    "    \n",
    "    M = 64\n",
    "    # set pX\n",
    "    px = sklearn.mixture.GMM(n_components=M, covariance_type='full', n_iter=100)\n",
    "    px.weights_ = weights\n",
    "    px.means_ = meanX\n",
    "    px.covars_ = covarXX\n",
    "    \n",
    "    # set Ab\n",
    "    sddim = jmean.shape[1] // 2\n",
    "    covXXinv = np.zeros((M, sddim, sddim))\n",
    "    for m in range(M):\n",
    "        covXXinv[m] = np.linalg.inv(covarXX[m])\n",
    "    A = np.zeros((M, sddim, sddim))\n",
    "    b = np.zeros((M, sddim))\n",
    "    cond_cov_inv = np.zeros((M, sddim, sddim))\n",
    "    for m in range(M):\n",
    "        A[m] = covarYX[m] @ covXXinv[m]\n",
    "        b[m] = fitted_target[m] - A[m] @ meanX[m]\n",
    "        cond_cov_inv[m] = np.linalg.inv(covarYY[m] - A[m] @ covarXY[m])\n",
    "        \n",
    "    # _gmmmap\n",
    "    T, sddim = source.shape\n",
    "    wseq = px.predict_proba(source)\n",
    "    cseq = np.argmax(wseq, axis=1)\n",
    "    mseq = np.zeros((T, sddim))\n",
    "    covseq = np.zeros((T, sddim, sddim))\n",
    "    for t in range(T):\n",
    "        m = cseq[t]\n",
    "        mseq[t] = fitted_target[m] + A[m] @ (source[t] - meanX[m])\n",
    "        covseq[t] = cond_cov_inv[m]\n",
    "        \n",
    "    # _mlpg\n",
    "    T, sddim = mseq.shape\n",
    "    W = construct_static_and_delta_matrix(T, sddim // 2)\n",
    "    D = get_diagonal_precision_matrix(T, sddim, covseq)\n",
    "    WD = W.T @ D\n",
    "    WDW = WD @ W\n",
    "    WDM = WD @ mseq.flatten()\n",
    "    \n",
    "    output = scipy.sparse.linalg.spsolve(WDW, WDM, use_umfpack=False).reshape(T, sddim // 2)\n",
    "    return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:57: DeprecationWarning: Class GMM is deprecated; The class GMM is deprecated in 0.18 and will be  removed in 0.20. Use class GaussianMixture instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:57: DeprecationWarning: Class GMM is deprecated; The class GMM is deprecated in 0.18 and will be  removed in 0.20. Use class GaussianMixture instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:57: DeprecationWarning: Class GMM is deprecated; The class GMM is deprecated in 0.18 and will be  removed in 0.20. Use class GaussianMixture instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:57: DeprecationWarning: Class GMM is deprecated; The class GMM is deprecated in 0.18 and will be  removed in 0.20. Use class GaussianMixture instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:57: DeprecationWarning: Class GMM is deprecated; The class GMM is deprecated in 0.18 and will be  removed in 0.20. Use class GaussianMixture instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:57: DeprecationWarning: Class GMM is deprecated; The class GMM is deprecated in 0.18 and will be  removed in 0.20. Use class GaussianMixture instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:57: DeprecationWarning: Class GMM is deprecated; The class GMM is deprecated in 0.18 and will be  removed in 0.20. Use class GaussianMixture instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:57: DeprecationWarning: Class GMM is deprecated; The class GMM is deprecated in 0.18 and will be  removed in 0.20. Use class GaussianMixture instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:57: DeprecationWarning: Class GMM is deprecated; The class GMM is deprecated in 0.18 and will be  removed in 0.20. Use class GaussianMixture instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:57: DeprecationWarning: Class GMM is deprecated; The class GMM is deprecated in 0.18 and will be  removed in 0.20. Use class GaussianMixture instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:57: DeprecationWarning: Class GMM is deprecated; The class GMM is deprecated in 0.18 and will be  removed in 0.20. Use class GaussianMixture instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:57: DeprecationWarning: Class GMM is