nnsvs_test_ofuton_p_utagoe_db

nnsvs_test_ofuton_p_utagoe_db.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "nnsvs_test_ofuton_p_utagoe_db",
      "provenance": [],
      "authorship_tag": "ABX9TyNU7fta7Wc4+HHVBJuLYMup",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/taroushirani/c94e6c4db059cc46abb5ab6d33aa885d/nnsvs_test_ofuton_p_utagoe_db.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XpoUEOtIWx9X",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        },
        "outputId": "7a03a845-3be9-4095-cbf2-f72d379c0ef9"
      },
      "source": [
        "# google driveの設定\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "#symbolic linkを張る\n",
        "!ln -s \"/content/drive/My Drive\" /content/gdrive"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FCY9PjEUXT5i",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "outputId": "0edf7153-4593-46f7-a581-85361bf9adde"
      },
      "source": [
        "# numpy, cythonのアップグレード\n",
        "! pip install -U numpy cython"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting numpy\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/93/0b/71ae818646c1a80fbe6776d41f480649523ed31243f1f34d9d7e41d70195/numpy-1.19.0-cp36-cp36m-manylinux2010_x86_64.whl (14.6MB)\n",
            "\u001b[K     |████████████████████████████████| 14.6MB 209kB/s \n",
            "\u001b[?25hRequirement already up-to-date: cython in /usr/local/lib/python3.6/dist-packages (0.29.20)\n",
            "\u001b[31mERROR: datascience 0.10.6 has requirement folium==0.2.1, but you'll have folium 0.8.3 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: albumentations 0.1.12 has requirement imgaug<0.2.7,>=0.2.5, but you'll have imgaug 0.2.9 which is incompatible.\u001b[0m\n",
            "Installing collected packages: numpy\n",
            "  Found existing installation: numpy 1.18.5\n",
            "    Uninstalling numpy-1.18.5:\n",
            "      Successfully uninstalled numpy-1.18.5\n",
            "Successfully installed numpy-1.19.0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "numpy"
                ]
              }
            }
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iV4ghgxzXaNt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "12c1c555-aa44-4df4-b131-c33bd9a62a4d"
      },
      "source": [
        "# hts_engine_APIとsinsyのビルド(pysinsyに必要)\n",
        "! git clone  -q https://github.com/r9y9/hts_engine_API\n",
        "! cd hts_engine_API/src && ./waf configure --prefix=/usr/ && ./waf build > hts_engine_API_build.log 2>&1 && ./waf install\n",
        "! git clone  -q https://github.com/r9y9/sinsy\n",
        "! cd sinsy/src/ && mkdir -p build && cd build && cmake -DCMAKE_BUILD_TYPE=Release -DBUILD_SHARED_LIBS=ON -DCMAKE_INSTALL_PREFIX=/usr/ .. && make -j  > sinsy_build.log 2>&1 &&  make install"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[32m\u001b[0mSetting top to                           :\u001b[0m \u001b[0m\u001b[32m\u001b[32m/content/hts_engine_API/src\u001b[0m \u001b[0m\n",
            "\u001b[32m\u001b[0mSetting out to                           :\u001b[0m \u001b[0m\u001b[32m\u001b[32m/content/hts_engine_API/src/build\u001b[0m \u001b[0m\n",
            "\u001b[32m\u001b[0mChecking for waf version in 1.7.11-2.1.0 :\u001b[0m \u001b[0m\u001b[32m\u001b[32mok\u001b[0m \u001b[0m\n",
            "\u001b[32m\u001b[0mChecking for 'gcc' (C compiler)          :\u001b[0m \u001b[0m\u001b[32m\u001b[32m/usr/bin/gcc\u001b[0m \u001b[0m\n",
            "\u001b[32m\u001b[0mChecking for header stdlib.h             :\u001b[0m \u001b[0m\u001b[32m\u001b[32myes\u001b[0m \u001b[0m\n",
            "\u001b[32m\u001b[0mChecking for header string.h             :\u001b[0m \u001b[0m\u001b[32m\u001b[32myes\u001b[0m \u001b[0m\n",
            "\n",
            "hts_engine_API has been configured as follows:\n",
            "\n",
            "[Build information]\n",
            "Package:                 hts_engine_API-1.0.9\n",
            "build (compile on):      x86_64-linux\n",
            "host endian:             little\n",
            "Compiler:                gcc\n",
            "Compiler version:        7.5.0\n",
            "CFLAGS:                  -O2 -Wall -fno-common -Wstrict-prototypes\n",
            "\n",
            "\u001b[32m'configure' finished successfully (0.811s)\u001b[0m\n",
            "\u001b[32mWaf: Entering directory `/content/hts_engine_API/src/build'\u001b[0m\n",
            "\u001b[32m\u001b[0m+ install \u001b[01;34m/usr/include/HTS_hidden.h\u001b[0m (from lib/HTS_hidden.h)\u001b[0m\n",
            "\u001b[32m\u001b[0m+ install \u001b[01;34m/usr/include/HTS_engine.h\u001b[0m (from include/HTS_engine.h)\u001b[0m\n",
            "\u001b[32m\u001b[0m+ symlink \u001b[01;34m/usr/lib/libhts_engine_API.so\u001b[0m (to libhts_engine_API.so.1.0.9)\u001b[0m\n",
            "\u001b[32m\u001b[0m+ install \u001b[01;34m/usr/lib/libhts_engine_API.so.1.0.9\u001b[0m (from build/lib/libhts_engine_API.so)\u001b[0m\n",
            "\u001b[32m\u001b[0m+ install \u001b[01;34m/usr/bin/hts_engine\u001b[0m (from build/bin/hts_engine)\u001b[0m\n",
            "\u001b[32m\u001b[0m+ symlink \u001b[01;34m/usr/lib/libhts_engine_API.so.1\u001b[0m (to libhts_engine_API.so.1.0.9)\u001b[0m\n",
            "\u001b[32m\u001b[0m+ install \u001b[01;34m/usr/lib/pkgconfig/hts_engine_API.pc\u001b[0m (from build/hts_engine_API.pc)\u001b[0m\n",
            "\u001b[32mWaf: Leaving directory `/content/hts_engine_API/src/build'\u001b[0m\n",
            "\u001b[32m'install' finished successfully (0.067s)\u001b[0m\n",
            "-- The C compiler identification is GNU 7.5.0\n",
            "-- The CXX compiler identification is GNU 7.5.0\n",
            "-- Check for working C compiler: /usr/bin/cc\n",
            "-- Check for working C compiler: /usr/bin/cc -- works\n",
            "-- Detecting C compiler ABI info\n",
            "-- Detecting C compiler ABI info - done\n",
            "-- Detecting C compile features\n",
            "-- Detecting C compile features - done\n",
            "-- Check for working CXX compiler: /usr/bin/c++\n",
            "-- Check for working CXX compiler: /usr/bin/c++ -- works\n",
            "-- Detecting CXX compiler ABI info\n",
            "-- Detecting CXX compiler ABI info - done\n",
            "-- Detecting CXX compile features\n",
            "-- Detecting CXX compile features - done\n",
            "-- Configuring done\n",
            "-- Generating done\n",
            "-- Build files have been written to: /content/sinsy/src/build\n",
            "[ 95%] Built target sinsy\n",
            "[100%] Built target sinsy-bin\n",
            "\u001b[36mInstall the project...\u001b[0m\n",
            "-- Install configuration: \"Release\"\n",
            "-- Installing: /usr/lib/libsinsy.so.0.9.2\n",
            "-- Installing: /usr/lib/libsinsy.so.0.9\n",
            "-- Installing: /usr/lib/libsinsy.so\n",
            "-- Installing: /usr/bin/sinsy\n",
            "-- Set runtime path of \"/usr/bin/sinsy\" to \"\"\n",
            "-- Installing: /usr/include/sinsy\n",
            "-- Installing: /usr/include/sinsy/sinsy.h\n",
            "-- Installing: /usr/include/sinsy/ILabelOutput.h\n",
            "-- Installing: /usr/include/sinsy/LabelStrings.h\n",
            "-- Installing: /usr/lib/sinsy/dic\n",
            "-- Installing: /usr/lib/sinsy/dic/japanese.macron\n",
            "-- Installing: /usr/lib/sinsy/dic/japanese.utf_8.conf\n",
            "-- Installing: /usr/lib/sinsy/dic/japanese.euc_jp.table\n",
            "-- Installing: /usr/lib/sinsy/dic/japanese.utf_8.table\n",
            "-- Installing: /usr/lib/sinsy/dic/japanese.shift_jis.conf\n",
            "-- Installing: /usr/lib/sinsy/dic/japanese.shift_jis.table\n",
            "-- Installing: /usr/lib/sinsy/dic/japanese.euc_jp.conf\n",
            "-- Installing: /usr/lib/pkgconfig/sinsy.pc\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UdRQ5pMuYtFj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "d0566b18-b9f2-455a-8048-00979a6f10f6"
      },
      "source": [
        "# Python dependencies\n",
        "! git clone -q https://github.com/r9y9/pysinsy\n",
        "! cd pysinsy && export SINSY_INSTALL_PREFIX=/usr/ && pip install -q .\n",
        "! git clone -q https://github.com/r9y9/nnmnkwii\n",
        "! cd nnmnkwii && pip install -q .\n",
        "! git clone -q https://github.com/r9y9/nnsvs \n",
        "! cd nnsvs && pip install -q .\n",
        "! pip install -q jaconv"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  Building wheel for pysinsy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[K     |████████████████████████████████| 419kB 4.5MB/s \n",
            "\u001b[K     |████████████████████████████████| 368kB 13.5MB/s \n",
            "\u001b[?25h  Building wheel for nnmnkwii (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for pysptk (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for bandmat (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[K     |████████████████████████████████| 81kB 3.6MB/s \n",
            "\u001b[K     |████████████████████████████████| 1.6MB 13.0MB/s \n",
            "\u001b[K     |████████████████████████████████| 81kB 12.3MB/s \n",
            "\u001b[?25h  Building wheel for nnsvs (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for librosa (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for pyworld (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for jaconv (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4d3gou5qfhRv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# レシピのダウンロード\n",
        "! cd nnsvs/egs && git clone -q https://github.com/taroushirani/nnsvs_ofuton_p_utagoe_db.git"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_QhtzUCKfvjc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "5fd968f8-4690-445d-99a6-4a054bbbba55"
      },
      "source": [
        "# おふとんP歌声DBの解凍(あらかじめGoogle Driveにアップロードしてある)\n",
        "! cd /content/gdrive && unzip /content/gdrive/OFUTON_P_UTAGOE_DB.zip"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  /content/gdrive/OFUTON_P_UTAGOE_DB.zip\n",
            "replace OFUTON_P_UTAGOE_DB/Note.txt? [y]es, [n]o, [A]ll, [N]one, [r]ename: N\n",
            "N\n",
            "N\n",
            "N\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m-TVJk1SGW_n",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "RECIPE_ROOT=\"/content/nnsvs/egs/nnsvs_ofuton_p_utagoe_db/00-svs-world/\""
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hT4crM1wgCd7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# db_root を /content/gdrive/OFUTON_P_UTAGOE_DB に変更\n",
        "! sed -i 's#[$]HOME\\/data#\\/content\\/gdrive#g' $RECIPE_ROOT/run.sh "
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Veas7tJiGj8E",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# stage -1の実行(データのダウンロード)\n",
        "! cd $RECIPE_ROOT && bash run.sh --stage -1 --stop-stage -1"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8Ce6Fq2fGt17",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "0af6294a-74d3-447b-97e0-46e584ed349a"
      },
      "source": [
        "# stage 0の実行(データの準備)\n",
        "! cd $RECIPE_ROOT && bash run.sh --stage 0 --stop-stage 0"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "stage 0: Data preparation\n",
            "akai_kutsu.lab 0.0\n",
            "akatombo.lab 0.0\n",
            "amefuri.lab 0.0\n",
            "antagata_dokosa.lab 0.0\n",
            "aogeba_tootoshi.lab 0.0\n",
            "arupusu_itimanjaku.lab 0.0\n",
            "chatsumi.lab 1.0\n",
            "chouchou.lab 0.0\n",
            "daikokusama.lab 0.0\n",
            "donguri_korokoro.lab 0.0\n",
            "furusato.lab 0.0\n",
            "genkotsuyamano_tanukisan.lab 0.0\n",
            "haruga_kita.lab 0.0\n",
            "haruno_ogawa.lab 0.0\n",
            "haruyo_koi.lab 0.0\n",
            "hato.lab 0.0\n",
            "hato_low.lab 0.0\n",
            "hiraita_hiraita.lab 0.0\n",
            "hotaruno_hikari.lab 0.0\n",
            "issunboushi.lab 0.0\n",
            "kagome_kagome.lab 0.0\n",
            "kagome_kagome_low.lab 0.0\n",
            "kamomeno_suiheisan.lab 0.0\n",
            "katatsumuri.lab 0.0\n",
            "kintaro.lab 0.0\n",
            "koganemushi.lab 0.0\n",
            "koinobori.lab 0.0\n",
            "momiji.lab 0.0\n",
            "momotarou.lab 0.0\n",
            "morobito_kozorite.lab 0.0\n",
            "mushino_koe.lab 0.0\n",
            "my_grandfathers_clock_1_1.lab 0.0\n",
            "my_grandfathers_clock_1_2.lab 0.0\n",
            "my_grandfathers_clock_1_3.lab 0.0\n",
            "my_grandfathers_clock_2_1.lab 0.0\n",
            "my_grandfathers_clock_2_2.lab 0.0\n",
            "my_grandfathers_clock_2_3.lab 0.0\n",
            "my_grandfathers_clock_3_1.lab 0.0\n",
            "my_grandfathers_clock_3_2.lab 0.0\n",
            "my_grandfathers_clock_3_3.lab 0.0\n",
            "nanatsunoko.lab 0.0\n",
            "oborodukiyo.lab 0.0\n",
            "sakura_sakura.lab 0.0\n",
            "seikurabe.lab 0.0\n",
            "shabondama.lab 0.0\n",
            "shoujoujino_tanukibayashi.lab 0.0\n",
            "tetsudou_shouka.lab 0.0\n",
            "tombi.lab 0.0\n",
            "tooryanse.lab 0.0\n",
            "urashima_taro.lab 0.0\n",
            "usagi.lab 0.0\n",
            "usagito_kame.lab 0.0\n",
            "warewa_uminoko.lab 0.0\n",
            "yuki.lab 0.0\n",
            "yurikagono_uta.lab 0.0\n",
            "zuizuizukkorobashi.lab 0.0\n",
            "zuizuizukkorobashi_low.lab 0.0\n",
            "akai_kutsu.lab: segment duration min 8.68, max 13.41, mean 9.69\n",
            "akatombo.lab: segment duration min 6.84, max 10.55, mean 8.64\n",
            "amefuri.lab: segment duration min 16.53, max 16.94, mean 16.73\n",
            "antagata_dokosa.lab: segment duration min 6.59, max 15.54, mean 11.64\n",
            "aogeba_tootoshi.lab: segment duration min 8.21, max 12.59, mean 9.01\n",
            "arupusu_itimanjaku.lab: segment duration min 9.03, max 12.41, mean 11.12\n",
            "chatsumi.lab: segment duration min 7.77, max 8.75, mean 8.46\n",
            "chouchou.lab: segment duration min 9.06, max 14.09, mean 10.45\n",
            "daikokusama.lab: segment duration min 21.41, max 21.61, mean 21.54\n",
            "donguri_korokoro.lab: segment duration min 21.61, max 23.82, mean 22.72\n",
            "furusato.lab: segment duration min 6.46, max 14.34, mean 10.32\n",
            "genkotsuyamano_tanukisan.lab: segment duration min 6.67, max 9.39, mean 8.03\n",
            "haruga_kita.lab: segment duration min 8.47, max 13.25, mean 9.48\n",
            "haruno_ogawa.lab: segment duration min 8.89, max 13.79, mean 9.67\n",
            "haruyo_koi.lab: segment duration min 6.31, max 9.42, mean 8.18\n",
            "hato.lab: segment duration min 6.24, max 9.26, mean 8.40\n",
            "hato_low.lab: segment duration min 4.42, max 9.03, mean 6.55\n",
            "hiraita_hiraita.lab: segment duration min 8.99, max 17.62, mean 11.49\n",
            "hotaruno_hikari.lab: segment duration min 8.84, max 15.36, mean 9.82\n",
            "issunboushi.lab: segment duration min 9.31, max 11.88, mean 10.54\n",
            "kagome_kagome.lab: segment duration min 21.20, max 21.20, mean 21.20\n",
            "kagome_kagome_low.lab: segment duration min 5.92, max 8.58, mean 7.66\n",
            "kamomeno_suiheisan.lab: segment duration min 6.54, max 14.29, mean 8.94\n",
            "katatsumuri.lab: segment duration min 6.64, max 9.45, mean 8.10\n",
            "kintaro.lab: segment duration min 16.70, max 16.84, mean 16.77\n",
            "koganemushi.lab: segment duration min 8.41, max 18.19, mean 13.51\n",
            "koinobori.lab: segment duration min 6.46, max 9.83, mean 7.51\n",
            "momiji.lab: segment duration min 37.64, max 42.24, mean 39.94\n",
            "momotarou.lab: segment duration min 6.96, max 9.37, mean 8.46\n",
            "morobito_kozorite.lab: segment duration min 8.53, max 14.49, mean 12.25\n",
            "mushino_koe.lab: segment duration min 6.60, max 18.75, mean 8.91\n",
            "my_grandfathers_clock_1_1.lab: segment duration min 10.02, max 23.39, mean 14.00\n",
            "my_grandfathers_clock_1_2.lab: segment duration min 9.22, max 13.64, mean 11.30\n",
            "my_grandfathers_clock_1_3.lab: segment duration min 9.26, max 13.55, mean 11.22\n",
            "my_grandfathers_clock_2_1.lab: segment duration min 9.05, max 13.40, mean 11.13\n",
            "my_grandfathers_clock_2_2.lab: segment duration min 9.02, max 13.35, mean 11.12\n",
            "my_grandfathers_clock_2_3.lab: segment duration min 5.11, max 13.53, mean 9.54\n",
            "my_grandfathers_clock_3_1.lab: segment duration min 9.19, max 13.53, mean 11.30\n",
            "my_grandfathers_clock_3_2.lab: segment duration min 9.37, max 13.46, mean 11.26\n",
            "my_grandfathers_clock_3_3.lab: segment duration min 9.38, max 13.60, mean 11.25\n",
            "nanatsunoko.lab: segment duration min 8.78, max 13.59, mean 9.71\n",
            "oborodukiyo.lab: segment duration min 6.67, max 11.46, mean 7.49\n",
            "sakura_sakura.lab: segment duration min 6.35, max 9.15, mean 7.42\n",
            "seikurabe.lab: segment duration min 6.29, max 10.55, mean 7.11\n",
            "shabondama.lab: segment duration min 52.56, max 52.56, mean 52.56\n",
            "shoujoujino_tanukibayashi.lab: segment duration min 6.25, max 18.41, mean 12.24\n",
            "tetsudou_shouka.lab: segment duration min 6.31, max 9.18, mean 7.95\n",
            "tombi.lab: segment duration min 3.99, max 13.41, mean 7.57\n",
            "tooryanse.lab: segment duration min 7.02, max 13.92, mean 9.69\n",
            "urashima_taro.lab: segment duration min 6.69, max 9.61, mean 8.01\n",
            "usagi.lab: segment duration min 16.59, max 16.59, mean 16.59\n",
            "usagito_kame.lab: segment duration min 6.42, max 9.11, mean 7.95\n",
            "warewa_uminoko.lab: segment duration min 7.17, max 12.01, mean 8.36\n",
            "yuki.lab: segment duration min 21.38, max 21.58, mean 21.48\n",
            "yurikagono_uta.lab: segment duration min 8.60, max 13.27, mean 9.64\n",
            "zuizuizukkorobashi.lab: segment duration min 6.29, max 8.72, mean 7.17\n",
            "zuizuizukkorobashi_low.lab: segment duration min 6.39, max 9.09, mean 7.30\n",
            "akai_kutsu.lab: segment lengths: 9.11, 13.41, 8.68, 8.90, 8.78, 9.28, \n",
            "akatombo.lab: segment lengths: 10.04, 6.84, 10.55, 6.96, 10.40, 7.06, \n",
            "amefuri.lab: segment lengths: 16.53, 16.71, 16.94, \n",
            "antagata_dokosa.lab: segment lengths: 15.54, 12.79, 6.59, \n",
            "aogeba_tootoshi.lab: segment lengths: 10.43, 12.59, 8.70, 8.70, 8.41, 8.37, 8.50, 8.60, 8.22, 8.48, 8.21, 8.88, \n",
            "arupusu_itimanjaku.lab: segment lengths: 12.41, 11.04, 12.01, 9.03, \n",
            "chatsumi.lab: segment lengths: 8.66, 8.75, 8.53, 7.77, 8.62, 8.72, 8.73, 7.89, \n",
            "chouchou.lab: segment lengths: 9.06, 14.09, 9.07, 9.59, 9.20, 13.88, 9.12, 9.61, \n",
            "daikokusama.lab: segment lengths: 21.41, 21.61, 21.61, \n",
            "donguri_korokoro.lab: segment lengths: 21.61, 23.82, \n",
            "furusato.lab: segment lengths: 9.95, 6.46, 14.00, 10.59, 6.48, 14.04, 10.50, 6.48, 14.34, \n",
            "genkotsuyamano_tanukisan.lab: segment lengths: 6.67, 9.39, \n",
            "haruga_kita.lab: segment lengths: 13.25, 8.66, 8.47, 8.64, 8.99, 8.83, \n",
            "haruno_ogawa.lab: segment lengths: 13.79, 8.89, 9.15, 9.11, 9.12, 8.98, 9.08, 9.24, \n",
            "haruyo_koi.lab: segment lengths: 6.31, 8.98, 7.01, 9.15, 9.42, \n",
            "hato.lab: segment lengths: 9.03, 9.26, 9.08, 6.24, \n",
            "hato_low.lab: segment lengths: 6.59, 6.68, 4.42, 9.03, 6.01, \n",
            "hiraita_hiraita.lab: segment lengths: 8.99, 9.09, 12.63, 9.12, 17.62, \n",
            "hotaruno_hikari.lab: segment lengths: 15.36, 8.93, 8.84, 9.21, 8.96, 8.89, 8.93, 9.46, \n",
            "issunboushi.lab: segment lengths: 11.54, 9.33, 11.83, 9.31, 11.88, 9.34, \n",
            "kagome_kagome.lab: segment lengths: 21.20, \n",
            "kagome_kagome_low.lab: segment lengths: 8.46, 8.58, 5.92, \n",
            "kamomeno_suiheisan.lab: segment lengths: 6.54, 14.29, 6.80, 9.22, 7.12, 9.06, 9.58, \n",
            "katatsumuri.lab: segment lengths: 6.64, 9.41, 6.89, 9.45, \n",
            "kintaro.lab: segment lengths: 16.70, 16.84, \n",
            "koganemushi.lab: segment lengths: 13.94, 8.41, 18.19, \n",
            "koinobori.lab: segment lengths: 9.83, 6.46, 6.75, 7.02, \n",
            "momiji.lab: segment lengths: 42.24, 37.64, \n",
            "momotarou.lab: segment lengths: 9.01, 9.21, 6.96, 9.14, 7.08, 9.37, \n",
            "morobito_kozorite.lab: segment lengths: 13.08, 14.49, 8.53, 14.41, 8.55, 14.43, \n",
            "mushino_koe.lab: segment lengths: 6.60, 6.91, 6.69, 7.13, 18.75, 7.34, \n",
            "my_grandfathers_clock_1_1.lab: segment lengths: 11.36, 11.43, 13.79, 23.39, 10.02, \n",
            "my_grandfathers_clock_1_2.lab: segment lengths: 11.28, 11.19, 11.25, 11.19, 13.64, 9.22, \n",
            "my_grandfathers_clock_1_3.lab: segment lengths: 11.11, 11.05, 11.23, 11.13, 13.55, 9.26, \n",
            "my_grandfathers_clock_2_1.lab: segment lengths: 11.12, 10.95, 11.35, 10.92, 13.40, 9.05, \n",
            "my_grandfathers_clock_2_2.lab: segment lengths: 10.92, 11.04, 11.05, 11.31, 13.35, 9.02, \n",
            "my_grandfathers_clock_2_3.lab: segment lengths: 10.99, 11.00, 5.63, 5.11, 11.21, 13.53, 9.29, \n",
            "my_grandfathers_clock_3_1.lab: segment lengths: 11.18, 11.21, 11.33, 11.33, 13.53, 9.19, \n",
            "my_grandfathers_clock_3_2.lab: segment lengths: 11.10, 11.30, 11.19, 11.12, 13.46, 9.37, \n",
            "my_grandfathers_clock_3_3.lab: segment lengths: 11.14, 11.01, 11.32, 11.05, 13.60, 9.38, \n",
            "nanatsunoko.lab: segment lengths: 13.59, 8.87, 9.10, 8.78, 9.03, 8.88, \n",
            "oborodukiyo.lab: segment lengths: 11.46, 6.92, 6.67, 7.11, 6.72, 6.71, 6.82, 7.49, \n",
            "sakura_sakura.lab: segment lengths: 6.62, 6.69, 9.04, 6.59, 6.35, 9.15, 9.07, 6.78, 6.44, \n",
            "seikurabe.lab: segment lengths: 9.97, 6.39, 6.35, 6.32, 6.63, 6.67, 10.55, 6.36, 6.39, 6.29, 6.43, 6.94, \n",
            "shabondama.lab: segment lengths: 52.56, \n",
            "shoujoujino_tanukibayashi.lab: segment lengths: 18.41, 12.49, 11.83, 6.25, \n",
            "tetsudou_shouka.lab: segment lengths: 6.60, 9.06, 7.32, 9.06, 9.18, 7.09, 9.00, 6.31, \n",
            "tombi.lab: segment lengths: 13.41, 8.64, 6.84, 6.35, 3.99, 8.66, 8.81, 6.83, 6.39, 5.75, \n",
            "tooryanse.lab: segment lengths: 8.95, 13.92, 9.06, 9.49, 7.02, \n",
            "urashima_taro.lab: segment lengths: 6.69, 9.04, 7.15, 9.01, 9.61, 6.85, 9.04, 6.70, \n",
            "usagi.lab: segment lengths: 16.59, \n",
            "usagito_kame.lab: segment lengths: 6.52, 9.11, 7.15, 9.11, 9.11, 7.15, 9.04, 6.42, \n",
            "warewa_uminoko.lab: segment lengths: 11.06, 7.32, 7.31, 7.22, 11.71, 7.34, 7.17, 7.32, 12.01, 7.22, 7.22, 7.42, \n",
            "yuki.lab: segment lengths: 21.38, 21.58, \n",
            "yurikagono_uta.lab: segment lengths: 8.97, 13.27, 9.08, 8.60, 9.12, 8.79, \n",
            "zuizuizukkorobashi.lab: segment lengths: 6.38, 8.06, 6.40, 8.72, 6.29, \n",
            "zuizuizukkorobashi_low.lab: segment lengths: 6.39, 7.97, 6.54, 9.09, 6.49, \n",
            "Segmentation stats: min 3.99, max 52.56, mean 10.11\n",
            "Total number of segments: 321\n",
            "Prepare data for time-lag models\n",
            "  0% 0/57 [00:00<?, ?it/s]akai_kutsu: Global offset (in sec): -0.045\n",
            "akai_kutsu_seg0.lab offset (in sec): -0.01\n",
            "akai_kutsu.lab: 1/10 time-lags are excluded.\n",
            "akai_kutsu_seg1.lab offset (in sec): -0.049999999999999996\n",
            "akai_kutsu.lab: 3/22 time-lags are excluded.\n",
            "akai_kutsu_seg2.lab offset (in sec): -0.034999999999999996\n",
            "akai_kutsu.lab: 1/18 time-lags are excluded.\n",
            "akai_kutsu_seg3.lab offset (in sec): -0.06\n",
            "akai_kutsu.lab: 4/16 time-lags are excluded.\n",
            "akai_kutsu_seg4.lab offset (in sec): -0.034999999999999996\n",
            "akai_kutsu.lab: 1/17 time-lags are excluded.\n",
            "akai_kutsu_seg5.lab offset (in sec): -0.034999999999999996\n",
            "akai_kutsu.lab: 1/16 time-lags are excluded.\n",
            "akatombo: Global offset (in sec): -0.04\n",
            "akatombo_seg0.lab offset (in sec): -0.02\n",
            "akatombo_seg1.lab offset (in sec): -0.04\n",
            "akatombo_seg2.lab offset (in sec): -0.03\n",
            "akatombo_seg3.lab offset (in sec): -0.04\n",
            "akatombo.lab: 1/17 time-lags are excluded.\n",
            "akatombo_seg4.lab offset (in sec): -0.06\n",
            "akatombo_seg5.lab offset (in sec): -0.03\n",
            "akatombo.lab: 1/17 time-lags are excluded.\n",
            "amefuri: Global offset (in sec): -0.105\n",
            "amefuri_seg0.lab offset (in sec): -0.11\n",
            "amefuri.lab: 3/40 time-lags are excluded.\n",
            "amefuri_seg1.lab offset (in sec): -0.095\n",
            "amefuri.lab: 3/40 time-lags are excluded.\n",
            "amefuri_seg2.lab offset (in sec): -0.105\n",
            "amefuri.lab: 2/40 time-lags are excluded.\n",
            "antagata_dokosa: Global offset (in sec): -0.11\n",
            "antagata_dokosa_seg0.lab offset (in sec): -0.105\n",
            "antagata_dokosa.lab: 2/37 time-lags are excluded.\n",
            "antagata_dokosa_seg1.lab offset (in sec): -0.11499999999999999\n",
            "antagata_dokosa.lab: 2/40 time-lags are excluded.\n",
            "antagata_dokosa_seg2.lab offset (in sec): -0.08499999999999999\n",
            "antagata_dokosa.lab: 3/12 time-lags are excluded.\n",
            "aogeba_tootoshi: Global offset (in sec): -0.06999999999999999\n",
            "aogeba_tootoshi_seg0.lab offset (in sec): -0.075\n",
            "aogeba_tootoshi.lab: 2/8 time-lags are excluded.\n",
            "aogeba_tootoshi_seg1.lab offset (in sec): -0.06999999999999999\n",
            "aogeba_tootoshi.lab: 1/22 time-lags are excluded.\n",
            "aogeba_tootoshi_seg2.lab offset (in sec): -0.095\n",
            "aogeba_tootoshi.lab: 1/15 time-lags are excluded.\n",
            "aogeba_tootoshi_seg3.lab offset (in sec): -0.034999999999999996\n",
            "aogeba_tootoshi.lab: 2/15 time-lags are excluded.\n",
            "aogeba_tootoshi_seg4.lab offset (in sec): -0.095\n",
            "aogeba_tootoshi.lab: 1/15 time-lags are excluded.\n",
            "aogeba_tootoshi_seg5.lab offset (in sec): -0.06999999999999999\n",
            "aogeba_tootoshi_seg6.lab offset (in sec): -0.06999999999999999\n",
            "aogeba_tootoshi_seg7.lab offset (in sec): -0.045\n",
            "aogeba_tootoshi_seg8.lab offset (in sec): -0.09\n",
            "aogeba_tootoshi.lab: 1/15 time-lags are excluded.\n",
            "aogeba_tootoshi_seg9.lab offset (in sec): -0.08499999999999999\n",
            "aogeba_tootoshi.lab: 3/15 time-lags are excluded.\n",
            "aogeba_tootoshi_seg10.lab offset (in sec): -0.04\n",
            "aogeba_tootoshi.lab: 2/15 time-lags are excluded.\n",
            "aogeba_tootoshi_seg11.lab offset (in sec): -0.02\n",
            "arupusu_itimanjaku: Global offset (in sec): -0.065\n",
            "arupusu_itimanjaku_seg0.lab offset (in sec): -0.065\n",
            "arupusu_itimanjaku.lab: 1/31 time-lags are excluded.\n",
            "arupusu_itimanjaku_seg1.lab offset (in sec): -0.055\n",
            "arupusu_itimanjaku_seg2.lab offset (in sec): -0.055\n",
            "arupusu_itimanjaku.lab: 1/28 time-lags are excluded.\n",
            "arupusu_itimanjaku_seg3.lab offset (in sec): -0.075\n",
