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taroushirani/nnsvs_dev20220717_oniku_kurumi_utagoe_db_dev_latest_training_20220717.ipynb

Created July 24, 2022 02:18
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nnsvs_dev20220717_oniku_kurumi_utagoe_db_dev_latest_training_20220717.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/taroushirani/2c3a77dc75f3d1ae4ab2add461cec0a8/nnsvs_dev20220717_oniku_kurumi_utagoe_db_dev_latest_training_20220717.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "giSmNuVzDdcL"
},
"source": [
"# Setup"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "I7FTMYdyUzgW"
},
"source": [
"# Miscellaneous setting\n",
"## Check GPU"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "C6wUuWIupBz3",
"outputId": "c8d6d050-ecc1-419d-a10a-3e42649cba86"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Sun Jul 17 09:47:11 2022 \n",
"+-----------------------------------------------------------------------------+\n",
"| NVIDIA-SMI 460.32.03 Driver Version: 460.32.03 CUDA Version: 11.2 |\n",
"|-------------------------------+----------------------+----------------------+\n",
"| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n",
"| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n",
"| | | MIG M. |\n",
"|===============================+======================+======================|\n",
"| 0 Tesla P100-PCIE... Off | 00000000:00:04.0 Off | 0 |\n",
"| N/A 35C P0 27W / 250W | 0MiB / 16280MiB | 0% Default |\n",
"| | | N/A |\n",
"+-------------------------------+----------------------+----------------------+\n",
" \n",
"+-----------------------------------------------------------------------------+\n",
"| Processes: |\n",
"| GPU GI CI PID Type Process name GPU Memory |\n",
"| ID ID Usage |\n",
"|=============================================================================|\n",
"| No running processes found |\n",
"+-----------------------------------------------------------------------------+\n"
]
}
],
"source": [
"! nvidia-smi"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "J76ifcTqBr88"
},
"source": [
"## Setting Google drive accessible"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "UAHxQFcMOBR2",
"outputId": "a5279898-1d07-4429-c2bd-654e39fc5a0f"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mounted at /content/drive\n"
]
}
],
"source": [
"from google.colab import drive\n",
"drive.mount('/content/drive')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "7b6A4YPEOEjt"
},
"outputs": [],
"source": [
"!ln -s \"/content/drive/My Drive\" /content/gdrive"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "K2ghaLcEOz5C"
},
"source": [
"\n",
"## Update numpy and cython"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "FCY9PjEUXT5i",
"outputId": "be785830-4ee7-4dc1-8fc2-8f5e27bc5320"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (1.21.6)\n",
"Requirement already satisfied: cython in /usr/local/lib/python3.7/dist-packages (0.29.30)\n"
]
}
],
"source": [
"! pip install -U numpy cython"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Ci9XLYz5RRp2"
},
"source": [
"## Install pysinsy (binary-indep version)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "BbzKX7A1PHia",
"outputId": "53ec3b2a-cabe-4604-bf5c-f2a3e6ea603a"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Collecting pysinsy\n",
" Downloading pysinsy-0.0.4.tar.gz (1.4 MB)\n",
"\u001b[K |████████████████████████████████| 1.4 MB 4.1 MB/s \n",
"\u001b[?25h Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
" Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
" Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n",
"Requirement already satisfied: numpy>=1.8.0 in /usr/local/lib/python3.7/dist-packages (from pysinsy) (1.21.6)\n",
"Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from pysinsy) (1.15.0)\n",
"Requirement already satisfied: cython>=0.21.0 in /usr/local/lib/python3.7/dist-packages (from pysinsy) (0.29.30)\n",
"Building wheels for collected packages: pysinsy\n",
" Building wheel for pysinsy (PEP 517) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for pysinsy: filename=pysinsy-0.0.4-cp37-cp37m-linux_x86_64.whl size=3222731 sha256=6c0311ea2ea632d1c3edb7ca9dda8b945b57d1803faf2d175b6de2571d22e5cf\n",
" Stored in directory: /root/.cache/pip/wheels/f7/f7/0c/b80b7529235c74a8febbfefad50edcab5082f6b134929e9225\n",
"Successfully built pysinsy\n",
"Installing collected packages: pysinsy\n",
"Successfully installed pysinsy-0.0.4\n"
]
}
],
"source": [
"! pip install pysinsy"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "kC4w4OdDSrLx"
},
"source": [
"## Install nnmnkwii (development version)\n",
"We can also use \"pip install git+https://github.com/r9y9/nnmnkwii\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "tZwFN2dLTi8L",
"outputId": "421c1b8e-8678-4ad8-a1ce-211c9ff8f889"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Processing /content/nnmnkwii\n",
" Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
" Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
" Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n",
"Collecting pysptk>=0.1.17\n",
" Downloading pysptk-0.1.21.tar.gz (420 kB)\n",
"\u001b[K |████████████████████████████████| 420 kB 3.9 MB/s \n",
"\u001b[?25h Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
" Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
" Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n",
"Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from nnmnkwii==0.1.2+86cec77) (1.7.3)\n",
"Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from nnmnkwii==0.1.2+86cec77) (4.64.0)\n",
"Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from nnmnkwii==0.1.2+86cec77) (1.0.2)\n",
"Requirement already satisfied: cython>=0.28.0 in /usr/local/lib/python3.7/dist-packages (from nnmnkwii==0.1.2+86cec77) (0.29.30)\n",
"Requirement already satisfied: fastdtw in /usr/local/lib/python3.7/dist-packages (from nnmnkwii==0.1.2+86cec77) (0.3.4)\n",
"Requirement already satisfied: decorator in /usr/local/lib/python3.7/dist-packages (from pysptk>=0.1.17->nnmnkwii==0.1.2+86cec77) (4.4.2)\n",
"Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from pysptk>=0.1.17->nnmnkwii==0.1.2+86cec77) (1.15.0)\n",
"Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from fastdtw->nnmnkwii==0.1.2+86cec77) (1.21.6)\n",
"Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->nnmnkwii==0.1.2+86cec77) (1.1.0)\n",
"Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->nnmnkwii==0.1.2+86cec77) (3.1.0)\n",
"Building wheels for collected packages: nnmnkwii, pysptk\n",
" Building wheel for nnmnkwii (PEP 517) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for nnmnkwii: filename=nnmnkwii-0.1.2+86cec77-cp37-cp37m-linux_x86_64.whl size=2893525 sha256=af64330815021a0dd89484d5585a5c7975bc363aa0477a3a76810bb62e742bf9\n",
" Stored in directory: /tmp/pip-ephem-wheel-cache-9l0953od/wheels/a5/5f/60/65c8ed7bf189bb4a268d31f2e20f86205c5b15cd7510dc4e66\n",
" Building wheel for pysptk (PEP 517) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for pysptk: filename=pysptk-0.1.21-cp37-cp37m-linux_x86_64.whl size=952300 sha256=d52422211e11db41587b653913d823c37d2bfa5bf6a60e6ac34608050a019cc1\n",
" Stored in directory: /root/.cache/pip/wheels/ab/3d/14/d7179b072549e93b6b5d76eb8b455f3a9d39a10f314660a385\n",
"Successfully built nnmnkwii pysptk\n",
"Installing collected packages: pysptk, nnmnkwii\n",
"Successfully installed nnmnkwii-0.1.2+86cec77 pysptk-0.1.21\n"
]
}
],
"source": [
"! git clone -q https://github.com/r9y9/nnmnkwii\n",
"! cd nnmnkwii && pip install . --use-feature=in-tree-build"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "24jlkljnTttP"
},
"source": [
"## Install NNSVS"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "UdRQ5pMuYtFj",
"outputId": "8c927906-b0ad-4966-dd07-acdadf3b6455"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Processing /content/nnsvs\n",
" Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
" Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
" Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n",
"Requirement already satisfied: cython in /usr/local/lib/python3.7/dist-packages (from nnsvs==0.0.3) (0.29.30)\n",
"Requirement already satisfied: pysptk in /usr/local/lib/python3.7/dist-packages (from nnsvs==0.0.3) (0.1.21)\n",
"Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from nnsvs==0.0.3) (1.21.6)\n",
"Collecting pyworld\n",
" Downloading pyworld-0.3.0.tar.gz (212 kB)\n",
"\u001b[K |████████████████████████████████| 212 kB 4.2 MB/s \n",
"\u001b[?25h Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
" Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
" Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n",
"Requirement already satisfied: torch>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from nnsvs==0.0.3) (1.12.0+cu113)\n",
"Requirement already satisfied: tensorboard in /usr/local/lib/python3.7/dist-packages (from nnsvs==0.0.3) (2.8.0)\n",
"Requirement already satisfied: torchaudio in /usr/local/lib/python3.7/dist-packages (from nnsvs==0.0.3) (0.12.0+cu113)\n",
"Requirement already satisfied: librosa>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from nnsvs==0.0.3) (0.8.1)\n",
"Collecting hydra-core<1.2.0,>=1.1.0\n",
" Downloading hydra_core-1.1.2-py3-none-any.whl (147 kB)\n",
"\u001b[K |████████████████████████████████| 147 kB 55.5 MB/s \n",
"\u001b[?25hRequirement already satisfied: pysinsy in /usr/local/lib/python3.7/dist-packages (from nnsvs==0.0.3) (0.0.4)\n",
"Requirement already satisfied: nnmnkwii in /usr/local/lib/python3.7/dist-packages (from nnsvs==0.0.3) (0.1.2+86cec77)\n",
"Collecting hydra-colorlog>=1.1.0\n",
" Downloading hydra_colorlog-1.2.0-py3-none-any.whl (3.6 kB)\n",
"Collecting colorlog\n",
" Downloading colorlog-6.6.0-py2.py3-none-any.whl (11 kB)\n",
"Collecting omegaconf==2.1.*\n",
" Downloading omegaconf-2.1.2-py3-none-any.whl (74 kB)\n",
"\u001b[K |████████████████████████████████| 74 kB 3.3 MB/s \n",
"\u001b[?25hCollecting importlib-resources<5.3\n",
" Downloading importlib_resources-5.2.3-py3-none-any.whl (27 kB)\n",
"Collecting antlr4-python3-runtime==4.8\n",
" Downloading antlr4-python3-runtime-4.8.tar.gz (112 kB)\n",
"\u001b[K |████████████████████████████████| 112 kB 71.3 MB/s \n",
"\u001b[?25hCollecting PyYAML>=5.1.0\n",
" Downloading PyYAML-6.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (596 kB)\n",
"\u001b[K |████████████████████████████████| 596 kB 66.2 MB/s \n",
"\u001b[?25hRequirement already satisfied: zipp>=3.1.0 in /usr/local/lib/python3.7/dist-packages (from importlib-resources<5.3->hydra-core<1.2.0,>=1.1.0->nnsvs==0.0.3) (3.8.0)\n",
"Requirement already satisfied: soundfile>=0.10.2 in /usr/local/lib/python3.7/dist-packages (from librosa>=0.7.0->nnsvs==0.0.3) (0.10.3.post1)\n",
"Requirement already satisfied: pooch>=1.0 in /usr/local/lib/python3.7/dist-packages (from librosa>=0.7.0->nnsvs==0.0.3) (1.6.0)\n",
"Requirement already satisfied: joblib>=0.14 in /usr/local/lib/python3.7/dist-packages (from librosa>=0.7.0->nnsvs==0.0.3) (1.1.0)\n",