deprecated; The class GMM is deprecated in 0.18 and will be  removed in 0.20. Use class GaussianMixture instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:57: DeprecationWarning: Class GMM is deprecated; The class GMM is deprecated in 0.18 and will be  removed in 0.20. Use class GaussianMixture instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:57: DeprecationWarning: Class GMM is deprecated; The class GMM is deprecated in 0.18 and will be  removed in 0.20. Use class GaussianMixture instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:57: DeprecationWarning: Class GMM is deprecated; The class GMM is deprecated in 0.18 and will be  removed in 0.20. Use class GaussianMixture instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:57: DeprecationWarning: Class GMM is deprecated; The class GMM is deprecated in 0.18 and will be  removed in 0.20. Use class GaussianMixture instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:57: DeprecationWarning: Class GMM is deprecated; The class GMM is deprecated in 0.18 and will be  removed in 0.20. Use class GaussianMixture instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:57: DeprecationWarning: Class GMM is deprecated; The class GMM is deprecated in 0.18 and will be  removed in 0.20. Use class GaussianMixture instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:57: DeprecationWarning: Class GMM is deprecated; The class GMM is deprecated in 0.18 and will be  removed in 0.20. Use class GaussianMixture instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:57: DeprecationWarning: Class GMM is deprecated; The class GMM is deprecated in 0.18 and will be  removed in 0.20. Use class GaussianMixture instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:57: DeprecationWarning: Class GMM is deprecated; The class GMM is deprecated in 0.18 and will be  removed in 0.20. Use class GaussianMixture instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:57: DeprecationWarning: Class GMM is deprecated; The class GMM is deprecated in 0.18 and will be  removed in 0.20. Use class GaussianMixture instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:57: DeprecationWarning: Class GMM is deprecated; The class GMM is deprecated in 0.18 and will be  removed in 0.20. Use class GaussianMixture instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:57: DeprecationWarning: Class GMM is deprecated; The class GMM is deprecated in 0.18 and will be  removed in 0.20. Use class GaussianMixture instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:57: DeprecationWarning: Class GMM is deprecated; The class GMM is deprecated in 0.18 and will be  removed in 0.20. Use class GaussianMixture instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:57: DeprecationWarning: Class GMM is deprecated; The class GMM is deprecated in 0.18 and will be  removed in 0.20. Use class GaussianMixture instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:57: DeprecationWarning: Class GMM is deprecated; The class GMM is deprecated in 0.18 and will be  removed in 0.20. Use class GaussianMixture instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:57: DeprecationWarning: Class GMM is deprecated; The class GMM is deprecated in 0.18 and will be  removed in 0.20. Use class GaussianMixture instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:57: DeprecationWarning: Class GMM is deprecated; The class GMM is deprecated in 0.18 and will be  removed in 0.20. Use class GaussianMixture instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:57: DeprecationWarning: Class GMM is deprecated; The class GMM is deprecated in 0.18 and will be  removed in 0.20. Use class GaussianMixture instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:57: DeprecationWarning: Class GMM is deprecated; The class GMM is deprecated in 0.18 and will be  removed in 0.20. Use class GaussianMixture instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:57: DeprecationWarning: Class