            "chatsumi: Global offset (in sec): 0.015\n",
            "chatsumi_seg0.lab offset (in sec): 0.024999999999999998\n",
            "chatsumi_seg1.lab offset (in sec): 0.005\n",
            "chatsumi.lab: 1/14 time-lags are excluded.\n",
            "chatsumi_seg2.lab offset (in sec): 0.024999999999999998\n",
            "chatsumi_seg3.lab offset (in sec): -0.06\n",
            "chatsumi.lab: 1/12 time-lags are excluded.\n",
            "chatsumi_seg4.lab offset (in sec): 0.034999999999999996\n",
            "chatsumi.lab: 1/13 time-lags are excluded.\n",
            "chatsumi_seg5.lab offset (in sec): 0.02\n",
            "chatsumi_seg6.lab offset (in sec): 0.01\n",
            "chatsumi.lab: 1/14 time-lags are excluded.\n",
            "chatsumi_seg7.lab offset (in sec): 0.049999999999999996\n",
            "chatsumi.lab: 2/12 time-lags are excluded.\n",
            "chouchou: Global offset (in sec): -0.105\n",
            "chouchou_seg0.lab offset (in sec): -0.145\n",
            "chouchou.lab: 1/6 time-lags are excluded.\n",
            "chouchou_seg1.lab offset (in sec): -0.09\n",
            "chouchou.lab: 2/24 time-lags are excluded.\n",
            "chouchou_seg2.lab offset (in sec): -0.11\n",
            "chouchou.lab: 4/15 time-lags are excluded.\n",
            "chouchou_seg3.lab offset (in sec): -0.09\n",
            "chouchou.lab: 2/15 time-lags are excluded.\n",
            "chouchou_seg4.lab offset (in sec): -0.055\n",
            "chouchou_seg5.lab offset (in sec): -0.11\n",
            "chouchou.lab: 4/24 time-lags are excluded.\n",
            "chouchou_seg6.lab offset (in sec): -0.075\n",
            "chouchou_seg7.lab offset (in sec): -0.09\n",
            "chouchou.lab: 3/15 time-lags are excluded.\n",
            "daikokusama: Global offset (in sec): -0.09999999999999999\n",
            "daikokusama_seg0.lab offset (in sec): -0.095\n",
            "daikokusama.lab: 3/49 time-lags are excluded.\n",
            "daikokusama_seg1.lab offset (in sec): -0.095\n",
            "daikokusama.lab: 2/49 time-lags are excluded.\n",
            "daikokusama_seg2.lab offset (in sec): -0.11\n",
            "daikokusama.lab: 2/49 time-lags are excluded.\n",
            "donguri_korokoro: Global offset (in sec): -0.075\n",
            "donguri_korokoro_seg0.lab offset (in sec): -0.08\n",
            "donguri_korokoro.lab: 1/49 time-lags are excluded.\n",
            "donguri_korokoro_seg1.lab offset (in sec): -0.075\n",
            "donguri_korokoro.lab: 3/49 time-lags are excluded.\n",
            "furusato: Global offset (in sec): -0.09\n",
            "furusato_seg0.lab offset (in sec): -0.08499999999999999\n",
            "furusato.lab: 1/11 time-lags are excluded.\n",
            "furusato_seg1.lab offset (in sec): -0.11499999999999999\n",
            "furusato_seg2.lab offset (in sec): -0.08\n",
            "furusato.lab: 3/28 time-lags are excluded.\n",
            "furusato_seg3.lab offset (in sec): -0.075\n",
            "furusato_seg4.lab offset (in sec): -0.15\n",
            "furusato.lab: 2/10 time-lags are excluded.\n",
            "furusato_seg5.lab offset (in sec): -0.06999999999999999\n",
            "furusato.lab: 4/27 time-lags are excluded.\n",
            "furusato_seg6.lab offset (in sec): -0.065\n",
            "furusato.lab: 2/11 time-lags are excluded.\n",
            "furusato_seg7.lab offset (in sec): -0.095\n",
            "furusato_seg8.lab offset (in sec): -0.06999999999999999\n",
            "furusato.lab: 6/27 time-lags are excluded.\n",
            "genkotsuyamano_tanukisan: Global offset (in sec): -0.12\n",
            "genkotsuyamano_tanukisan_seg0.lab offset (in sec): -0.105\n",
            "genkotsuyamano_tanukisan.lab: 1/12 time-lags are excluded.\n",
            "genkotsuyamano_tanukisan_seg1.lab offset (in sec): -0.12\n",
            "genkotsuyamano_tanukisan.lab: 3/26 time-lags are excluded.\n",
            "haruga_kita: Global offset (in sec): -0.09\n",
            "haruga_kita_seg0.lab offset (in sec): -0.06999999999999999\n",
            "haruga_kita_seg1.lab offset (in sec): -0.09\n",
            "haruga_kita.lab: 1/16 time-lags are excluded.\n",
            "haruga_kita_seg2.lab offset (in sec): -0.095\n",
            "haruga_kita.lab: 1/16 time-lags are excluded.\n",
            "haruga_kita_seg3.lab offset (in sec): -0.075\n",
            "haruga_kita.lab: 1/16 time-lags are excluded.\n",
            "haruga_kita_seg4.lab offset (in sec): -0.08\n",
            "haruga_kita.lab: 1/16 time-lags are excluded.\n",
            "haruga_kita_seg5.lab offset (in sec): -0.09999999999999999\n",
            "haruga_kita.lab: 1/16 time-lags are excluded.\n",
            "haruno_ogawa: Global offset (in sec): -0.095\n",
            "haruno_ogawa_seg0.lab offset (in sec): -0.08499999999999999\n",
            "haruno_ogawa.lab: 2/16 time-lags are excluded.\n",
            "haruno_ogawa_seg1.lab offset (in sec): -0.08\n",
            "haruno_ogawa.lab: 2/16 time-lags are excluded.\n",
            "haruno_ogawa_seg2.lab offset (in sec): -0.105\n",
            "haruno_ogawa.lab: 1/16 time-lags are excluded.\n",
            "haruno_ogawa_seg3.lab offset (in sec): -0.095\n",
            "haruno_ogawa.lab: 2/16 time-lags are excluded.\n",
            "haruno_ogawa_seg4.lab offset (in sec): -0.095\n",
            "haruno_ogawa.lab: 2/16 time-lags are excluded.\n",
            "haruno_ogawa_seg5.lab offset (in sec): -0.049999999999999996\n",
            "haruno_ogawa_seg6.lab offset (in sec): -0.09999999999999999\n",
            "haruno_ogawa.lab: 1/16 time-lags are excluded.\n",
            "haruno_ogawa_seg7.lab offset (in sec): -0.12\n",
            " 25% 14/57 [00:00<00:00, 129.68it/s]haruyo_koi: Global offset (in sec): -0.09\n",
            "haruyo_koi_seg0.lab offset (in sec): -0.125\n",
            "haruyo_koi.lab: 1/11 time-lags are excluded.\n",
            "haruyo_koi_seg1.lab offset (in sec): -0.08499999999999999\n",
            "haruyo_koi_seg2.lab offset (in sec): -0.095\n",
            "haruyo_koi.lab: 1/12 time-lags are excluded.\n",
            "haruyo_koi_seg3.lab offset (in sec): -0.08\n",
            "haruyo_koi.lab: 1/24 time-lags are excluded.\n",
            "haruyo_koi_seg4.lab offset (in sec): -0.09\n",
            "hato: Global offset (in sec): -0.11\n",
            "hato_seg0.lab offset (in sec): -0.145\n",
            "hato.lab: 1/8 time-lags are excluded.\n",
            "hato_seg1.lab offset (in sec): -0.12\n",
            "hato.lab: 2/26 time-lags are excluded.\n",
            "hato_seg2.lab offset (in sec): -0.09\n",
            "hato.lab: 1/21 time-lags are excluded.\n",
            "hato_seg3.lab offset (in sec): -0.105\n",
            "hato_low: Global offset (in sec): -0.09999999999999999\n",
            "hato_low_seg0.lab offset (in sec): -0.105\n",
            "hato_low_seg1.lab offset (in sec): -0.095\n",
            "hato_low_seg2.lab offset (in sec): -0.11\n",
            "hato_low.lab: 3/13 time-lags are excluded.\n",
            "hato_low_seg3.lab offset (in sec): -0.105\n",
            "hato_low.lab: 1/21 time-lags are excluded.\n",
            "hato_low_seg4.lab offset (in sec): -0.08\n",
            "hiraita_hiraita: Global offset (in sec): -0.09\n",
            "hiraita_hiraita_seg0.lab offset (in sec): -0.11\n",
            "hiraita_hiraita_seg1.lab offset (in sec): -0.105\n",
            "hiraita_hiraita.lab: 1/22 time-lags are excluded.\n",
            "hiraita_hiraita_seg2.lab offset (in sec): -0.08499999999999999\n",
            "hiraita_hiraita.lab: 2/22 time-lags are excluded.\n",
            "hiraita_hiraita_seg3.lab offset (in sec): -0.08\n",
            "hiraita_hiraita_seg4.lab offset (in sec): -0.06\n",
            "hiraita_hiraita.lab: 1/34 time-lags are excluded.\n",
            "hotaruno_hikari: Global offset (in sec): -0.09999999999999999\n",
            "hotaruno_hikari_seg0.lab offset (in sec): -0.105\n",
            "hotaruno_hikari.lab: 2/15 time-lags are excluded.\n",
            "hotaruno_hikari_seg1.lab offset (in sec): -0.12\n",
            "hotaruno_hikari_seg2.lab offset (in sec): -0.095\n",
            "hotaruno_hikari.lab: 1/15 time-lags are excluded.\n",
            "hotaruno_hikari_seg3.lab offset (in sec): -0.075\n",
            "hotaruno_hikari_seg4.lab offset (in sec): -0.09999999999999999\n",
            "hotaruno_hikari.lab: 1/15 time-lags are excluded.\n",
            "hotaruno_hikari_seg5.lab offset (in sec): -0.08\n",
            "hotaruno_hikari_seg6.lab offset (in sec): -0.11\n",
            "hotaruno_hikari.lab: 1/15 time-lags are excluded.\n",
            "hotaruno_hikari_seg7.lab offset (in sec): -0.06\n",
            "hotaruno_hikari.lab: 1/15 time-lags are excluded.\n",
            "issunboushi: Global offset (in sec): -0.09999999999999999\n",
            "issunboushi_seg0.lab offset (in sec): -0.105\n",
            "issunboushi.lab: 1/29 time-lags are excluded.\n",
            "issunboushi_seg1.lab offset (in sec): -0.08499999999999999\n",
            "issunboushi.lab: 1/25 time-lags are excluded.\n",
            "issunboushi_seg2.lab offset (in sec): -0.09999999999999999\n",
            "issunboushi.lab: 2/29 time-lags are excluded.\n",
            "issunboushi_seg3.lab offset (in sec): -0.105\n",
            "issunboushi.lab: 4/25 time-lags are excluded.\n",
            "issunboushi_seg4.lab offset (in sec): -0.095\n",
            "issunboushi.lab: 1/29 time-lags are excluded.\n",
            "issunboushi_seg5.lab offset (in sec): -0.08499999999999999\n",
            "issunboushi.lab: 2/24 time-lags are excluded.\n",
            "kagome_kagome: Global offset (in sec): -0.065\n",
            "kagome_kagome_seg0.lab offset (in sec): -0.065\n",
            "kagome_kagome.lab: 2/51 time-lags are excluded.\n",
            "kagome_kagome_low: Global offset (in sec): -0.075\n",
            "kagome_kagome_low_seg0.lab offset (in sec): -0.09\n",
            "kagome_kagome_low.lab: 1/17 time-lags are excluded.\n",
            "kagome_kagome_low_seg1.lab offset (in sec): -0.065\n",
            "kagome_kagome_low.lab: 1/27 time-lags are excluded.\n",
            "kagome_kagome_low_seg2.lab offset (in sec): -0.06999999999999999\n",
            "kamomeno_suiheisan: Global offset (in sec): -0.095\n",
            "kamomeno_suiheisan_seg0.lab offset (in sec): -0.125\n",
            "kamomeno_suiheisan_seg1.lab offset (in sec): -0.075\n",
            "kamomeno_suiheisan.lab: 2/43 time-lags are excluded.\n",
            "kamomeno_suiheisan_seg2.lab offset (in sec): -0.16999999999999998\n",
            "kamomeno_suiheisan_seg3.lab offset (in sec): -0.095\n",
            "kamomeno_suiheisan.lab: 4/28 time-lags are excluded.\n",
            "kamomeno_suiheisan_seg4.lab offset (in sec): -0.065\n",
            "kamomeno_suiheisan_seg5.lab offset (in sec): -0.13499999999999998\n",
            "kamomeno_suiheisan.lab: 1/21 time-lags are excluded.\n",
            "kamomeno_suiheisan_seg6.lab offset (in sec): -0.06999999999999999\n",
            "kamomeno_suiheisan.lab: 1/32 time-lags are excluded.\n",
            "katatsumuri: Global offset (in sec): -0.065\n",
            "katatsumuri_seg0.lab offset (in sec): -0.09\n",
            "katatsumuri.lab: 1/13 time-lags are excluded.\n",
            "katatsumuri_seg1.lab offset (in sec): -0.049999999999999996\n",
            "katatsumuri.lab: 1/27 time-lags are excluded.\n",
            "katatsumuri_seg2.lab offset (in sec): -0.055\n",
            "katatsumuri_seg3.lab offset (in sec): -0.06\n",
            "kintaro: Global offset (in sec): -0.055\n",
            "kintaro_seg0.lab offset (in sec): -0.065\n",
            "kintaro.lab: 3/45 time-lags are excluded.\n",
            "kintaro_seg1.lab offset (in sec): -0.04\n",
            "kintaro.lab: 2/45 time-lags are excluded.\n",
            "koganemushi: Global offset (in sec): -0.06999999999999999\n",
            "koganemushi_seg0.lab offset (in sec): -0.06\n",
            "koganemushi_seg1.lab offset (in sec): -0.06999999999999999\n",
            "koganemushi.lab: 1/15 time-lags are excluded.\n",
            "koganemushi_seg2.lab offset (in sec): -0.06\n",
            "koganemushi.lab: 1/41 time-lags are excluded.\n",
            "koinobori: Global offset (in sec): -0.055\n",
            "koinobori_seg0.lab offset (in sec): -0.024999999999999998\n",
            "koinobori.lab: 1/13 time-lags are excluded.\n",
            "koinobori_seg1.lab offset (in sec): -0.065\n",
            "koinobori.lab: 1/13 time-lags are excluded.\n",
            "koinobori_seg2.lab offset (in sec): -0.08\n",
            "koinobori.lab: 1/14 time-lags are excluded.\n",
            "koinobori_seg3.lab offset (in sec): -0.034999999999999996\n",
            "koinobori.lab: 1/13 time-lags are excluded.\n",
            "momiji: Global offset (in sec): -0.075\n",
            "momiji_seg0.lab offset (in sec): -0.065\n",
            "momiji.lab: 9/60 time-lags are excluded.\n",
            "momiji_seg1.lab offset (in sec): -0.08\n",
            "momiji.lab: 2/60 time-lags are excluded.\n",
            "momotarou: Global offset (in sec): -0.09\n",
            "momotarou_seg0.lab offset (in sec): -0.055\n",
            "momotarou.lab: 4/14 time-lags are excluded.\n",
            "momotarou_seg1.lab offset (in sec): -0.09\n",
            "momotarou_seg2.lab offset (in sec): -0.12\n",
            "momotarou.lab: 1/10 time-lags are excluded.\n",
            "momotarou_seg3.lab offset (in sec): -0.095\n",
            "momotarou.lab: 1/25 time-lags are excluded.\n",
            "momotarou_seg4.lab offset (in sec): -0.11\n",
            "momotarou_seg5.lab offset (in sec): -0.08499999999999999\n",
            "momotarou.lab: 2/24 time-lags are excluded.\n",
            "morobito_kozorite: Global offset (in sec): -0.065\n",
            "morobito_kozorite_seg0.lab offset (in sec): -0.09999999999999999\n",
            "morobito_kozorite.lab: 3/15 time-lags are excluded.\n",
            "morobito_kozorite_seg1.lab offset (in sec): -0.065\n",
            "morobito_kozorite.lab: 1/45 time-lags are excluded.\n",
            "morobito_kozorite_seg2.lab offset (in sec): -0.075\n",
            "morobito_kozorite.lab: 1/15 time-lags are excluded.\n",
            "morobito_kozorite_seg3.lab offset (in sec): -0.049999999999999996\n",
            "morobito_kozorite.lab: 4/45 time-lags are excluded.\n",
            "morobito_kozorite_seg4.lab offset (in sec): -0.08\n",
            "morobito_kozorite.lab: 1/15 time-lags are excluded.\n",
            "morobito_kozorite_seg5.lab offset (in sec): -0.04\n",
            "morobito_kozorite.lab: 3/45 time-lags are excluded.\n",
            "mushino_koe: Global offset (in sec): -0.045\n",
            "mushino_koe_seg0.lab offset (in sec): -0.095\n",
            "mushino_koe_seg1.lab offset (in sec): -0.06\n",
            "mushino_koe.lab: 1/28 time-lags are excluded.\n",
            "mushino_koe_seg2.lab offset (in sec): -0.04\n",
            "mushino_koe_seg3.lab offset (in sec): -0.049999999999999996\n",
            "mushino_koe_seg4.lab offset (in sec): -0.024999999999999998\n",
            "mushino_koe.lab: 4/68 time-lags are excluded.\n",
            "mushino_koe_seg5.lab offset (in sec): -0.045\n",
            " 54% 31/57 [00:00<00:00, 136.56it/s]my_grandfathers_clock_1_1: Global offset (in sec): -0.09\n",
            "my_grandfathers_clock_1_1_seg0.lab offset (in sec): -0.095\n",
            "my_grandfathers_clock_1_1.lab: 3/21 time-lags are excluded.\n",
            "my_grandfathers_clock_1_1_seg1.lab offset (in sec): -0.095\n",
            "my_grandfathers_clock_1_1.lab: 2/22 time-lags are excluded.\n",
            "my_grandfathers_clock_1_1_seg2.lab offset (in sec): -0.049999999999999996\n",
            "my_grandfathers_clock_1_1.lab: 2/27 time-lags are excluded.\n",
            "my_grandfathers_clock_1_1_seg3.lab offset (in sec): -0.08\n",
            "my_grandfathers_clock_1_1.lab: 5/41 time-lags are excluded.\n",
            "my_grandfathers_clock_1_1_seg4.lab offset (in sec): -0.08\n",
            "my_grandfathers_clock_1_1.lab: 1/13 time-lags are excluded.\n",
            "my_grandfathers_clock_1_2: Global offset (in sec): -0.065\n",
            "my_grandfathers_clock_1_2_seg0.lab offset (in sec): -0.06\n",
            "my_grandfathers_clock_1_2_seg1.lab offset (in sec): -0.06\n",
            "my_grandfathers_clock_1_2_seg2.lab offset (in sec): -0.04\n",
            "my_grandfathers_clock_1_2.lab: 2/23 time-lags are excluded.\n",
            "my_grandfathers_clock_1_2_seg3.lab offset (in sec): -0.049999999999999996\n",
            "my_grandfathers_clock_1_2.lab: 3/17 time-lags are excluded.\n",
            "my_grandfathers_clock_1_2_seg4.lab offset (in sec): -0.065\n",
            "my_grandfathers_clock_1_2_seg5.lab offset (in sec): -0.065\n",
            "my_grandfathers_clock_1_3: Global offset (in sec): -0.08499999999999999\n",
            "my_grandfathers_clock_1_3_seg0.lab offset (in sec): -0.06999999999999999\n",
            "my_grandfathers_clock_1_3.lab: 1/21 time-lags are excluded.\n",
            "my_grandfathers_clock_1_3_seg1.lab offset (in sec): -0.065\n",
            "my_grandfathers_clock_1_3_seg2.lab offset (in sec): -0.075\n",
            "my_grandfathers_clock_1_3_seg3.lab offset (in sec): -0.09\n",
            "my_grandfathers_clock_1_3.lab: 1/17 time-lags are excluded.\n",
            "my_grandfathers_clock_1_3_seg4.lab offset (in sec): -0.075\n",
            "my_grandfathers_clock_1_3.lab: 2/27 time-lags are excluded.\n",
            "my_grandfathers_clock_1_3_seg5.lab offset (in sec): -0.095\n",
            "my_grandfathers_clock_2_1: Global offset (in sec): -0.049999999999999996\n",
            "my_grandfathers_clock_2_1_seg0.lab offset (in sec): -0.049999999999999996\n",
            "my_grandfathers_clock_2_1_seg1.lab offset (in sec): -0.045\n",
            "my_grandfathers_clock_2_1_seg2.lab offset (in sec): -0.02\n",
            "my_grandfathers_clock_2_1.lab: 1/23 time-lags are excluded.\n",
            "my_grandfathers_clock_2_1_seg3.lab offset (in sec): -0.055\n",
            "my_grandfathers_clock_2_1.lab: 3/17 time-lags are excluded.\n",
            "my_grandfathers_clock_2_1_seg4.lab offset (in sec): -0.049999999999999996\n",
            "my_grandfathers_clock_2_1.lab: 2/28 time-lags are excluded.\n",
            "my_grandfathers_clock_2_1_seg5.lab offset (in sec): -0.09\n",
            "my_grandfathers_clock_2_1.lab: 1/13 time-lags are excluded.\n",
            "my_grandfathers_clock_2_2: Global offset (in sec): -0.065\n",
            "my_grandfathers_clock_2_2_seg0.lab offset (in sec): -0.04\n",
            "my_grandfathers_clock_2_2_seg1.lab offset (in sec): -0.03\n",
            "my_grandfathers_clock_2_2.lab: 1/22 time-lags are excluded.\n",
            "my_grandfathers_clock_2_2_seg2.lab offset (in sec): -0.049999999999999996\n",
            "my_grandfathers_clock_2_2.lab: 1/23 time-lags are excluded.\n",
            "my_grandfathers_clock_2_2_seg3.lab offset (in sec): -0.08\n",
            "my_grandfathers_clock_2_2.lab: 1/17 time-lags are excluded.\n",
            "my_grandfathers_clock_2_2_seg4.lab offset (in sec): -0.06999999999999999\n",
            "my_grandfathers_clock_2_2.lab: 2/28 time-lags are excluded.\n",
            "my_grandfathers_clock_2_2_seg5.lab offset (in sec): -0.13499999999999998\n",
            "my_grandfathers_clock_2_3: Global offset (in sec): -0.08\n",
            "my_grandfathers_clock_2_3_seg0.lab offset (in sec): -0.06999999999999999\n",
            "my_grandfathers_clock_2_3.lab: 2/21 time-lags are excluded.\n",
            "my_grandfathers_clock_2_3_seg1.lab offset (in sec): -0.055\n",
            "my_grandfathers_clock_2_3_seg2.lab offset (in sec): -0.095\n",
            "my_grandfathers_clock_2_3.lab: 1/13 time-lags are excluded.\n",
            "my_grandfathers_clock_2_3_seg3.lab offset (in sec): -0.06999999999999999\n",
            "my_grandfathers_clock_2_3_seg4.lab offset (in sec): -0.075\n",
            "my_grandfathers_clock_2_3.lab: 1/17 time-lags are excluded.\n",
            "my_grandfathers_clock_2_3_seg5.lab offset (in sec): -0.09999999999999999\n",
            "my_grandfathers_clock_2_3.lab: 1/28 time-lags are excluded.\n",
            "my_grandfathers_clock_2_3_seg6.lab offset (in sec): -0.06\n",
            "my_grandfathers_clock_2_3.lab: 1/13 time-lags are excluded.\n",
            "my_grandfathers_clock_3_1: Global offset (in sec): -0.03\n",
            "my_grandfathers_clock_3_1_seg0.lab offset (in sec): -0.06999999999999999\n",
            "my_grandfathers_clock_3_1.lab: 3/21 time-lags are excluded.\n",
            "my_grandfathers_clock_3_1_seg1.lab offset (in sec): -0.01\n",
            "my_grandfathers_clock_3_1.lab: 2/22 time-lags are excluded.\n",
            "my_grandfathers_clock_3_1_seg2.lab offset (in sec): -0.01\n",
            "my_grandfathers_clock_3_1_seg3.lab offset (in sec): -0.024999999999999998\n",
            "my_grandfathers_clock_3_1.lab: 1/17 time-lags are excluded.\n",
            "my_grandfathers_clock_3_1_seg4.lab offset (in sec): -0.015\n",
            "my_grandfathers_clock_3_1.lab: 1/27 time-lags are excluded.\n",
            "my_grandfathers_clock_3_1_seg5.lab offset (in sec): -0.065\n",
            "my_grandfathers_clock_3_1.lab: 1/13 time-lags are excluded.\n",
            "my_grandfathers_clock_3_2: Global offset (in sec): -0.075\n",
            "my_grandfathers_clock_3_2_seg0.lab offset (in sec): -0.045\n",
            "my_grandfathers_clock_3_2.lab: 1/21 time-lags are excluded.\n",
            "my_grandfathers_clock_3_2_seg1.lab offset (in sec): -0.04\n",
            "my_grandfathers_clock_3_2.lab: 2/22 time-lags are excluded.\n",
            "my_grandfathers_clock_3_2_seg2.lab offset (in sec): -0.06999999999999999\n",
            "my_grandfathers_clock_3_2.lab: 1/24 time-lags are excluded.\n",
            "my_grandfathers_clock_3_2_seg3.lab offset (in sec): -0.08499999999999999\n",
            "my_grandfathers_clock_3_2_seg4.lab offset (in sec): -0.08499999999999999\n",
            "my_grandfathers_clock_3_2.lab: 3/28 time-lags are excluded.\n",
            "my_grandfathers_clock_3_2_seg5.lab offset (in sec): -0.09\n",
            "my_grandfathers_clock_3_2.lab: 3/13 time-lags are excluded.\n",
            "my_grandfathers_clock_3_3: Global offset (in sec): -0.04\n",
            "my_grandfathers_clock_3_3_seg0.lab offset (in sec): -0.04\n",
            "my_grandfathers_clock_3_3.lab: 1/21 time-lags are excluded.\n",
            "my_grandfathers_clock_3_3_seg1.lab offset (in sec): -0.04\n",
            "my_grandfathers_clock_3_3.lab: 2/22 time-lags are excluded.\n",
            "my_grandfathers_clock_3_3_seg2.lab offset (in sec): -0.01\n",
            "my_grandfathers_clock_3_3.lab: 3/23 time-lags are excluded.\n",
            "my_grandfathers_clock_3_3_seg3.lab offset (in sec): -0.01\n",
            "my_grandfathers_clock_3_3.lab: 2/17 time-lags are excluded.\n",
            "my_grandfathers_clock_3_3_seg4.lab offset (in sec): -0.06\n",
            "my_grandfathers_clock_3_3.lab: 2/27 time-lags are excluded.\n",
            "my_grandfathers_clock_3_3_seg5.lab offset (in sec): -0.024999999999999998\n",
            "nanatsunoko: Global offset (in sec): -0.075\n",
            "nanatsunoko_seg0.lab offset (in sec): -0.08499999999999999\n",
            "nanatsunoko.lab: 1/18 time-lags are excluded.\n",
            "nanatsunoko_seg1.lab offset (in sec): -0.075\n",
            "nanatsunoko.lab: 1/17 time-lags are excluded.\n",
            "nanatsunoko_seg2.lab offset (in sec): -0.095\n",
            "nanatsunoko.lab: 2/15 time-lags are excluded.\n",
            "nanatsunoko_seg3.lab offset (in sec): -0.095\n",
            "nanatsunoko.lab: 3/13 time-lags are excluded.\n",
            "nanatsunoko_seg4.lab offset (in sec): -0.045\n",
            "nanatsunoko.lab: 4/18 time-lags are excluded.\n",
            "nanatsunoko_seg5.lab offset (in sec): -0.04\n",
            "nanatsunoko.lab: 1/16 time-lags are excluded.\n",
            "oborodukiyo: Global offset (in sec): -0.065\n",
            "oborodukiyo_seg0.lab offset (in sec): -0.08\n",
            "oborodukiyo.lab: 3/16 time-lags are excluded.\n",
            "oborodukiyo_seg1.lab offset (in sec): -0.075\n",
            "oborodukiyo_seg2.lab offset (in sec): -0.034999999999999996\n",
            "oborodukiyo.lab: 2/17 time-lags are excluded.\n",
            "oborodukiyo_seg3.lab offset (in sec): -0.04\n",
            "oborodukiyo.lab: 1/16 time-lags are excluded.\n",
            "oborodukiyo_seg4.lab offset (in sec): -0.055\n",
            "oborodukiyo_seg5.lab offset (in sec): -0.065\n",
            "oborodukiyo_seg6.lab offset (in sec): -0.08499999999999999\n",
            "oborodukiyo.lab: 1/17 time-lags are excluded.\n",
            "oborodukiyo_seg7.lab offset (in sec): -0.045\n",
            "oborodukiyo.lab: 1/16 time-lags are excluded.\n",
            "sakura_sakura: Global offset (in sec): -0.03\n",
            "sakura_sakura_seg0.lab offset (in sec): -0.075\n",
            "sakura_sakura.lab: 1/3 time-lags are excluded.\n",
            "sakura_sakura_seg1.lab offset (in sec): -0.049999999999999996\n",
            "sakura_sakura.lab: 2/12 time-lags are excluded.\n",
            "sakura_sakura_seg2.lab offset (in sec): -0.03\n",
            "sakura_sakura.lab: 1/17 time-lags are excluded.\n",
            "sakura_sakura_seg3.lab offset (in sec): -0.04\n",
            "sakura_sakura.lab: 1/12 time-lags are excluded.\n",
            "sakura_sakura_seg4.lab offset (in sec): -0.01\n",
            "sakura_sakura.lab: 2/10 time-lags are excluded.\n",
            "sakura_sakura_seg5.lab offset (in sec): -0.034999999999999996\n",
            "sakura_sakura.lab: 2/16 time-lags are excluded.\n",
            "sakura_sakura_seg6.lab offset (in sec): -0.034999999999999996\n",
            "sakura_sakura.lab: 1/17 time-lags are excluded.\n",
            "sakura_sakura_seg7.lab offset (in sec): 0.0\n",
            "sakura_sakura_seg8.lab offset (in sec): 0.0\n",
            "seikurabe: Global offset (in sec): -0.09\n",
            "seikurabe_seg0.lab offset (in sec): -0.08499999999999999\n",
            "seikurabe_seg1.lab offset (in sec): -0.125\n",
            "seikurabe.lab: 2/12 time-lags are excluded.\n",
            "seikurabe_seg2.lab offset (in sec): -0.09\n",
            "seikurabe.lab: 1/12 time-lags are excluded.\n",
            "seikurabe_seg3.lab offset (in sec): -0.09\n",
            "seikurabe.lab: 1/12 time-lags are excluded.\n",
            "seikurabe_seg4.lab offset (in sec): -0.095\n",
            "seikurabe_seg5.lab offset (in sec): -0.09\n",
            "seikurabe.lab: 1/14 time-lags are excluded.\n",
            "seikurabe_seg6.lab offset (in sec): -0.08\n",
            "seikurabe_seg7.lab offset (in sec): -0.09\n",
            "seikurabe.lab: 1/12 time-lags are excluded.\n",
            "seikurabe_seg8.lab offset (in sec): -0.09\n",
            "seikurabe.lab: 1/12 time-lags are excluded.\n",
            "seikurabe_seg9.lab offset (in sec): -0.09999999999999999\n",
            "seikurabe.lab: 2/12 time-lags are excluded.\n",
            "seikurabe_seg10.lab offset (in sec): -0.065\n",
            "seikurabe.lab: 1/14 time-lags are excluded.\n",
            "seikurabe_seg11.lab offset (in sec): -0.06\n",
            "seikurabe.lab: 1/14 time-lags are excluded.\n",
            " 77% 44/57 [00:00<00:00, 129.25it/s]shabondama: Global offset (in sec): -0.08499999999999999\n",
            "shabondama_seg0.lab offset (in sec): -0.08499999999999999\n",
            "shabondama.lab: 8/73 time-lags are excluded.\n",
            "shoujoujino_tanukibayashi: Global offset (in sec): -0.04\n",
            "shoujoujino_tanukibayashi_seg0.lab offset (in sec): -0.04\n",
            "shoujoujino_tanukibayashi.lab: 4/55 time-lags are excluded.\n",
            "shoujoujino_tanukibayashi_seg1.lab offset (in sec): -0.03\n",
            "shoujoujino_tanukibayashi_seg2.lab offset (in sec): -0.049999999999999996\n",