"Requirement already satisfied: audioread>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from librosa>=0.7.0->nnsvs==0.0.3) (2.1.9)\n",
"Requirement already satisfied: scikit-learn!=0.19.0,>=0.14.0 in /usr/local/lib/python3.7/dist-packages (from librosa>=0.7.0->nnsvs==0.0.3) (1.0.2)\n",
"Requirement already satisfied: decorator>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from librosa>=0.7.0->nnsvs==0.0.3) (4.4.2)\n",
"Requirement already satisfied: numba>=0.43.0 in /usr/local/lib/python3.7/dist-packages (from librosa>=0.7.0->nnsvs==0.0.3) (0.51.2)\n",
"Requirement already satisfied: scipy>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from librosa>=0.7.0->nnsvs==0.0.3) (1.7.3)\n",
"Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from librosa>=0.7.0->nnsvs==0.0.3) (21.3)\n",
"Requirement already satisfied: resampy>=0.2.2 in /usr/local/lib/python3.7/dist-packages (from librosa>=0.7.0->nnsvs==0.0.3) (0.3.1)\n",
"Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from numba>=0.43.0->librosa>=0.7.0->nnsvs==0.0.3) (57.4.0)\n",
"Requirement already satisfied: llvmlite<0.35,>=0.34.0.dev0 in /usr/local/lib/python3.7/dist-packages (from numba>=0.43.0->librosa>=0.7.0->nnsvs==0.0.3) (0.34.0)\n",
"Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->librosa>=0.7.0->nnsvs==0.0.3) (3.0.9)\n",
"Requirement already satisfied: appdirs>=1.3.0 in /usr/local/lib/python3.7/dist-packages (from pooch>=1.0->librosa>=0.7.0->nnsvs==0.0.3) (1.4.4)\n",
"Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.7/dist-packages (from pooch>=1.0->librosa>=0.7.0->nnsvs==0.0.3) (2.23.0)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->pooch>=1.0->librosa>=0.7.0->nnsvs==0.0.3) (2022.6.15)\n",
"Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->pooch>=1.0->librosa>=0.7.0->nnsvs==0.0.3) (1.24.3)\n",
"Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->pooch>=1.0->librosa>=0.7.0->nnsvs==0.0.3) (3.0.4)\n",
"Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->pooch>=1.0->librosa>=0.7.0->nnsvs==0.0.3) (2.10)\n",
"Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn!=0.19.0,>=0.14.0->librosa>=0.7.0->nnsvs==0.0.3) (3.1.0)\n",
"Requirement already satisfied: cffi>=1.0 in /usr/local/lib/python3.7/dist-packages (from soundfile>=0.10.2->librosa>=0.7.0->nnsvs==0.0.3) (1.15.1)\n",
"Requirement already satisfied: pycparser in /usr/local/lib/python3.7/dist-packages (from cffi>=1.0->soundfile>=0.10.2->librosa>=0.7.0->nnsvs==0.0.3) (2.21)\n",
"Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch>=1.6.0->nnsvs==0.0.3) (4.1.1)\n",
"Requirement already satisfied: fastdtw in /usr/local/lib/python3.7/dist-packages (from nnmnkwii->nnsvs==0.0.3) (0.3.4)\n",
"Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from nnmnkwii->nnsvs==0.0.3) (4.64.0)\n",
"Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from pysptk->nnsvs==0.0.3) (1.15.0)\n",
"Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard->nnsvs==0.0.3) (0.6.1)\n",
"Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard->nnsvs==0.0.3) (1.8.1)\n",
"Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.7/dist-packages (from tensorboard->nnsvs==0.0.3) (1.0.1)\n",
"Requirement already satisfied: absl-py>=0.4 in /usr/local/lib/python3.7/dist-packages (from tensorboard->nnsvs==0.0.3) (1.1.0)\n",
"Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.7/dist-packages (from tensorboard->nnsvs==0.0.3) (0.4.6)\n",
"Requirement already satisfied: grpcio>=1.24.3 in /usr/local/lib/python3.7/dist-packages (from tensorboard->nnsvs==0.0.3) (1.47.0)\n",
"Requirement already satisfied: google-auth<3,>=1.6.3 in /usr/local/lib/python3.7/dist-packages (from tensorboard->nnsvs==0.0.3) (1.35.0)\n",
"Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.7/dist-packages (from tensorboard->nnsvs==0.0.3) (0.37.1)\n",
"Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.7/dist-packages (from tensorboard->nnsvs==0.0.3) (3.3.7)\n",
"Requirement already satisfied: protobuf>=3.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard->nnsvs==0.0.3) (3.17.3)\n",
"Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tensorboard->nnsvs==0.0.3) (4.8)\n",
"Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tensorboard->nnsvs==0.0.3) (0.2.8)\n",
"Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tensorboard->nnsvs==0.0.3) (4.2.4)\n",
"Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard->nnsvs==0.0.3) (1.3.1)\n",
"Requirement already satisfied: importlib-metadata>=4.4 in /usr/local/lib/python3.7/dist-packages (from markdown>=2.6.8->tensorboard->nnsvs==0.0.3) (4.12.0)\n",
"Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.7/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard->nnsvs==0.0.3) (0.4.8)\n",
"Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard->nnsvs==0.0.3) (3.2.0)\n",
"Building wheels for collected packages: nnsvs, antlr4-python3-runtime, pyworld\n",
" Building wheel for nnsvs (PEP 517) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for nnsvs: filename=nnsvs-0.0.3-py3-none-any.whl size=2343874 sha256=dfddc303caf78670303aedff4cff1503a049433cd617f6f836a5ed807ffb89c9\n",
" Stored in directory: /tmp/pip-ephem-wheel-cache-irq6zjwf/wheels/db/2b/ae/0c12ddf83c351cf9d279be61d900a5a32727bfc3a54acb4457\n",
" Building wheel for antlr4-python3-runtime (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for antlr4-python3-runtime: filename=antlr4_python3_runtime-4.8-py3-none-any.whl size=141230 sha256=03374eb833a39f4f270dbacc2530e705b7226fe5adb4d62044d12581b8cec99a\n",
" Stored in directory: /root/.cache/pip/wheels/ca/33/b7/336836125fc9bb4ceaa4376d8abca10ca8bc84ddc824baea6c\n",
" Building wheel for pyworld (PEP 517) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for pyworld: filename=pyworld-0.3.0-cp37-cp37m-linux_x86_64.whl size=609473 sha256=7fd508150c3f3417f241df4042c8ca7864003258d1f9cbedd89fb11a15408c91\n",
" Stored in directory: /root/.cache/pip/wheels/e7/7c/11/c775fffa0e1e7b05a6604b4323408a77f80fb4ab304d96b5c6\n",
"Successfully built nnsvs antlr4-python3-runtime pyworld\n",
"Installing collected packages: PyYAML, antlr4-python3-runtime, omegaconf, importlib-resources, hydra-core, colorlog, pyworld, hydra-colorlog, nnsvs\n",
" Attempting uninstall: PyYAML\n",
" Found existing installation: PyYAML 3.13\n",
" Uninstalling PyYAML-3.13:\n",
" Successfully uninstalled PyYAML-3.13\n",
" Attempting uninstall: importlib-resources\n",
" Found existing installation: importlib-resources 5.8.0\n",
" Uninstalling importlib-resources-5.8.0:\n",
" Successfully uninstalled importlib-resources-5.8.0\n",
"Successfully installed PyYAML-6.0 antlr4-python3-runtime-4.8 colorlog-6.6.0 hydra-colorlog-1.2.0 hydra-core-1.1.2 importlib-resources-5.2.3 nnsvs-0.0.3 omegaconf-2.1.2 pyworld-0.3.0\n"
]
}
],
"source": [
"! git clone -b dev20220717 -q https://github.com/taroushirani/nnsvs\n",
"! cd nnsvs && pip install . --use-feature=in-tree-build"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "zVs9OAsrVTve"
},
"source": [
"## Recipe setting"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "nJHNL9htIP4D"
},
"outputs": [],
"source": [
"RECIPE_ROOT=\"/content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "XTb5MRRHUGAh",
"outputId": "aec6834f-abd4-47f6-a2ab-653d18f8411e"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Collecting jaconv\n",
" Downloading jaconv-0.3.tar.gz (15 kB)\n",
"Building wheels for collected packages: jaconv\n",
" Building wheel for jaconv (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for jaconv: filename=jaconv-0.3-py3-none-any.whl size=15564 sha256=e38e37482c60b9cf1ecbd0f276e0857a527acb2742d24c0e9c0f9b7170ff366c\n",
" Stored in directory: /root/.cache/pip/wheels/8f/4f/c2/a2a3b14d0e94f855f4aa8887bf0267bee9ecfb8e62a9ee2d92\n",
"Successfully built jaconv\n",
"Installing collected packages: jaconv\n",
"Successfully installed jaconv-0.3\n"
]
}
],
"source": [
"! pip install jaconv"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "iCTC2-NSmdSN",
"outputId": "a5afdf57-899e-4d2e-e69b-d42cbc7815ce"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (3.2.2)\n",
"Requirement already satisfied: mlflow in /usr/local/lib/python3.7/dist-packages (1.27.0)\n",
"Collecting optuna\n",
" Downloading optuna-2.10.1-py3-none-any.whl (308 kB)\n",
"\u001b[K |████████████████████████████████| 308 kB 3.9 MB/s \n",
"\u001b[?25hCollecting hydra-optuna-sweeper\n",
" Downloading hydra_optuna_sweeper-1.2.0-py3-none-any.whl (8.5 kB)\n",
"Requirement already satisfied: protobuf in /usr/local/lib/python3.7/dist-packages (3.17.3)\n",
"Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (3.0.9)\n",
"Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (0.11.0)\n",
"Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (1.4.3)\n",
"Requirement already satisfied: numpy>=1.11 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (1.21.6)\n",
"Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (2.8.2)\n",
"Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib) (4.1.1)\n",
"Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib) (1.15.0)\n",
"Requirement already satisfied: querystring-parser in /usr/local/lib/python3.7/dist-packages (from mlflow) (1.2.4)\n",
"Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from mlflow) (1.7.3)\n",
"Requirement already satisfied: alembic in /usr/local/lib/python3.7/dist-packages (from mlflow) (1.8.1)\n",
"Requirement already satisfied: Flask in /usr/local/lib/python3.7/dist-packages (from mlflow) (1.1.4)\n",
"Requirement already satisfied: docker>=4.0.0 in /usr/local/lib/python3.7/dist-packages (from mlflow) (5.0.3)\n",
"Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.7/dist-packages (from mlflow) (6.0)\n",
"Requirement already satisfied: sqlparse>=0.3.1 in /usr/local/lib/python3.7/dist-packages (from mlflow) (0.4.2)\n",
"Requirement already satisfied: gitpython>=2.1.0 in /usr/local/lib/python3.7/dist-packages (from mlflow) (3.1.27)\n",
"Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from mlflow) (1.3.5)\n",
"Requirement already satisfied: prometheus-flask-exporter in /usr/local/lib/python3.7/dist-packages (from mlflow) (0.20.2)\n",
"Requirement already satisfied: click>=7.0 in /usr/local/lib/python3.7/dist-packages (from mlflow) (7.1.2)\n",
"Requirement already satisfied: importlib-metadata!=4.7.0,>=3.7.0 in /usr/local/lib/python3.7/dist-packages (from mlflow) (4.12.0)\n",
"Requirement already satisfied: entrypoints in /usr/local/lib/python3.7/dist-packages (from mlflow) (0.4)\n",
"Requirement already satisfied: pytz in /usr/local/lib/python3.7/dist-packages (from mlflow) (2022.1)\n",
"Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from mlflow) (21.3)\n",
"Requirement already satisfied: cloudpickle in /usr/local/lib/python3.7/dist-packages (from mlflow) (1.3.0)\n",
"Requirement already satisfied: requests>=2.17.3 in /usr/local/lib/python3.7/dist-packages (from mlflow) (2.23.0)\n",