GMM is deprecated; The class GMM is deprecated in 0.18 and will be  removed in 0.20. Use class GaussianMixture instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:57: DeprecationWarning: Class GMM is deprecated; The class GMM is deprecated in 0.18 and will be  removed in 0.20. Use class GaussianMixture instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:57: DeprecationWarning: Class GMM is deprecated; The class GMM is deprecated in 0.18 and will be  removed in 0.20. Use class GaussianMixture instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:57: DeprecationWarning: Class GMM is deprecated; The class GMM is deprecated in 0.18 and will be  removed in 0.20. Use class GaussianMixture instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:57: DeprecationWarning: Class GMM is deprecated; The class GMM is deprecated in 0.18 and will be  removed in 0.20. Use class GaussianMixture instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:57: DeprecationWarning: Class GMM is deprecated; The class GMM is deprecated in 0.18 and will be  removed in 0.20. Use class GaussianMixture instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:57: DeprecationWarning: Class GMM is deprecated; The class GMM is deprecated in 0.18 and will be  removed in 0.20. Use class GaussianMixture instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:57: DeprecationWarning: Class GMM is deprecated; The class GMM is deprecated in 0.18 and will be  removed in 0.20. Use class GaussianMixture instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:57: DeprecationWarning: Class GMM is deprecated; The class GMM is deprecated in 0.18 and will be  removed in 0.20. Use class GaussianMixture instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:57: DeprecationWarning: Class GMM is deprecated; The class GMM is deprecated in 0.18 and will be  removed in 0.20. Use class GaussianMixture instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:57: DeprecationWarning: Class GMM is deprecated; The class GMM is deprecated in 0.18 and will be  removed in 0.20. Use class GaussianMixture instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:57: DeprecationWarning: Class GMM is deprecated; The class GMM is deprecated in 0.18 and will be  removed in 0.20. Use class GaussianMixture instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:57: DeprecationWarning: Class GMM is deprecated; The class GMM is deprecated in 0.18 and will be  removed in 0.20. Use class GaussianMixture instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:57: DeprecationWarning: Class GMM is deprecated; The class GMM is deprecated in 0.18 and will be  removed in 0.20. Use class GaussianMixture instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/higa/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:75: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "# learn cvgvstats \n",
    "cv_mceps = []\n",
    "for i in range(len(src_mceplist)):\n",
    "    temp_mcep = src_mceplist[i]\n",
    "    temp_mcep_0th = temp_mcep[:, 0]\n",
    "    temp_mcep = temp_mcep[:, 1:]\n",
    "    sta_mcep = static_delta(temp_mcep)\n",
    "    cvmcep_wopow = np.array(mcepconvert(sta_mcep, init_W, init_jmean, init_meanX,\n",
    "                                        init_covXX, init_covXY, init_covYX, init_covYY,\n",
    "                                        fitted_source, fitted_target))\n",
    "    cvmcep = np.c_[temp_mcep_0th, cvmcep_wopow]\n",
    "    cv_mceps.append(cvmcep)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "cvgvstats = gv.estimate(cv_mceps)\n",
    "\n",
    "for i in range(len(src_mceplist)):\n",
    "    cvmcep_wGV = gv.postfilter(cv_mceps[i], targvstats, cvgvstats=cvgvstats)\n",
    "    cvf0 = f0statis.convert(src_f0list[i], srcf0stats, tarf0stats)\n",
    "    wav = synthesizer.synthesis(cvf0, cvmcep_wGV, src_aplist[i], rmcep=src_mceplist[i])\n",
    "    sf.write(output_path + \"cv_{}_.wav\".format(i), wav, 16000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(src_mceplist)):\n",