            "shoujoujino_tanukibayashi.lab: 4/31 time-lags are excluded.\n",
            "shoujoujino_tanukibayashi_seg3.lab offset (in sec): -0.02\n",
            "tetsudou_shouka: Global offset (in sec): -0.12\n",
            "tetsudou_shouka_seg0.lab offset (in sec): -0.125\n",
            "tetsudou_shouka.lab: 4/11 time-lags are excluded.\n",
            "tetsudou_shouka_seg1.lab offset (in sec): -0.11\n",
            "tetsudou_shouka.lab: 1/25 time-lags are excluded.\n",
            "tetsudou_shouka_seg2.lab offset (in sec): -0.11\n",
            "tetsudou_shouka_seg3.lab offset (in sec): -0.11\n",
            "tetsudou_shouka.lab: 1/25 time-lags are excluded.\n",
            "tetsudou_shouka_seg4.lab offset (in sec): -0.09999999999999999\n",
            "tetsudou_shouka.lab: 1/25 time-lags are excluded.\n",
            "tetsudou_shouka_seg5.lab offset (in sec): -0.12\n",
            "tetsudou_shouka_seg6.lab offset (in sec): -0.13\n",
            "tetsudou_shouka.lab: 4/25 time-lags are excluded.\n",
            "tetsudou_shouka_seg7.lab offset (in sec): -0.11499999999999999\n",
            "tombi: Global offset (in sec): -0.075\n",
            "tombi_seg0.lab offset (in sec): -0.049999999999999996\n",
            "tombi.lab: 3/17 time-lags are excluded.\n",
            "tombi_seg1.lab offset (in sec): -0.06\n",
            "tombi_seg2.lab offset (in sec): -0.11\n",
            "tombi.lab: 4/14 time-lags are excluded.\n",
            "tombi_seg3.lab offset (in sec): -0.08\n",
            "tombi.lab: 2/12 time-lags are excluded.\n",
            "tombi_seg4.lab offset (in sec): -0.08499999999999999\n",
            "tombi.lab: 1/7 time-lags are excluded.\n",
            "tombi_seg5.lab offset (in sec): -0.04\n",
            "tombi.lab: 1/17 time-lags are excluded.\n",
            "tombi_seg6.lab offset (in sec): -0.065\n",
            "tombi.lab: 2/17 time-lags are excluded.\n",
            "tombi_seg7.lab offset (in sec): -0.09999999999999999\n",
            "tombi.lab: 2/14 time-lags are excluded.\n",
            "tombi_seg8.lab offset (in sec): -0.049999999999999996\n",
            "tombi.lab: 1/12 time-lags are excluded.\n",
            "tombi_seg9.lab offset (in sec): -0.075\n",
            "tombi.lab: 1/7 time-lags are excluded.\n",
            "tooryanse: Global offset (in sec): -0.075\n",
            "tooryanse_seg0.lab offset (in sec): -0.12\n",
            "tooryanse_seg1.lab offset (in sec): -0.08\n",
            "tooryanse.lab: 1/36 time-lags are excluded.\n",
            "tooryanse_seg2.lab offset (in sec): -0.075\n",
            "tooryanse_seg3.lab offset (in sec): -0.06\n",
            "tooryanse.lab: 1/28 time-lags are excluded.\n",
            "tooryanse_seg4.lab offset (in sec): -0.034999999999999996\n",
            "tooryanse.lab: 1/16 time-lags are excluded.\n",
            "urashima_taro: Global offset (in sec): -0.08\n",
            "urashima_taro_seg0.lab offset (in sec): -0.105\n",
            "urashima_taro_seg1.lab offset (in sec): -0.065\n",
            "urashima_taro.lab: 3/24 time-lags are excluded.\n",
            "urashima_taro_seg2.lab offset (in sec): -0.075\n",
            "urashima_taro_seg3.lab offset (in sec): -0.065\n",
            "urashima_taro.lab: 2/25 time-lags are excluded.\n",
            "urashima_taro_seg4.lab offset (in sec): -0.065\n",
            "urashima_taro.lab: 6/26 time-lags are excluded.\n",
            "urashima_taro_seg5.lab offset (in sec): -0.11\n",
            "urashima_taro.lab: 3/12 time-lags are excluded.\n",
            "urashima_taro_seg6.lab offset (in sec): -0.09\n",
            "urashima_taro.lab: 2/25 time-lags are excluded.\n",
            "urashima_taro_seg7.lab offset (in sec): -0.075\n",
            "usagi: Global offset (in sec): -0.06\n",
            "usagi_seg0.lab offset (in sec): -0.055\n",
            "usagi.lab: 2/31 time-lags are excluded.\n",
            "usagito_kame: Global offset (in sec): -0.13499999999999998\n",
            "usagito_kame_seg0.lab offset (in sec): -0.13499999999999998\n",
            "usagito_kame_seg1.lab offset (in sec): -0.12\n",
            "usagito_kame.lab: 2/25 time-lags are excluded.\n",
            "usagito_kame_seg2.lab offset (in sec): -0.13999999999999999\n",
            "usagito_kame_seg3.lab offset (in sec): -0.13\n",
            "usagito_kame.lab: 3/26 time-lags are excluded.\n",
            "usagito_kame_seg4.lab offset (in sec): -0.13\n",
            "usagito_kame_seg5.lab offset (in sec): -0.13\n",
            "usagito_kame.lab: 1/12 time-lags are excluded.\n",
            "usagito_kame_seg6.lab offset (in sec): -0.13499999999999998\n",
            "usagito_kame.lab: 1/24 time-lags are excluded.\n",
            "usagito_kame_seg7.lab offset (in sec): -0.125\n",
            "warewa_uminoko: Global offset (in sec): -0.08499999999999999\n",
            "warewa_uminoko_seg0.lab offset (in sec): -0.06999999999999999\n",
            "warewa_uminoko_seg1.lab offset (in sec): -0.105\n",
            "warewa_uminoko.lab: 3/13 time-lags are excluded.\n",
            "warewa_uminoko_seg2.lab offset (in sec): -0.11\n",
            "warewa_uminoko_seg3.lab offset (in sec): -0.095\n",
            "warewa_uminoko.lab: 2/12 time-lags are excluded.\n",
            "warewa_uminoko_seg4.lab offset (in sec): -0.045\n",
            "warewa_uminoko.lab: 3/13 time-lags are excluded.\n",
            "warewa_uminoko_seg5.lab offset (in sec): -0.065\n",
            "warewa_uminoko.lab: 3/13 time-lags are excluded.\n",
            "warewa_uminoko_seg6.lab offset (in sec): -0.09999999999999999\n",
            "warewa_uminoko.lab: 2/12 time-lags are excluded.\n",
            "warewa_uminoko_seg7.lab offset (in sec): -0.075\n",
            "warewa_uminoko.lab: 3/13 time-lags are excluded.\n",
            "warewa_uminoko_seg8.lab offset (in sec): -0.105\n",
            "warewa_uminoko_seg9.lab offset (in sec): -0.09\n",
            "warewa_uminoko.lab: 2/12 time-lags are excluded.\n",
            "warewa_uminoko_seg10.lab offset (in sec): -0.08499999999999999\n",
            "warewa_uminoko.lab: 2/13 time-lags are excluded.\n",
            "warewa_uminoko_seg11.lab offset (in sec): -0.09\n",
            "warewa_uminoko.lab: 2/12 time-lags are excluded.\n",
            "yuki: Global offset (in sec): -0.065\n",
            "yuki_seg0.lab offset (in sec): -0.065\n",
            "yuki.lab: 3/55 time-lags are excluded.\n",
            "yuki_seg1.lab offset (in sec): -0.065\n",
            "yuki.lab: 2/54 time-lags are excluded.\n",
            "yurikagono_uta: Global offset (in sec): -0.06999999999999999\n",
            "yurikagono_uta_seg0.lab offset (in sec): -0.04\n",
            "yurikagono_uta_seg1.lab offset (in sec): -0.06999999999999999\n",
            "yurikagono_uta.lab: 1/25 time-lags are excluded.\n",
            "yurikagono_uta_seg2.lab offset (in sec): -0.055\n",
            "yurikagono_uta.lab: 2/18 time-lags are excluded.\n",
            "yurikagono_uta_seg3.lab offset (in sec): -0.06999999999999999\n",
            "yurikagono_uta.lab: 1/15 time-lags are excluded.\n",
            "yurikagono_uta_seg4.lab offset (in sec): -0.06999999999999999\n",
            "yurikagono_uta.lab: 1/18 time-lags are excluded.\n",
            "yurikagono_uta_seg5.lab offset (in sec): -0.06999999999999999\n",
            "zuizuizukkorobashi: Global offset (in sec): -0.075\n",
            "zuizuizukkorobashi_seg0.lab offset (in sec): -0.09\n",
            "zuizuizukkorobashi.lab: 1/15 time-lags are excluded.\n",
            "zuizuizukkorobashi_seg1.lab offset (in sec): -0.11\n",
            "zuizuizukkorobashi.lab: 2/23 time-lags are excluded.\n",
            "zuizuizukkorobashi_seg2.lab offset (in sec): -0.045\n",
            "zuizuizukkorobashi.lab: 3/19 time-lags are excluded.\n",
            "zuizuizukkorobashi_seg3.lab offset (in sec): -0.055\n",
            "zuizuizukkorobashi_seg4.lab offset (in sec): -0.065\n",
            "zuizuizukkorobashi_low: Global offset (in sec): -0.08499999999999999\n",
            "zuizuizukkorobashi_low_seg0.lab offset (in sec): -0.09\n",
            "zuizuizukkorobashi_low_seg1.lab offset (in sec): -0.105\n",
            "zuizuizukkorobashi_low.lab: 4/23 time-lags are excluded.\n",
            "zuizuizukkorobashi_low_seg2.lab offset (in sec): -0.08\n",
            "zuizuizukkorobashi_low.lab: 1/19 time-lags are excluded.\n",
            "zuizuizukkorobashi_low_seg3.lab offset (in sec): -0.075\n",
            "zuizuizukkorobashi_low.lab: 2/27 time-lags are excluded.\n",
            "zuizuizukkorobashi_low_seg4.lab offset (in sec): -0.075\n",
            "zuizuizukkorobashi_low.lab: 1/18 time-lags are excluded.\n",
            "100% 57/57 [00:00<00:00, 133.84it/s]\n",
            "Prepare data for duration models\n",
            "100% 57/57 [00:00<00:00, 636.28it/s]\n",
            "Prepare data for acoustic models\n",
            "100% 57/57 [05:40<00:00,  5.98s/it]\n",
            "train/dev/eval split\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tmI-uuTPGxxO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "9db5f33c-64e2-4820-e9f0-752e4a75170d"
      },
      "source": [
        "# stage1の実行(言語特徴量, 持続長, タイムラグ, 音響特徴量の抽出)\n",
        "! cd $RECIPE_ROOT && bash run.sh --stage 1 --stop-stage 1"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "stage 1: Feature generation\n",
            "[\u001b[36m2020-06-29 00:31:24,579\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - acoustic:\n",
            "  enabled: true\n",
            "  f0_ceil: 700\n",
            "  f0_floor: 150\n",
            "  frame_period: 5\n",
            "  interp_unvoiced_aperiodicity: true\n",
            "  label_dir: data/acoustic/label_phone_align\n",
            "  mgc_order: 59\n",
            "  num_windows: 3\n",
            "  question_path: null\n",
            "  relative_f0: true\n",
            "  subphone_features: coarse_coding\n",
            "  use_harvest: true\n",
            "  wav_dir: data/acoustic/wav\n",
            "duration:\n",
            "  enabled: true\n",
            "  label_dir: data/duration/label_phone_align\n",
            "  question_path: null\n",
            "log_f0_conditioning: true\n",
            "out_dir: dump/ofuton_p/org/train_no_dev/\n",
            "question_path: ./conf/jp_qst001_nnsvs.hed\n",
            "timelag:\n",
            "  enabled: true\n",
            "  label_phone_align_dir: data/timelag/label_phone_align\n",
            "  label_phone_score_dir: data/timelag/label_phone_score\n",
            "  question_path: null\n",
            "utt_list: data/list/train_no_dev.list\n",
            "verbose: 100\n",
            "\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:31:24,621\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - mkdirs: /content/nnsvs/egs/nnsvs_ofuton_p_utagoe_db/00-svs-world/dump/ofuton_p/org/train_no_dev/in_timelag\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:31:24,621\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - mkdirs: /content/nnsvs/egs/nnsvs_ofuton_p_utagoe_db/00-svs-world/dump/ofuton_p/org/train_no_dev/out_timelag\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:31:24,621\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - mkdirs: /content/nnsvs/egs/nnsvs_ofuton_p_utagoe_db/00-svs-world/dump/ofuton_p/org/train_no_dev/in_duration\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:31:24,621\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - mkdirs: /content/nnsvs/egs/nnsvs_ofuton_p_utagoe_db/00-svs-world/dump/ofuton_p/org/train_no_dev/out_duration\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:31:24,621\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - mkdirs: /content/nnsvs/egs/nnsvs_ofuton_p_utagoe_db/00-svs-world/dump/ofuton_p/org/train_no_dev/in_acoustic\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:31:24,622\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - mkdirs: /content/nnsvs/egs/nnsvs_ofuton_p_utagoe_db/00-svs-world/dump/ofuton_p/org/train_no_dev/out_acoustic\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:31:24,627\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Timelag linguistic feature dim: 420\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:31:24,628\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Timelag feature dim: 1\u001b[0m\n",
            "100% 311/311 [00:03<00:00, 94.12it/s] \n",
            "[\u001b[36m2020-06-29 00:31:27,942\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Duration linguistic feature dim: 420\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:31:27,942\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Duration feature dim: 1\u001b[0m\n",
            "100% 311/311 [00:06<00:00, 51.09it/s]\n",
            "[\u001b[36m2020-06-29 00:31:34,052\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Acoustic linguistic feature dim: 424\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:31:36,989\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Acoustic feature dim: 199\u001b[0m\n",
            "100% 311/311 [21:36<00:00,  4.17s/it]\n",
            "[\u001b[36m2020-06-29 00:53:15,537\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - acoustic:\n",
            "  enabled: true\n",
            "  f0_ceil: 700\n",
            "  f0_floor: 150\n",
            "  frame_period: 5\n",
            "  interp_unvoiced_aperiodicity: true\n",
            "  label_dir: data/acoustic/label_phone_align\n",
            "  mgc_order: 59\n",
            "  num_windows: 3\n",
            "  question_path: null\n",
            "  relative_f0: true\n",
            "  subphone_features: coarse_coding\n",
            "  use_harvest: true\n",
            "  wav_dir: data/acoustic/wav\n",
            "duration:\n",
            "  enabled: true\n",
            "  label_dir: data/duration/label_phone_align\n",
            "  question_path: null\n",
            "log_f0_conditioning: true\n",
            "out_dir: dump/ofuton_p/org/dev/\n",
            "question_path: ./conf/jp_qst001_nnsvs.hed\n",
            "timelag:\n",
            "  enabled: true\n",
            "  label_phone_align_dir: data/timelag/label_phone_align\n",
            "  label_phone_score_dir: data/timelag/label_phone_score\n",
            "  question_path: null\n",
            "utt_list: data/list/dev.list\n",
            "verbose: 100\n",
            "\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:53:15,573\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - mkdirs: /content/nnsvs/egs/nnsvs_ofuton_p_utagoe_db/00-svs-world/dump/ofuton_p/org/dev/in_timelag\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:53:15,573\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - mkdirs: /content/nnsvs/egs/nnsvs_ofuton_p_utagoe_db/00-svs-world/dump/ofuton_p/org/dev/out_timelag\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:53:15,573\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - mkdirs: /content/nnsvs/egs/nnsvs_ofuton_p_utagoe_db/00-svs-world/dump/ofuton_p/org/dev/in_duration\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:53:15,574\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - mkdirs: /content/nnsvs/egs/nnsvs_ofuton_p_utagoe_db/00-svs-world/dump/ofuton_p/org/dev/out_duration\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:53:15,574\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - mkdirs: /content/nnsvs/egs/nnsvs_ofuton_p_utagoe_db/00-svs-world/dump/ofuton_p/org/dev/in_acoustic\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:53:15,574\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - mkdirs: /content/nnsvs/egs/nnsvs_ofuton_p_utagoe_db/00-svs-world/dump/ofuton_p/org/dev/out_acoustic\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:53:15,583\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Timelag linguistic feature dim: 420\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:53:15,584\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Timelag feature dim: 1\u001b[0m\n",
            "100% 4/4 [00:00<00:00, 67.76it/s]\n",
            "[\u001b[36m2020-06-29 00:53:15,660\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Duration linguistic feature dim: 420\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:53:15,661\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Duration feature dim: 1\u001b[0m\n",
            "100% 4/4 [00:00<00:00, 39.34it/s]\n",
            "[\u001b[36m2020-06-29 00:53:15,799\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Acoustic linguistic feature dim: 424\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:53:19,652\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Acoustic feature dim: 199\u001b[0m\n",
            "100% 4/4 [00:18<00:00,  4.65s/it]\n",
            "[\u001b[36m2020-06-29 00:53:39,837\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - acoustic:\n",
            "  enabled: true\n",
            "  f0_ceil: 700\n",
            "  f0_floor: 150\n",
            "  frame_period: 5\n",
            "  interp_unvoiced_aperiodicity: true\n",
            "  label_dir: data/acoustic/label_phone_align\n",
            "  mgc_order: 59\n",
            "  num_windows: 3\n",
            "  question_path: null\n",
            "  relative_f0: true\n",
            "  subphone_features: coarse_coding\n",
            "  use_harvest: true\n",
            "  wav_dir: data/acoustic/wav\n",
            "duration:\n",
            "  enabled: true\n",
            "  label_dir: data/duration/label_phone_align\n",
            "  question_path: null\n",
            "log_f0_conditioning: true\n",
            "out_dir: dump/ofuton_p/org/eval/\n",
            "question_path: ./conf/jp_qst001_nnsvs.hed\n",
            "timelag:\n",
            "  enabled: true\n",
            "  label_phone_align_dir: data/timelag/label_phone_align\n",
            "  label_phone_score_dir: data/timelag/label_phone_score\n",
            "  question_path: null\n",
            "utt_list: data/list/eval.list\n",
            "verbose: 100\n",
            "\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:53:39,871\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - mkdirs: /content/nnsvs/egs/nnsvs_ofuton_p_utagoe_db/00-svs-world/dump/ofuton_p/org/eval/in_timelag\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:53:39,871\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - mkdirs: /content/nnsvs/egs/nnsvs_ofuton_p_utagoe_db/00-svs-world/dump/ofuton_p/org/eval/out_timelag\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:53:39,871\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - mkdirs: /content/nnsvs/egs/nnsvs_ofuton_p_utagoe_db/00-svs-world/dump/ofuton_p/org/eval/in_duration\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:53:39,871\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - mkdirs: /content/nnsvs/egs/nnsvs_ofuton_p_utagoe_db/00-svs-world/dump/ofuton_p/org/eval/out_duration\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:53:39,872\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - mkdirs: /content/nnsvs/egs/nnsvs_ofuton_p_utagoe_db/00-svs-world/dump/ofuton_p/org/eval/in_acoustic\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:53:39,872\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - mkdirs: /content/nnsvs/egs/nnsvs_ofuton_p_utagoe_db/00-svs-world/dump/ofuton_p/org/eval/out_acoustic\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:53:39,880\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Timelag linguistic feature dim: 420\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:53:39,880\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Timelag feature dim: 1\u001b[0m\n",
            "100% 6/6 [00:00<00:00, 107.50it/s]\n",
            "[\u001b[36m2020-06-29 00:53:39,953\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Duration linguistic feature dim: 420\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:53:39,954\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Duration feature dim: 1\u001b[0m\n",
            "100% 6/6 [00:00<00:00, 60.00it/s]\n",
            "[\u001b[36m2020-06-29 00:53:40,092\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Acoustic linguistic feature dim: 424\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:53:42,631\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Acoustic feature dim: 199\u001b[0m\n",
            "100% 6/6 [00:12<00:00,  2.00s/it]\n",
            "[2020-06-29 00:53:55,563][nnsvs][INFO] - list_path: train_list.txt\n",
            "out_path: dump/ofuton_p/org/in_timelag_scaler.joblib\n",
            "scaler:\n",
            "  class: sklearn.preprocessing.MinMaxScaler\n",
            "  params: {}\n",
            "verbose: 100\n",
            "\n",
            "[2020-06-29 00:53:55,723][nnsvs][INFO] - data min:\n",
            "[ 0.         0.         0.         0.         0.         0.\n",
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            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
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            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
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            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         1.         1.         1.         1.\n",
            "  1.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         4.4116693  4.4116693  4.4116693\n",
            "  1.         1.        -1.        -1.        -1.        -1.\n",
            " -1.         1.         1.         1.        -1.        -1.\n",
            " -1.        -1.        -1.        -1.        -1.        -1.\n",
            " -1.         0.         1.         7.         3.         0.\n",
            "  0.        -1.        -1.        -1.        -1.        -1.\n",
            " -1.        -1.        -1.        -1.        -1.        -1.\n",
            " -1.        -1.        -1.        -1.        -1.        -1.\n",
            " -1.        -1.        -1.        -1.        -1.        -1.\n",
            " -1.        -1.        -1.        -1.        -1.        -1.\n",
            " -1.        -1.        -1.        -1.        -1.        -1.       ]\n",
            "[2020-06-29 00:53:55,725][nnsvs][INFO] - data max:\n",
            "[  0.         1.         0.         1.         1.         1.\n",
            "   1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.         1.         0.         1.\n",
            "   1.         1.         1.         1.         0.         1.\n",
            "   1.         0.         1.         1.         1.         1.\n",
            "   1.         1.         0.         0.         1.         0.\n",
            "   0.         0.         0.         0.         1.         0.\n",
            "   0.         0.         0.         0.         1.         0.\n",
            "   0.         0.         0.         0.         0.         0.\n",
            "   0.         0.         0.         0.         1.         1.\n",
            "   0.         0.         1.         0.         1.         0.\n",
            "   0.         1.         0.         0.         1.         0.\n",
            "   0.         0.         0.         0.         1.         0.\n",
            "   0.         0.         0.         0.         0.         0.\n",
            "   1.         0.         1.         0.         0.         0.\n",
            "   0.         0.         0.         0.         0.         0.\n",
            "   1.         0.         0.         0.         0.         1.\n",
            "   1.         1.         1.         1.         0.         1.\n",
            "   1.         0.         1.         1.         0.         1.\n",
            "   1.         0.         1.         1.         0.         1.\n",
            "   1.         0.         1.         1.         1.         1.\n",
            "   1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         0.         0.         0.         1.\n",
            "   0.         0.         1.         1.         0.         1.\n",
            "   1.         0.         1.         1.         1.         1.\n",
            "   1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         0.         1.         1.         1.\n",
            "   1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.         1.         0.         1.\n",
            "   1.         1.         1.         1.         0.         1.\n",
            "   1.         0.         1.         1.         1.         1.\n",
            "   1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         0.         0.         1.         1.\n",
            "   0.         0.         1.         1.         1.         1.\n",
            "   1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         0.         1.         1.         1.\n",
            "   1.         1.         1.         1.         1.         0.\n",
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            "   0.         0.         0.         1.         0.         0.\n",
            "   0.         0.         1.         1.         0.         1.\n",
            "   1.         0.         1.         1.         0.         0.\n",
            "   0.         1.         1.         1.         0.         0.\n",
            "   1.         0.         0.         1.         1.         1.\n",
            "   0.         0.         1.         0.         0.         0.\n",
            "   0.         0.         1.         1.         1.         1.\n",
            "   1.         0.         0.         1.         1.         1.\n",
            "   0.         0.         1.         5.855726   5.855726   5.855726\n",
            "   1.         4.        -1.         2.         4.         1.\n",
            "   1.         4.         1.         1.         4.         1.\n",
            "   1.        11.        11.         1.       187.        66.\n",
            "  11.        11.         1.       215.        75.         0.\n",
            "   0.        -1.        -1.        -1.        -1.        -1.\n",
            "  -1.        -1.        -1.        -1.        -1.        -1.\n",
            "  -1.        -1.        -1.        -1.        -1.        -1.\n",
            "  -1.        -1.        -1.        -1.        -1.        -1.\n",
            "  -1.        -1.        -1.        -1.        -1.        -1.\n",
            "  -1.        11.        11.         1.       215.        75.      ]\n",
            "'dump/ofuton_p/org/in_timelag_scaler.joblib' -> 'dump/ofuton_p/norm/in_timelag_scaler.joblib'\n",
            "[2020-06-29 00:53:56,545][nnsvs][INFO] - list_path: train_list.txt\n",
            "out_path: dump/ofuton_p/org/in_duration_scaler.joblib\n",
            "scaler:\n",
            "  class: sklearn.preprocessing.MinMaxScaler\n",
            "  params: {}\n",
            "verbose: 100\n",
            "\n",
            "[2020-06-29 00:53:56,799][nnsvs][INFO] - data min:\n",
            "[ 0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
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            "  0.         0.         0.         0.         0.         0.\n",
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            "  0.         0.         0.         0.         0.         0.\n",
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            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
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            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
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            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
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            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
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            "  0.         0.         0.         0.         0.         0.\n",
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            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         1.         1.         1.         1.\n",