"Requirement already satisfied: databricks-cli>=0.8.7 in /usr/local/lib/python3.7/dist-packages (from mlflow) (0.17.0)\n",
"Requirement already satisfied: sqlalchemy>=1.4.0 in /usr/local/lib/python3.7/dist-packages (from mlflow) (1.4.39)\n",
"Requirement already satisfied: gunicorn in /usr/local/lib/python3.7/dist-packages (from mlflow) (20.1.0)\n",
"Requirement already satisfied: pyjwt>=1.7.0 in /usr/local/lib/python3.7/dist-packages (from databricks-cli>=0.8.7->mlflow) (2.4.0)\n",
"Requirement already satisfied: oauthlib>=3.1.0 in /usr/local/lib/python3.7/dist-packages (from databricks-cli>=0.8.7->mlflow) (3.2.0)\n",
"Requirement already satisfied: tabulate>=0.7.7 in /usr/local/lib/python3.7/dist-packages (from databricks-cli>=0.8.7->mlflow) (0.8.10)\n",
"Requirement already satisfied: websocket-client>=0.32.0 in /usr/local/lib/python3.7/dist-packages (from docker>=4.0.0->mlflow) (1.3.3)\n",
"Requirement already satisfied: gitdb<5,>=4.0.1 in /usr/local/lib/python3.7/dist-packages (from gitpython>=2.1.0->mlflow) (4.0.9)\n",
"Requirement already satisfied: smmap<6,>=3.0.1 in /usr/local/lib/python3.7/dist-packages (from gitdb<5,>=4.0.1->gitpython>=2.1.0->mlflow) (5.0.0)\n",
"Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata!=4.7.0,>=3.7.0->mlflow) (3.8.0)\n",
"Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.17.3->mlflow) (3.0.4)\n",
"Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.17.3->mlflow) (1.24.3)\n",
"Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.17.3->mlflow) (2.10)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.17.3->mlflow) (2022.6.15)\n",
"Requirement already satisfied: greenlet!=0.4.17 in /usr/local/lib/python3.7/dist-packages (from sqlalchemy>=1.4.0->mlflow) (1.1.2)\n",
"Requirement already satisfied: colorlog in /usr/local/lib/python3.7/dist-packages (from optuna) (6.6.0)\n",
"Collecting cliff\n",
" Downloading cliff-3.10.1-py3-none-any.whl (81 kB)\n",
"\u001b[K |████████████████████████████████| 81 kB 11.6 MB/s \n",
"\u001b[?25hRequirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from optuna) (4.64.0)\n",
"Collecting cmaes>=0.8.2\n",
" Downloading cmaes-0.8.2-py3-none-any.whl (15 kB)\n",
"Requirement already satisfied: hydra-core>=1.1.0.dev7 in /usr/local/lib/python3.7/dist-packages (from hydra-optuna-sweeper) (1.1.2)\n",
"Requirement already satisfied: omegaconf==2.1.* in /usr/local/lib/python3.7/dist-packages (from hydra-core>=1.1.0.dev7->hydra-optuna-sweeper) (2.1.2)\n",
"Requirement already satisfied: importlib-resources<5.3 in /usr/local/lib/python3.7/dist-packages (from hydra-core>=1.1.0.dev7->hydra-optuna-sweeper) (5.2.3)\n",
"Requirement already satisfied: antlr4-python3-runtime==4.8 in /usr/local/lib/python3.7/dist-packages (from hydra-core>=1.1.0.dev7->hydra-optuna-sweeper) (4.8)\n",
"Requirement already satisfied: Mako in /usr/local/lib/python3.7/dist-packages (from alembic->mlflow) (1.2.1)\n",
"Collecting pbr!=2.1.0,>=2.0.0\n",
" Downloading pbr-5.9.0-py2.py3-none-any.whl (112 kB)\n",
"\u001b[K |████████████████████████████████| 112 kB 71.3 MB/s \n",
"\u001b[?25hRequirement already satisfied: PrettyTable>=0.7.2 in /usr/local/lib/python3.7/dist-packages (from cliff->optuna) (3.3.0)\n",
"Collecting autopage>=0.4.0\n",
" Downloading autopage-0.5.1-py3-none-any.whl (29 kB)\n",
"Collecting cmd2>=1.0.0\n",
" Downloading cmd2-2.4.2-py3-none-any.whl (147 kB)\n",
"\u001b[K |████████████████████████████████| 147 kB 63.4 MB/s \n",
"\u001b[?25hCollecting stevedore>=2.0.1\n",
" Downloading stevedore-3.5.0-py3-none-any.whl (49 kB)\n",
"\u001b[K |████████████████████████████████| 49 kB 7.2 MB/s \n",
"\u001b[?25hRequirement already satisfied: attrs>=16.3.0 in /usr/local/lib/python3.7/dist-packages (from cmd2>=1.0.0->cliff->optuna) (21.4.0)\n",
"Collecting pyperclip>=1.6\n",
" Downloading pyperclip-1.8.2.tar.gz (20 kB)\n",
"Requirement already satisfied: wcwidth>=0.1.7 in /usr/local/lib/python3.7/dist-packages (from cmd2>=1.0.0->cliff->optuna) (0.2.5)\n",
"Requirement already satisfied: Werkzeug<2.0,>=0.15 in /usr/local/lib/python3.7/dist-packages (from Flask->mlflow) (1.0.1)\n",
"Requirement already satisfied: itsdangerous<2.0,>=0.24 in /usr/local/lib/python3.7/dist-packages (from Flask->mlflow) (1.1.0)\n",
"Requirement already satisfied: Jinja2<3.0,>=2.10.1 in /usr/local/lib/python3.7/dist-packages (from Flask->mlflow) (2.11.3)\n",
"Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.7/dist-packages (from Jinja2<3.0,>=2.10.1->Flask->mlflow) (2.0.1)\n",
"Requirement already satisfied: setuptools>=3.0 in /usr/local/lib/python3.7/dist-packages (from gunicorn->mlflow) (57.4.0)\n",
"Requirement already satisfied: prometheus-client in /usr/local/lib/python3.7/dist-packages (from prometheus-flask-exporter->mlflow) (0.14.1)\n",
"Building wheels for collected packages: pyperclip\n",
" Building wheel for pyperclip (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for pyperclip: filename=pyperclip-1.8.2-py3-none-any.whl size=11137 sha256=c5130fa0d36edb39ccc67bcbf3dea7a56311f81db202410b280f61005834f138\n",
" Stored in directory: /root/.cache/pip/wheels/9f/18/84/8f69f8b08169c7bae2dde6bd7daf0c19fca8c8e500ee620a28\n",
"Successfully built pyperclip\n",
"Installing collected packages: pyperclip, pbr, stevedore, cmd2, autopage, cmaes, cliff, optuna, hydra-optuna-sweeper\n",
"Successfully installed autopage-0.5.1 cliff-3.10.1 cmaes-0.8.2 cmd2-2.4.2 hydra-optuna-sweeper-1.2.0 optuna-2.10.1 pbr-5.9.0 pyperclip-1.8.2 stevedore-3.5.0\n"
]
}
],
"source": [
"! pip install matplotlib mlflow optuna hydra-optuna-sweeper protobuf"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "egbw2IYYw1DH"
},
"outputs": [],
"source": [
"! sed -i 's#\\~\\/data#\\/content\\/gdrive#g' $RECIPE_ROOT/config.yaml"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "kvC_xsSOWDUh"
},
"source": [
"# Data preparation"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "HO_LXEu1BGG8"
},
"outputs": [],
"source": [
"#! cd $RECIPE_ROOT && bash run.sh --stage -1 --stop-stage -1"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "0bLMxIs2x2Qr",
"outputId": "db84bf5f-165f-4aee-8862-c98c7e9f8247"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"stage 0: Data preparation\n",
"Convert musicxml to label files.\n",
"100% 56/56 [00:13<00:00, 4.01it/s]\n",
"Copy original label files.\n",
"100% 56/56 [00:11<00:00, 4.84it/s]\n",
"Round label files.\n",
"100% 56/56 [00:00<00:00, 1307.50it/s]\n",
"100% 56/56 [00:00<00:00, 903.64it/s]\n",
"100% 56/56 [00:00<00:00, 1339.84it/s]\n",
"Copy original label files.\n",
"100% 56/56 [00:00<00:00, 507.85it/s]\n",
"Round label files.\n",
"100% 56/56 [00:00<00:00, 1224.00it/s]\n",
"100% 56/56 [00:00<00:00, 780.13it/s]\n",
"100% 56/56 [00:00<00:00, 994.73it/s]\n",
"0it [00:00, ?it/s]Consecutive pau/sil-s are detected.\n",
"Consecutive pau/sil-s are detected.\n",
"Consecutive pau/sil-s are detected.\n",
"akai_kutsu.lab 0.0\n",
"Consecutive pau/sil-s are detected.\n",
"Consecutive pau/sil-s are detected.\n",
"Consecutive pau/sil-s are detected.\n",
"akatonbo.lab 0.0\n",
"Consecutive pau/sil-s are detected.\n",
"Consecutive pau/sil-s are detected.\n",
"Consecutive pau/sil-s are detected.\n",
"amehuri.lab 0.0\n",
"Consecutive pau/sil-s are detected.\n",
"Consecutive pau/sil-s are detected.\n",
"Consecutive pau/sil-s are detected.\n",
"aogeba_toutoshi.lab 0.0\n",
"Consecutive pau/sil-s are detected.\n",
"Consecutive pau/sil-s are detected.\n",
"aoimeno_ningyou.lab 0.0\n",
"Consecutive pau/sil-s are detected.\n",
"Consecutive pau/sil-s are detected.\n",
"Consecutive pau/sil-s are detected.\n",
"arupusu_ichimanjaku.lab 0.0\n",
"Consecutive pau/sil-s are detected.\n",
"Consecutive pau/sil-s are detected.\n",
"chatsumi.lab 0.0\n",
"Consecutive pau/sil-s are detected.\n",
"Consecutive pau/sil-s are detected.\n",
"chouchou.lab 0.0\n",
"Consecutive pau/sil-s are detected.\n",
"die_moldau.lab 0.0\n",
"Consecutive pau/sil-s are detected.\n",
"Consecutive pau/sil-s are detected.\n",
"donguri_korokoro.lab 0.0\n",
"Consecutive pau/sil-s are detected.\n",
"Consecutive pau/sil-s are detected.\n",
"fujino_yama.lab 0.0\n",
"Consecutive pau/sil-s are detected.\n",
"Consecutive pau/sil-s are detected.\n",
"Consecutive pau/sil-s are detected.\n",
"furusato.lab 0.0\n",
"Consecutive pau/sil-s are detected.\n",
"Consecutive pau/sil-s are detected.\n",
"goin_home.lab 0.0\n",
"Consecutive pau/sil-s are detected.\n",
"hamabeno_uta.lab 0.0\n",
"Consecutive pau/sil-s are detected.\n",
"Consecutive pau/sil-s are detected.\n",
"Consecutive pau/sil-s are detected.\n",
"haruga_kita.lab 0.0\n",
"Consecutive pau/sil-s are detected.\n",
"Consecutive pau/sil-s are detected.\n",
"Consecutive pau/sil-s are detected.\n",
"haruno_ogawa.lab 0.0\n",
"Consecutive pau/sil-s are detected.\n",
"Consecutive pau/sil-s are detected.\n",
"haruyo_koi.lab 0.0\n",
"Consecutive pau/sil-s are detected.\n",
"Consecutive pau/sil-s are detected.\n",
"hato.lab 0.0\n",
"Consecutive pau/sil-s are detected.\n",
"Consecutive pau/sil-s are detected.\n",
"hiraita_hiraita.lab 0.0\n",
"Consecutive pau/sil-s are detected.\n",
"Consecutive pau/sil-s are detected.\n",
"hoshinoyo.lab 0.0\n",
"Consecutive pau/sil-s are detected.\n",
"Consecutive pau/sil-s are detected.\n",
"hotaruno_hikari.lab 0.0\n",
"Consecutive pau/sil-s are detected.\n",
"jugoya_otsukisan.lab 0.0\n",
"Consecutive pau/sil-s are detected.\n",
"Consecutive pau/sil-s are detected.\n",
"Consecutive pau/sil-s are detected.\n",
"kachushano_uta.lab 0.0\n",
"Consecutive pau/sil-s are detected.\n",
"kagome_kagome.lab 0.0\n",
"Consecutive pau/sil-s are detected.\n",
"Consecutive pau/sil-s are detected.\n",
"Consecutive pau/sil-s are detected.\n",
"kamomeno_suiheisan.lab 0.0\n",
"Consecutive pau/sil-s are detected.\n",
"Consecutive pau/sil-s are detected.\n",
"katatsumuri.lab 0.0\n",
"Consecutive pau/sil-s are detected.\n",
"Consecutive pau/sil-s are detected.\n",
"kintarou.lab 0.0\n",
"Consecutive pau/sil-s are detected.\n",
"Consecutive pau/sil-s are detected.\n",
"koganemushi.lab 0.0\n",
"Consecutive pau/sil-s are detected.\n",
"koinobori.lab 0.0\n",
"Consecutive pau/sil-s are detected.\n",
"Consecutive pau/sil-s are detected.\n",
"Consecutive pau/sil-s are detected.\n",
"makibano_asa.lab 0.0\n",
"Consecutive pau/sil-s are detected.\n",
"Consecutive pau/sil-s are detected.\n",
"Consecutive pau/sil-s are detected.\n",
"mansikka_on_punanen_marja.lab 0.0\n",
"Consecutive pau/sil-s are detected.\n",
"Consecutive pau/sil-s are detected.\n",
"miwataseba.lab 0.0\n",
"Consecutive pau/sil-s are detected.\n",
"Consecutive pau/sil-s are detected.\n",
"momiji.lab 0.0\n",
"33it [00:00, 321.72it/s]Consecutive pau/sil-s are detected.\n",
"Consecutive pau/sil-s are detected.\n",
"Consecutive pau/sil-s are detected.\n",
"momotarou.lab 0.0\n",
"Consecutive pau/sil-s are detected.\n",
"Consecutive pau/sil-s are detected.\n",
"Consecutive pau/sil-s are detected.\n",
"morobito_kozorite.lab 0.0\n",
"Consecutive pau/sil-s are detected.\n",
"Consecutive pau/sil-s are detected.\n",
"mushino_koe.lab 0.0\n",