    "    wav = synthesizer.synthesis(src_f0list[i], src_mceplist[i], src_aplist[i])\n",
    "    sf.write(output_path + \"mcep_{}_.wav\".format(i), wav, 16000)\n",
    "    wav = synthesizer.synthesis_sp(src_f0list[i], src_splist[i], src_aplist[i])\n",
    "    sf.write(output_path + \"ansys_{}_.wav\".format(i), wav, 16000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(506,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fda8d59ce48>]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LwqgQdu6j3QVk5NcybZIX3h6OvPbZMR5dv5f7/76D0upWvjM/nOhQ87Cu3WPp4Z/7X6Kr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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fda72866518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cvf0 = f0statis.convert(src_f0list[0], srcf0stats, tarf0stats)\n",
    "print(cvf0.shape)\n",
    "plt.plot(cvf0)\n",
    "plt.plot(src_f0list[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "cvmcep_wGV = gv.postfilter(cv_mceps[0], srcgvstats, cvgvstats=cvgvstats)\n",
    "cvf0 = f0statis.convert(src_f0list[0], srcf0stats, tarf0stats)\n",
    "wav = synthesizer.synthesis(cvf0, cvmcep_wGV, src_aplist[0], rmcep=src_mceplist[0])\n",
    "sf.write(output_path + \"te.wav\", wav, 16000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "extract acoustic featuers: ./utterance/pre-stored-ej/adaptation/EJM04/V01/T01/TIMIT/000/A11.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/adaptation/EJM04/V01/T01/TIMIT/000/A14.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/adaptation/EJM04/V01/T01/TIMIT/000/A17.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/adaptation/EJM04/V01/T01/TIMIT/000/A18.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/adaptation/EJM04/V01/T01/TIMIT/000/A19.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/adaptation/EJM04/V01/T01/TIMIT/000/A20.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/adaptation/EJM04/V01/T01/TIMIT/000/A21.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/adaptation/EJM04/V01/T01/TIMIT/000/A22.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/adaptation/EJM04/V01/T01/TIMIT/000/A23.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/adaptation/EJM04/V01/T01/TIMIT/000/A24.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/adaptation/EJM04/V01/T01/TIMIT/000/A25.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/adaptation/EJM04/V01/T01/TIMIT/000/A26.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/adaptation/EJM04/V01/T01/TIMIT/000/A27.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/adaptation/EJM04/V01/T01/TIMIT/000/A28.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/adaptation/EJM04/V01/T01/TIMIT/000/A29.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/adaptation/EJM04/V01/T01/TIMIT/000/A30.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/adaptation/EJM04/V01/T01/TIMIT/000/A31.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/adaptation/EJM04/V01/T01/TIMIT/000/A32.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/adaptation/EJM04/V01/T01/TIMIT/000/A33.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/adaptation/EJM04/V01/T01/TIMIT/000/A34.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/adaptation/EJM04/V01/T01/TIMIT/000/A35.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/adaptation/EJM04/V01/T01/TIMIT/000/A36.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/adaptation/EJM04/V01/T01/TIMIT/000/A37.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/adaptation/EJM04/V01/T01/TIMIT/000/A38.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/adaptation/EJM04/V01/T01/TIMIT/000/A39.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/adaptation/EJM04/V01/T01/TIMIT/000/A40.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/adaptation/EJM04/V01/T01/TIMIT/000/A41.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/adaptation/EJM04/V01/T01/TIMIT/000/A42.