            "  1.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         4.4116693  4.4116693  4.4116693\n",
            "  1.         1.        -1.        -1.        -1.        -1.\n",
            " -1.         1.         1.         1.        -1.        -1.\n",
            " -1.        -1.        -1.        -1.        -1.        -1.\n",
            " -1.         0.         1.         7.         3.         0.\n",
            "  0.        -1.        -1.        -1.        -1.        -1.\n",
            " -1.        -1.        -1.        -1.        -1.        -1.\n",
            " -1.        -1.        -1.        -1.        -1.        -1.\n",
            " -1.        -1.        -1.        -1.        -1.        -1.\n",
            " -1.        -1.        -1.        -1.        -1.        -1.\n",
            " -1.        -1.        -1.        -1.        -1.        -1.       ]\n",
            "[2020-06-29 00:53:56,801][nnsvs][INFO] - data max:\n",
            "[  1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.         1.         0.         1.\n",
            "   1.         1.         1.         1.         0.         1.\n",
            "   1.         0.         1.         1.         1.         1.\n",
            "   1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         0.         0.         1.         1.\n",
            "   0.         0.         1.         1.         1.         1.\n",
            "   1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.         1.         0.         1.\n",
            "   1.         1.         1.         1.         0.         1.\n",
            "   1.         0.         1.         1.         1.         1.\n",
            "   1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         0.         0.         1.         1.\n",
            "   0.         0.         1.         1.         1.         1.\n",
            "   1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.         1.         0.         1.\n",
            "   1.         1.         1.         1.         0.         1.\n",
            "   1.         0.         1.         1.         1.         1.\n",
            "   1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         0.         0.         1.         1.\n",
            "   0.         0.         1.         1.         1.         1.\n",
            "   1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.         1.         1.         0.\n",
            "   0.         1.         0.         0.         0.         0.\n",
            "   0.         0.         1.         0.         0.         0.\n",
            "   0.         0.         0.         1.         0.         0.\n",
            "   0.         0.         1.         1.         0.         1.\n",
            "   1.         0.         1.         1.         0.         0.\n",
            "   0.         1.         1.         1.         0.         0.\n",
            "   1.         0.         0.         1.         1.         1.\n",
            "   0.         0.         1.         0.         0.         0.\n",
            "   0.         0.         1.         1.         1.         1.\n",
            "   1.         0.         0.         1.         1.         1.\n",
            "   0.         0.         1.         5.855726   5.855726   5.855726\n",
            "   4.         4.        -1.         2.         4.         1.\n",
            "   1.         4.         1.         1.         4.         1.\n",
            "   1.        11.        11.         1.       187.        72.\n",
            "  11.        11.         1.       419.       168.         0.\n",
            "   0.        -1.        -1.        -1.        -1.        -1.\n",
            "  -1.        -1.        -1.        -1.        -1.        -1.\n",
            "  -1.        -1.        -1.        -1.        -1.        -1.\n",
            "  -1.        -1.        -1.        -1.        -1.        -1.\n",
            "  -1.        -1.        -1.        -1.        -1.        -1.\n",
            "  -1.        11.        11.         1.       215.        75.      ]\n",
            "'dump/ofuton_p/org/in_duration_scaler.joblib' -> 'dump/ofuton_p/norm/in_duration_scaler.joblib'\n",
            "[2020-06-29 00:53:57,610][nnsvs][INFO] - list_path: train_list.txt\n",
            "out_path: dump/ofuton_p/org/in_acoustic_scaler.joblib\n",
            "scaler:\n",
            "  class: sklearn.preprocessing.MinMaxScaler\n",
            "  params: {}\n",
            "verbose: 100\n",
            "\n",
            "[2020-06-29 00:53:58,707][nnsvs][INFO] - data min:\n",
            "[ 0.          0.          0.          0.          0.          0.\n",
            "  0.          0.          0.          0.          0.          0.\n",
            "  0.          0.          0.          0.          0.          0.\n",
            "  0.          0.          0.          0.          0.          0.\n",
            "  0.          0.          0.          0.          0.          0.\n",
            "  0.          0.          0.          0.          0.          0.\n",
            "  0.          0.          0.          0.          0.          0.\n",
            "  0.          0.          0.          0.          0.          0.\n",
            "  0.          0.          0.          0.          0.          0.\n",
            "  0.          0.          0.          0.          0.          0.\n",
            "  0.          0.          0.          0.          0.          0.\n",
            "  0.          0.          0.          0.          0.          0.\n",
            "  0.          0.          0.          0.          0.          0.\n",
            "  0.          0.          0.          0.          0.          0.\n",
            "  0.          0.          0.          0.          0.          0.\n",
            "  0.          0.          0.          0.          0.          0.\n",
            "  0.          0.          0.          0.          0.          0.\n",
            "  0.          0.          0.          0.          0.          0.\n",
            "  0.          0.          0.          0.          0.          0.\n",
            "  0.          0.          0.          0.          0.          0.\n",
            "  0.          0.          0.          0.          0.          0.\n",
            "  0.          0.          0.          0.          0.          0.\n",
            "  0.          0.          0.          0.          0.          0.\n",
            "  0.          0.          0.          0.          0.          0.\n",
            "  0.          0.          0.          0.          0.          0.\n",
            "  0.          0.          0.          0.          0.          0.\n",
            "  0.          0.          0.          0.          0.          0.\n",
            "  0.          0.          0.          0.          0.          0.\n",
            "  0.          0.          0.          0.          0.          0.\n",
            "  0.          0.          0.          0.          0.          0.\n",
            "  0.          0.          0.          0.          0.          0.\n",
            "  0.          0.          0.          0.          0.          0.\n",
            "  0.          0.          0.          0.          0.          0.\n",
            "  0.          0.          0.          0.          0.          0.\n",
            "  0.          0.          0.          0.          0.          0.\n",
            "  0.          0.          0.          0.          0.          0.\n",
            "  0.          0.          0.          0.          0.          0.\n",
            "  0.          0.          0.          0.          0.          0.\n",
            "  0.          0.          0.          0.          0.          0.\n",
            "  0.          0.          0.          0.          0.          0.\n",
            "  0.          0.          0.          0.          0.          0.\n",
            "  0.          0.          0.          0.          0.          0.\n",
            "  0.          0.          0.          0.          0.          0.\n",
            "  0.          0.          0.          0.          0.          0.\n",
            "  0.          0.          0.          0.          0.          0.\n",
            "  0.          0.          0.          0.          0.          0.\n",
            "  0.          0.          0.          0.          0.          0.\n",
            "  0.          0.          0.          0.          0.          0.\n",
            "  0.          0.          0.          0.          0.          0.\n",
            "  0.          0.          0.          0.          0.          0.\n",
            "  0.          0.          1.          0.          0.          0.\n",
            "  0.          0.          0.          0.          0.          0.\n",
            "  0.          0.          0.          0.          0.          0.\n",
            "  0.          0.          0.          0.          0.          0.\n",
            "  0.          0.          0.          0.          0.          0.\n",
            "  0.          0.          0.          0.          0.          0.\n",
            "  0.          0.          0.          0.          0.          0.\n",
            "  0.          0.          1.          1.          1.          1.\n",
            "  1.          0.          0.          0.          0.          0.\n",
            "  0.          0.          0.          4.4116693   4.4116693   4.4116693\n",
            "  1.          1.         -1.         -1.         -1.         -1.\n",
            " -1.          1.          1.          1.         -1.         -1.\n",
            " -1.         -1.         -1.         -1.         -1.         -1.\n",
            " -1.          0.          1.          7.          3.          0.\n",
            "  0.         -1.         -1.         -1.         -1.         -1.\n",
            " -1.         -1.         -1.         -1.         -1.         -1.\n",
            " -1.         -1.         -1.         -1.         -1.         -1.\n",
            " -1.         -1.         -1.         -1.         -1.         -1.\n",
            " -1.         -1.         -1.         -1.         -1.         -1.\n",
            " -1.         -1.         -1.         -1.         -1.         -1.\n",
            "  0.04404987  0.45900714  0.04404987  1.        ]\n",
            "[2020-06-29 00:53:58,710][nnsvs][INFO] - data max:\n",
            "[  1.           1.           1.           1.           1.\n",
            "   1.           1.           1.           1.           1.\n",
            "   1.           1.           1.           1.           1.\n",
            "   1.           0.           1.           1.           1.\n",
            "   1.           1.           0.           1.           1.\n",
            "   0.           1.           1.           1.           1.\n",
            "   1.           1.           1.           1.           1.\n",
            "   1.           1.           1.           1.           1.\n",
            "   1.           1.           1.           1.           1.\n",
            "   1.           1.           1.           1.           1.\n",
            "   1.           1.           1.           1.           1.\n",
            "   1.           0.           0.           1.           1.\n",
            "   0.           0.           1.           1.           1.\n",
            "   1.           1.           1.           1.           1.\n",
            "   1.           1.           1.           1.           1.\n",
            "   1.           1.           1.           1.           1.\n",
            "   1.           1.           1.           1.           1.\n",
            "   1.           1.           1.           1.           1.\n",
            "   1.           1.           1.           1.           1.\n",
            "   1.           1.           1.           1.           1.\n",
            "   1.           1.           1.           1.           1.\n",
            "   1.           1.           1.           1.           1.\n",
            "   1.           1.           0.           1.           1.\n",
            "   1.           1.           1.           0.           1.\n",
            "   1.           0.           1.           1.           1.\n",
            "   1.           1.           1.           1.           1.\n",
            "   1.           1.           1.           1.           1.\n",
            "   1.           1.           1.           1.           1.\n",
            "   1.           1.           1.           1.           1.\n",
            "   1.           1.           1.           1.           1.\n",
            "   1.           1.           0.           0.           1.\n",
            "   1.           0.           0.           1.           1.\n",
            "   1.           1.           1.           1.           1.\n",
            "   1.           1.           1.           1.           1.\n",
            "   1.           1.           1.           1.           1.\n",
            "   1.           1.           1.           1.           1.\n",
            "   1.           1.           1.           1.           1.\n",
            "   1.           1.           1.           1.           1.\n",
            "   1.           1.           1.           1.           1.\n",
            "   1.           1.           1.           1.           1.\n",
            "   1.           1.           1.           1.           1.\n",
            "   1.           1.           1.           0.           1.\n",
            "   1.           1.           1.           1.           0.\n",
            "   1.           1.           0.           1.           1.\n",
            "   1.           1.           1.           1.           1.\n",
            "   1.           1.           1.           1.           1.\n",
            "   1.           1.           1.           1.           1.\n",
            "   1.           1.           1.           1.           1.\n",
            "   1.           1.           1.           1.           1.\n",
            "   1.           1.           1.           0.           0.\n",
            "   1.           1.           0.           0.           1.\n",
            "   1.           1.           1.           1.           1.\n",
            "   1.           1.           1.           1.           1.\n",
            "   1.           1.           1.           1.           1.\n",
            "   1.           1.           1.           1.           1.\n",
            "   1.           1.           1.           1.           1.\n",
            "   1.           1.           1.           1.           1.\n",
            "   1.           1.           1.           1.           1.\n",
            "   1.           1.           1.           0.           0.\n",
            "   1.           0.           0.           0.           0.\n",
            "   0.           0.           1.           0.           0.\n",
            "   0.           0.           0.           0.           1.\n",
            "   0.           0.           0.           0.           1.\n",
            "   1.           0.           1.           1.           0.\n",
            "   1.           1.           0.           0.           0.\n",
            "   1.           1.           1.           0.           0.\n",
            "   1.           0.           0.           1.           1.\n",
            "   1.           0.           0.           1.           0.\n",
            "   0.           0.           0.           0.           1.\n",
            "   1.           1.           1.           1.           0.\n",
            "   0.           1.           1.           1.           0.\n",
            "   0.           1.           5.855726     5.855726     5.855726\n",
            "   4.           4.          -1.           2.           4.\n",
            "   1.           1.           4.           1.           1.\n",
            "   4.           1.           1.          11.          11.\n",
            "   1.         187.          72.          11.          11.\n",
            "   1.         419.         168.           0.           0.\n",
            "  -1.          -1.          -1.          -1.          -1.\n",
            "  -1.          -1.          -1.          -1.          -1.\n",
            "  -1.          -1.          -1.          -1.          -1.\n",
            "  -1.          -1.          -1.          -1.          -1.\n",
            "  -1.          -1.          -1.          -1.          -1.\n",
            "  -1.          -1.          -1.          -1.          -1.\n",
            "  11.          11.           1.         215.          75.\n",
            "   0.99733615   0.99733615   0.99733615 820.        ]\n",
            "'dump/ofuton_p/org/in_acoustic_scaler.joblib' -> 'dump/ofuton_p/norm/in_acoustic_scaler.joblib'\n",
            "[2020-06-29 00:53:59,520][nnsvs][INFO] - list_path: train_list.txt\n",
            "out_path: dump/ofuton_p/org/out_timelag_scaler.joblib\n",
            "scaler:\n",
            "  class: sklearn.preprocessing.StandardScaler\n",
            "  params: {}\n",
            "verbose: 100\n",
            "\n",
            "[2020-06-29 00:53:59,722][nnsvs][INFO] - mean:\n",
            "[0.73342518]\n",
            "[2020-06-29 00:53:59,722][nnsvs][INFO] - std:\n",
            "[8.84255003]\n",
            "'dump/ofuton_p/org/out_timelag_scaler.joblib' -> 'dump/ofuton_p/norm/out_timelag_scaler.joblib'\n",
            "[2020-06-29 00:54:00,588][nnsvs][INFO] - list_path: train_list.txt\n",
            "out_path: dump/ofuton_p/org/out_duration_scaler.joblib\n",
            "scaler:\n",
            "  class: sklearn.preprocessing.StandardScaler\n",
            "  params: {}\n",
            "verbose: 100\n",
            "\n",
            "[2020-06-29 00:54:00,784][nnsvs][INFO] - mean:\n",
            "[54.91732528]\n",
            "[2020-06-29 00:54:00,784][nnsvs][INFO] - std:\n",
            "[60.16458363]\n",
            "'dump/ofuton_p/org/out_duration_scaler.joblib' -> 'dump/ofuton_p/norm/out_duration_scaler.joblib'\n",
            "[2020-06-29 00:54:01,628][nnsvs][INFO] - list_path: train_list.txt\n",
            "out_path: dump/ofuton_p/org/out_acoustic_scaler.joblib\n",
            "scaler:\n",
            "  class: sklearn.preprocessing.StandardScaler\n",
            "  params: {}\n",
            "verbose: 100\n",
            "\n",
            "[2020-06-29 00:54:03,211][nnsvs][INFO] - mean:\n",
            "[ 4.48926286e+00  2.97738155e+00 -2.91884945e-01  6.45235902e-01\n",
            " -1.00614775e-01  6.54349805e-01  6.34388516e-02 -8.30964667e-02\n",
            " -9.20893713e-02  2.71247570e-01 -1.00649429e-01 -6.77840504e-02\n",
            " -1.41360167e-01  1.63447164e-01 -5.64226461e-02 -2.03896975e-01\n",
            " -5.75325741e-02  8.86676556e-02 -5.90037380e-02 -7.32930201e-02\n",
            " -1.78592527e-02  2.05292001e-02 -7.26582866e-02  1.19229324e-01\n",
            " -8.19775023e-02  4.74537804e-02 -3.24075710e-02  3.08261003e-02\n",
            "  4.12904096e-02  1.43013676e-02 -2.96003808e-02  7.67341694e-02\n",
            " -6.02285109e-02  3.67615707e-02  1.78320143e-02 -2.81091364e-02\n",
            "  3.91657386e-03  9.85617921e-03 -2.86082749e-02  3.10848263e-02\n",
            " -2.30870027e-02  4.24013389e-03 -1.17542802e-02  1.88583942e-02\n",
            " -1.38665788e-02  5.44744954e-03 -3.11447554e-03  1.48186223e-02\n",
            " -1.19160509e-02 -2.75431900e-04  1.29378540e-02 -6.11846726e-03\n",
            " -1.49864954e-03  7.06798926e-03 -4.81458815e-03  3.74347837e-03\n",
            " -1.44612219e-03  5.23740129e-04 -5.59670536e-03  8.01284669e-03\n",
            "  1.57268045e-04  1.32559408e-04  5.50871281e-05  4.34797274e-05\n",
            "  1.68756348e-05  4.93717621e-05  1.98767606e-05 -4.66752920e-06\n",
            "  8.59068062e-06  3.25091861e-05  1.61273339e-06 -4.45396267e-06\n",
            "  4.00147813e-07  2.38012813e-05  7.64756097e-06 -1.06114406e-05\n",
            "  1.55647913e-05  1.51681867e-05  1.42526293e-07  4.63573998e-06\n",
            " -6.53584903e-06 -1.03769077e-05 -7.01639191e-06  3.92296501e-06\n",
            " -5.75906031e-06 -6.83822294e-06 -5.89954444e-06  4.66451417e-06\n",
            "  5.72005987e-06 -7.07712378e-06 -4.60696814e-06  5.15718574e-06\n",
            " -4.80055142e-06  2.41557779e-06  9.52538261e-08 -4.66958504e-06\n",
            "  1.17861852e-06 -2.42326697e-06 -4.13439283e-06 -3.43042102e-06\n",
            " -1.69875148e-06 -3.30997166e-06 -2.17783666e-06  2.54081858e-06\n",
            " -1.72423759e-06 -1.33102531e-06  1.30597727e-06  1.67030843e-06\n",
            " -2.10995412e-06  2.86645623e-07  2.43354550e-06 -1.59007966e-06\n",
            "  6.54938133e-07  3.84398823e-07 -2.74632212e-07  7.95648174e-07\n",
            "  6.96134620e-07  7.39473353e-07 -9.34550047e-07 -4.26649085e-07\n",
            " -1.32312427e-03 -1.84876271e-03 -3.05418120e-04 -6.41706284e-04\n",
            " -1.99452853e-04 -4.32408685e-04 -2.31824128e-04 -6.56324357e-05\n",
            " -1.43100662e-04 -1.26032882e-04  1.32927659e-05 -4.27734088e-05\n",
            " -5.22294892e-06 -7.09492448e-05  1.74572146e-05  6.71909170e-05\n",
            "  2.30302999e-06 -4.92426447e-05  3.22425786e-05  1.67978747e-05\n",
            "  2.78223923e-05  1.55220879e-05  5.43268106e-05 -5.03140257e-05\n",
            "  5.62128735e-05 -1.80580397e-05  3.46641936e-05 -1.71242010e-05\n",
            " -4.87179974e-06  2.16595603e-06  3.10293087e-05 -4.71190397e-05\n",
            "  3.79820273e-05 -2.66634695e-05  9.18557222e-06  2.86063706e-06\n",
            "  1.00459601e-05 -7.07226702e-06  2.77563873e-05 -1.43094832e-05\n",
            "  2.32335454e-05 -8.94884925e-06  1.73407378e-05 -1.58240729e-05\n",
            "  9.67414741e-06 -5.85404009e-07  3.25312214e-06 -1.18162865e-05\n",
            "  1.16396230e-05 -6.30235149e-06 -5.05632170e-08 -1.66738750e-07\n",
            " -1.10338438e-06  2.27707375e-06 -2.31321337e-06  1.50078330e-06\n",
            " -4.51007713e-06  3.27121809e-07  2.99135481e-06  2.72827903e-07\n",
            " -5.79674038e-03  5.44635917e-06  2.10636908e-05  8.41008758e-01\n",
            " -1.41878815e+01 -9.26621885e+00 -7.28834103e+00 -5.09372915e+00\n",
            " -4.36185330e+00  3.63857417e-04  1.56232293e-04  1.43463933e-04\n",
            "  2.08561030e-05  4.26869365e-05  4.00363858e-03  3.18195146e-03\n",
            "  3.31092074e-03  2.90256445e-03  2.96820442e-03]\n",
            "[2020-06-29 00:54:03,213][nnsvs][INFO] - std:\n",
            "[1.79201074 0.97229052 0.43389168 0.31486011 0.30160703 0.34802819\n",
            " 0.38400011 0.34369319 0.31528761 0.22169372 0.17668416 0.19605052\n",
            " 0.20236178 0.16200776 0.17534768 0.17599711 0.17294366 0.15149844\n",
            " 0.14371782 0.13197993 0.13875385 0.13511918 0.14427937 0.12884297\n",
            " 0.11039449 0.11870753 0.10421255 0.11494491 0.10825022 0.1083141\n",
            " 0.10188893 0.09607891 0.09453858 0.09706703 0.10103088 0.09474976\n",
            " 0.09115526 0.09024867 0.08478478 0.08111446 0.07632886 0.07762267\n",
            " 0.07521336 0.07316142 0.07224926 0.07016255 0.06800952 0.06545666\n",
            " 0.06351626 0.06335577 0.06347015 0.0623366  0.06058766 0.05858666\n",
            " 0.05664507 0.05542949 0.0544544  0.05340965 0.05243242 0.05148207\n",
            " 0.34197671 0.10535069 0.0858115  0.06805183 0.0698238  0.06371546\n",
            " 0.06492903 0.06390249 0.06014709 0.05665489 0.05383046 0.05511184\n",
            " 0.05362892 0.05077423 0.04980198 0.04972662 0.04679259 0.04604398\n",
            " 0.04430617 0.04339124 0.04279286 0.04149237 0.04069633 0.03955876\n",
            " 0.03845222 0.03805506 0.03721631 0.03657307 0.03595842 0.03523453\n",
            " 0.03464097 0.03417351 0.03334314 0.03282699 0.03234205 0.03174121\n",
            " 0.03113692 0.03061703 0.03015665 0.02963262 0.02914003 0.02866706\n",
            " 0.02821198 0.02784697 0.02743659 0.02699947 0.02660914 0.02626353\n",
            " 0.02601805 0.02566573 0.02531606 0.02503787 0.02470106 0.02444943\n",
            " 0.02409506 0.02379527 0.02346215 0.02321056 0.0229619  0.02264629\n",
            " 0.95067789 0.22741301 0.22452869 0.18614215 0.19832816 0.1854116\n",
            " 0.17620564 0.18682531 0.17289661 0.16924965 0.15571504 0.15983521\n",
            " 0.1554852  0.14910783 0.14280538 0.1431537  0.13258551 0.12967116\n",
            " 0.12432488 0.12078882 0.11890827 0.11524051 0.11160263 0.1088205\n",
            " 0.1050244  0.10339223 0.10091265 0.09868278 0.09679328 0.09467894\n",
            " 0.09345121 0.09228151 0.08969745 0.08858049 0.087493   0.08559784\n",
            " 0.08416775 0.08298582 0.08200882 0.08077635 0.07904161 0.07770294\n",
            " 0.07662254 0.07584374 0.07478761 0.07336304 0.07203313 0.07133429\n",
            " 0.07064425 0.06963421 0.06852607 0.06757136 0.06686524 0.066193\n",
            " 0.06525083 0.06430101 0.06349325 0.0627604  0.06208101 0.06138205\n",
            " 0.10327463 0.01551749 0.02162095 0.36566792 8.00330603 6.07410643\n",
            " 4.68184088 3.10054723 2.47154248 1.53122039 1.6307418  1.70622076\n",
            " 1.40898576 1.33588955 3.67739233 4.25948026 4.55921912 3.93342993\n",
            " 3.82205952]\n",
            "'dump/ofuton_p/org/out_acoustic_scaler.joblib' -> 'dump/ofuton_p/norm/out_acoustic_scaler.joblib'\n",
            "[\u001b[36m2020-06-29 00:54:04,098\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - in_dir: dump/ofuton_p/org/train_no_dev/in_timelag/\n",
            "inverse: false\n",
            "num_workers: 4\n",
            "out_dir: dump/ofuton_p/norm/train_no_dev/in_timelag/\n",
            "scaler_path: dump/ofuton_p/org/in_timelag_scaler.joblib\n",
            "verbose: 100\n",
            "\u001b[0m\n",
            "100% 311/311 [00:00<00:00, 1204.23it/s]\n",
            "[\u001b[36m2020-06-29 00:54:05,244\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - in_dir: dump/ofuton_p/org/train_no_dev/in_duration/\n",
            "inverse: false\n",
            "num_workers: 4\n",
            "out_dir: dump/ofuton_p/norm/train_no_dev/in_duration/\n",
            "scaler_path: dump/ofuton_p/org/in_duration_scaler.joblib\n",
            "verbose: 100\n",
            "\u001b[0m\n",
            "100% 311/311 [00:00<00:00, 1252.19it/s]\n",
            "[\u001b[36m2020-06-29 00:54:06,425\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - in_dir: dump/ofuton_p/org/train_no_dev/in_acoustic/\n",
            "inverse: false\n",
            "num_workers: 4\n",
            "out_dir: dump/ofuton_p/norm/train_no_dev/in_acoustic/\n",
            "scaler_path: dump/ofuton_p/org/in_acoustic_scaler.joblib\n",
            "verbose: 100\n",
            "\u001b[0m\n",
            "100% 311/311 [00:03<00:00, 90.62it/s]\n",
            "[\u001b[36m2020-06-29 00:54:10,998\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - in_dir: dump/ofuton_p/org/train_no_dev/out_timelag/\n",
            "inverse: false\n",
            "num_workers: 4\n",
            "out_dir: dump/ofuton_p/norm/train_no_dev/out_timelag/\n",
            "scaler_path: dump/ofuton_p/org/out_timelag_scaler.joblib\n",
            "verbose: 100\n",
            "\u001b[0m\n",
            "100% 311/311 [00:00<00:00, 1398.90it/s]\n",
            "[\u001b[36m2020-06-29 00:54:12,116\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - in_dir: dump/ofuton_p/org/train_no_dev/out_duration/\n",
            "inverse: false\n",
            "num_workers: 4\n",
            "out_dir: dump/ofuton_p/norm/train_no_dev/out_duration/\n",
            "scaler_path: dump/ofuton_p/org/out_duration_scaler.joblib\n",
            "verbose: 100\n",
            "\u001b[0m\n",
            "100% 311/311 [00:00<00:00, 1430.58it/s]\n",