"Consecutive pau/sil-s are detected.\n",
"nanatsunoko.lab 0.0\n",
"Consecutive pau/sil-s are detected.\n",
"Consecutive pau/sil-s are detected.\n",
"Consecutive pau/sil-s are detected.\n",
"nonakano_bara.lab 0.0\n",
"Consecutive pau/sil-s are detected.\n",
"Consecutive pau/sil-s are detected.\n",
"Consecutive pau/sil-s are detected.\n",
"peichika.lab 0.0\n",
"Consecutive pau/sil-s are detected.\n",
"Consecutive pau/sil-s are detected.\n",
"romance_anonimo.lab 0.0\n",
"Consecutive pau/sil-s are detected.\n",
"Consecutive pau/sil-s are detected.\n",
"sakura_sakura.lab 0.0\n",
"Consecutive pau/sil-s are detected.\n",
"shabondama.lab 0.0\n",
"Consecutive pau/sil-s are detected.\n",
"Consecutive pau/sil-s are detected.\n",
"Consecutive pau/sil-s are detected.\n",
"shoujoujino_tanukibayashi.lab 0.0\n",
"Consecutive pau/sil-s are detected.\n",
"Consecutive pau/sil-s are detected.\n",
"Consecutive pau/sil-s are detected.\n",
"sousyunfu.lab 0.0\n",
"Consecutive pau/sil-s are detected.\n",
"Consecutive pau/sil-s are detected.\n",
"Consecutive pau/sil-s are detected.\n",
"tetsudou_shouka.lab 0.0\n",
"Consecutive pau/sil-s are detected.\n",
"Consecutive pau/sil-s are detected.\n",
"Consecutive pau/sil-s are detected.\n",
"the_other_day_i_met_a_bear.lab 0.0\n",
"Consecutive pau/sil-s are detected.\n",
"tonbi.lab 1.0\n",
"Consecutive pau/sil-s are detected.\n",
"toryanse.lab 0.0\n",
"Consecutive pau/sil-s are detected.\n",
"troika.lab 0.0\n",
"Consecutive pau/sil-s are detected.\n",
"Consecutive pau/sil-s are detected.\n",
"Consecutive pau/sil-s are detected.\n",
"twinkle_twinkle_little_star.lab 0.0\n",
"Consecutive pau/sil-s are detected.\n",
"Consecutive pau/sil-s are detected.\n",
"Consecutive pau/sil-s are detected.\n",
"urashima_tarou.lab 0.0\n",
"Consecutive pau/sil-s are detected.\n",
"usagi.lab 0.0\n",
"Consecutive pau/sil-s are detected.\n",
"Consecutive pau/sil-s are detected.\n",
"usagito_kame.lab 6.0\n",
"Consecutive pau/sil-s are detected.\n",
"Consecutive pau/sil-s are detected.\n",
"yuki.lab 0.0\n",
"Consecutive pau/sil-s are detected.\n",
"Consecutive pau/sil-s are detected.\n",
"Consecutive pau/sil-s are detected.\n",
"yurikagono_uta.lab 0.0\n",
"Consecutive pau/sil-s are detected.\n",
"zuizui_zukkorobashi.lab 0.0\n",
"56it [00:00, 319.51it/s]\n",
"56it [00:00, 821.91it/s]\n",
"56it [00:00, 144.75it/s]\n",
"56it [00:00, 141.29it/s]\n",
"56it [00:00, 148.67it/s]\n",
"akai_kutsu.lab: segment duration min 10.70, max 16.59, mean 12.82\n",
"akatonbo.lab: segment duration min 5.08, max 16.92, mean 11.33\n",
"amehuri.lab: segment duration min 5.56, max 15.72, mean 11.21\n",
"aogeba_toutoshi.lab: segment duration min 5.73, max 29.72, mean 11.16\n",
"aoimeno_ningyou.lab: segment duration min 5.44, max 12.48, mean 7.65\n",
"arupusu_ichimanjaku.lab: segment duration min 7.09, max 9.03, mean 8.15\n",
"chatsumi.lab: segment duration min 6.96, max 11.62, mean 7.97\n",
"chouchou.lab: segment duration min 5.02, max 11.43, mean 7.18\n",
"die_moldau.lab: segment duration min 5.17, max 19.72, mean 10.59\n",
"donguri_korokoro.lab: segment duration min 5.84, max 8.04, mean 7.31\n",
"fujino_yama.lab: segment duration min 7.24, max 14.39, mean 10.37\n",
"furusato.lab: segment duration min 8.11, max 12.72, mean 9.13\n",
"goin_home.lab: segment duration min 7.95, max 8.79, mean 8.51\n",
"hamabeno_uta.lab: segment duration min 5.62, max 12.87, mean 6.63\n",
"haruga_kita.lab: segment duration min 7.48, max 11.29, mean 8.86\n",
"haruno_ogawa.lab: segment duration min 8.62, max 13.21, mean 9.54\n",
"haruyo_koi.lab: segment duration min 6.42, max 10.33, mean 8.88\n",
"hato.lab: segment duration min 6.88, max 14.16, mean 11.55\n",
"hiraita_hiraita.lab: segment duration min 9.34, max 14.28, mean 12.16\n",
"hoshinoyo.lab: segment duration min 5.01, max 10.86, mean 7.10\n",
"hotaruno_hikari.lab: segment duration min 6.06, max 13.36, mean 9.66\n",
"jugoya_otsukisan.lab: segment duration min 8.72, max 13.53, mean 9.76\n",
"kachushano_uta.lab: segment duration min 8.98, max 12.76, mean 10.02\n",
"kagome_kagome.lab: segment duration min 7.57, max 11.50, mean 9.06\n",
"kamomeno_suiheisan.lab: segment duration min 5.86, max 9.16, mean 6.55\n",
"katatsumuri.lab: segment duration min 5.79, max 9.15, mean 6.82\n",
"kintarou.lab: segment duration min 5.55, max 8.42, mean 6.42\n",
"koganemushi.lab: segment duration min 8.18, max 18.71, mean 12.96\n",
"koinobori.lab: segment duration min 7.46, max 11.50, mean 8.53\n",
"makibano_asa.lab: segment duration min 5.23, max 17.76, mean 10.16\n",
"mansikka_on_punanen_marja.lab: segment duration min 10.05, max 23.52, mean 14.34\n",
"miwataseba.lab: segment duration min 9.17, max 20.23, mean 14.50\n",
"momiji.lab: segment duration min 7.46, max 10.00, mean 9.64\n",
"momotarou.lab: segment duration min 6.02, max 14.02, mean 10.56\n",
"morobito_kozorite.lab: segment duration min 11.60, max 30.07, mean 16.79\n",
"mushino_koe.lab: segment duration min 5.45, max 8.54, mean 7.57\n",
"nanatsunoko.lab: segment duration min 5.19, max 11.46, mean 9.13\n",
"nonakano_bara.lab: segment duration min 5.00, max 10.81, mean 6.04\n",
"peichika.lab: segment duration min 5.34, max 11.56, mean 6.97\n",
"romance_anonimo.lab: segment duration min 8.04, max 37.63, mean 28.05\n",
"sakura_sakura.lab: segment duration min 10.13, max 15.50, mean 13.31\n",
"shabondama.lab: segment duration min 5.78, max 12.62, mean 6.67\n",
"shoujoujino_tanukibayashi.lab: segment duration min 5.23, max 20.20, mean 13.06\n",
"sousyunfu.lab: segment duration min 5.04, max 5.89, mean 5.57\n",
"tetsudou_shouka.lab: segment duration min 17.75, max 18.52, mean 18.13\n",
"the_other_day_i_met_a_bear.lab: segment duration min 6.00, max 12.21, mean 8.78\n",
"tonbi.lab: segment duration min 9.29, max 12.09, mean 10.10\n",
"toryanse.lab: segment duration min 42.04, max 42.04, mean 42.04\n",
"troika.lab: segment duration min 5.40, max 9.15, mean 6.99\n",
"twinkle_twinkle_little_star.lab: segment duration min 5.27, max 11.24, mean 6.13\n",
"urashima_tarou.lab: segment duration min 20.22, max 21.22, mean 20.69\n",
"usagi.lab: segment duration min 18.36, max 18.36, mean 18.36\n",
"usagito_kame.lab: segment duration min 17.93, max 19.86, mean 18.90\n",
"yuki.lab: segment duration min 21.86, max 23.29, mean 22.57\n",
"yurikagono_uta.lab: segment duration min 8.05, max 19.04, mean 11.62\n",
"zuizui_zukkorobashi.lab: segment duration min 6.27, max 9.04, mean 7.30\n",
"akai_kutsu.lab: segment lengths: 16.59, 10.76, 14.01, 10.78, 14.06, 10.70, \n",
"akatonbo.lab: segment lengths: 11.57, 16.92, 9.09, 5.08, 11.09, 14.68, 10.89, \n",
"amehuri.lab: segment lengths: 15.72, 14.79, 5.56, 8.76, \n",
"aogeba_toutoshi.lab: segment lengths: 6.04, 5.73, 11.81, 5.97, 5.79, 12.58, 17.01, 12.00, 12.29, 12.81, 29.72, 5.98, 5.93, 12.53, \n",
"aoimeno_ningyou.lab: segment lengths: 12.48, 6.67, 9.14, 6.29, 5.69, 6.31, 5.96, 10.84, 5.44, \n",
"arupusu_ichimanjaku.lab: segment lengths: 9.03, 8.35, 8.12, 7.09, \n",
"chatsumi.lab: segment lengths: 11.62, 7.13, 7.14, 6.96, 9.67, 7.09, 7.11, 7.01, \n",
"chouchou.lab: segment lengths: 8.00, 7.97, 10.63, 5.21, 5.15, 5.02, 5.71, 5.22, 8.06, 10.72, 5.12, 5.09, 11.43, \n",
"die_moldau.lab: segment lengths: 5.17, 14.39, 9.93, 9.64, 9.60, 9.57, 9.80, 5.48, 5.19, 14.43, 19.72, 9.68, 9.55, 16.09, \n",
"donguri_korokoro.lab: segment lengths: 7.58, 7.49, 5.84, 7.59, 8.04, \n",
"fujino_yama.lab: segment lengths: 9.49, 14.30, 9.48, 9.47, 7.24, 14.39, 9.43, 9.17, \n",
"furusato.lab: segment lengths: 12.72, 8.23, 8.44, 8.39, 10.84, 8.35, 8.33, 8.37, 10.97, 8.11, 8.46, 8.32, \n",
"goin_home.lab: segment lengths: 8.61, 8.49, 8.79, 8.63, 8.46, 8.69, 8.18, 8.61, 8.46, 8.75, 8.57, 8.59, 8.34, 7.95, \n",
"hamabeno_uta.lab: segment lengths: 12.37, 6.21, 5.86, 6.21, 5.71, 6.26, 5.96, 6.15, 5.62, 6.19, 5.71, 6.22, 5.94, 6.19, 6.01, 6.19, 5.97, 6.20, 5.69, 12.87, 6.19, 6.16, \n",
"haruga_kita.lab: segment lengths: 11.29, 7.48, 9.56, 7.64, 9.59, 7.60, \n",
"haruno_ogawa.lab: segment lengths: 13.21, 8.71, 8.66, 8.89, 11.15, 8.62, 8.71, 8.89, 11.12, 8.62, 8.64, 9.23, \n",
"haruyo_koi.lab: segment lengths: 7.49, 10.03, 6.42, 10.10, 10.33, \n",
"hato.lab: segment lengths: 13.59, 6.88, 14.16, \n",
"hiraita_hiraita.lab: segment lengths: 10.79, 14.28, 9.34, 14.25, \n",
"hoshinoyo.lab: segment lengths: 10.82, 10.85, 10.86, 5.04, 5.17, 5.08, 8.25, 5.01, 5.10, 5.05, 10.73, 5.13, 5.15, \n",
"hotaruno_hikari.lab: segment lengths: 6.26, 6.18, 6.25, 6.06, 12.71, 13.36, 11.20, 6.13, 12.50, 12.76, 12.80, \n",
"jugoya_otsukisan.lab: segment lengths: 13.53, 9.04, 8.72, 8.86, 8.74, 9.66, \n",
"kachushano_uta.lab: segment lengths: 8.98, 9.05, 9.61, 12.65, 9.17, 9.21, 12.76, 9.13, 9.57, \n",
"kagome_kagome.lab: segment lengths: 7.57, 11.50, 8.10, \n",
"kamomeno_suiheisan.lab: segment lengths: 9.16, 5.86, 6.04, 5.95, 7.89, 5.93, 6.13, 6.01, 7.88, 5.88, 5.91, 5.98, \n",
"katatsumuri.lab: segment lengths: 9.15, 5.97, 5.79, 8.15, 5.90, 5.97, \n",
"kintarou.lab: segment lengths: 8.42, 5.63, 5.79, 7.08, 5.55, 6.05, \n",
"koganemushi.lab: segment lengths: 11.98, 8.18, 18.71, \n",
"koinobori.lab: segment lengths: 11.50, 7.46, 7.59, 7.58, \n",
"makibano_asa.lab: segment lengths: 17.62, 6.32, 6.82, 7.34, 15.99, 17.76, 5.23, 14.07, 6.38, 6.85, 7.33, \n",
"mansikka_on_punanen_marja.lab: segment lengths: 15.03, 10.05, 23.52, 12.67, 10.46, \n",
"miwataseba.lab: segment lengths: 20.23, 9.17, 19.15, 9.46, \n",
"momiji.lab: segment lengths: 9.84, 9.85, 9.97, 9.90, 7.46, 9.97, 9.89, 9.87, 10.00, \n",
"momotarou.lab: segment lengths: 6.02, 8.21, 14.02, 14.01, \n",
"morobito_kozorite.lab: segment lengths: 30.07, 11.75, 15.07, 11.60, 15.44, \n",
"mushino_koe.lab: segment lengths: 8.41, 8.54, 8.39, 5.67, 7.06, 8.48, 8.53, 5.45, \n",
"nanatsunoko.lab: segment lengths: 8.49, 8.30, 11.46, 11.35, 11.30, 5.58, 5.19, 11.40, \n",
"nonakano_bara.lab: segment lengths: 10.81, 5.16, 5.12, 5.18, 5.00, 5.12, 5.26, 8.29, 10.74, 5.04, 5.05, 5.32, 5.42, 8.04, 5.17, 5.01, 5.02, 5.01, 5.20, 5.75, \n",
"peichika.lab: segment lengths: 6.69, 6.76, 5.96, 5.56, 11.54, 6.64, 5.87, 5.34, 11.56, 6.69, 5.44, 5.58, \n",
"romance_anonimo.lab: segment lengths: 37.63, 8.04, 25.14, 35.71, 33.73, \n",
"sakura_sakura.lab: segment lengths: 15.50, 10.21, 15.45, 13.17, 10.13, 15.40, \n",
"shabondama.lab: segment lengths: 12.62, 6.11, 6.07, 5.96, 6.15, 5.90, 5.88, 5.78, 5.92, 6.29, \n",
"shoujoujino_tanukibayashi.lab: segment lengths: 20.20, 13.66, 13.15, 5.23, \n",
"sousyunfu.lab: segment lengths: 5.73, 5.08, 5.89, 5.04, 5.72, 5.62, 5.72, 5.19, 5.66, 5.42, 5.75, 5.31, 5.76, 5.67, 5.70, 5.42, 5.66, 5.56, 5.79, 5.22, 5.70, 5.66, 5.71, 5.75, \n",
"tetsudou_shouka.lab: segment lengths: 18.52, 17.75, 18.13, \n",
"the_other_day_i_met_a_bear.lab: segment lengths: 8.23, 7.83, 12.21, 7.85, 6.00, 7.72, 10.22, 10.56, 6.09, 7.83, 10.30, 10.49, \n",
"tonbi.lab: segment lengths: 11.81, 9.44, 9.52, 9.47, 12.09, 9.29, 9.64, 9.53, \n",
"toryanse.lab: segment lengths: 42.04, \n",
"troika.lab: segment lengths: 7.12, 6.42, 5.40, 9.15, 7.13, 6.29, 5.44, 8.79, 7.28, 6.20, 5.45, 9.15, \n",