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/adaptation/EJM04/V01/T01/TIMIT/000/A43.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/adaptation/EJM04/V01/T01/TIMIT/000/A44.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/adaptation/EJM04/V01/T01/TIMIT/000/A45.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/adaptation/EJM04/V01/T01/TIMIT/000/A46.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/adaptation/EJM04/V01/T01/TIMIT/000/A47.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/adaptation/EJM04/V01/T01/TIMIT/000/A48.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/adaptation/EJM04/V01/T01/TIMIT/000/A49.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/adaptation/EJM04/V01/T01/TIMIT/000/A50.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/adaptation/EJM04/V01/T01/TIMIT/000/A51.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/adaptation/EJM04/V01/T01/TIMIT/000/A52.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/adaptation/EJM04/V01/T01/TIMIT/000/A53.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/adaptation/EJM04/V01/T01/TIMIT/000/A54.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/adaptation/EJM04/V01/T01/TIMIT/000/A55.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/adaptation/EJM04/V01/T01/TIMIT/000/A56.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/adaptation/EJM04/V01/T01/TIMIT/000/A57.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/adaptation/EJM04/V01/T01/TIMIT/000/A58.wav\n",
      "extract acoustic featuers: ./utterance/pre-stored-ej/adaptation/EJM04/V01/T01/TIMIT/000/A59.wav\n"
     ]
    }
   ],
   "source": [
    "# org-cv distance\n",
    "with open(output_path + \"melcd_org-cv.txt\", \"w\") as outfile:\n",
    "    outfile.write(\"adapt1 org-cv mcd.\\n\")\n",
    "    for i in range(len(src_mceplist)):\n",
    "        temp_mcep = src_mceplist[i]\n",
    "        temp_mcep_0th = temp_mcep[:, 0]\n",
    "        temp_mcep = temp_mcep[:, 1:]\n",
    "        \n",
    "        temp_cv = cv_mceps[i]\n",
    "        temp_cv_0th = temp_cv[:, 0]\n",
    "        temp_cv = temp_cv[:, 1:]\n",
    "        \n",
    "        _, _, mcd = get_alignment(temp_mcep, temp_mcep_0th, temp_cv, temp_cv_0th, opow=-15, tpow=-15, sd=1)\n",
    "        outfile.write(\"{0},{1}\\n\".format(i, mcd))\n",
    "\n",
    "# cv-target distance\n",
    "# read target files and analyze mceps\n",
    "targets_mceplist = []\n",
    "targets_list = __same_path + 'adaptation/EJM04/V01/T01/TIMIT/000/*.wav'\n",
    "for files in sorted(glob.iglob(targets_list, recursive=True)):\n",
    "    wavf = files\n",
    "    x, fs = sf.read(wavf)\n",
    "    x = np.array(x, dtype=np.float)\n",
    "    x = low_cut_filter(x, fs, cutoff=70)\n",
    "    assert fs == 16000\n",
    "    print(\"extract acoustic featuers: \" + wavf)\n",
    "\n",
    "    f0, sp, ap = feat.analyze(x)\n",
    "    mcep = feat.mcep()\n",
    "    targets_mceplist.append(mcep)\n",
    "\n",
    "with open(output_path + \"melcd_cv-target.txt\", \"w\") as outfile:\n",
    "    outfile.write(\"adapt1 cv-target mcd.\\n\")\n",
    "    \n",
    "    for i in range(len(src_mceplist)):\n",
    "        temp_mcep = targets_mceplist[i]\n",
    "        temp_mcep_0th = temp_mcep[:, 0]\n",
    "        temp_mcep = temp_mcep[:, 1:]\n",
    "        \n",
    "        temp_cv = cv_mceps[i]\n",
    "        temp_cv_0th = temp_cv[:, 0]\n",
    "        temp_cv = temp_cv[:, 1:]\n",
    "        \n",
    "        _, _, mcd = get_alignment(temp_cv, temp_cv_0th, temp_mcep, temp_mcep_0th, opow=-15, tpow=-15, sd=1)\n",
    "        outfile.write(\"{0},{1}\\n\".format(i, mcd))\n",
    "\n",
    "# org-target distance\n",
    "with open(output_path + \"melcd_org-target.txt\", \"w\") as outfile:\n",
    "    outfile.write(\"adapt1 org-target mcd.\\n\")\n",
    "    \n",
    "    for i in range(len(src_mceplist)):\n",
    "\n",
    "        temp_mcep = src_mceplist[i]\n",
    "        temp_mcep_0th = temp_mcep[:, 0]\n",