            "[\u001b[36m2020-06-29 00:54:13,197\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - in_dir: dump/ofuton_p/org/train_no_dev/out_acoustic/\n",
            "inverse: false\n",
            "num_workers: 4\n",
            "out_dir: dump/ofuton_p/norm/train_no_dev/out_acoustic/\n",
            "scaler_path: dump/ofuton_p/org/out_acoustic_scaler.joblib\n",
            "verbose: 100\n",
            "\u001b[0m\n",
            "100% 311/311 [00:14<00:00, 20.99it/s]\n",
            "[\u001b[36m2020-06-29 00:54:29,489\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - in_dir: dump/ofuton_p/org/dev/in_timelag/\n",
            "inverse: false\n",
            "num_workers: 4\n",
            "out_dir: dump/ofuton_p/norm/dev/in_timelag/\n",
            "scaler_path: dump/ofuton_p/org/in_timelag_scaler.joblib\n",
            "verbose: 100\n",
            "\u001b[0m\n",
            "100% 4/4 [00:00<00:00, 123.71it/s]\n",
            "[\u001b[36m2020-06-29 00:54:30,505\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - in_dir: dump/ofuton_p/org/dev/in_duration/\n",
            "inverse: false\n",
            "num_workers: 4\n",
            "out_dir: dump/ofuton_p/norm/dev/in_duration/\n",
            "scaler_path: dump/ofuton_p/org/in_duration_scaler.joblib\n",
            "verbose: 100\n",
            "\u001b[0m\n",
            "100% 4/4 [00:00<00:00, 142.15it/s]\n",
            "[\u001b[36m2020-06-29 00:54:31,394\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - in_dir: dump/ofuton_p/org/dev/in_acoustic/\n",
            "inverse: false\n",
            "num_workers: 4\n",
            "out_dir: dump/ofuton_p/norm/dev/in_acoustic/\n",
            "scaler_path: dump/ofuton_p/org/in_acoustic_scaler.joblib\n",
            "verbose: 100\n",
            "\u001b[0m\n",
            "100% 4/4 [00:00<00:00, 73.56it/s]\n",
            "[\u001b[36m2020-06-29 00:54:32,315\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - in_dir: dump/ofuton_p/org/dev/out_timelag/\n",
            "inverse: false\n",
            "num_workers: 4\n",
            "out_dir: dump/ofuton_p/norm/dev/out_timelag/\n",
            "scaler_path: dump/ofuton_p/org/out_timelag_scaler.joblib\n",
            "verbose: 100\n",
            "\u001b[0m\n",
            "100% 4/4 [00:00<00:00, 168.83it/s]\n",
            "[\u001b[36m2020-06-29 00:54:33,225\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - in_dir: dump/ofuton_p/org/dev/out_duration/\n",
            "inverse: false\n",
            "num_workers: 4\n",
            "out_dir: dump/ofuton_p/norm/dev/out_duration/\n",
            "scaler_path: dump/ofuton_p/org/out_duration_scaler.joblib\n",
            "verbose: 100\n",
            "\u001b[0m\n",
            "100% 4/4 [00:00<00:00, 152.46it/s]\n",
            "[\u001b[36m2020-06-29 00:54:34,136\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - in_dir: dump/ofuton_p/org/dev/out_acoustic/\n",
            "inverse: false\n",
            "num_workers: 4\n",
            "out_dir: dump/ofuton_p/norm/dev/out_acoustic/\n",
            "scaler_path: dump/ofuton_p/org/out_acoustic_scaler.joblib\n",
            "verbose: 100\n",
            "\u001b[0m\n",
            "100% 4/4 [00:00<00:00, 83.83it/s]\n",
            "[\u001b[36m2020-06-29 00:54:35,045\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - in_dir: dump/ofuton_p/org/eval/in_timelag/\n",
            "inverse: false\n",
            "num_workers: 4\n",
            "out_dir: dump/ofuton_p/norm/eval/in_timelag/\n",
            "scaler_path: dump/ofuton_p/org/in_timelag_scaler.joblib\n",
            "verbose: 100\n",
            "\u001b[0m\n",
            "100% 6/6 [00:00<00:00, 217.83it/s]\n",
            "[\u001b[36m2020-06-29 00:54:35,914\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - in_dir: dump/ofuton_p/org/eval/in_duration/\n",
            "inverse: false\n",
            "num_workers: 4\n",
            "out_dir: dump/ofuton_p/norm/eval/in_duration/\n",
            "scaler_path: dump/ofuton_p/org/in_duration_scaler.joblib\n",
            "verbose: 100\n",
            "\u001b[0m\n",
            "100% 6/6 [00:00<00:00, 243.94it/s]\n",
            "[\u001b[36m2020-06-29 00:54:36,795\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - in_dir: dump/ofuton_p/org/eval/in_acoustic/\n",
            "inverse: false\n",
            "num_workers: 4\n",
            "out_dir: dump/ofuton_p/norm/eval/in_acoustic/\n",
            "scaler_path: dump/ofuton_p/org/in_acoustic_scaler.joblib\n",
            "verbose: 100\n",
            "\u001b[0m\n",
            "100% 6/6 [00:00<00:00, 128.88it/s]\n",
            "[\u001b[36m2020-06-29 00:54:37,721\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - in_dir: dump/ofuton_p/org/eval/out_timelag/\n",
            "inverse: false\n",
            "num_workers: 4\n",
            "out_dir: dump/ofuton_p/norm/eval/out_timelag/\n",
            "scaler_path: dump/ofuton_p/org/out_timelag_scaler.joblib\n",
            "verbose: 100\n",
            "\u001b[0m\n",
            "100% 6/6 [00:00<00:00, 336.30it/s]\n",
            "[\u001b[36m2020-06-29 00:54:38,631\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - in_dir: dump/ofuton_p/org/eval/out_duration/\n",
            "inverse: false\n",
            "num_workers: 4\n",
            "out_dir: dump/ofuton_p/norm/eval/out_duration/\n",
            "scaler_path: dump/ofuton_p/org/out_duration_scaler.joblib\n",
            "verbose: 100\n",
            "\u001b[0m\n",
            "100% 6/6 [00:00<00:00, 373.87it/s]\n",
            "[\u001b[36m2020-06-29 00:54:39,496\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - in_dir: dump/ofuton_p/org/eval/out_acoustic/\n",
            "inverse: false\n",
            "num_workers: 4\n",
            "out_dir: dump/ofuton_p/norm/eval/out_acoustic/\n",
            "scaler_path: dump/ofuton_p/org/out_acoustic_scaler.joblib\n",
            "verbose: 100\n",
            "\u001b[0m\n",
            "100% 6/6 [00:00<00:00, 121.18it/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ulblx4TgbU_d",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "130c1db5-d81e-48e9-f2ac-0df12cbf7ef7"
      },
      "source": [
        "# stage2-4の実行(タイムラグモデル, 持続長モデル, 音響モデルの学習)\n",
        "! cd $RECIPE_ROOT && bash run.sh --stage 2 --stop-stage 4"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "stage 2: Training time-lag model\n",
            "+ nnsvs-train data.train_no_dev.in_dir=dump/ofuton_p/norm/train_no_dev/in_timelag/ data.train_no_dev.out_dir=dump/ofuton_p/norm/train_no_dev/out_timelag/ data.dev.in_dir=dump/ofuton_p/norm/dev/in_timelag/ data.dev.out_dir=dump/ofuton_p/norm/dev/out_timelag/ model=timelag train.out_dir=exp/ofuton_p/timelag data.batch_size=8 resume.checkpoint=\n",
            "[\u001b[36m2020-06-29 00:54:42,600\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - cudnn:\n",
            "  benchmark: false\n",
            "  deterministic: false\n",
            "data:\n",
            "  batch_size: 8\n",
            "  dev:\n",
            "    in_dir: dump/ofuton_p/norm/dev/in_timelag/\n",
            "    out_dir: dump/ofuton_p/norm/dev/out_timelag/\n",
            "  num_workers: 2\n",
            "  pin_memory: true\n",
            "  train_no_dev:\n",
            "    in_dir: dump/ofuton_p/norm/train_no_dev/in_timelag/\n",
            "    out_dir: dump/ofuton_p/norm/train_no_dev/out_timelag/\n",
            "model:\n",
            "  has_dynamic_features:\n",
            "  - false\n",
            "  netG:\n",
            "    class: nnsvs.model.FeedForwardNet\n",
            "    params:\n",
            "      dropout: 0.5\n",
            "      hidden_dim: 128\n",
            "      in_dim: 420\n",
            "      num_layers: 2\n",
            "      out_dim: 1\n",
            "  stream_sizes:\n",
            "  - 1\n",
            "  stream_weights:\n",
            "  - 1\n",
            "optim:\n",
            "  lr_scheduler:\n",
            "    name: StepLR\n",
            "    params:\n",
            "      gamma: 0.5\n",
            "      step_size: 20\n",
            "  optimizer:\n",
            "    name: Adam\n",
            "    params:\n",
            "      betas:\n",
            "      - 0.5\n",
            "      - 0.999\n",
            "      lr: 0.001\n",
            "      weight_decay: 0.0\n",
            "resume:\n",
            "  checkpoint: null\n",
            "  load_optimizer: false\n",
            "train:\n",
            "  checkpoint_epoch_interval: 20\n",
            "  nepochs: 50\n",
            "  out_dir: exp/ofuton_p/timelag\n",
            "  stream_wise_loss: false\n",
            "verbose: 100\n",
            "\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:54:42,601\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - cudnn.deterministic: False\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:54:42,601\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - cudnn.benchmark: False\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:54:52,128\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 28, 420]), torch.Size([8, 28, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:54:52,130\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 20, 420]), torch.Size([8, 20, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:54:52,137\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 51, 420]), torch.Size([8, 51, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:54:52,139\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 35, 420]), torch.Size([8, 35, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:54:52,149\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 52, 420]), torch.Size([8, 52, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:54:52,152\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 36, 420]), torch.Size([8, 36, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:54:52,155\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 41, 420]), torch.Size([8, 41, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:54:52,165\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 23, 420]), torch.Size([8, 23, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:54:52,167\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 24, 420]), torch.Size([8, 24, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:54:52,177\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 32, 420]), torch.Size([8, 32, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:54:52,179\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 27, 420]), torch.Size([8, 27, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:54:52,189\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 42, 420]), torch.Size([8, 42, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:54:52,193\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 40, 420]), torch.Size([8, 40, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:54:52,200\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 41, 420]), torch.Size([8, 41, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:54:52,207\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 46, 420]), torch.Size([8, 46, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:54:52,212\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 46, 420]), torch.Size([8, 46, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:54:52,221\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 37, 420]), torch.Size([8, 37, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:54:52,224\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 27, 420]), torch.Size([8, 27, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:54:52,237\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 25, 420]), torch.Size([8, 25, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:54:52,237\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 42, 420]), torch.Size([8, 42, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:54:52,249\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 37, 420]), torch.Size([8, 37, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:54:52,249\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 28, 420]), torch.Size([8, 28, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:54:52,262\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 47, 420]), torch.Size([8, 47, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:54:52,263\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 58, 420]), torch.Size([8, 58, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:54:52,287\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 25, 420]), torch.Size([8, 25, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:54:52,287\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 26, 420]), torch.Size([8, 26, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:54:52,295\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 24, 420]), torch.Size([8, 24, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:54:52,296\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 27, 420]), torch.Size([8, 27, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:54:52,306\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 25, 420]), torch.Size([8, 25, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:54:52,306\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 47, 420]), torch.Size([8, 47, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:54:52,317\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 52, 420]), torch.Size([8, 52, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:54:52,318\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 48, 420]), torch.Size([8, 48, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:54:52,327\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 27, 420]), torch.Size([8, 27, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:54:52,330\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 35, 420]), torch.Size([8, 35, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:54:52,338\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 33, 420]), torch.Size([8, 33, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:54:52,343\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 44, 420]), torch.Size([8, 44, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:54:52,348\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 23, 420]), torch.Size([8, 23, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:54:52,356\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 43, 420]), torch.Size([8, 43, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:54:52,359\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([7, 65, 420]), torch.Size([7, 65, 1]), torch.Size([7])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:54:52,441\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([4, 49, 420]), torch.Size([4, 49, 1]), torch.Size([4])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:54:52,471\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Start utterance-wise training...\u001b[0m\n",
            "  0% 0/50 [00:00<?, ?it/s][\u001b[36m2020-06-29 00:54:53,118\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 1]: loss 0.8676283787458371\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:54:53,197\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 1]: loss 0.5206252932548523\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:54:53,205\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [Best loss 0.5206252932548523: checkpoint is saved at /content/nnsvs/egs/nnsvs_ofuton_p_utagoe_db/00-svs-world/exp/ofuton_p/timelag/best_loss.pth\u001b[0m\n",
            "  2% 1/50 [00:00<00:35,  1.36it/s][\u001b[36m2020-06-29 00:54:53,634\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 2]: loss 0.7298528888286688\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:54:53,713\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 2]: loss 0.48972010612487793\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:54:53,717\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [Best loss 0.48972010612487793: checkpoint is saved at /content/nnsvs/egs/nnsvs_ofuton_p_utagoe_db/00-svs-world/exp/ofuton_p/timelag/best_loss.pth\u001b[0m\n",
            "  4% 2/50 [00:01<00:31,  1.50it/s][\u001b[36m2020-06-29 00:54:54,150\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 3]: loss 0.6940622123388144\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:54:54,229\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 3]: loss 0.48258697986602783\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:54:54,233\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [Best loss 0.48258697986602783: checkpoint is saved at /content/nnsvs/egs/nnsvs_ofuton_p_utagoe_db/00-svs-world/exp/ofuton_p/timelag/best_loss.pth\u001b[0m\n",
            "  6% 3/50 [00:01<00:29,  1.61it/s][\u001b[36m2020-06-29 00:54:54,660\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 4]: loss 0.6781693979715689\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:54:54,736\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 4]: loss 0.47676047682762146\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:54:54,740\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [Best loss 0.47676047682762146: checkpoint is saved at /content/nnsvs/egs/nnsvs_ofuton_p_utagoe_db/00-svs-world/exp/ofuton_p/timelag/best_loss.pth\u001b[0m\n",
            "  8% 4/50 [00:02<00:27,  1.70it/s][\u001b[36m2020-06-29 00:54:55,179\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 5]: loss 0.6600459126325754\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:54:55,256\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 5]: loss 0.4796019494533539\u001b[0m\n",
            " 10% 5/50 [00:02<00:25,  1.77it/s][\u001b[36m2020-06-29 00:54:55,689\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 6]: loss 0.6409715658579117\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:54:55,768\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 6]: loss 0.6229339838027954\u001b[0m\n",
            " 12% 6/50 [00:03<00:24,  1.82it/s][\u001b[36m2020-06-29 00:54:56,200\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 7]: loss 0.6302179839366522\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:54:56,279\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 7]: loss 0.48287978768348694\u001b[0m\n",
            " 14% 7/50 [00:03<00:23,  1.86it/s][\u001b[36m2020-06-29 00:54:56,705\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 8]: loss 0.6134293965804272\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:54:56,784\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 8]: loss 0.522430419921875\u001b[0m\n",
            " 16% 8/50 [00:04<00:22,  1.89it/s][\u001b[36m2020-06-29 00:54:57,226\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 9]: loss 0.6030132518364832\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:54:57,302\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 9]: loss 0.48661187291145325\u001b[0m\n",
            " 18% 9/50 [00:04<00:21,  1.90it/s][\u001b[36m2020-06-29 00:54:57,773\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 10]: loss 0.5788880762381431\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:54:57,848\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 10]: loss 0.4899581968784332\u001b[0m\n",
            " 20% 10/50 [00:05<00:21,  1.88it/s][\u001b[36m2020-06-29 00:54:58,279\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 11]: loss 0.5740124865984305\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:54:58,355\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 11]: loss 0.4960295259952545\u001b[0m\n",
            " 22% 11/50 [00:05<00:20,  1.91it/s][\u001b[36m2020-06-29 00:54:58,785\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 12]: loss 0.5611398533368722\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:54:58,862\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 12]: loss 0.5124534964561462\u001b[0m\n",
            " 24% 12/50 [00:06<00:19,  1.93it/s][\u001b[36m2020-06-29 00:54:59,297\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 13]: loss 0.5492661855159662\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:54:59,374\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 13]: loss 0.5411194562911987\u001b[0m\n",
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            "[\u001b[36m2020-06-29 00:54:59,886\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 14]: loss 0.5406199097633362\u001b[0m\n",
            " 28% 14/50 [00:07<00:18,  1.94it/s][\u001b[36m2020-06-29 00:55:00,317\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 15]: loss 0.5358327909921988\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:00,396\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 15]: loss 0.5261401534080505\u001b[0m\n",
            " 30% 15/50 [00:07<00:17,  1.95it/s][\u001b[36m2020-06-29 00:55:00,830\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 16]: loss 0.527265557111838\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:00,907\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 16]: loss 0.6314839720726013\u001b[0m\n",
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            "[\u001b[36m2020-06-29 00:55:01,426\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 17]: loss 0.562506914138794\u001b[0m\n",
            " 34% 17/50 [00:08<00:16,  1.94it/s][\u001b[36m2020-06-29 00:55:01,850\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 18]: loss 0.5163060495486627\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:01,926\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 18]: loss 0.5477848649024963\u001b[0m\n",
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            "[\u001b[36m2020-06-29 00:55:02,440\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 19]: loss 0.5828137993812561\u001b[0m\n",
            " 38% 19/50 [00:09<00:15,  1.96it/s][\u001b[36m2020-06-29 00:55:02,869\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 20]: loss 0.49792053684210164\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:02,946\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 20]: loss 0.5319952368736267\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:02,949\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Checkpoint is saved at /content/nnsvs/egs/nnsvs_ofuton_p_utagoe_db/00-svs-world/exp/ofuton_p/timelag/checkpoint_epoch0020.pth\u001b[0m\n",
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            " 46% 23/50 [00:12<00:13,  1.95it/s][\u001b[36m2020-06-29 00:55:04,920\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 24]: loss 0.44369448453952104\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:04,996\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 24]: loss 0.5452682971954346\u001b[0m\n",
            " 48% 24/50 [00:12<00:13,  1.96it/s][\u001b[36m2020-06-29 00:55:05,429\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 25]: loss 0.4495898622732896\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:05,509\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 25]: loss 0.5567013025283813\u001b[0m\n",
            " 50% 25/50 [00:13<00:12,  1.96it/s][\u001b[36m2020-06-29 00:55:05,931\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 26]: loss 0.4305128990075527\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:06,008\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 26]: loss 0.5810406804084778\u001b[0m\n",
            " 52% 26/50 [00:13<00:12,  1.97it/s][\u001b[36m2020-06-29 00:55:06,440\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 27]: loss 0.4318363009355007\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:06,523\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 27]: loss 0.5699257254600525\u001b[0m\n",
            " 54% 27/50 [00:14<00:11,  1.96it/s][\u001b[36m2020-06-29 00:55:06,954\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 28]: loss 0.4302224746117225\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:07,030\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 28]: loss 0.564246416091919\u001b[0m\n",
            " 56% 28/50 [00:14<00:11,  1.97it/s][\u001b[36m2020-06-29 00:55:07,470\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 29]: loss 0.42641285825998354\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:07,549\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 29]: loss 0.5462740659713745\u001b[0m\n",
            " 58% 29/50 [00:15<00:10,  1.95it/s][\u001b[36m2020-06-29 00:55:07,976\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 30]: loss 0.42369050857348317\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:08,053\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 30]: loss 0.5923756957054138\u001b[0m\n",
            " 60% 30/50 [00:15<00:10,  1.96it/s][\u001b[36m2020-06-29 00:55:08,492\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 31]: loss 0.4157016422504034\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:08,578\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 31]: loss 0.5517420768737793\u001b[0m\n",
            " 62% 31/50 [00:16<00:09,  1.95it/s][\u001b[36m2020-06-29 00:55:09,011\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 32]: loss 0.40947157297378933\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:09,091\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 32]: loss 0.5709247589111328\u001b[0m\n",
            " 64% 32/50 [00:16<00:09,  1.95it/s][\u001b[36m2020-06-29 00:55:09,530\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 33]: loss 0.41036490904979217\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:09,616\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 33]: loss 0.5807896256446838\u001b[0m\n",
            " 66% 33/50 [00:17<00:08,  1.93it/s][\u001b[36m2020-06-29 00:55:10,055\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 34]: loss 0.4109112077798599\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:10,133\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 34]: loss 0.5516916513442993\u001b[0m\n",
            " 68% 34/50 [00:17<00:08,  1.93it/s][\u001b[36m2020-06-29 00:55:10,575\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 35]: loss 0.4069978579496726\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:10,654\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 35]: loss 0.5211539268493652\u001b[0m\n",
            " 70% 35/50 [00:18<00:07,  1.93it/s][\u001b[36m2020-06-29 00:55:11,084\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 36]: loss 0.3957623174557319\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:11,161\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 36]: loss 0.5465179681777954\u001b[0m\n",
            " 72% 36/50 [00:18<00:07,  1.94it/s][\u001b[36m2020-06-29 00:55:11,602\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 37]: loss 0.3964392558122293\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:11,680\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 37]: loss 0.5538304448127747\u001b[0m\n",
            " 74% 37/50 [00:19<00:06,  1.94it/s][\u001b[36m2020-06-29 00:55:12,111\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 38]: loss 0.3952847191920647\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:12,189\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 38]: loss 0.5488588213920593\u001b[0m\n",
            " 76% 38/50 [00:19<00:06,  1.95it/s][\u001b[36m2020-06-29 00:55:12,627\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 39]: loss 0.38900307814280194\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:12,704\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 39]: loss 0.5725904107093811\u001b[0m\n",
            " 78% 39/50 [00:20<00:05,  1.94it/s][\u001b[36m2020-06-29 00:55:13,137\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 40]: loss 0.3772964202440702\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:13,215\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 40]: loss 0.5248964428901672\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:13,218\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Checkpoint is saved at /content/nnsvs/egs/nnsvs_ofuton_p_utagoe_db/00-svs-world/exp/ofuton_p/timelag/checkpoint_epoch0040.pth\u001b[0m\n",