"twinkle_twinkle_little_star.lab: segment lengths: 11.24, 5.36, 5.37, 5.31, 5.35, 5.97, 8.53, 5.40, 5.27, 5.29, 5.35, 5.64, 8.55, 5.37, 5.33, 5.38, 5.36, 6.17, \n",
"urashima_tarou.lab: segment lengths: 21.22, 20.22, 20.63, \n",
"usagi.lab: segment lengths: 18.36, \n",
"usagito_kame.lab: segment lengths: 19.86, 18.91, 17.93, \n",
"yuki.lab: segment lengths: 23.29, 21.86, \n",
"yurikagono_uta.lab: segment lengths: 12.36, 8.11, 19.04, 10.55, 8.05, \n",
"zuizui_zukkorobashi.lab: segment lengths: 6.65, 7.92, 6.62, 9.04, 6.27, \n",
"Segmentation stats: min 5.00, max 42.04, mean 9.26\n",
"Total number of segments: 451\n",
"/usr/local/lib/python3.7/dist-packages/resampy/interpn.py:114: NumbaWarning: The TBB threading layer requires TBB version 2019.5 or later i.e., TBB_INTERFACE_VERSION >= 11005. Found TBB_INTERFACE_VERSION = 9107. The TBB threading layer is disabled.\n",
" _resample_loop_p(x, t_out, interp_win, interp_delta, num_table, scale, y)\n",
"Prepare data for time-lag models\n",
" 0% 0/56 [00:00<?, ?it/s]akai_kutsu: Global offset (in sec): -0.045\n",
"akai_kutsu_seg0.lab offset (in sec): -0.045\n",
"akai_kutsu_seg1.lab offset (in sec): -0.045\n",
"akai_kutsu_seg2.lab offset (in sec): -0.04\n",
"akai_kutsu_seg3.lab offset (in sec): -0.034999999999999996\n",
"akai_kutsu_seg4.lab offset (in sec): -0.045\n",
"akai_kutsu_seg5.lab offset (in sec): -0.024999999999999998\n",
"akatonbo: Global offset (in sec): -0.045\n",
"akatonbo_seg0.lab offset (in sec): -0.06\n",
"akatonbo_seg1.lab offset (in sec): -0.034999999999999996\n",
"akatonbo_seg2.lab offset (in sec): -0.06\n",
"akatonbo_seg3.lab offset (in sec): -0.049999999999999996\n",
"akatonbo_seg4.lab offset (in sec): -0.034999999999999996\n",
"akatonbo.lab: 1/18 time-lags are excluded.\n",
"akatonbo_seg5.lab offset (in sec): -0.04\n",
"akatonbo.lab: 1/15 time-lags are excluded.\n",
"akatonbo_seg6.lab offset (in sec): -0.03\n",
"akatonbo.lab: 1/18 time-lags are excluded.\n",
"amehuri: Global offset (in sec): -0.049999999999999996\n",
"amehuri_seg0.lab offset (in sec): -0.049999999999999996\n",
"amehuri.lab: 2/41 time-lags are excluded.\n",
"amehuri_seg1.lab offset (in sec): -0.049999999999999996\n",
"amehuri.lab: 2/40 time-lags are excluded.\n",
"amehuri_seg2.lab offset (in sec): -0.045\n",
"amehuri_seg3.lab offset (in sec): -0.045\n",
"amehuri.lab: 1/27 time-lags are excluded.\n",
"aogeba_toutoshi: Global offset (in sec): -0.055\n",
"aogeba_toutoshi_seg0.lab offset (in sec): -0.08\n",
"aogeba_toutoshi_seg1.lab offset (in sec): -0.06999999999999999\n",
"aogeba_toutoshi_seg2.lab offset (in sec): -0.06\n",
"aogeba_toutoshi_seg3.lab offset (in sec): -0.06\n",
"aogeba_toutoshi_seg4.lab offset (in sec): -0.08\n",
"aogeba_toutoshi_seg5.lab offset (in sec): -0.075\n",
"aogeba_toutoshi.lab: 1/16 time-lags are excluded.\n",
"aogeba_toutoshi_seg6.lab offset (in sec): -0.045\n",
"aogeba_toutoshi_seg7.lab offset (in sec): -0.06999999999999999\n",
"aogeba_toutoshi_seg8.lab offset (in sec): -0.034999999999999996\n",
"aogeba_toutoshi_seg9.lab offset (in sec): -0.049999999999999996\n",
"aogeba_toutoshi.lab: 1/16 time-lags are excluded.\n",
"aogeba_toutoshi_seg10.lab offset (in sec): -0.055\n",
"aogeba_toutoshi_seg11.lab offset (in sec): -0.065\n",
"aogeba_toutoshi_seg12.lab offset (in sec): -0.06999999999999999\n",
"aogeba_toutoshi_seg13.lab offset (in sec): -0.04\n",
"aogeba_toutoshi.lab: 1/16 time-lags are excluded.\n",
"aoimeno_ningyou: Global offset (in sec): -0.049999999999999996\n",
"aoimeno_ningyou_seg0.lab offset (in sec): -0.049999999999999996\n",
"aoimeno_ningyou_seg1.lab offset (in sec): -0.045\n",
"aoimeno_ningyou_seg2.lab offset (in sec): -0.055\n",
"aoimeno_ningyou_seg3.lab offset (in sec): -0.045\n",
"aoimeno_ningyou_seg4.lab offset (in sec): -0.055\n",
"aoimeno_ningyou_seg5.lab offset (in sec): -0.049999999999999996\n",
"aoimeno_ningyou_seg6.lab offset (in sec): -0.06999999999999999\n",
"aoimeno_ningyou.lab: 1/14 time-lags are excluded.\n",
"aoimeno_ningyou_seg7.lab offset (in sec): -0.045\n",
"aoimeno_ningyou_seg8.lab offset (in sec): -0.02\n",
"arupusu_ichimanjaku: Global offset (in sec): -0.034999999999999996\n",
"arupusu_ichimanjaku_seg0.lab offset (in sec): -0.03\n",
"arupusu_ichimanjaku_seg1.lab offset (in sec): -0.034999999999999996\n",
"arupusu_ichimanjaku_seg2.lab offset (in sec): -0.049999999999999996\n",
"arupusu_ichimanjaku.lab: 2/29 time-lags are excluded.\n",
"arupusu_ichimanjaku_seg3.lab offset (in sec): -0.02\n",
"chatsumi: Global offset (in sec): -0.049999999999999996\n",
"chatsumi_seg0.lab offset (in sec): -0.055\n",
"chatsumi_seg1.lab offset (in sec): -0.055\n",
"chatsumi_seg2.lab offset (in sec): -0.04\n",
"chatsumi_seg3.lab offset (in sec): -0.06\n",
"chatsumi_seg4.lab offset (in sec): -0.065\n",
"chatsumi_seg5.lab offset (in sec): -0.034999999999999996\n",
"chatsumi_seg6.lab offset (in sec): -0.049999999999999996\n",
"chatsumi_seg7.lab offset (in sec): -0.06\n",
"chouchou: Global offset (in sec): -0.065\n",
"chouchou_seg0.lab offset (in sec): -0.095\n",
"chouchou_seg1.lab offset (in sec): -0.055\n",
"chouchou_seg2.lab offset (in sec): -0.055\n",
"chouchou_seg3.lab offset (in sec): -0.08\n",
"chouchou_seg4.lab offset (in sec): -0.049999999999999996\n",
"chouchou_seg5.lab offset (in sec): -0.06\n",
"chouchou_seg6.lab offset (in sec): -0.075\n",
"chouchou_seg7.lab offset (in sec): -0.04\n",
"chouchou_seg8.lab offset (in sec): -0.06\n",
"chouchou_seg9.lab offset (in sec): -0.06999999999999999\n",
"chouchou_seg10.lab offset (in sec): -0.055\n",
"chouchou_seg11.lab offset (in sec): -0.045\n",
"chouchou_seg12.lab offset (in sec): -0.055\n",
"die_moldau.lab: 1/17 time-lags are excluded.\n",
"die_moldau.lab: 2/13 time-lags are excluded.\n",
"die_moldau.lab: 1/17 time-lags are excluded.\n",
"die_moldau.lab: 2/26 time-lags are excluded.\n",
"die_moldau.lab: 2/14 time-lags are excluded.\n",
"donguri_korokoro: Global offset (in sec): -0.04\n",
"donguri_korokoro_seg0.lab offset (in sec): -0.03\n",
"donguri_korokoro.lab: 1/13 time-lags are excluded.\n",
"donguri_korokoro_seg1.lab offset (in sec): -0.03\n",
"donguri_korokoro_seg2.lab offset (in sec): -0.065\n",
"donguri_korokoro.lab: 2/11 time-lags are excluded.\n",
"donguri_korokoro_seg3.lab offset (in sec): -0.04\n",
"donguri_korokoro.lab: 2/26 time-lags are excluded.\n",
"donguri_korokoro_seg4.lab offset (in sec): -0.034999999999999996\n",
" 18% 10/56 [00:00<00:00, 99.41it/s]fujino_yama: Global offset (in sec): -0.045\n",
"fujino_yama_seg0.lab offset (in sec): -0.045\n",
"fujino_yama_seg1.lab offset (in sec): -0.034999999999999996\n",
"fujino_yama_seg2.lab offset (in sec): -0.045\n",
"fujino_yama_seg3.lab offset (in sec): -0.04\n",
"fujino_yama_seg4.lab offset (in sec): -0.055\n",
"fujino_yama_seg5.lab offset (in sec): -0.03\n",
"fujino_yama_seg6.lab offset (in sec): -0.06\n",
"fujino_yama_seg7.lab offset (in sec): -0.045\n",
"furusato: Global offset (in sec): -0.055\n",
"furusato_seg0.lab offset (in sec): -0.065\n",
"furusato_seg1.lab offset (in sec): -0.06\n",
"furusato_seg2.lab offset (in sec): -0.04\n",
"furusato_seg3.lab offset (in sec): -0.049999999999999996\n",
"furusato_seg4.lab offset (in sec): -0.06\n",
"furusato_seg5.lab offset (in sec): -0.065\n",
"furusato_seg6.lab offset (in sec): -0.034999999999999996\n",
"furusato_seg7.lab offset (in sec): -0.055\n",
"furusato_seg8.lab offset (in sec): -0.06999999999999999\n",
"furusato_seg9.lab offset (in sec): -0.034999999999999996\n",
"furusato_seg10.lab offset (in sec): -0.045\n",
"furusato_seg11.lab offset (in sec): -0.049999999999999996\n",
"goin_home: Global offset (in sec): -0.04\n",
"goin_home_seg0.lab offset (in sec): -0.04\n",
"goin_home_seg1.lab offset (in sec): -0.055\n",
"goin_home_seg2.lab offset (in sec): -0.065\n",
"goin_home_seg3.lab offset (in sec): -0.01\n",
"goin_home.lab: 1/12 time-lags are excluded.\n",
"goin_home_seg4.lab offset (in sec): -0.03\n",
"goin_home_seg5.lab offset (in sec): -0.049999999999999996\n",
"goin_home.lab: 2/12 time-lags are excluded.\n",
"goin_home_seg6.lab offset (in sec): -0.034999999999999996\n",
"goin_home_seg7.lab offset (in sec): -0.02\n",
"goin_home.lab: 1/12 time-lags are excluded.\n",
"goin_home_seg8.lab offset (in sec): -0.015\n",
"goin_home_seg9.lab offset (in sec): -0.015\n",
"goin_home_seg10.lab offset (in sec): -0.005\n",
"goin_home_seg11.lab offset (in sec): -0.01\n",
"goin_home.lab: 1/12 time-lags are excluded.\n",
"goin_home_seg12.lab offset (in sec): -0.049999999999999996\n",
"goin_home_seg13.lab offset (in sec): -0.049999999999999996\n",
"hamabeno_uta: Global offset (in sec): -0.049999999999999996\n",
"hamabeno_uta_seg0.lab offset (in sec): -0.04\n",
"hamabeno_uta_seg1.lab offset (in sec): -0.049999999999999996\n",
"hamabeno_uta.lab: 1/10 time-lags are excluded.\n",
"hamabeno_uta_seg2.lab offset (in sec): -0.065\n",
"hamabeno_uta_seg3.lab offset (in sec): -0.055\n",
"hamabeno_uta_seg4.lab offset (in sec): -0.06999999999999999\n",
"hamabeno_uta_seg5.lab offset (in sec): -0.04\n",
"hamabeno_uta.lab: 1/10 time-lags are excluded.\n",
"hamabeno_uta_seg6.lab offset (in sec): -0.024999999999999998\n",
"hamabeno_uta_seg7.lab offset (in sec): -0.049999999999999996\n",
"hamabeno_uta_seg8.lab offset (in sec): -0.04\n",
"hamabeno_uta_seg9.lab offset (in sec): -0.045\n",
"hamabeno_uta_seg10.lab offset (in sec): -0.06\n",
"hamabeno_uta_seg11.lab offset (in sec): -0.075\n",
"hamabeno_uta_seg12.lab offset (in sec): -0.065\n",
"hamabeno_uta_seg13.lab offset (in sec): -0.024999999999999998\n",
"hamabeno_uta_seg14.lab offset (in sec): -0.065\n",
"hamabeno_uta_seg15.lab offset (in sec): -0.06\n",
"hamabeno_uta_seg16.lab offset (in sec): -0.055\n",
"hamabeno_uta_seg17.lab offset (in sec): -0.055\n",
"hamabeno_uta_seg18.lab offset (in sec): -0.049999999999999996\n",
"hamabeno_uta_seg19.lab offset (in sec): -0.06999999999999999\n",
"hamabeno_uta.lab: 1/14 time-lags are excluded.\n",
"hamabeno_uta_seg20.lab offset (in sec): -0.045\n",
"hamabeno_uta_seg21.lab offset (in sec): -0.049999999999999996\n",
"haruga_kita: Global offset (in sec): -0.049999999999999996\n",
"haruga_kita_seg0.lab offset (in sec): -0.049999999999999996\n",
"haruga_kita_seg1.lab offset (in sec): -0.055\n",
"haruga_kita_seg2.lab offset (in sec): -0.04\n",
"haruga_kita_seg3.lab offset (in sec): -0.065\n",
"haruga_kita_seg4.lab offset (in sec): -0.03\n",
"haruga_kita_seg5.lab offset (in sec): -0.045\n",
"haruno_ogawa: Global offset (in sec): -0.055\n",
"haruno_ogawa_seg0.lab offset (in sec): -0.049999999999999996\n",
"haruno_ogawa_seg1.lab offset (in sec): -0.06\n",
"haruno_ogawa.lab: 1/16 time-lags are excluded.\n",
"haruno_ogawa_seg2.lab offset (in sec): -0.04\n",
"haruno_ogawa_seg3.lab offset (in sec): -0.06\n",
"haruno_ogawa_seg4.lab offset (in sec): -0.045\n",
"haruno_ogawa_seg5.lab offset (in sec): -0.06\n",
"haruno_ogawa_seg6.lab offset (in sec): -0.04\n",
"haruno_ogawa_seg7.lab offset (in sec): -0.055\n",
"haruno_ogawa_seg8.lab offset (in sec): -0.049999999999999996\n",