    "        temp_mcep = temp_mcep[:, 1:]\n",
    "        \n",
    "        temp_mcep2 = targets_mceplist[i]\n",
    "        temp_mcep2_0th = temp_mcep2[:, 0]\n",
    "        temp_mcep2 = temp_mcep2[:, 1:]\n",
    "        \n",
    "        \n",
    "        _, _, mcd = get_alignment(temp_mcep, temp_mcep_0th, temp_mcep2, temp_mcep2_0th, opow=-15, tpow=-15, sd=1)\n",
    "        outfile.write(\"{0},{1}\\n\".format(i, mcd))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set_style(\"whitegrid\")\n",
    "plt.rcParams['figure.figsize'] = (16, 5)\n",
    "\n",
    "for i in range(len(cv_mceps)):\n",
    "    this_cv_mcep = cv_mceps[i]\n",
    "    this_src_mcep = src_mceplist[i]\n",
    "    this_tar_mcep = targets_mceplist[i]\n",
    "\n",
    "    # get alignement \n",
    "    twf = estimate_twf(this_src_mcep[:, 1:], this_tar_mcep[:, 1:], distance='melcd', fast=False, otflag='org')\n",
    "    alignend_tar = this_tar_mcep[twf[1]]\n",
    "\n",
    "    # save mcep fig\n",
    "    range_s = this_cv_mcep.shape[0]\n",
    "    scale = [x for x in range(range_s)]\n",
    "    mcep_sample_s = [this_src_mcep[x][0] for x in range(range_s)]\n",
    "    mcep_sample_c = [this_cv_mcep[x][0] for x in range(range_s)]\n",
    "    mcep_sample_t = [alignend_tar[x][0] for x in range(range_s)]\n",
    "\n",
    "    plt.subplot(311)\n",
    "    plt.plot(scale, mcep_sample_s, label=\"source\", linewidth=1.0, linestyle='solid')\n",
    "    plt.plot(scale, mcep_sample_c, label=\"convert\", linewidth=1.0, linestyle='dashed')\n",
    "    plt.plot(scale, mcep_sample_t, label=\"target\", linewidth=1.0, linestyle='dashdot')\n",
    "    plt.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3, ncol=3, mode=\"expand\", borderaxespad=0.)\n",
    "    plt.xlim(0, range_s)\n",
    "\n",
    "    mcep_sample_s = [this_src_mcep[x][1] for x in range(range_s)]\n",
    "    mcep_sample_c = [this_cv_mcep[x][1] for x in range(range_s)]\n",
    "    mcep_sample_t = [alignend_tar[x][1] for x in range(range_s)]\n",
    "\n",
    "    plt.subplot(312)\n",
    "    plt.plot(scale, mcep_sample_s, label=\"source\", linewidth=1.0, linestyle='solid')\n",
    "    plt.plot(scale, mcep_sample_c, label=\"convert\", linewidth=1.0, linestyle='dashed')\n",
    "    plt.plot(scale, mcep_sample_t, label=\"target\", linewidth=1.0, linestyle='dashdot')\n",
    "    plt.ylabel(\"Mel-cepstram amplitude\")\n",
    "    plt.xlim(0, range_s)\n",
    "\n",
    "    mcep_sample_s = [this_src_mcep[x][2] for x in range(range_s)]\n",
    "    mcep_sample_c = [this_cv_mcep[x][2] for x in range(range_s)]\n",
    "    mcep_sample_t = [alignend_tar[x][2] for x in range(range_s)]\n",
    "\n",
    "    plt.subplot(313)\n",
    "    plt.plot(scale, mcep_sample_s, label=\"source\", linewidth=1.0, linestyle='solid')\n",
    "    plt.plot(scale, mcep_sample_c, label=\"convert\", linewidth=1.0, linestyle='dashed')\n",
    "    plt.plot(scale, mcep_sample_t, label=\"target\", linewidth=1.0, linestyle='dashdot')\n",
    "    plt.xlabel(\"Frame\")\n",
    "    plt.xlim(0, range_s)\n",
    "\n",
    "    plt.savefig(output_path + \"mcep_{}_.png\".format(i) , format='png', dpi=300)\n",
    "    plt.close()\n",
    "\n",
    "    # save f0 fig\n",
    "    cvf0 = f0statis.convert(src_f0list[i], srcf0stats, tarf0stats)\n",
    "    plt.plot(scale, src_f0list[i], label=\"source\", linewidth=1.0, linestyle='solid')\n",
    "    plt.plot(scale, cvf0, label=\"convert\", linewidth=1.0, linestyle=\"dashed\")\n",
    "    plt.xlabel(\"Frame\")\n",
    "    plt.ylabel(\"Frequency [Hz]\")\n",
    "    plt.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3, ncol=2, mode=\"expand\", borderaxespad=0.)\n",
    "    plt.xlim(0, len(cvf0))\n",
    "    \n",
    "    plt.savefig(output_path + \"f0_{}_.png\".format(i), format='png', dpi=300)\n",
    "    plt.close()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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