            " 80% 40/50 [00:20<00:05,  1.94it/s][\u001b[36m2020-06-29 00:55:13,669\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 41]: loss 0.3662317647383763\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:13,748\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 41]: loss 0.5592123866081238\u001b[0m\n",
            " 82% 41/50 [00:21<00:04,  1.93it/s][\u001b[36m2020-06-29 00:55:14,175\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 42]: loss 0.3570793668429057\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:14,252\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 42]: loss 0.5476083755493164\u001b[0m\n",
            " 84% 42/50 [00:21<00:04,  1.94it/s][\u001b[36m2020-06-29 00:55:14,689\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 43]: loss 0.3537774108923398\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:14,766\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 43]: loss 0.5459064841270447\u001b[0m\n",
            " 86% 43/50 [00:22<00:03,  1.94it/s][\u001b[36m2020-06-29 00:55:15,207\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 44]: loss 0.35438200372916\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:15,285\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 44]: loss 0.5752795934677124\u001b[0m\n",
            " 88% 44/50 [00:22<00:03,  1.94it/s][\u001b[36m2020-06-29 00:55:15,729\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 45]: loss 0.3532890673631277\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:15,805\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 45]: loss 0.569737434387207\u001b[0m\n",
            " 90% 45/50 [00:23<00:02,  1.93it/s][\u001b[36m2020-06-29 00:55:16,219\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 46]: loss 0.3521780577989725\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:16,296\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 46]: loss 0.5685356259346008\u001b[0m\n",
            " 92% 46/50 [00:23<00:02,  1.96it/s][\u001b[36m2020-06-29 00:55:16,741\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 47]: loss 0.3467780030690707\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:16,821\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 47]: loss 0.5900439023971558\u001b[0m\n",
            " 94% 47/50 [00:24<00:01,  1.95it/s][\u001b[36m2020-06-29 00:55:17,253\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 48]: loss 0.34725269828087246\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:17,330\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 48]: loss 0.5882500410079956\u001b[0m\n",
            " 96% 48/50 [00:24<00:01,  1.95it/s][\u001b[36m2020-06-29 00:55:17,775\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 49]: loss 0.34079391146317506\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:17,852\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 49]: loss 0.5899452567100525\u001b[0m\n",
            " 98% 49/50 [00:25<00:00,  1.94it/s][\u001b[36m2020-06-29 00:55:18,276\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 50]: loss 0.33584932104135173\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:18,352\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 50]: loss 0.5809425115585327\u001b[0m\n",
            "100% 50/50 [00:25<00:00,  1.93it/s]\n",
            "[\u001b[36m2020-06-29 00:55:18,356\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Checkpoint is saved at /content/nnsvs/egs/nnsvs_ofuton_p_utagoe_db/00-svs-world/exp/ofuton_p/timelag/checkpoint_epoch0050.pth\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:18,357\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - The best loss was 0.47676047682762146\u001b[0m\n",
            "+ set +x\n",
            "stage 3: Training phoneme duration model\n",
            "+ nnsvs-train data.train_no_dev.in_dir=dump/ofuton_p/norm/train_no_dev/in_duration/ data.train_no_dev.out_dir=dump/ofuton_p/norm/train_no_dev/out_duration/ data.dev.in_dir=dump/ofuton_p/norm/dev/in_duration/ data.dev.out_dir=dump/ofuton_p/norm/dev/out_duration/ model=duration train.out_dir=exp/ofuton_p/duration data.batch_size=8 resume.checkpoint=\n",
            "[\u001b[36m2020-06-29 00:55:19,902\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - cudnn:\n",
            "  benchmark: false\n",
            "  deterministic: false\n",
            "data:\n",
            "  batch_size: 8\n",
            "  dev:\n",
            "    in_dir: dump/ofuton_p/norm/dev/in_duration/\n",
            "    out_dir: dump/ofuton_p/norm/dev/out_duration/\n",
            "  num_workers: 2\n",
            "  pin_memory: true\n",
            "  train_no_dev:\n",
            "    in_dir: dump/ofuton_p/norm/train_no_dev/in_duration/\n",
            "    out_dir: dump/ofuton_p/norm/train_no_dev/out_duration/\n",
            "model:\n",
            "  has_dynamic_features:\n",
            "  - false\n",
            "  netG:\n",
            "    class: nnsvs.model.LSTMRNN\n",
            "    params:\n",
            "      bidirectional: true\n",
            "      dropout: 0.5\n",
            "      hidden_dim: 64\n",
            "      in_dim: 420\n",
            "      num_layers: 2\n",
            "      out_dim: 1\n",
            "  stream_sizes:\n",
            "  - 1\n",
            "  stream_weights:\n",
            "  - 1\n",
            "optim:\n",
            "  lr_scheduler:\n",
            "    name: StepLR\n",
            "    params:\n",
            "      gamma: 0.5\n",
            "      step_size: 20\n",
            "  optimizer:\n",
            "    name: Adam\n",
            "    params:\n",
            "      betas:\n",
            "      - 0.5\n",
            "      - 0.999\n",
            "      lr: 0.001\n",
            "      weight_decay: 0.0\n",
            "resume:\n",
            "  checkpoint: null\n",
            "  load_optimizer: false\n",
            "train:\n",
            "  checkpoint_epoch_interval: 20\n",
            "  nepochs: 50\n",
            "  out_dir: exp/ofuton_p/duration\n",
            "  stream_wise_loss: false\n",
            "verbose: 100\n",
            "\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:19,902\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - cudnn.deterministic: False\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:19,902\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - cudnn.benchmark: False\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:22,768\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 116, 420]), torch.Size([8, 116, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:22,768\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 89, 420]), torch.Size([8, 89, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:22,782\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 69, 420]), torch.Size([8, 69, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:22,782\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 47, 420]), torch.Size([8, 47, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:22,785\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 55, 420]), torch.Size([8, 55, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:22,792\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 74, 420]), torch.Size([8, 74, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:22,803\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 96, 420]), torch.Size([8, 96, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:22,807\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 91, 420]), torch.Size([8, 91, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:22,815\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 76, 420]), torch.Size([8, 76, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:22,821\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 56, 420]), torch.Size([8, 56, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:22,828\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 71, 420]), torch.Size([8, 71, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:22,835\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 73, 420]), torch.Size([8, 73, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:22,842\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 57, 420]), torch.Size([8, 57, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:22,848\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 46, 420]), torch.Size([8, 46, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:22,856\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 114, 420]), torch.Size([8, 114, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:22,869\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 109, 420]), torch.Size([8, 109, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:22,872\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 95, 420]), torch.Size([8, 95, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:22,885\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 47, 420]), torch.Size([8, 47, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:22,888\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 114, 420]), torch.Size([8, 114, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:22,897\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 45, 420]), torch.Size([8, 45, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:22,900\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 40, 420]), torch.Size([8, 40, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:22,913\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 52, 420]), torch.Size([8, 52, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:22,918\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 146, 420]), torch.Size([8, 146, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:22,932\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 94, 420]), torch.Size([8, 94, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:22,935\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 110, 420]), torch.Size([8, 110, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:22,944\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 40, 420]), torch.Size([8, 40, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:22,947\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 76, 420]), torch.Size([8, 76, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:22,960\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 38, 420]), torch.Size([8, 38, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:22,960\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 47, 420]), torch.Size([8, 47, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:22,973\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 51, 420]), torch.Size([8, 51, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:22,973\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 54, 420]), torch.Size([8, 54, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:22,987\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 49, 420]), torch.Size([8, 49, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:22,987\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 37, 420]), torch.Size([8, 37, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:23,016\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 68, 420]), torch.Size([8, 68, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:23,016\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 59, 420]), torch.Size([8, 59, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:23,030\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 96, 420]), torch.Size([8, 96, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:23,030\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 39, 420]), torch.Size([8, 39, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:23,041\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 42, 420]), torch.Size([8, 42, 1]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:23,041\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([7, 77, 420]), torch.Size([7, 77, 1]), torch.Size([7])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:23,125\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([4, 99, 420]), torch.Size([4, 99, 1]), torch.Size([4])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:23,156\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Start utterance-wise training...\u001b[0m\n",
            "  0% 0/50 [00:00<?, ?it/s][\u001b[36m2020-06-29 00:55:24,095\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 1]: loss 0.7696707546710968\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:24,183\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 1]: loss 0.5257578492164612\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:24,191\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [Best loss 0.5257578492164612: checkpoint is saved at /content/nnsvs/egs/nnsvs_ofuton_p_utagoe_db/00-svs-world/exp/ofuton_p/duration/best_loss.pth\u001b[0m\n",
            "  2% 1/50 [00:01<00:50,  1.03s/it][\u001b[36m2020-06-29 00:55:25,067\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 2]: loss 0.42218287938680404\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:25,155\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 2]: loss 0.3603571355342865\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:25,164\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [Best loss 0.3603571355342865: checkpoint is saved at /content/nnsvs/egs/nnsvs_ofuton_p_utagoe_db/00-svs-world/exp/ofuton_p/duration/best_loss.pth\u001b[0m\n",
            "  4% 2/50 [00:02<00:48,  1.02s/it][\u001b[36m2020-06-29 00:55:26,075\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 3]: loss 0.2933512589870355\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:26,163\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 3]: loss 0.3150295913219452\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:26,172\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [Best loss 0.3150295913219452: checkpoint is saved at /content/nnsvs/egs/nnsvs_ofuton_p_utagoe_db/00-svs-world/exp/ofuton_p/duration/best_loss.pth\u001b[0m\n",
            "  6% 3/50 [00:03<00:47,  1.01s/it][\u001b[36m2020-06-29 00:55:27,164\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 4]: loss 0.22007325081489024\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:27,252\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 4]: loss 0.2888471186161041\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:27,261\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [Best loss 0.2888471186161041: checkpoint is saved at /content/nnsvs/egs/nnsvs_ofuton_p_utagoe_db/00-svs-world/exp/ofuton_p/duration/best_loss.pth\u001b[0m\n",
            "  8% 4/50 [00:04<00:47,  1.04s/it][\u001b[36m2020-06-29 00:55:28,172\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 5]: loss 0.16207289848572168\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:28,263\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 5]: loss 0.23293447494506836\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:28,271\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [Best loss 0.23293447494506836: checkpoint is saved at /content/nnsvs/egs/nnsvs_ofuton_p_utagoe_db/00-svs-world/exp/ofuton_p/duration/best_loss.pth\u001b[0m\n",
            " 10% 5/50 [00:05<00:46,  1.03s/it][\u001b[36m2020-06-29 00:55:29,186\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 6]: loss 0.13027630440699747\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:29,274\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 6]: loss 0.2358989715576172\u001b[0m\n",
            " 12% 6/50 [00:06<00:44,  1.02s/it][\u001b[36m2020-06-29 00:55:30,195\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 7]: loss 0.1068820643883485\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:30,283\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 7]: loss 0.160216823220253\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:30,292\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [Best loss 0.160216823220253: checkpoint is saved at /content/nnsvs/egs/nnsvs_ofuton_p_utagoe_db/00-svs-world/exp/ofuton_p/duration/best_loss.pth\u001b[0m\n",
            " 14% 7/50 [00:07<00:43,  1.02s/it][\u001b[36m2020-06-29 00:55:31,205\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 8]: loss 0.09452595590398861\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:31,295\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 8]: loss 0.13782134652137756\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:31,304\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [Best loss 0.13782134652137756: checkpoint is saved at /content/nnsvs/egs/nnsvs_ofuton_p_utagoe_db/00-svs-world/exp/ofuton_p/duration/best_loss.pth\u001b[0m\n",
            " 16% 8/50 [00:08<00:42,  1.02s/it][\u001b[36m2020-06-29 00:55:32,205\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 9]: loss 0.08334273176315503\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:32,292\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 9]: loss 0.10318423062562943\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:32,301\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [Best loss 0.10318423062562943: checkpoint is saved at /content/nnsvs/egs/nnsvs_ofuton_p_utagoe_db/00-svs-world/exp/ofuton_p/duration/best_loss.pth\u001b[0m\n",
            " 18% 9/50 [00:09<00:41,  1.01s/it][\u001b[36m2020-06-29 00:55:33,195\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 10]: loss 0.07469143412816219\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:33,288\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 10]: loss 0.0958767980337143\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:33,297\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [Best loss 0.0958767980337143: checkpoint is saved at /content/nnsvs/egs/nnsvs_ofuton_p_utagoe_db/00-svs-world/exp/ofuton_p/duration/best_loss.pth\u001b[0m\n",
            " 20% 10/50 [00:10<00:40,  1.01s/it][\u001b[36m2020-06-29 00:55:34,196\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 11]: loss 0.0710254142490717\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:34,283\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 11]: loss 0.10568759590387344\u001b[0m\n",
            " 22% 11/50 [00:11<00:39,  1.00s/it][\u001b[36m2020-06-29 00:55:35,174\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 12]: loss 0.06580377799960282\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:35,261\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 12]: loss 0.0836733877658844\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:35,270\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [Best loss 0.0836733877658844: checkpoint is saved at /content/nnsvs/egs/nnsvs_ofuton_p_utagoe_db/00-svs-world/exp/ofuton_p/duration/best_loss.pth\u001b[0m\n",
            " 24% 12/50 [00:12<00:37,  1.00it/s][\u001b[36m2020-06-29 00:55:36,156\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 13]: loss 0.06107512393440956\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:36,245\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 13]: loss 0.08291873335838318\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:36,254\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [Best loss 0.08291873335838318: checkpoint is saved at /content/nnsvs/egs/nnsvs_ofuton_p_utagoe_db/00-svs-world/exp/ofuton_p/duration/best_loss.pth\u001b[0m\n",
            " 26% 13/50 [00:13<00:36,  1.01it/s][\u001b[36m2020-06-29 00:55:37,170\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 14]: loss 0.06096622371711792\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:37,260\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 14]: loss 0.08062079548835754\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:37,269\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [Best loss 0.08062079548835754: checkpoint is saved at /content/nnsvs/egs/nnsvs_ofuton_p_utagoe_db/00-svs-world/exp/ofuton_p/duration/best_loss.pth\u001b[0m\n",
            " 28% 14/50 [00:14<00:35,  1.00it/s][\u001b[36m2020-06-29 00:55:38,172\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 15]: loss 0.05534081839216061\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:38,270\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 15]: loss 0.08445099741220474\u001b[0m\n",
            " 30% 15/50 [00:15<00:35,  1.00s/it][\u001b[36m2020-06-29 00:55:39,161\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 16]: loss 0.054293893373165376\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:39,249\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 16]: loss 0.07428047060966492\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:39,257\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [Best loss 0.07428047060966492: checkpoint is saved at /content/nnsvs/egs/nnsvs_ofuton_p_utagoe_db/00-svs-world/exp/ofuton_p/duration/best_loss.pth\u001b[0m\n",
            " 32% 16/50 [00:16<00:33,  1.00it/s][\u001b[36m2020-06-29 00:55:40,150\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 17]: loss 0.05640542440307446\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:40,238\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 17]: loss 0.0746166780591011\u001b[0m\n",
            " 34% 17/50 [00:17<00:32,  1.01it/s][\u001b[36m2020-06-29 00:55:41,144\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 18]: loss 0.049176036642912105\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:41,237\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 18]: loss 0.06502970308065414\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:41,247\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [Best loss 0.06502970308065414: checkpoint is saved at /content/nnsvs/egs/nnsvs_ofuton_p_utagoe_db/00-svs-world/exp/ofuton_p/duration/best_loss.pth\u001b[0m\n",
            " 36% 18/50 [00:18<00:31,  1.00it/s][\u001b[36m2020-06-29 00:55:42,156\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 19]: loss 0.04579867957494198\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:42,244\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 19]: loss 0.06719603389501572\u001b[0m\n",
            " 38% 19/50 [00:19<00:30,  1.00it/s][\u001b[36m2020-06-29 00:55:43,140\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 20]: loss 0.04732830779483685\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:43,232\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 20]: loss 0.0713282898068428\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:43,240\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Checkpoint is saved at /content/nnsvs/egs/nnsvs_ofuton_p_utagoe_db/00-svs-world/exp/ofuton_p/duration/checkpoint_epoch0020.pth\u001b[0m\n",
            " 40% 20/50 [00:20<00:29,  1.00it/s][\u001b[36m2020-06-29 00:55:44,151\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 21]: loss 0.0391255675886686\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:44,241\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 21]: loss 0.05277719348669052\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:44,250\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [Best loss 0.05277719348669052: checkpoint is saved at /content/nnsvs/egs/nnsvs_ofuton_p_utagoe_db/00-svs-world/exp/ofuton_p/duration/best_loss.pth\u001b[0m\n",
            " 42% 21/50 [00:21<00:29,  1.00s/it][\u001b[36m2020-06-29 00:55:45,122\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 22]: loss 0.03789642515281836\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:45,211\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 22]: loss 0.057269178330898285\u001b[0m\n",
            " 44% 22/50 [00:22<00:27,  1.01it/s][\u001b[36m2020-06-29 00:55:46,091\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 23]: loss 0.03639821072992606\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:46,177\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 23]: loss 0.05812638998031616\u001b[0m\n",
            " 46% 23/50 [00:23<00:26,  1.02it/s][\u001b[36m2020-06-29 00:55:47,063\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 24]: loss 0.036480072408150405\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:47,152\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 24]: loss 0.05398786813020706\u001b[0m\n",
            " 48% 24/50 [00:23<00:25,  1.02it/s][\u001b[36m2020-06-29 00:55:48,066\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 25]: loss 0.03437684643536042\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:48,157\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 25]: loss 0.060619160532951355\u001b[0m\n",
            " 50% 25/50 [00:25<00:24,  1.01it/s][\u001b[36m2020-06-29 00:55:49,056\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 26]: loss 0.03481677986490421\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:49,147\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 26]: loss 0.06930438429117203\u001b[0m\n",
            " 52% 26/50 [00:25<00:23,  1.01it/s][\u001b[36m2020-06-29 00:55:50,021\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 27]: loss 0.033368515996978834\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:50,112\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 27]: loss 0.04768173024058342\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:50,124\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [Best loss 0.04768173024058342: checkpoint is saved at /content/nnsvs/egs/nnsvs_ofuton_p_utagoe_db/00-svs-world/exp/ofuton_p/duration/best_loss.pth\u001b[0m\n",
            " 54% 27/50 [00:26<00:22,  1.02it/s][\u001b[36m2020-06-29 00:55:50,994\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 28]: loss 0.03340840535477186\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:51,089\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 28]: loss 0.06222198158502579\u001b[0m\n",
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            "[\u001b[36m2020-06-29 00:55:52,075\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 29]: loss 0.05739280581474304\u001b[0m\n",
            " 58% 29/50 [00:28<00:20,  1.02it/s][\u001b[36m2020-06-29 00:55:52,974\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 30]: loss 0.03447901653364683\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:53,063\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 30]: loss 0.04668226093053818\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:53,072\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [Best loss 0.04668226093053818: checkpoint is saved at /content/nnsvs/egs/nnsvs_ofuton_p_utagoe_db/00-svs-world/exp/ofuton_p/duration/best_loss.pth\u001b[0m\n",
            " 60% 30/50 [00:29<00:19,  1.01it/s][\u001b[36m2020-06-29 00:55:53,954\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 31]: loss 0.03291515270486856\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:54,042\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 31]: loss 0.050229646265506744\u001b[0m\n",
            " 62% 31/50 [00:30<00:18,  1.02it/s][\u001b[36m2020-06-29 00:55:54,940\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 32]: loss 0.033331478922030866\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:55,027\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 32]: loss 0.05496009811758995\u001b[0m\n",
            " 64% 32/50 [00:31<00:17,  1.02it/s][\u001b[36m2020-06-29 00:55:55,923\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 33]: loss 0.03252475340970051\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:56,014\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 33]: loss 0.05962199345231056\u001b[0m\n",
            " 66% 33/50 [00:32<00:16,  1.02it/s][\u001b[36m2020-06-29 00:55:56,900\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 34]: loss 0.032176210664403744\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:56,988\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 34]: loss 0.04174267128109932\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:55:56,997\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [Best loss 0.04174267128109932: checkpoint is saved at /content/nnsvs/egs/nnsvs_ofuton_p_utagoe_db/00-svs-world/exp/ofuton_p/duration/best_loss.pth\u001b[0m\n",