"haruno_ogawa_seg9.lab offset (in sec): -0.049999999999999996\n",
"haruno_ogawa_seg10.lab offset (in sec): -0.04\n",
"haruno_ogawa_seg11.lab offset (in sec): -0.03\n",
"haruyo_koi: Global offset (in sec): -0.034999999999999996\n",
"haruyo_koi_seg0.lab offset (in sec): -0.01\n",
"haruyo_koi_seg1.lab offset (in sec): -0.03\n",
"haruyo_koi.lab: 1/25 time-lags are excluded.\n",
"haruyo_koi_seg2.lab offset (in sec): -0.03\n",
"haruyo_koi_seg3.lab offset (in sec): -0.03\n",
"haruyo_koi.lab: 1/24 time-lags are excluded.\n",
"haruyo_koi_seg4.lab offset (in sec): -0.034999999999999996\n",
"haruyo_koi.lab: 1/25 time-lags are excluded.\n",
"hato: Global offset (in sec): -0.045\n",
"hato_seg0.lab offset (in sec): -0.024999999999999998\n",
"hato.lab: 2/21 time-lags are excluded.\n",
"hato_seg1.lab offset (in sec): -0.04\n",
"hato_seg2.lab offset (in sec): -0.024999999999999998\n",
"hiraita_hiraita: Global offset (in sec): -0.034999999999999996\n",
"hiraita_hiraita_seg0.lab offset (in sec): -0.049999999999999996\n",
"hiraita_hiraita_seg1.lab offset (in sec): -0.03\n",
"hiraita_hiraita_seg2.lab offset (in sec): -0.03\n",
"hiraita_hiraita_seg3.lab offset (in sec): -0.03\n",
"hoshinoyo: Global offset (in sec): -0.055\n",
"hoshinoyo_seg0.lab offset (in sec): -0.065\n",
"hoshinoyo_seg1.lab offset (in sec): -0.049999999999999996\n",
"hoshinoyo_seg2.lab offset (in sec): -0.049999999999999996\n",
"hoshinoyo.lab: 1/16 time-lags are excluded.\n",
"hoshinoyo_seg3.lab offset (in sec): -0.045\n",
"hoshinoyo_seg4.lab offset (in sec): -0.06999999999999999\n",
"hoshinoyo_seg5.lab offset (in sec): -0.06\n",
"hoshinoyo_seg6.lab offset (in sec): -0.055\n",
"hoshinoyo_seg7.lab offset (in sec): -0.045\n",
"hoshinoyo_seg8.lab offset (in sec): -0.02\n",
"hoshinoyo_seg9.lab offset (in sec): -0.049999999999999996\n",
"hoshinoyo_seg10.lab offset (in sec): -0.049999999999999996\n",
"hoshinoyo_seg11.lab offset (in sec): -0.065\n",
"hoshinoyo_seg12.lab offset (in sec): -0.045\n",
" 36% 20/56 [00:00<00:00, 95.57it/s]hotaruno_hikari: Global offset (in sec): -0.049999999999999996\n",
"hotaruno_hikari_seg0.lab offset (in sec): -0.055\n",
"hotaruno_hikari_seg1.lab offset (in sec): -0.03\n",
"hotaruno_hikari_seg2.lab offset (in sec): -0.04\n",
"hotaruno_hikari_seg3.lab offset (in sec): -0.02\n",
"hotaruno_hikari_seg4.lab offset (in sec): -0.055\n",
"hotaruno_hikari.lab: 1/16 time-lags are excluded.\n",
"hotaruno_hikari_seg5.lab offset (in sec): -0.024999999999999998\n",
"hotaruno_hikari.lab: 1/17 time-lags are excluded.\n",
"hotaruno_hikari_seg6.lab offset (in sec): -0.049999999999999996\n",
"hotaruno_hikari_seg7.lab offset (in sec): -0.04\n",
"hotaruno_hikari_seg8.lab offset (in sec): -0.045\n",
"hotaruno_hikari_seg9.lab offset (in sec): -0.045\n",
"hotaruno_hikari.lab: 1/16 time-lags are excluded.\n",
"hotaruno_hikari_seg10.lab offset (in sec): -0.034999999999999996\n",
"hotaruno_hikari.lab: 1/16 time-lags are excluded.\n",
"jugoya_otsukisan: Global offset (in sec): -0.055\n",
"jugoya_otsukisan_seg0.lab offset (in sec): -0.06\n",
"jugoya_otsukisan.lab: 2/16 time-lags are excluded.\n",
"jugoya_otsukisan_seg1.lab offset (in sec): -0.075\n",
"jugoya_otsukisan_seg2.lab offset (in sec): -0.045\n",
"jugoya_otsukisan_seg3.lab offset (in sec): -0.065\n",
"jugoya_otsukisan_seg4.lab offset (in sec): -0.045\n",
"jugoya_otsukisan_seg5.lab offset (in sec): -0.045\n",
"kachushano_uta: Global offset (in sec): -0.04\n",
"kachushano_uta_seg0.lab offset (in sec): -0.04\n",
"kachushano_uta_seg1.lab offset (in sec): -0.03\n",
"kachushano_uta_seg2.lab offset (in sec): -0.045\n",
"kachushano_uta_seg3.lab offset (in sec): -0.04\n",
"kachushano_uta_seg4.lab offset (in sec): -0.03\n",
"kachushano_uta_seg5.lab offset (in sec): -0.045\n",
"kachushano_uta_seg6.lab offset (in sec): -0.06\n",
"kachushano_uta_seg7.lab offset (in sec): -0.015\n",
"kachushano_uta.lab: 2/17 time-lags are excluded.\n",
"kachushano_uta_seg8.lab offset (in sec): -0.034999999999999996\n",
"kagome_kagome: Global offset (in sec): -0.03\n",
"kagome_kagome_seg0.lab offset (in sec): -0.03\n",
"kagome_kagome_seg1.lab offset (in sec): -0.01\n",
"kagome_kagome.lab: 1/27 time-lags are excluded.\n",
"kagome_kagome_seg2.lab offset (in sec): -0.045\n",
"kamomeno_suiheisan: Global offset (in sec): -0.049999999999999996\n",
"kamomeno_suiheisan_seg0.lab offset (in sec): -0.055\n",
"kamomeno_suiheisan.lab: 1/10 time-lags are excluded.\n",
"kamomeno_suiheisan_seg1.lab offset (in sec): 0.0\n",
"kamomeno_suiheisan.lab: 1/11 time-lags are excluded.\n",
"kamomeno_suiheisan_seg2.lab offset (in sec): -0.055\n",
"kamomeno_suiheisan.lab: 1/18 time-lags are excluded.\n",
"kamomeno_suiheisan_seg3.lab offset (in sec): -0.049999999999999996\n",
"kamomeno_suiheisan_seg4.lab offset (in sec): -0.06\n",
"kamomeno_suiheisan.lab: 1/10 time-lags are excluded.\n",
"kamomeno_suiheisan_seg5.lab offset (in sec): -0.065\n",
"kamomeno_suiheisan.lab: 1/11 time-lags are excluded.\n",
"kamomeno_suiheisan_seg6.lab offset (in sec): -0.049999999999999996\n",
"kamomeno_suiheisan.lab: 2/18 time-lags are excluded.\n",
"kamomeno_suiheisan_seg7.lab offset (in sec): -0.034999999999999996\n",
"kamomeno_suiheisan_seg8.lab offset (in sec): -0.049999999999999996\n",
"kamomeno_suiheisan.lab: 1/10 time-lags are excluded.\n",
"kamomeno_suiheisan_seg9.lab offset (in sec): -0.055\n",
"kamomeno_suiheisan_seg10.lab offset (in sec): -0.055\n",
"kamomeno_suiheisan_seg11.lab offset (in sec): -0.045\n",
"katatsumuri: Global offset (in sec): -0.065\n",
"katatsumuri_seg0.lab offset (in sec): -0.055\n",
"katatsumuri_seg1.lab offset (in sec): -0.04\n",
"katatsumuri_seg2.lab offset (in sec): -0.08\n",
"katatsumuri_seg3.lab offset (in sec): -0.06\n",
"katatsumuri_seg4.lab offset (in sec): -0.045\n",
"katatsumuri_seg5.lab offset (in sec): -0.09999999999999999\n",
"katatsumuri.lab: 1/13 time-lags are excluded.\n",
"kintarou: Global offset (in sec): -0.04\n",
"kintarou_seg0.lab offset (in sec): -0.04\n",
"kintarou_seg1.lab offset (in sec): -0.049999999999999996\n",
"kintarou_seg2.lab offset (in sec): -0.055\n",
"kintarou_seg3.lab offset (in sec): -0.034999999999999996\n",
"kintarou_seg4.lab offset (in sec): -0.055\n",
"kintarou_seg5.lab offset (in sec): -0.01\n",
"koganemushi: Global offset (in sec): -0.034999999999999996\n",
"koganemushi_seg0.lab offset (in sec): -0.04\n",
"koganemushi.lab: 1/24 time-lags are excluded.\n",
"koganemushi_seg1.lab offset (in sec): -0.015\n",
"koganemushi_seg2.lab offset (in sec): -0.03\n",
"koinobori: Global offset (in sec): -0.049999999999999996\n",
"koinobori_seg0.lab offset (in sec): -0.04\n",
"koinobori_seg1.lab offset (in sec): -0.045\n",
"koinobori_seg2.lab offset (in sec): -0.045\n",
"koinobori_seg3.lab offset (in sec): -0.049999999999999996\n",
"koinobori.lab: 1/13 time-lags are excluded.\n",
"makibano_asa: Global offset (in sec): -0.034999999999999996\n",
"makibano_asa_seg0.lab offset (in sec): -0.034999999999999996\n",
"makibano_asa_seg1.lab offset (in sec): -0.04\n",
"makibano_asa_seg2.lab offset (in sec): -0.04\n",
"makibano_asa_seg3.lab offset (in sec): -0.034999999999999996\n",
"makibano_asa_seg4.lab offset (in sec): -0.024999999999999998\n",
"makibano_asa_seg5.lab offset (in sec): -0.034999999999999996\n",
"makibano_asa_seg6.lab offset (in sec): -0.015\n",
"makibano_asa_seg7.lab offset (in sec): -0.03\n",
"makibano_asa_seg8.lab offset (in sec): -0.034999999999999996\n",
"makibano_asa.lab: 1/14 time-lags are excluded.\n",
"makibano_asa_seg9.lab offset (in sec): -0.02\n",
"makibano_asa_seg10.lab offset (in sec): -0.02\n",
"mansikka_on_punanen_marja: Global offset (in sec): -0.04\n",
"mansikka_on_punanen_marja_seg0.lab offset (in sec): -0.045\n",
"mansikka_on_punanen_marja_seg1.lab offset (in sec): -0.04\n",
"mansikka_on_punanen_marja_seg2.lab offset (in sec): -0.034999999999999996\n",
"mansikka_on_punanen_marja.lab: 1/41 time-lags are excluded.\n",
"mansikka_on_punanen_marja_seg3.lab offset (in sec): -0.034999999999999996\n",
"mansikka_on_punanen_marja.lab: 1/20 time-lags are excluded.\n",
"mansikka_on_punanen_marja_seg4.lab offset (in sec): -0.045\n",
"miwataseba: Global offset (in sec): -0.049999999999999996\n",
"miwataseba_seg0.lab offset (in sec): -0.045\n",
"miwataseba.lab: 2/38 time-lags are excluded.\n",
"miwataseba_seg1.lab offset (in sec): -0.055\n",
"miwataseba_seg2.lab offset (in sec): -0.06\n",
"miwataseba_seg3.lab offset (in sec): -0.03\n",
" 57% 32/56 [00:00<00:00, 103.39it/s]momiji: Global offset (in sec): -0.04\n",
"momiji_seg0.lab offset (in sec): -0.04\n",
"momiji_seg1.lab offset (in sec): -0.045\n",
"momiji_seg2.lab offset (in sec): -0.034999999999999996\n",
"momiji_seg3.lab offset (in sec): -0.034999999999999996\n",
"momiji_seg4.lab offset (in sec): -0.04\n",
"momiji_seg5.lab offset (in sec): -0.049999999999999996\n",
"momiji_seg6.lab offset (in sec): -0.034999999999999996\n",
"momiji_seg7.lab offset (in sec): -0.034999999999999996\n",
"momiji.lab: 2/17 time-lags are excluded.\n",
"momiji_seg8.lab offset (in sec): -0.02\n",
"momotarou: Global offset (in sec): -0.04\n",
"momotarou_seg0.lab offset (in sec): -0.049999999999999996\n",
"momotarou_seg1.lab offset (in sec): -0.034999999999999996\n",
"momotarou_seg2.lab offset (in sec): -0.045\n",
"momotarou.lab: 1/34 time-lags are excluded.\n",
"momotarou_seg3.lab offset (in sec): -0.034999999999999996\n",
"morobito_kozorite: Global offset (in sec): -0.034999999999999996\n",
"morobito_kozorite_seg0.lab offset (in sec): -0.03\n",
"morobito_kozorite.lab: 1/64 time-lags are excluded.\n",
"morobito_kozorite_seg1.lab offset (in sec): -0.049999999999999996\n",
"morobito_kozorite_seg2.lab offset (in sec): -0.034999999999999996\n",
"morobito_kozorite.lab: 1/48 time-lags are excluded.\n",
"morobito_kozorite_seg3.lab offset (in sec): -0.04\n",
"morobito_kozorite_seg4.lab offset (in sec): -0.03\n",
"morobito_kozorite.lab: 3/46 time-lags are excluded.\n",
"mushino_koe: Global offset (in sec): -0.034999999999999996\n",
"mushino_koe_seg0.lab offset (in sec): -0.034999999999999996\n",
"mushino_koe_seg1.lab offset (in sec): -0.02\n",
"mushino_koe.lab: 1/28 time-lags are excluded.\n",
"mushino_koe_seg2.lab offset (in sec): -0.045\n",
"mushino_koe.lab: 4/25 time-lags are excluded.\n",
"mushino_koe_seg3.lab offset (in sec): -0.034999999999999996\n",
"mushino_koe_seg4.lab offset (in sec): -0.06\n",
"mushino_koe_seg5.lab offset (in sec): -0.03\n",
"mushino_koe_seg6.lab offset (in sec): -0.024999999999999998\n",
"mushino_koe.lab: 1/25 time-lags are excluded.\n",
"mushino_koe_seg7.lab offset (in sec): -0.045\n",
"nanatsunoko: Global offset (in sec): -0.045\n",
"nanatsunoko_seg0.lab offset (in sec): -0.08\n",
"nanatsunoko_seg1.lab offset (in sec): -0.06\n",
"nanatsunoko_seg2.lab offset (in sec): -0.034999999999999996\n",
"nanatsunoko_seg3.lab offset (in sec): -0.04\n",
"nanatsunoko.lab: 1/16 time-lags are excluded.\n",
"nanatsunoko_seg4.lab offset (in sec): -0.034999999999999996\n",
"nanatsunoko_seg5.lab offset (in sec): -0.04\n",
"nanatsunoko_seg6.lab offset (in sec): -0.049999999999999996\n",
"nanatsunoko_seg7.lab offset (in sec): -0.005\n",
"nanatsunoko.lab: 1/18 time-lags are excluded.\n",
"nonakano_bara: Global offset (in sec): -0.04\n",
"nonakano_bara_seg0.lab offset (in sec): -0.04\n",
"nonakano_bara_seg1.lab offset (in sec): -0.065\n",
"nonakano_bara_seg2.lab offset (in sec): -0.055\n",
"nonakano_bara_seg3.lab offset (in sec): -0.04\n",
"nonakano_bara_seg4.lab offset (in sec): -0.045\n",
"nonakano_bara_seg5.lab offset (in sec): -0.02\n",
"nonakano_bara_seg6.lab offset (in sec): -0.04\n",
"nonakano_bara_seg7.lab offset (in sec): -0.02\n",
"nonakano_bara_seg8.lab offset (in sec): -0.02\n",
"nonakano_bara_seg9.lab offset (in sec): -0.055\n",
"nonakano_bara_seg10.lab offset (in sec): -0.045\n",
"nonakano_bara_seg11.lab offset (in sec): -0.03\n",
"nonakano_bara_seg12.lab offset (in sec): -0.024999999999999998\n",
"nonakano_bara_seg13.lab offset (in sec): -0.034999999999999996\n",
"nonakano_bara_seg14.lab offset (in sec): -0.04\n",
"nonakano_bara_seg15.lab offset (in sec): -0.055\n",
"nonakano_bara_seg16.lab offset (in sec): -0.045\n",
"nonakano_bara_seg17.lab offset (in sec): -0.04\n",
"nonakano_bara_seg18.lab offset (in sec): -0.03\n",
"nonakano_bara_seg19.lab offset (in sec): -0.034999999999999996\n",
"peichika: Global offset (in sec): -0.049999999999999996\n",
"peichika_seg0.lab offset (in sec): -0.049999999999999996\n",
"peichika_seg1.lab offset (in sec): -0.06\n",
"peichika_seg2.lab offset (in sec): -0.04\n",
"peichika_seg3.lab offset (in sec): -0.065\n",
"peichika_seg4.lab offset (in sec): -0.055\n",
"peichika_seg5.lab offset (in sec): -0.03\n",
"peichika_seg6.lab offset (in sec): -0.034999999999999996\n",
"peichika_seg7.lab offset (in sec): -0.045\n",
"peichika_seg8.lab offset (in sec): -0.065\n",
"peichika.lab: 1/14 time-lags are excluded.\n",
"peichika_seg9.lab offset (in sec): -0.055\n",
"peichika_seg10.lab offset (in sec): -0.045\n",
"peichika_seg11.lab offset (in sec): -0.055\n",
"romance_anonimo: Global offset (in sec): -0.049999999999999996\n",
"romance_anonimo_seg0.lab offset (in sec): -0.04\n",
"romance_anonimo.lab: 1/52 time-lags are excluded.\n",
"romance_anonimo_seg1.lab offset (in sec): -0.075\n",
"romance_anonimo_seg2.lab offset (in sec): -0.045\n",
"romance_anonimo_seg3.lab offset (in sec): -0.06999999999999999\n",
"romance_anonimo.lab: 4/53 time-lags are excluded.\n",
"romance_anonimo_seg4.lab offset (in sec): -0.04\n",
"romance_anonimo.lab: 3/52 time-lags are excluded.\n",
"sakura_sakura: Global offset (in sec): -0.045\n",
"sakura_sakura_seg0.lab offset (in sec): -0.034999999999999996\n",
"sakura_sakura.lab: 2/17 time-lags are excluded.\n",
"sakura_sakura_seg1.lab offset (in sec): -0.024999999999999998\n",
"sakura_sakura_seg2.lab offset (in sec): -0.034999999999999996\n",
"sakura_sakura.lab: 1/23 time-lags are excluded.\n",
"sakura_sakura_seg3.lab offset (in sec): -0.04\n",
"sakura_sakura.lab: 1/16 time-lags are excluded.\n",
"sakura_sakura_seg4.lab offset (in sec): -0.024999999999999998\n",
"sakura_sakura_seg5.lab offset (in sec): -0.02\n",
"shabondama: Global offset (in sec): -0.06\n",
"shabondama_seg0.lab offset (in sec): -0.034999999999999996\n",
"shabondama.lab: 1/8 time-lags are excluded.\n",
"shabondama_seg1.lab offset (in sec): -0.06999999999999999\n",
"shabondama_seg2.lab offset (in sec): -0.045\n",
"shabondama_seg3.lab offset (in sec): -0.03\n",
"shabondama_seg4.lab offset (in sec): -0.045\n",
"shabondama.lab: 1/9 time-lags are excluded.\n",
"shabondama_seg5.lab offset (in sec): -0.065\n",
"shabondama_seg6.lab offset (in sec): -0.08\n",
"shabondama_seg7.lab offset (in sec): -0.049999999999999996\n",
"shabondama_seg8.lab offset (in sec): -0.065\n",
"shabondama_seg9.lab offset (in sec): -0.065\n",
"shabondama.lab: 1/9 time-lags are excluded.\n",
"shoujoujino_tanukibayashi: Global offset (in sec): -0.04\n",
"shoujoujino_tanukibayashi_seg0.lab offset (in sec): -0.045\n",
"shoujoujino_tanukibayashi.lab: 4/50 time-lags are excluded.\n",
"shoujoujino_tanukibayashi_seg1.lab offset (in sec): -0.01\n",
"shoujoujino_tanukibayashi.lab: 2/43 time-lags are excluded.\n",
"shoujoujino_tanukibayashi_seg2.lab offset (in sec): -0.055\n",
"shoujoujino_tanukibayashi.lab: 1/27 time-lags are excluded.\n",
"shoujoujino_tanukibayashi_seg3.lab offset (in sec): -0.03\n",
"shoujoujino_tanukibayashi.lab: 1/17 time-lags are excluded.\n",
" 77% 43/56 [00:00<00:00, 90.69it/s] sousyunfu: Global offset (in sec): -0.034999999999999996\n",
"sousyunfu_seg0.lab offset (in sec): -0.045\n",
"sousyunfu_seg1.lab offset (in sec): -0.06999999999999999\n",
"sousyunfu_seg2.lab offset (in sec): -0.024999999999999998\n",
"sousyunfu.lab: 2/7 time-lags are excluded.\n",
"sousyunfu_seg3.lab offset (in sec): -0.02\n",
"sousyunfu_seg4.lab offset (in sec): -0.03\n",
"sousyunfu_seg5.lab offset (in sec): -0.02\n",
"sousyunfu_seg6.lab offset (in sec): -0.02\n",
"sousyunfu_seg7.lab offset (in sec): 0.005\n",
"sousyunfu_seg8.lab offset (in sec): -0.015\n",
"sousyunfu.lab: 1/7 time-lags are excluded.\n",
"sousyunfu_seg9.lab offset (in sec): -0.034999999999999996\n",
"sousyunfu_seg10.lab offset (in sec): -0.04\n",
"sousyunfu_seg11.lab offset (in sec): -0.03\n",
"sousyunfu_seg12.lab offset (in sec): -0.06\n",
"sousyunfu_seg13.lab offset (in sec): -0.03\n",
"sousyunfu_seg14.lab offset (in sec): -0.06\n",
"sousyunfu_seg15.lab offset (in sec): -0.02\n",
"sousyunfu_seg16.lab offset (in sec): -0.045\n",
"sousyunfu_seg17.lab offset (in sec): -0.055\n",
"sousyunfu_seg18.lab offset (in sec): -0.049999999999999996\n",
"sousyunfu_seg19.lab offset (in sec): -0.03\n",
"sousyunfu_seg20.lab offset (in sec): -0.045\n",
"sousyunfu_seg21.lab offset (in sec): -0.02\n",
"sousyunfu_seg22.lab offset (in sec): -0.055\n",
"sousyunfu_seg23.lab offset (in sec): 0.0\n",
"tetsudou_shouka: Global offset (in sec): -0.065\n",
"tetsudou_shouka_seg0.lab offset (in sec): -0.065\n",
"tetsudou_shouka.lab: 2/49 time-lags are excluded.\n",
"tetsudou_shouka_seg1.lab offset (in sec): -0.065\n",
"tetsudou_shouka_seg2.lab offset (in sec): -0.065\n",
"the_other_day_i_met_a_bear: Global offset (in sec): -0.06\n",
"the_other_day_i_met_a_bear_seg0.lab offset (in sec): -0.02\n",
"the_other_day_i_met_a_bear.lab: 1/4 time-lags are excluded.\n",
"the_other_day_i_met_a_bear_seg1.lab offset (in sec): -0.09\n",
"the_other_day_i_met_a_bear.lab: 1/9 time-lags are excluded.\n",
"the_other_day_i_met_a_bear_seg2.lab offset (in sec): -0.045\n",
"the_other_day_i_met_a_bear_seg3.lab offset (in sec): -0.055\n",
"the_other_day_i_met_a_bear_seg4.lab offset (in sec): -0.045\n",
"the_other_day_i_met_a_bear_seg5.lab offset (in sec): -0.08\n",
"the_other_day_i_met_a_bear_seg6.lab offset (in sec): -0.04\n",
"the_other_day_i_met_a_bear_seg7.lab offset (in sec): -0.015\n",
"the_other_day_i_met_a_bear.lab: 1/11 time-lags are excluded.\n",
"the_other_day_i_met_a_bear_seg8.lab offset (in sec): -0.045\n",
"the_other_day_i_met_a_bear.lab: 2/4 time-lags are excluded.\n",
"the_other_day_i_met_a_bear_seg9.lab offset (in sec): -0.065\n",
"the_other_day_i_met_a_bear_seg10.lab offset (in sec): -0.065\n",
"the_other_day_i_met_a_bear.lab: 2/12 time-lags are excluded.\n",
"the_other_day_i_met_a_bear_seg11.lab offset (in sec): -0.04\n",
"tonbi: Global offset (in sec): -0.04\n",
"tonbi_seg0.lab offset (in sec): -0.02\n",
"tonbi.lab: 1/17 time-lags are excluded.\n",
"tonbi_seg1.lab offset (in sec): -0.04\n",
"tonbi.lab: 1/17 time-lags are excluded.\n",
"tonbi_seg2.lab offset (in sec): -0.015\n",
"tonbi.lab: 1/19 time-lags are excluded.\n",
"tonbi_seg3.lab offset (in sec): -0.04\n",
"tonbi.lab: 1/15 time-lags are excluded.\n",
"tonbi_seg4.lab offset (in sec): -0.024999999999999998\n",
"tonbi_seg5.lab offset (in sec): -0.04\n",
"tonbi_seg6.lab offset (in sec): -0.045\n",
"tonbi.lab: 1/19 time-lags are excluded.\n",
"tonbi_seg7.lab offset (in sec): -0.034999999999999996\n",
"tonbi.lab: 1/15 time-lags are excluded.\n",
"toryanse: Global offset (in sec): -0.04\n",
"toryanse_seg0.lab offset (in sec): -0.04\n",
"toryanse.lab: 1/109 time-lags are excluded.\n",
"troika: Global offset (in sec): -0.055\n",
"troika_seg0.lab offset (in sec): -0.04\n",
"troika_seg1.lab offset (in sec): -0.034999999999999996\n",
"troika_seg2.lab offset (in sec): -0.06\n",
"troika.lab: 1/8 time-lags are excluded.\n",
"troika_seg3.lab offset (in sec): -0.049999999999999996\n",
"troika_seg4.lab offset (in sec): -0.065\n",
"troika_seg5.lab offset (in sec): -0.08499999999999999\n",
"troika.lab: 1/7 time-lags are excluded.\n",
"troika_seg6.lab offset (in sec): -0.075\n",
"troika.lab: 1/8 time-lags are excluded.\n",
"troika_seg7.lab offset (in sec): -0.04\n",
"troika_seg8.lab offset (in sec): -0.04\n",
"troika.lab: 1/10 time-lags are excluded.\n",
"troika_seg9.lab offset (in sec): -0.034999999999999996\n",
"troika_seg10.lab offset (in sec): -0.095\n",
"troika_seg11.lab offset (in sec): -0.04\n",
"twinkle_twinkle_little_star: Global offset (in sec): -0.065\n",
"twinkle_twinkle_little_star_seg0.lab offset (in sec): -0.034999999999999996\n",
"twinkle_twinkle_little_star.lab: 1/7 time-lags are excluded.\n",
"twinkle_twinkle_little_star_seg1.lab offset (in sec): -0.045\n",
"twinkle_twinkle_little_star_seg2.lab offset (in sec): -0.075\n",
"twinkle_twinkle_little_star_seg3.lab offset (in sec): -0.055\n",
"twinkle_twinkle_little_star_seg4.lab offset (in sec): -0.045\n",
"twinkle_twinkle_little_star_seg5.lab offset (in sec): -0.06\n",
"twinkle_twinkle_little_star.lab: 1/7 time-lags are excluded.\n",
"twinkle_twinkle_little_star_seg6.lab offset (in sec): -0.09999999999999999\n",
"twinkle_twinkle_little_star.lab: 1/7 time-lags are excluded.\n",
"twinkle_twinkle_little_star_seg7.lab offset (in sec): -0.06999999999999999\n",
"twinkle_twinkle_little_star_seg8.lab offset (in sec): -0.04\n",
"twinkle_twinkle_little_star_seg9.lab offset (in sec): -0.04\n",
"twinkle_twinkle_little_star.lab: 1/7 time-lags are excluded.\n",
"twinkle_twinkle_little_star_seg10.lab offset (in sec): -0.045\n",
"twinkle_twinkle_little_star_seg11.lab offset (in sec): -0.055\n",
"twinkle_twinkle_little_star_seg12.lab offset (in sec): -0.095\n",
"twinkle_twinkle_little_star_seg13.lab offset (in sec): -0.08499999999999999\n",
"twinkle_twinkle_little_star_seg14.lab offset (in sec): -0.055\n",
"twinkle_twinkle_little_star_seg15.lab offset (in sec): -0.06999999999999999\n",
"twinkle_twinkle_little_star.lab: 1/7 time-lags are excluded.\n",
"twinkle_twinkle_little_star_seg16.lab offset (in sec): -0.06\n",
"twinkle_twinkle_little_star_seg17.lab offset (in sec): -0.13\n",
"urashima_tarou: Global offset (in sec): -0.045\n",
"urashima_tarou_seg0.lab offset (in sec): -0.045\n",
"urashima_tarou_seg1.lab offset (in sec): -0.049999999999999996\n",
"urashima_tarou_seg2.lab offset (in sec): -0.04\n",
"usagi: Global offset (in sec): -0.065\n",
"usagi_seg0.lab offset (in sec): -0.055\n",
"usagito_kame: Global offset (in sec): -0.049999999999999996\n",
"usagito_kame_seg0.lab offset (in sec): -0.04\n",