            " 68% 34/50 [00:33<00:15,  1.02it/s][\u001b[36m2020-06-29 00:55:57,900\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 35]: loss 0.03167091037791509\u001b[0m\n",
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            "[\u001b[36m2020-06-29 00:55:59,952\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 37]: loss 0.05189818888902664\u001b[0m\n",
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            "[\u001b[36m2020-06-29 00:56:00,935\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 38]: loss 0.04904894530773163\u001b[0m\n",
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            "[\u001b[36m2020-06-29 00:56:02,869\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 40]: loss 0.05155593156814575\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:56:02,877\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Checkpoint is saved at /content/nnsvs/egs/nnsvs_ofuton_p_utagoe_db/00-svs-world/exp/ofuton_p/duration/checkpoint_epoch0040.pth\u001b[0m\n",
            " 80% 40/50 [00:39<00:09,  1.02it/s][\u001b[36m2020-06-29 00:56:03,773\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 41]: loss 0.028408904918111287\u001b[0m\n",
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            " 90% 45/50 [00:44<00:04,  1.01it/s][\u001b[36m2020-06-29 00:56:08,734\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 46]: loss 0.026332561643077776\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:56:08,824\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 46]: loss 0.04440055787563324\u001b[0m\n",
            " 92% 46/50 [00:45<00:03,  1.01it/s][\u001b[36m2020-06-29 00:56:09,740\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 47]: loss 0.025669355637943134\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:56:09,830\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 47]: loss 0.05605297535657883\u001b[0m\n",
            " 94% 47/50 [00:46<00:02,  1.00it/s][\u001b[36m2020-06-29 00:56:10,745\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 48]: loss 0.02541032017996678\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:56:10,833\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 48]: loss 0.04681495949625969\u001b[0m\n",
            " 96% 48/50 [00:47<00:01,  1.00it/s][\u001b[36m2020-06-29 00:56:11,754\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 49]: loss 0.024851728923236713\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:56:11,845\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 49]: loss 0.046374280005693436\u001b[0m\n",
            " 98% 49/50 [00:48<00:01,  1.00s/it][\u001b[36m2020-06-29 00:56:12,775\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 50]: loss 0.02456905253422566\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:56:12,873\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 50]: loss 0.04929352551698685\u001b[0m\n",
            "100% 50/50 [00:49<00:00,  1.01it/s]\n",
            "[\u001b[36m2020-06-29 00:56:12,882\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Checkpoint is saved at /content/nnsvs/egs/nnsvs_ofuton_p_utagoe_db/00-svs-world/exp/ofuton_p/duration/checkpoint_epoch0050.pth\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:56:12,887\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - The best loss was 0.04174267128109932\u001b[0m\n",
            "+ set +x\n",
            "stage 4: Training acoustic model\n",
            "+ nnsvs-train data.train_no_dev.in_dir=dump/ofuton_p/norm/train_no_dev/in_acoustic/ data.train_no_dev.out_dir=dump/ofuton_p/norm/train_no_dev/out_acoustic/ data.dev.in_dir=dump/ofuton_p/norm/dev/in_acoustic/ data.dev.out_dir=dump/ofuton_p/norm/dev/out_acoustic/ model=acoustic train.out_dir=exp/ofuton_p/acoustic data.batch_size=8 resume.checkpoint=\n",
            "[\u001b[36m2020-06-29 00:56:14,338\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - cudnn:\n",
            "  benchmark: false\n",
            "  deterministic: false\n",
            "data:\n",
            "  batch_size: 8\n",
            "  dev:\n",
            "    in_dir: dump/ofuton_p/norm/dev/in_acoustic/\n",
            "    out_dir: dump/ofuton_p/norm/dev/out_acoustic/\n",
            "  num_workers: 2\n",
            "  pin_memory: true\n",
            "  train_no_dev:\n",
            "    in_dir: dump/ofuton_p/norm/train_no_dev/in_acoustic/\n",
            "    out_dir: dump/ofuton_p/norm/train_no_dev/out_acoustic/\n",
            "model:\n",
            "  has_dynamic_features:\n",
            "  - true\n",
            "  - true\n",
            "  - false\n",
            "  - true\n",
            "  netG:\n",
            "    class: nnsvs.model.Conv1dResnet\n",
            "    params:\n",
            "      dropout: 0.1\n",
            "      hidden_dim: 128\n",
            "      in_dim: 424\n",
            "      num_layers: 6\n",
            "      out_dim: 199\n",
            "  num_windows: 3\n",
            "  stream_sizes:\n",
            "  - 180\n",
            "  - 3\n",
            "  - 1\n",
            "  - 15\n",
            "  stream_weights: null\n",
            "optim:\n",
            "  lr_scheduler:\n",
            "    name: StepLR\n",
            "    params:\n",
            "      gamma: 0.5\n",
            "      step_size: 20\n",
            "  optimizer:\n",
            "    name: Adam\n",
            "    params:\n",
            "      betas:\n",
            "      - 0.5\n",
            "      - 0.999\n",
            "      lr: 0.001\n",
            "      weight_decay: 0.0\n",
            "resume:\n",
            "  checkpoint: null\n",
            "  load_optimizer: false\n",
            "train:\n",
            "  checkpoint_epoch_interval: 20\n",
            "  nepochs: 50\n",
            "  out_dir: exp/ofuton_p/acoustic\n",
            "  stream_wise_loss: false\n",
            "verbose: 100\n",
            "\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:56:14,338\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - cudnn.deterministic: False\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:56:14,338\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - cudnn.benchmark: False\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:56:17,455\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 2260, 424]), torch.Size([8, 2260, 199]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:56:17,657\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 3525, 424]), torch.Size([8, 3525, 199]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:56:17,875\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 3751, 424]), torch.Size([8, 3751, 199]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:56:17,875\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 2483, 424]), torch.Size([8, 2483, 199]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:56:18,257\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 4293, 424]), torch.Size([8, 4293, 199]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:56:18,258\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 3638, 424]), torch.Size([8, 3638, 199]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:56:18,305\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 2729, 424]), torch.Size([8, 2729, 199]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:56:18,477\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 4062, 424]), torch.Size([8, 4062, 199]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:56:18,754\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 4282, 424]), torch.Size([8, 4282, 199]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:56:19,087\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 8449, 424]), torch.Size([8, 8449, 199]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:56:19,094\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 2899, 424]), torch.Size([8, 2899, 199]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:56:19,123\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 2558, 424]), torch.Size([8, 2558, 199]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:56:19,168\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 2883, 424]), torch.Size([8, 2883, 199]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:56:19,767\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 4678, 424]), torch.Size([8, 4678, 199]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:56:19,768\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 2307, 424]), torch.Size([8, 2307, 199]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:56:19,946\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 4074, 424]), torch.Size([8, 4074, 199]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:56:19,947\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 1829, 424]), torch.Size([8, 1829, 199]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:56:20,201\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 2719, 424]), torch.Size([8, 2719, 199]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:56:20,201\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 3108, 424]), torch.Size([8, 3108, 199]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:56:20,429\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 1916, 424]), torch.Size([8, 1916, 199]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:56:20,541\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 3682, 424]), torch.Size([8, 3682, 199]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:56:20,645\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 2242, 424]), torch.Size([8, 2242, 199]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:56:21,206\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 3306, 424]), torch.Size([8, 3306, 199]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:56:21,412\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 7529, 424]), torch.Size([8, 7529, 199]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:56:21,548\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 2266, 424]), torch.Size([8, 2266, 199]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:56:21,548\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 3087, 424]), torch.Size([8, 3087, 199]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:56:22,387\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 2869, 424]), torch.Size([8, 2869, 199]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:56:22,387\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 3340, 424]), torch.Size([8, 3340, 199]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:56:23,098\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 1857, 424]), torch.Size([8, 1857, 199]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:56:23,195\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 4323, 424]), torch.Size([8, 4323, 199]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:56:23,885\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 2527, 424]), torch.Size([8, 2527, 199]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:56:23,885\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 10513, 424]), torch.Size([8, 10513, 199]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:56:24,778\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 2807, 424]), torch.Size([8, 2807, 199]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:56:24,778\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 4275, 424]), torch.Size([8, 4275, 199]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:56:26,085\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 3319, 424]), torch.Size([8, 3319, 199]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:56:26,086\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 2223, 424]), torch.Size([8, 2223, 199]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:56:26,500\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 2004, 424]), torch.Size([8, 2004, 199]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:56:26,500\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([8, 2711, 424]), torch.Size([8, 2711, 199]), torch.Size([8])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:56:27,112\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([7, 4074, 424]), torch.Size([7, 4074, 199]), torch.Size([7])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:56:27,310\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - torch.Size([4, 4240, 424]), torch.Size([4, 4240, 199]), torch.Size([4])\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:56:27,345\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Start utterance-wise training...\u001b[0m\n",
            "  0% 0/50 [00:00<?, ?it/s][\u001b[36m2020-06-29 00:56:47,979\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 1]: loss 0.9782553131763752\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:56:48,255\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 1]: loss 1.3761334419250488\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:56:48,282\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [Best loss 1.3761334419250488: checkpoint is saved at /content/nnsvs/egs/nnsvs_ofuton_p_utagoe_db/00-svs-world/exp/ofuton_p/acoustic/best_loss.pth\u001b[0m\n",
            "  2% 1/50 [00:20<17:05, 20.93s/it][\u001b[36m2020-06-29 00:56:57,240\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 2]: loss 0.9470159503129812\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:56:57,512\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 2]: loss 1.3343477249145508\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:56:57,539\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [Best loss 1.3343477249145508: checkpoint is saved at /content/nnsvs/egs/nnsvs_ofuton_p_utagoe_db/00-svs-world/exp/ofuton_p/acoustic/best_loss.pth\u001b[0m\n",
            "  4% 2/50 [00:30<13:56, 17.43s/it][\u001b[36m2020-06-29 00:57:05,589\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 3]: loss 0.9351954368444589\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:57:05,863\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 3]: loss 1.3298050165176392\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:57:05,891\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [Best loss 1.3298050165176392: checkpoint is saved at /content/nnsvs/egs/nnsvs_ofuton_p_utagoe_db/00-svs-world/exp/ofuton_p/acoustic/best_loss.pth\u001b[0m\n",
            "  6% 3/50 [00:38<11:31, 14.71s/it][\u001b[36m2020-06-29 00:57:13,579\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 4]: loss 0.929673847479698\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:57:13,860\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 4]: loss 1.322392225265503\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:57:13,888\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [Best loss 1.322392225265503: checkpoint is saved at /content/nnsvs/egs/nnsvs_ofuton_p_utagoe_db/00-svs-world/exp/ofuton_p/acoustic/best_loss.pth\u001b[0m\n",
            "  8% 4/50 [00:46<09:43, 12.69s/it][\u001b[36m2020-06-29 00:57:21,671\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 5]: loss 0.9229546999320005\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:57:21,943\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 5]: loss 1.3198856115341187\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:57:21,970\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [Best loss 1.3198856115341187: checkpoint is saved at /content/nnsvs/egs/nnsvs_ofuton_p_utagoe_db/00-svs-world/exp/ofuton_p/acoustic/best_loss.pth\u001b[0m\n",
            " 10% 5/50 [00:54<08:28, 11.31s/it][\u001b[36m2020-06-29 00:57:29,802\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 6]: loss 0.9255235057610732\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:57:30,080\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 6]: loss 1.314720630645752\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:57:30,106\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [Best loss 1.314720630645752: checkpoint is saved at /content/nnsvs/egs/nnsvs_ofuton_p_utagoe_db/00-svs-world/exp/ofuton_p/acoustic/best_loss.pth\u001b[0m\n",
            " 12% 6/50 [01:02<07:35, 10.36s/it][\u001b[36m2020-06-29 00:57:38,085\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 7]: loss 0.9203949585939065\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:57:38,359\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 7]: loss 1.321893572807312\u001b[0m\n",
            " 14% 7/50 [01:11<06:58,  9.73s/it][\u001b[36m2020-06-29 00:57:46,246\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 8]: loss 0.9220483776850578\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:57:46,527\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 8]: loss 1.3231027126312256\u001b[0m\n",
            " 16% 8/50 [01:19<06:28,  9.26s/it][\u001b[36m2020-06-29 00:57:54,541\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 9]: loss 0.9194884758729202\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:57:54,813\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 9]: loss 1.315779447555542\u001b[0m\n",
            " 18% 9/50 [01:27<06:07,  8.97s/it][\u001b[36m2020-06-29 00:58:02,504\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 10]: loss 0.9189002987666007\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:58:02,779\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 10]: loss 1.3130617141723633\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:58:02,803\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [Best loss 1.3130617141723633: checkpoint is saved at /content/nnsvs/egs/nnsvs_ofuton_p_utagoe_db/00-svs-world/exp/ofuton_p/acoustic/best_loss.pth\u001b[0m\n",
            " 20% 10/50 [01:35<05:46,  8.67s/it][\u001b[36m2020-06-29 00:58:10,725\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 11]: loss 0.9151663688512949\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:58:11,000\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 11]: loss 1.3163172006607056\u001b[0m\n",
            " 22% 11/50 [01:43<05:32,  8.53s/it][\u001b[36m2020-06-29 00:58:18,813\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 12]: loss 0.9115892526430961\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:58:19,100\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 12]: loss 1.309708833694458\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:58:19,125\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [Best loss 1.309708833694458: checkpoint is saved at /content/nnsvs/egs/nnsvs_ofuton_p_utagoe_db/00-svs-world/exp/ofuton_p/acoustic/best_loss.pth\u001b[0m\n",
            " 24% 12/50 [01:51<05:19,  8.41s/it][\u001b[36m2020-06-29 00:58:26,815\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 13]: loss 0.9116065945380774\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:58:27,093\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 13]: loss 1.3097045421600342\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:58:27,118\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [Best loss 1.3097045421600342: checkpoint is saved at /content/nnsvs/egs/nnsvs_ofuton_p_utagoe_db/00-svs-world/exp/ofuton_p/acoustic/best_loss.pth\u001b[0m\n",
            " 26% 13/50 [01:59<05:06,  8.28s/it][\u001b[36m2020-06-29 00:58:35,296\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 14]: loss 0.9124152003190457\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:58:35,570\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 14]: loss 1.3115395307540894\u001b[0m\n",
            " 28% 14/50 [02:08<05:00,  8.33s/it][\u001b[36m2020-06-29 00:58:43,422\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 15]: loss 0.9081941430385296\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:58:43,696\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 15]: loss 1.3093565702438354\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:58:43,722\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [Best loss 1.3093565702438354: checkpoint is saved at /content/nnsvs/egs/nnsvs_ofuton_p_utagoe_db/00-svs-world/exp/ofuton_p/acoustic/best_loss.pth\u001b[0m\n",
            " 30% 15/50 [02:16<04:49,  8.28s/it][\u001b[36m2020-06-29 00:58:51,597\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 16]: loss 0.9054661011084532\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:58:51,881\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 16]: loss 1.304992914199829\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:58:51,905\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [Best loss 1.304992914199829: checkpoint is saved at /content/nnsvs/egs/nnsvs_ofuton_p_utagoe_db/00-svs-world/exp/ofuton_p/acoustic/best_loss.pth\u001b[0m\n",
            " 32% 16/50 [02:24<04:40,  8.25s/it][\u001b[36m2020-06-29 00:59:00,089\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 17]: loss 0.9067050508963757\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:59:00,373\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 17]: loss 1.3043919801712036\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:59:00,400\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [Best loss 1.3043919801712036: checkpoint is saved at /content/nnsvs/egs/nnsvs_ofuton_p_utagoe_db/00-svs-world/exp/ofuton_p/acoustic/best_loss.pth\u001b[0m\n",
            " 34% 17/50 [02:33<04:34,  8.32s/it][\u001b[36m2020-06-29 00:59:08,226\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 18]: loss 0.9027322056965951\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:59:08,500\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 18]: loss 1.300652265548706\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:59:08,524\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [Best loss 1.300652265548706: checkpoint is saved at /content/nnsvs/egs/nnsvs_ofuton_p_utagoe_db/00-svs-world/exp/ofuton_p/acoustic/best_loss.pth\u001b[0m\n",
            " 36% 18/50 [02:41<04:24,  8.26s/it][\u001b[36m2020-06-29 00:59:16,484\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 19]: loss 0.9076463045218052\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:59:16,766\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 19]: loss 1.3098055124282837\u001b[0m\n",
            " 38% 19/50 [02:49<04:15,  8.26s/it][\u001b[36m2020-06-29 00:59:24,586\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 20]: loss 0.901579085068825\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:59:24,864\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 20]: loss 1.2982866764068604\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:59:24,889\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [Best loss 1.2982866764068604: checkpoint is saved at /content/nnsvs/egs/nnsvs_ofuton_p_utagoe_db/00-svs-world/exp/ofuton_p/acoustic/best_loss.pth\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:59:24,909\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Checkpoint is saved at /content/nnsvs/egs/nnsvs_ofuton_p_utagoe_db/00-svs-world/exp/ofuton_p/acoustic/checkpoint_epoch0020.pth\u001b[0m\n",
            " 40% 20/50 [02:57<04:06,  8.23s/it][\u001b[36m2020-06-29 00:59:32,675\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 21]: loss 0.8954258545851096\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:59:32,950\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 21]: loss 1.298293113708496\u001b[0m\n",
            " 42% 21/50 [03:05<03:56,  8.17s/it][\u001b[36m2020-06-29 00:59:40,775\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 22]: loss 0.8948056758978428\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:59:41,050\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 22]: loss 1.2937413454055786\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:59:41,074\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [Best loss 1.2937413454055786: checkpoint is saved at /content/nnsvs/egs/nnsvs_ofuton_p_utagoe_db/00-svs-world/exp/ofuton_p/acoustic/best_loss.pth\u001b[0m\n",
            " 44% 22/50 [03:13<03:48,  8.15s/it][\u001b[36m2020-06-29 00:59:48,898\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 23]: loss 0.8945205777119367\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:59:49,170\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 23]: loss 1.3002300262451172\u001b[0m\n",
            " 46% 23/50 [03:21<03:39,  8.14s/it][\u001b[36m2020-06-29 00:59:57,151\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 24]: loss 0.894290946997129\u001b[0m\n",
            "[\u001b[36m2020-06-29 00:59:57,440\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 24]: loss 1.2988635301589966\u001b[0m\n",
            " 48% 24/50 [03:30<03:32,  8.18s/it][\u001b[36m2020-06-29 01:00:05,276\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 25]: loss 0.8921997531866416\u001b[0m\n",
            "[\u001b[36m2020-06-29 01:00:05,552\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 25]: loss 1.2969133853912354\u001b[0m\n",
            " 50% 25/50 [03:38<03:23,  8.16s/it][\u001b[36m2020-06-29 01:00:13,359\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 26]: loss 0.8938662088834323\u001b[0m\n",
            "[\u001b[36m2020-06-29 01:00:13,641\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 26]: loss 1.2987892627716064\u001b[0m\n",
            " 52% 26/50 [03:46<03:15,  8.14s/it][\u001b[36m2020-06-29 01:00:21,392\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 27]: loss 0.8919895887374878\u001b[0m\n",
            "[\u001b[36m2020-06-29 01:00:21,673\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 27]: loss 1.2995136976242065\u001b[0m\n",
            " 54% 27/50 [03:54<03:06,  8.11s/it][\u001b[36m2020-06-29 01:00:29,657\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 28]: loss 0.8940006754337213\u001b[0m\n",
            "[\u001b[36m2020-06-29 01:00:29,930\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 28]: loss 1.2975661754608154\u001b[0m\n",
            " 56% 28/50 [04:02<02:59,  8.15s/it][\u001b[36m2020-06-29 01:00:37,884\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 29]: loss 0.8915121478912158\u001b[0m\n",
            "[\u001b[36m2020-06-29 01:00:38,161\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 29]: loss 1.2969090938568115\u001b[0m\n",
            " 58% 29/50 [04:10<02:51,  8.17s/it][\u001b[36m2020-06-29 01:00:45,986\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 30]: loss 0.8910356897574204\u001b[0m\n",
            "[\u001b[36m2020-06-29 01:00:46,268\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 30]: loss 1.2952594757080078\u001b[0m\n",
            " 60% 30/50 [04:18<02:43,  8.15s/it][\u001b[36m2020-06-29 01:00:54,198\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 31]: loss 0.8891152984056717\u001b[0m\n",
            "[\u001b[36m2020-06-29 01:00:54,483\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 31]: loss 1.2953050136566162\u001b[0m\n",
            " 62% 31/50 [04:27<02:35,  8.17s/it][\u001b[36m2020-06-29 01:01:02,239\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 32]: loss 0.8871628061319009\u001b[0m\n",
            "[\u001b[36m2020-06-29 01:01:02,516\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 32]: loss 1.2962087392807007\u001b[0m\n",
            " 64% 32/50 [04:35<02:26,  8.13s/it][\u001b[36m2020-06-29 01:01:10,323\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 33]: loss 0.8883530619816903\u001b[0m\n",
            "[\u001b[36m2020-06-29 01:01:10,597\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 33]: loss 1.2959929704666138\u001b[0m\n",
            " 66% 33/50 [04:43<02:17,  8.12s/it][\u001b[36m2020-06-29 01:01:18,655\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 34]: loss 0.8897519661830022\u001b[0m\n",
            "[\u001b[36m2020-06-29 01:01:18,933\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 34]: loss 1.2960550785064697\u001b[0m\n",
            " 68% 34/50 [04:51<02:10,  8.18s/it][\u001b[36m2020-06-29 01:01:26,862\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 35]: loss 0.8867274660330552\u001b[0m\n",
            "[\u001b[36m2020-06-29 01:01:27,147\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 35]: loss 1.2959848642349243\u001b[0m\n",
            " 70% 35/50 [04:59<02:02,  8.19s/it][\u001b[36m2020-06-29 01:01:35,046\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 36]: loss 0.8846113162162976\u001b[0m\n",
            "[\u001b[36m2020-06-29 01:01:35,326\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 36]: loss 1.2996642589569092\u001b[0m\n",
            " 72% 36/50 [05:07<01:54,  8.19s/it][\u001b[36m2020-06-29 01:01:43,036\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 37]: loss 0.8885665077429551\u001b[0m\n",
            "[\u001b[36m2020-06-29 01:01:43,311\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 37]: loss 1.2911489009857178\u001b[0m\n",
            "[\u001b[36m2020-06-29 01:01:43,339\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [Best loss 1.2911489009857178: checkpoint is saved at /content/nnsvs/egs/nnsvs_ofuton_p_utagoe_db/00-svs-world/exp/ofuton_p/acoustic/best_loss.pth\u001b[0m\n",
            " 74% 37/50 [05:15<01:45,  8.14s/it][\u001b[36m2020-06-29 01:01:51,209\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 38]: loss 0.8872875265586071\u001b[0m\n",
            "[\u001b[36m2020-06-29 01:01:51,483\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 38]: loss 1.2959154844284058\u001b[0m\n",
            " 76% 38/50 [05:24<01:37,  8.14s/it][\u001b[36m2020-06-29 01:01:59,293\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 39]: loss 0.8873509825804294\u001b[0m\n",
            "[\u001b[36m2020-06-29 01:01:59,569\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 39]: loss 1.293765902519226\u001b[0m\n",
            " 78% 39/50 [05:32<01:29,  8.12s/it][\u001b[36m2020-06-29 01:02:07,707\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 40]: loss 0.8849113675264212\u001b[0m\n",
            "[\u001b[36m2020-06-29 01:02:07,982\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 40]: loss 1.2990245819091797\u001b[0m\n",
            "[\u001b[36m2020-06-29 01:02:08,018\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Checkpoint is saved at /content/nnsvs/egs/nnsvs_ofuton_p_utagoe_db/00-svs-world/exp/ofuton_p/acoustic/checkpoint_epoch0040.pth\u001b[0m\n",
            " 80% 40/50 [05:40<01:22,  8.22s/it][\u001b[36m2020-06-29 01:02:15,633\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 41]: loss 0.8817672836474884\u001b[0m\n",
            "[\u001b[36m2020-06-29 01:02:15,904\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 41]: loss 1.2948044538497925\u001b[0m\n",
            " 82% 41/50 [05:48<01:13,  8.12s/it][\u001b[36m2020-06-29 01:02:23,585\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 42]: loss 0.8802249370477139\u001b[0m\n",
            "[\u001b[36m2020-06-29 01:02:23,855\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 42]: loss 1.291769027709961\u001b[0m\n",
            " 84% 42/50 [05:56<01:04,  8.07s/it][\u001b[36m2020-06-29 01:02:31,750\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 43]: loss 0.8807655236659906\u001b[0m\n",
            "[\u001b[36m2020-06-29 01:02:32,024\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 43]: loss 1.2929301261901855\u001b[0m\n",
            " 86% 43/50 [06:04<00:56,  8.10s/it][\u001b[36m2020-06-29 01:02:40,078\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 44]: loss 0.8792540889519912\u001b[0m\n",
            "[\u001b[36m2020-06-29 01:02:40,358\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 44]: loss 1.2946337461471558\u001b[0m\n",
            " 88% 44/50 [06:13<00:49,  8.17s/it][\u001b[36m2020-06-29 01:02:48,323\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 45]: loss 0.8808710024907038\u001b[0m\n",
            "[\u001b[36m2020-06-29 01:02:48,603\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 45]: loss 1.2897744178771973\u001b[0m\n",
            "[\u001b[36m2020-06-29 01:02:48,629\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [Best loss 1.2897744178771973: checkpoint is saved at /content/nnsvs/egs/nnsvs_ofuton_p_utagoe_db/00-svs-world/exp/ofuton_p/acoustic/best_loss.pth\u001b[0m\n",
            " 90% 45/50 [06:21<00:40,  8.20s/it][\u001b[36m2020-06-29 01:02:56,290\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 46]: loss 0.8782063447512113\u001b[0m\n",
            "[\u001b[36m2020-06-29 01:02:56,565\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 46]: loss 1.2959206104278564\u001b[0m\n",
            " 92% 46/50 [06:29<00:32,  8.12s/it][\u001b[36m2020-06-29 01:03:04,423\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 47]: loss 0.8785934830323244\u001b[0m\n",
            "[\u001b[36m2020-06-29 01:03:04,698\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 47]: loss 1.2918572425842285\u001b[0m\n",
            " 94% 47/50 [06:37<00:24,  8.12s/it][\u001b[36m2020-06-29 01:03:12,856\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 48]: loss 0.8778351866281949\u001b[0m\n",
            "[\u001b[36m2020-06-29 01:03:13,137\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 48]: loss 1.292864441871643\u001b[0m\n",
            " 96% 48/50 [06:45<00:16,  8.22s/it][\u001b[36m2020-06-29 01:03:20,913\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 49]: loss 0.8788996476393479\u001b[0m\n",
            "[\u001b[36m2020-06-29 01:03:21,191\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 49]: loss 1.2919691801071167\u001b[0m\n",
            " 98% 49/50 [06:53<00:08,  8.17s/it][\u001b[36m2020-06-29 01:03:29,176\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 50]: loss 0.8781188359627357\u001b[0m\n",
            "[\u001b[36m2020-06-29 01:03:29,455\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 50]: loss 1.2903828620910645\u001b[0m\n",
            "100% 50/50 [07:02<00:00,  8.44s/it]\n",
            "[\u001b[36m2020-06-29 01:03:29,482\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Checkpoint is saved at /content/nnsvs/egs/nnsvs_ofuton_p_utagoe_db/00-svs-world/exp/ofuton_p/acoustic/checkpoint_epoch0050.pth\u001b[0m\n",
            "[\u001b[36m2020-06-29 01:03:29,498\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - The best loss was 1.2897744178771973\u001b[0m\n",
            "+ set +x\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YnPWRwTgqUhg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "8bbd5627-aac7-48fb-ffb3-94d2ecfa52a0"
      },
      "source": [
        "# stage5-6の実行(モデルデータの出力および歌声合成)\n",
        "! cd $RECIPE_ROOT && bash run.sh --stage 5 --stop-stage 6"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "stage 5: Generation features from timelag/duration/acoustic models\n",
            "+ nnsvs-generate model.checkpoint=exp/ofuton_p/timelag/latest.pth model.model_yaml=exp/ofuton_p/timelag/model.yaml out_scaler_path=dump/ofuton_p/norm/out_timelag_scaler.joblib in_dir=dump/ofuton_p/norm/dev/in_timelag/ out_dir=exp/ofuton_p/timelag/predicted/dev/latest/\n",
            "[\u001b[36m2020-06-29 01:03:33,047\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - in_dir: dump/ofuton_p/norm/dev/in_timelag/\n",
            "model:\n",
            "  checkpoint: exp/ofuton_p/timelag/latest.pth\n",
            "  model_yaml: exp/ofuton_p/timelag/model.yaml\n",
            "out_dir: exp/ofuton_p/timelag/predicted/dev/latest/\n",
            "out_scaler_path: dump/ofuton_p/norm/out_timelag_scaler.joblib\n",
            "verbose: 100\n",
            "\u001b[0m\n",
            "100% 4/4 [00:00<00:00, 287.40it/s]\n",
            "+ set +x\n",
            "+ nnsvs-generate model.checkpoint=exp/ofuton_p/duration/latest.pth model.model_yaml=exp/ofuton_p/duration/model.yaml out_scaler_path=dump/ofuton_p/norm/out_duration_scaler.joblib in_dir=dump/ofuton_p/norm/dev/in_duration/ out_dir=exp/ofuton_p/duration/predicted/dev/latest/\n",
            "[\u001b[36m2020-06-29 01:03:37,710\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - in_dir: dump/ofuton_p/norm/dev/in_duration/\n",
            "model:\n",
            "  checkpoint: exp/ofuton_p/duration/latest.pth\n",
            "  model_yaml: exp/ofuton_p/duration/model.yaml\n",
            "out_dir: exp/ofuton_p/duration/predicted/dev/latest/\n",
            "out_scaler_path: dump/ofuton_p/norm/out_duration_scaler.joblib\n",
            "verbose: 100\n",
            "\u001b[0m\n",
            "100% 4/4 [00:00<00:00, 91.20it/s]\n",
            "+ set +x\n",
            "+ nnsvs-generate model.checkpoint=exp/ofuton_p/acoustic/latest.pth model.model_yaml=exp/ofuton_p/acoustic/model.yaml out_scaler_path=dump/ofuton_p/norm/out_acoustic_scaler.joblib in_dir=dump/ofuton_p/norm/dev/in_acoustic/ out_dir=exp/ofuton_p/acoustic/predicted/dev/latest/\n",
            "[\u001b[36m2020-06-29 01:03:42,413\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - in_dir: dump/ofuton_p/norm/dev/in_acoustic/\n",
            "model:\n",
            "  checkpoint: exp/ofuton_p/acoustic/latest.pth\n",
            "  model_yaml: exp/ofuton_p/acoustic/model.yaml\n",
            "out_dir: exp/ofuton_p/acoustic/predicted/dev/latest/\n",
            "out_scaler_path: dump/ofuton_p/norm/out_acoustic_scaler.joblib\n",
            "verbose: 100\n",
            "\u001b[0m\n",
            "100% 4/4 [00:00<00:00, 30.09it/s]\n",
            "+ set +x\n",
            "+ nnsvs-generate model.checkpoint=exp/ofuton_p/timelag/latest.pth model.model_yaml=exp/ofuton_p/timelag/model.yaml out_scaler_path=dump/ofuton_p/norm/out_timelag_scaler.joblib in_dir=dump/ofuton_p/norm/eval/in_timelag/ out_dir=exp/ofuton_p/timelag/predicted/eval/latest/\n",
            "[\u001b[36m2020-06-29 01:03:47,148\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - in_dir: dump/ofuton_p/norm/eval/in_timelag/\n",
            "model:\n",
            "  checkpoint: exp/ofuton_p/timelag/latest.pth\n",
            "  model_yaml: exp/ofuton_p/timelag/model.yaml\n",
            "out_dir: exp/ofuton_p/timelag/predicted/eval/latest/\n",
            "out_scaler_path: dump/ofuton_p/norm/out_timelag_scaler.joblib\n",
            "verbose: 100\n",
            "\u001b[0m\n",
            "100% 6/6 [00:00<00:00, 613.35it/s]\n",
            "+ set +x\n",
            "+ nnsvs-generate model.checkpoint=exp/ofuton_p/duration/latest.pth model.model_yaml=exp/ofuton_p/duration/model.yaml out_scaler_path=dump/ofuton_p/norm/out_duration_scaler.joblib in_dir=dump/ofuton_p/norm/eval/in_duration/ out_dir=exp/ofuton_p/duration/predicted/eval/latest/\n",
            "[\u001b[36m2020-06-29 01:03:51,708\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - in_dir: dump/ofuton_p/norm/eval/in_duration/\n",
            "model:\n",
            "  checkpoint: exp/ofuton_p/duration/latest.pth\n",
            "  model_yaml: exp/ofuton_p/duration/model.yaml\n",
            "out_dir: exp/ofuton_p/duration/predicted/eval/latest/\n",
            "out_scaler_path: dump/ofuton_p/norm/out_duration_scaler.joblib\n",
            "verbose: 100\n",
            "\u001b[0m\n",
            "100% 6/6 [00:00<00:00, 145.65it/s]\n",
            "+ set +x\n",
            "+ nnsvs-generate model.checkpoint=exp/ofuton_p/acoustic/latest.pth model.model_yaml=exp/ofuton_p/acoustic/model.yaml out_scaler_path=dump/ofuton_p/norm/out_acoustic_scaler.joblib in_dir=dump/ofuton_p/norm/eval/in_acoustic/ out_dir=exp/ofuton_p/acoustic/predicted/eval/latest/\n",
            "[\u001b[36m2020-06-29 01:03:56,310\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - in_dir: dump/ofuton_p/norm/eval/in_acoustic/\n",
            "model:\n",
            "  checkpoint: exp/ofuton_p/acoustic/latest.pth\n",
            "  model_yaml: exp/ofuton_p/acoustic/model.yaml\n",
            "out_dir: exp/ofuton_p/acoustic/predicted/eval/latest/\n",
            "out_scaler_path: dump/ofuton_p/norm/out_acoustic_scaler.joblib\n",
            "verbose: 100\n",
            "\u001b[0m\n",
            "100% 6/6 [00:00<00:00, 11.35it/s]\n",
            "+ set +x\n",
            "stage 6: Synthesis waveforms\n",
            "+ nnsvs-synthesis question_path=conf/jp_qst001_nnsvs.hed timelag.checkpoint=exp/ofuton_p/timelag/latest.pth timelag.in_scaler_path=dump/ofuton_p/norm/in_timelag_scaler.joblib timelag.out_scaler_path=dump/ofuton_p/norm/out_timelag_scaler.joblib timelag.model_yaml=exp/ofuton_p/timelag/model.yaml duration.checkpoint=exp/ofuton_p/duration/latest.pth duration.in_scaler_path=dump/ofuton_p/norm/in_duration_scaler.joblib duration.out_scaler_path=dump/ofuton_p/norm/out_duration_scaler.joblib duration.model_yaml=exp/ofuton_p/duration/model.yaml acoustic.checkpoint=exp/ofuton_p/acoustic/latest.pth acoustic.in_scaler_path=dump/ofuton_p/norm/in_acoustic_scaler.joblib acoustic.out_scaler_path=dump/ofuton_p/norm/out_acoustic_scaler.joblib acoustic.model_yaml=exp/ofuton_p/acoustic/model.yaml utt_list=./data/list/dev.list in_dir=data/acoustic/label_phone_score/ out_dir=exp/ofuton_p/synthesis/dev/latest/label_phone_score ground_truth_duration=false\n",
            "[\u001b[36m2020-06-29 01:04:01,438\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - acoustic:\n",
            "  checkpoint: exp/ofuton_p/acoustic/latest.pth\n",
            "  has_dynamic_features:\n",
            "  - true\n",
            "  - true\n",
            "  - false\n",
            "  - true\n",
            "  in_scaler_path: dump/ofuton_p/norm/in_acoustic_scaler.joblib\n",
            "  model_yaml: exp/ofuton_p/acoustic/model.yaml\n",
            "  num_windows: 3\n",
            "  out_scaler_path: dump/ofuton_p/norm/out_acoustic_scaler.joblib\n",
            "  post_filter: true\n",
            "  question_path: null\n",
            "  relative_f0: true\n",
            "  stream_sizes:\n",
            "  - 180\n",
            "  - 3\n",
            "  - 1\n",
            "  - 15\n",
            "  subphone_features: coarse_coding\n",
            "device: cuda\n",
            "duration:\n",
            "  checkpoint: exp/ofuton_p/duration/latest.pth\n",
            "  has_dynamic_features:\n",
            "  - false\n",
            "  in_scaler_path: dump/ofuton_p/norm/in_duration_scaler.joblib\n",
            "  model_yaml: exp/ofuton_p/duration/model.yaml\n",
            "  out_scaler_path: dump/ofuton_p/norm/out_duration_scaler.joblib\n",
            "  question_path: null\n",
            "  stream_sizes:\n",
            "  - 1\n",
            "frame_period: 5\n",
            "ground_truth_duration: false\n",
            "in_dir: data/acoustic/label_phone_score/\n",
            "label_path: null\n",
            "log_f0_conditioning: true\n",
            "out_dir: exp/ofuton_p/synthesis/dev/latest/label_phone_score\n",
            "out_wav_path: null\n",
            "question_path: conf/jp_qst001_nnsvs.hed\n",
            "sample_rate: 48000\n",
            "timelag:\n",
            "  allowed_range:\n",
            "  - -20\n",
            "  - 20\n",
            "  allowed_range_rest:\n",
            "  - -40\n",
            "  - 40\n",
            "  checkpoint: exp/ofuton_p/timelag/latest.pth\n",
            "  has_dynamic_features:\n",
            "  - false\n",
            "  in_scaler_path: dump/ofuton_p/norm/in_timelag_scaler.joblib\n",
            "  model_yaml: exp/ofuton_p/timelag/model.yaml\n",
            "  out_scaler_path: dump/ofuton_p/norm/out_timelag_scaler.joblib\n",
            "  question_path: null\n",
            "  stream_sizes:\n",
            "  - 1\n",
            "utt_list: ./data/list/dev.list\n",
            "verbose: 100\n",
            "\u001b[0m\n",
            "[\u001b[36m2020-06-29 01:04:04,287\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Processes 4 utterances...\u001b[0m\n",
            "100% 4/4 [00:15<00:00,  3.96s/it]\n",
            "+ set +x\n",
            "+ nnsvs-synthesis question_path=conf/jp_qst001_nnsvs.hed timelag.checkpoint=exp/ofuton_p/timelag/latest.pth timelag.in_scaler_path=dump/ofuton_p/norm/in_timelag_scaler.joblib timelag.out_scaler_path=dump/ofuton_p/norm/out_timelag_scaler.joblib timelag.model_yaml=exp/ofuton_p/timelag/model.yaml duration.checkpoint=exp/ofuton_p/duration/latest.pth duration.in_scaler_path=dump/ofuton_p/norm/in_duration_scaler.joblib duration.out_scaler_path=dump/ofuton_p/norm/out_duration_scaler.joblib duration.model_yaml=exp/ofuton_p/duration/model.yaml acoustic.checkpoint=exp/ofuton_p/acoustic/latest.pth acoustic.in_scaler_path=dump/ofuton_p/norm/in_acoustic_scaler.joblib acoustic.out_scaler_path=dump/ofuton_p/norm/out_acoustic_scaler.joblib acoustic.model_yaml=exp/ofuton_p/acoustic/model.yaml utt_list=./data/list/dev.list in_dir=data/acoustic/label_phone_align/ out_dir=exp/ofuton_p/synthesis/dev/latest/label_phone_align ground_truth_duration=true\n",
            "[\u001b[36m2020-06-29 01:04:21,956\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - acoustic:\n",
            "  checkpoint: exp/ofuton_p/acoustic/latest.pth\n",
            "  has_dynamic_features:\n",
            "  - true\n",
            "  - true\n",
            "  - false\n",
            "  - true\n",
            "  in_scaler_path: dump/ofuton_p/norm/in_acoustic_scaler.joblib\n",
            "  model_yaml: exp/ofuton_p/acoustic/model.yaml\n",
            "  num_windows: 3\n",
            "  out_scaler_path: dump/ofuton_p/norm/out_acoustic_scaler.joblib\n",
            "  post_filter: true\n",
            "  question_path: null\n",
            "  relative_f0: true\n",
            "  stream_sizes:\n",
            "  - 180\n",
            "  - 3\n",
            "  - 1\n",
            "  - 15\n",
            "  subphone_features: coarse_coding\n",
            "device: cuda\n",
            "duration:\n",
            "  checkpoint: exp/ofuton_p/duration/latest.pth\n",
            "  has_dynamic_features:\n",
            "  - false\n",
            "  in_scaler_path: dump/ofuton_p/norm/in_duration_scaler.joblib\n",
            "  model_yaml: exp/ofuton_p/duration/model.yaml\n",
            "  out_scaler_path: dump/ofuton_p/norm/out_duration_scaler.joblib\n",
            "  question_path: null\n",
            "  stream_sizes:\n",
            "  - 1\n",
            "frame_period: 5\n",
            "ground_truth_duration: true\n",
            "in_dir: data/acoustic/label_phone_align/\n",
            "label_path: null\n",
            "log_f0_conditioning: true\n",
            "out_dir: exp/ofuton_p/synthesis/dev/latest/label_phone_align\n",
            "out_wav_path: null\n",
            "question_path: conf/jp_qst001_nnsvs.hed\n",
            "sample_rate: 48000\n",
            "timelag:\n",
            "  allowed_range:\n",
            "  - -20\n",
            "  - 20\n",
            "  allowed_range_rest:\n",
            "  - -40\n",
            "  - 40\n",
            "  checkpoint: exp/ofuton_p/timelag/latest.pth\n",
            "  has_dynamic_features:\n",
            "  - false\n",
            "  in_scaler_path: dump/ofuton_p/norm/in_timelag_scaler.joblib\n",
            "  model_yaml: exp/ofuton_p/timelag/model.yaml\n",
            "  out_scaler_path: dump/ofuton_p/norm/out_timelag_scaler.joblib\n",
            "  question_path: null\n",
            "  stream_sizes:\n",
            "  - 1\n",
            "utt_list: ./data/list/dev.list\n",
            "verbose: 100\n",
            "\u001b[0m\n",
            "[\u001b[36m2020-06-29 01:04:24,864\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Processes 4 utterances...\u001b[0m\n",
            "100% 4/4 [00:16<00:00,  4.12s/it]\n",
            "+ set +x\n",
            "+ nnsvs-synthesis question_path=conf/jp_qst001_nnsvs.hed timelag.checkpoint=exp/ofuton_p/timelag/latest.pth timelag.in_scaler_path=dump/ofuton_p/norm/in_timelag_scaler.joblib timelag.out_scaler_path=dump/ofuton_p/norm/out_timelag_scaler.joblib timelag.model_yaml=exp/ofuton_p/timelag/model.yaml duration.checkpoint=exp/ofuton_p/duration/latest.pth duration.in_scaler_path=dump/ofuton_p/norm/in_duration_scaler.joblib duration.out_scaler_path=dump/ofuton_p/norm/out_duration_scaler.joblib duration.model_yaml=exp/ofuton_p/duration/model.yaml acoustic.checkpoint=exp/ofuton_p/acoustic/latest.pth acoustic.in_scaler_path=dump/ofuton_p/norm/in_acoustic_scaler.joblib acoustic.out_scaler_path=dump/ofuton_p/norm/out_acoustic_scaler.joblib acoustic.model_yaml=exp/ofuton_p/acoustic/model.yaml utt_list=./data/list/eval.list in_dir=data/acoustic/label_phone_score/ out_dir=exp/ofuton_p/synthesis/eval/latest/label_phone_score ground_truth_duration=false\n",
            "[\u001b[36m2020-06-29 01:04:43,145\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - acoustic:\n",
            "  checkpoint: exp/ofuton_p/acoustic/latest.pth\n",
            "  has_dynamic_features:\n",
            "  - true\n",
            "  - true\n",
            "  - false\n",
            "  - true\n",
            "  in_scaler_path: dump/ofuton_p/norm/in_acoustic_scaler.joblib\n",
            "  model_yaml: exp/ofuton_p/acoustic/model.yaml\n",
            "  num_windows: 3\n",
            "  out_scaler_path: dump/ofuton_p/norm/out_acoustic_scaler.joblib\n",
            "  post_filter: true\n",
            "  question_path: null\n",
            "  relative_f0: true\n",
            "  stream_sizes:\n",
            "  - 180\n",
            "  - 3\n",
            "  - 1\n",
            "  - 15\n",
            "  subphone_features: coarse_coding\n",
            "device: cuda\n",
            "duration:\n",
            "  checkpoint: exp/ofuton_p/duration/latest.pth\n",
            "  has_dynamic_features:\n",
            "  - false\n",
            "  in_scaler_path: dump/ofuton_p/norm/in_duration_scaler.joblib\n",
            "  model_yaml: exp/ofuton_p/duration/model.yaml\n",
            "  out_scaler_path: dump/ofuton_p/norm/out_duration_scaler.joblib\n",
            "  question_path: null\n",
            "  stream_sizes:\n",
            "  - 1\n",
            "frame_period: 5\n",
            "ground_truth_duration: false\n",
            "in_dir: data/acoustic/label_phone_score/\n",
            "label_path: null\n",
            "log_f0_conditioning: true\n",
            "out_dir: exp/ofuton_p/synthesis/eval/latest/label_phone_score\n",
            "out_wav_path: null\n",
            "question_path: conf/jp_qst001_nnsvs.hed\n",
            "sample_rate: 48000\n",
            "timelag:\n",
            "  allowed_range:\n",
            "  - -20\n",
            "  - 20\n",
            "  allowed_range_rest:\n",
            "  - -40\n",
            "  - 40\n",
            "  checkpoint: exp/ofuton_p/timelag/latest.pth\n",
            "  has_dynamic_features:\n",
            "  - false\n",
            "  in_scaler_path: dump/ofuton_p/norm/in_timelag_scaler.joblib\n",
            "  model_yaml: exp/ofuton_p/timelag/model.yaml\n",
            "  out_scaler_path: dump/ofuton_p/norm/out_timelag_scaler.joblib\n",
            "  question_path: null\n",
            "  stream_sizes:\n",
            "  - 1\n",
            "utt_list: ./data/list/eval.list\n",
            "verbose: 100\n",
            "\u001b[0m\n",
            "[\u001b[36m2020-06-29 01:04:46,031\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Processes 6 utterances...\u001b[0m\n",
            "100% 6/6 [00:24<00:00,  4.08s/it]\n",
            "+ set +x\n",
            "+ nnsvs-synthesis question_path=conf/jp_qst001_nnsvs.hed timelag.checkpoint=exp/ofuton_p/timelag/latest.pth timelag.in_scaler_path=dump/ofuton_p/norm/in_timelag_scaler.joblib timelag.out_scaler_path=dump/ofuton_p/norm/out_timelag_scaler.joblib timelag.model_yaml=exp/ofuton_p/timelag/model.yaml duration.checkpoint=exp/ofuton_p/duration/latest.pth duration.in_scaler_path=dump/ofuton_p/norm/in_duration_scaler.joblib duration.out_scaler_path=dump/ofuton_p/norm/out_duration_scaler.joblib duration.model_yaml=exp/ofuton_p/duration/model.yaml acoustic.checkpoint=exp/ofuton_p/acoustic/latest.pth acoustic.in_scaler_path=dump/ofuton_p/norm/in_acoustic_scaler.joblib acoustic.out_scaler_path=dump/ofuton_p/norm/out_acoustic_scaler.joblib acoustic.model_yaml=exp/ofuton_p/acoustic/model.yaml utt_list=./data/list/eval.list in_dir=data/acoustic/label_phone_align/ out_dir=exp/ofuton_p/synthesis/eval/latest/label_phone_align ground_truth_duration=true\n",
            "[\u001b[36m2020-06-29 01:05:12,314\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - acoustic:\n",
            "  checkpoint: exp/ofuton_p/acoustic/latest.pth\n",
            "  has_dynamic_features:\n",
            "  - true\n",
            "  - true\n",
            "  - false\n",
            "  - true\n",
            "  in_scaler_path: dump/ofuton_p/norm/in_acoustic_scaler.joblib\n",
            "  model_yaml: exp/ofuton_p/acoustic/model.yaml\n",
            "  num_windows: 3\n",
            "  out_scaler_path: dump/ofuton_p/norm/out_acoustic_scaler.joblib\n",
            "  post_filter: true\n",
            "  question_path: null\n",
            "  relative_f0: true\n",
            "  stream_sizes:\n",
            "  - 180\n",
            "  - 3\n",
            "  - 1\n",
            "  - 15\n",
            "  subphone_features: coarse_coding\n",
            "device: cuda\n",
            "duration:\n",
            "  checkpoint: exp/ofuton_p/duration/latest.pth\n",
            "  has_dynamic_features:\n",
            "  - false\n",
            "  in_scaler_path: dump/ofuton_p/norm/in_duration_scaler.joblib\n",
            "  model_yaml: exp/ofuton_p/duration/model.yaml\n",
            "  out_scaler_path: dump/ofuton_p/norm/out_duration_scaler.joblib\n",
            "  question_path: null\n",
            "  stream_sizes:\n",
            "  - 1\n",
            "frame_period: 5\n",
            "ground_truth_duration: true\n",
            "in_dir: data/acoustic/label_phone_align/\n",
            "label_path: null\n",
            "log_f0_conditioning: true\n",
            "out_dir: exp/ofuton_p/synthesis/eval/latest/label_phone_align\n",
            "out_wav_path: null\n",
            "question_path: conf/jp_qst001_nnsvs.hed\n",
            "sample_rate: 48000\n",
            "timelag:\n",
            "  allowed_range:\n",
            "  - -20\n",
            "  - 20\n",
            "  allowed_range_rest:\n",
            "  - -40\n",
            "  - 40\n",
            "  checkpoint: exp/ofuton_p/timelag/latest.pth\n",
            "  has_dynamic_features:\n",
            "  - false\n",
            "  in_scaler_path: dump/ofuton_p/norm/in_timelag_scaler.joblib\n",
            "  model_yaml: exp/ofuton_p/timelag/model.yaml\n",
            "  out_scaler_path: dump/ofuton_p/norm/out_timelag_scaler.joblib\n",
            "  question_path: null\n",
            "  stream_sizes:\n",
            "  - 1\n",
            "utt_list: ./data/list/eval.list\n",
            "verbose: 100\n",
            "\u001b[0m\n",
            "[\u001b[36m2020-06-29 01:05:15,163\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Processes 6 utterances...\u001b[0m\n",
            "100% 6/6 [00:24<00:00,  4.09s/it]\n",
            "+ set +x\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w_Wy9DNLQhLv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "00f53489-18d7-49f0-887f-a43a56b3f17e"
      },
      "source": [
        "import IPython\n",
        "from IPython.display import Audio\n",
        "from glob import glob\n",
        "from os.path import join\n",
        "sample_rate=48000\n",
        "synthesized_wav_paths = sorted(glob(join(RECIPE_ROOT, \"exp/**/*.wav\"),  recursive=True))\n",
        "\n",
        "for wav_path in synthesized_wav_paths:\n",
        "    print(wav_path)\n",
        "    IPython.display.display(Audio(wav_path, rate=sample_rate))"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/nnsvs/egs/nnsvs_ofuton_p_utagoe_db/00-svs-world/exp/ofuton_p/synthesis/dev/latest/label_phone_align/kagome_kagome_low_seg0.wav\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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