"usagito_kame_seg1.lab offset (in sec): -0.055\n",
"usagito_kame.lab: 1/48 time-lags are excluded.\n",
"usagito_kame_seg2.lab offset (in sec): -0.04\n",
" 95% 53/56 [00:00<00:00, 92.98it/s]yuki: Global offset (in sec): -0.045\n",
"yuki_seg0.lab offset (in sec): -0.04\n",
"yuki.lab: 1/61 time-lags are excluded.\n",
"yuki_seg1.lab offset (in sec): -0.04\n",
"yuki.lab: 1/60 time-lags are excluded.\n",
"yurikagono_uta: Global offset (in sec): -0.04\n",
"yurikagono_uta_seg0.lab offset (in sec): -0.03\n",
"yurikagono_uta.lab: 1/18 time-lags are excluded.\n",
"yurikagono_uta_seg1.lab offset (in sec): -0.03\n",
"yurikagono_uta_seg2.lab offset (in sec): -0.04\n",
"yurikagono_uta_seg3.lab offset (in sec): -0.055\n",
"yurikagono_uta_seg4.lab offset (in sec): -0.024999999999999998\n",
"zuizui_zukkorobashi: Global offset (in sec): -0.045\n",
"zuizui_zukkorobashi_seg0.lab offset (in sec): -0.049999999999999996\n",
"zuizui_zukkorobashi_seg1.lab offset (in sec): -0.034999999999999996\n",
"zuizui_zukkorobashi_seg2.lab offset (in sec): -0.034999999999999996\n",
"zuizui_zukkorobashi_seg3.lab offset (in sec): -0.034999999999999996\n",
"zuizui_zukkorobashi_seg4.lab offset (in sec): -0.034999999999999996\n",
"100% 56/56 [00:00<00:00, 96.31it/s]\n",
"Prepare data for duration models\n",
"100% 56/56 [00:00<00:00, 464.49it/s]\n",
"Prepare data for acoustic models\n",
"100% 56/56 [07:13<00:00, 7.75s/it]\n",
"train/dev/eval split\n"
]
}
],
"source": [
"#! cd $RECIPE_ROOT && bash run.sh --stage -0 --stop-stage 0"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "QnYwVu94gjF4",
"outputId": "707e7d9a-d551-4948-f190-09e9cc004e61"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"stage 1: Feature generation\n",
"++ nnsvs-prepare-features utt_list=data/list/train_no_dev.list out_dir=dump/oniku_kurumi/org/train_no_dev/ question_path=../../_common/hed/jp_dev.hed timelag=defaults duration=defaults acoustic=static_deltadelta_sinevib acoustic.sample_rate=48000 acoustic.trajectory_smoothing=false acoustic.trajectory_smoothing_cutoff=50\n",
"/usr/local/lib/python3.7/dist-packages/resampy/interpn.py:114: NumbaWarning: The TBB threading layer requires TBB version 2019.5 or later i.e., TBB_INTERFACE_VERSION >= 11005. Found TBB_INTERFACE_VERSION = 9107. The TBB threading layer is disabled.\n",
" _resample_loop_p(x, t_out, interp_win, interp_delta, num_table, scale, y)\n",
"[\u001b[36m2022-07-17 07:26:25,516\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - timelag:\n",
" enabled: true\n",
" question_path: null\n",
" label_phone_score_dir: data/timelag/label_phone_score\n",
" label_phone_align_dir: data/timelag/label_phone_align\n",
"duration:\n",
" enabled: true\n",
" question_path: null\n",
" label_dir: data/duration/label_phone_align\n",
"acoustic:\n",
" enabled: true\n",
" question_path: null\n",
" wav_dir: data/acoustic/wav\n",
" label_dir: data/acoustic/label_phone_align\n",
" sample_rate: 48000\n",
" subphone_features: coarse_coding\n",
" f0_floor: 150\n",
" f0_ceil: 700\n",
" use_harvest: true\n",
" d4c_threshold: 0.85\n",
" frame_period: 5\n",
" mgc_order: 59\n",
" num_windows: 3\n",
" relative_f0: false\n",
" interp_unvoiced_aperiodicity: true\n",
" vibrato_mode: sine\n",
" trajectory_smoothing: false\n",
" trajectory_smoothing_cutoff: 50\n",
" correct_vuv: false\n",
"verbose: 100\n",
"utt_list: data/list/train_no_dev.list\n",
"out_dir: dump/oniku_kurumi/org/train_no_dev/\n",
"max_workers: null\n",
"question_path: ../../_common/hed/jp_dev.hed\n",
"log_f0_conditioning: true\n",
"\u001b[0m\n",
"[\u001b[36m2022-07-17 07:26:25,553\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - mkdirs: /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/dump/oniku_kurumi/org/train_no_dev/in_timelag\u001b[0m\n",
"[\u001b[36m2022-07-17 07:26:25,554\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - mkdirs: /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/dump/oniku_kurumi/org/train_no_dev/out_timelag\u001b[0m\n",
"[\u001b[36m2022-07-17 07:26:25,554\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - mkdirs: /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/dump/oniku_kurumi/org/train_no_dev/in_duration\u001b[0m\n",
"[\u001b[36m2022-07-17 07:26:25,554\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - mkdirs: /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/dump/oniku_kurumi/org/train_no_dev/out_duration\u001b[0m\n",
"[\u001b[36m2022-07-17 07:26:25,555\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - mkdirs: /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/dump/oniku_kurumi/org/train_no_dev/in_acoustic\u001b[0m\n",
"[\u001b[36m2022-07-17 07:26:25,555\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - mkdirs: /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/dump/oniku_kurumi/org/train_no_dev/out_acoustic\u001b[0m\n",
"[\u001b[36m2022-07-17 07:26:25,578\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Timelag linguistic feature dim: 337\u001b[0m\n",
"[\u001b[36m2022-07-17 07:26:25,578\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Timelag feature dim: 1\u001b[0m\n",
"100% 442/442 [00:07<00:00, 56.65it/s]\n",
"[\u001b[36m2022-07-17 07:26:33,475\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Duration linguistic feature dim: 337\u001b[0m\n",
"[\u001b[36m2022-07-17 07:26:33,476\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Duration feature dim: 1\u001b[0m\n",
"100% 442/442 [00:12<00:00, 34.71it/s]\n",
"[\u001b[36m2022-07-17 07:26:46,315\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Acoustic linguistic feature dim: 341\u001b[0m\n",
"[\u001b[36m2022-07-17 07:26:53,592\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Acoustic feature dim: 206\u001b[0m\n",
" 0% 0/442 [00:00<?, ?it/s]Rate: 5.496828752642706, Extent: 24.970238833613728\n",
" 2% 7/442 [00:36<33:56, 4.68s/it]Rate: 4.273504273504273, Extent: 27.796228798245828\n",
" 2% 8/442 [00:40<34:03, 4.71s/it]Rate: 5.257623554153523, Extent: 26.301474147696844\n",
" 3% 12/442 [00:55<31:33, 4.40s/it]Rate: 5.231388329979879, Extent: 28.47998116010446\n",
" 3% 13/442 [00:58<28:00, 3.92s/it]Rate: 5.389221556886227, Extent: 35.98746835707006\n",
"Rate: 4.854368932038835, Extent: 41.00032827424384\n",
" 8% 34/442 [02:20<20:18, 2.99s/it]Rate: 6.267806267806268, Extent: 22.281267545263166\n",
" 12% 54/442 [03:16<19:56, 3.08s/it]Rate: 4.814814814814815, Extent: 25.228192071675146\n",
" 13% 57/442 [03:25<20:59, 3.27s/it]Rate: 4.830917874396135, Extent: 39.554192661255186\n",
" 13% 58/442 [03:27<18:33, 2.90s/it]Rate: 5.263157894736842, Extent: 15.226496757957102\n",
" 14% 63/442 [03:36<11:14, 1.78s/it]Rate: 4.406779661016949, Extent: 24.656660871931447\n",
" 15% 65/442 [03:47<21:13, 3.38s/it]Rate: 5.362982341399608, Extent: 33.28671823608857\n",
" 15% 68/442 [04:05<35:05, 5.63s/it]Rate: 5.300859598853869, Extent: 28.837320965100044\n",
" 16% 69/442 [04:08<29:33, 4.75s/it]Rate: 3.870967741935484, Extent: 32.14984004326617\n",
" 19% 82/442 [04:51<13:44, 2.29s/it]Rate: 3.3175355450236967, Extent: 75.41375214090827\n",
" 20% 90/442 [05:25<24:57, 4.25s/it]Rate: 5.607476635514019, Extent: 24.096058860845915\n",
" 22% 97/442 [05:45<18:35, 3.23s/it]Rate: 4.774535809018568, Extent: 25.054688356373845\n",
" 22% 99/442 [05:51<18:09, 3.17s/it]Rate: 5.05050505050505, Extent: 32.774031561727405\n",
" 27% 118/442 [06:51<14:31, 2.69s/it]Rate: 5.154639175257732, Extent: 27.009765887244612\n",
" 28% 123/442 [07:05<12:17, 2.31s/it]Rate: 5.0724637681159415, Extent: 37.879238363104186\n",
" 29% 129/442 [07:19<10:37, 2.04s/it]Rate: 5.319148936170213, Extent: 46.98310457360149\n",
"Rate: 5.9880239520958085, Extent: 34.87633221264032\n",
" 32% 140/442 [07:48<11:23, 2.26s/it]Rate: 5.20446096654275, Extent: 33.29093519416639\n",
"Rate: 6.0344827586206895, Extent: 25.677191719766597\n",
" 32% 141/442 [07:55<18:17, 3.65s/it]Rate: 5.761316872427984, Extent: 31.80359576257329\n",
" 33% 146/442 [08:09<14:14, 2.89s/it]Rate: 5.20446096654275, Extent: 28.57935877919785\n",
" 33% 147/442 [08:19<23:40, 4.82s/it]Rate: 6.7114093959731544, Extent: 80.2917360762629\n",
" 34% 150/442 [08:26<16:05, 3.31s/it]Rate: 5.780346820809249, Extent: 22.71172160693404\n",
" 35% 154/442 [08:42<17:25, 3.63s/it]Rate: 4.946996466431095, Extent: 35.998922090470124\n",
" 36% 157/442 [08:50<14:25, 3.04s/it]Rate: 5.154639175257732, Extent: 16.62105426784192\n",
" 37% 162/442 [09:10<17:17, 3.71s/it]Rate: 6.796116504854369, Extent: 22.8663232665562\n",
"Rate: 4.878048780487805, Extent: 55.74130752725296\n",
" 37% 164/442 [09:18<17:49, 3.85s/it]Rate: 4.854368932038835, Extent: 49.34959884337741\n",
" 38% 167/442 [09:26<14:09, 3.09s/it]Rate: 4.1841004184100425, Extent: 23.683679790311544\n",
" 39% 173/442 [09:42<11:36, 2.59s/it]Rate: 4.216867469879518, Extent: 19.134939674894277\n",
" 40% 177/442 [09:50<10:28, 2.37s/it]Rate: 5.128205128205128, Extent: 25.036943698698103\n",
" 40% 178/442 [09:52<10:14, 2.33s/it]Rate: 4.366812227074236, Extent: 25.352965478728947\n",
" 41% 180/442 [10:04<18:25, 4.22s/it]Rate: 4.405286343612335, Extent: 27.516412561890956\n",
" 41% 183/442 [10:12<14:44, 3.41s/it]Rate: 4.878048780487805, Extent: 26.868161040627\n",
" 42% 184/442 [10:15<14:31, 3.38s/it]Rate: 5.05050505050505, Extent: 23.618962457504857\n",
" 42% 186/442 [10:20<11:49, 2.77s/it]Rate: 5.780346820809249, Extent: 21.545594246698876\n",
"Rate: 4.262295081967213, Extent: 30.787960347592335\n",
" 43% 189/442 [10:37<17:49, 4.23s/it]Rate: 6.3559322033898304, Extent: 23.822234436707156\n",
" 43% 192/442 [10:47<15:24, 3.70s/it]Rate: 5.042016806722689, Extent: 22.07303464735297\n",
" 44% 194/442 [10:53<14:55, 3.61s/it]Rate: 4.697986577181208, Extent: 28.133458319835363\n",
" 45% 199/442 [11:12<14:51, 3.67s/it]Rate: 5.5464926590538335, Extent: 35.367880254710066\n",
" 47% 208/442 [11:40<08:57, 2.30s/it]Rate: 6.382978723404256, Extent: 43.03049509196343\n",
"Rate: 3.4428794992175273, Extent: 40.63775307313856\n",
" 48% 210/442 [11:46<09:07, 2.36s/it]Rate: 4.545454545454546, Extent: 30.83168041066174\n",
" 48% 213/442 [11:53<09:44, 2.55s/it]Rate: 4.15335463258786, Extent: 36.254156458433044\n",
" 49% 217/442 [12:02<08:09, 2.18s/it]Rate: 6.024096385542168, Extent: 34.34362629550223\n",
"Rate: 4.830917874396135, Extent: 48.61864613602957\n",
" 50% 219/442 [12:08<08:41, 2.34s/it]Rate: 5.426356589147288, Extent: 33.93083930432061\n",
" 50% 220/442 [12:11<10:03, 2.72s/it]Rate: 4.615384615384615, Extent: 53.620309817766305\n",
"Rate: 4.946996466431095, Extent: 35.067659253336906\n",
" 50% 222/442 [12:17<10:45, 2.93s/it]Rate: 5.4945054945054945, Extent: 46.87342643293878\n",
" 52% 228/442 [12:34<12:24, 3.48s/it]Rate: 3.8135593220338984, Extent: 47.46756414928556\n",
"Rate: 5.907172995780591, Extent: 29.251546750597235\n",
" 53% 234/442 [12:57<11:46, 3.39s/it]Rate: 3.674540682414698, Extent: 56.008289634907996\n",
" 54% 240/442 [13:22<14:01, 4.17s/it]Rate: 4.672897196261683, Extent: 38.75254153096985\n",
" 56% 247/442 [13:47<11:50, 3.65s/it]Rate: 4.0, Extent: 38.06049796032412\n",
"Rate: 6.25, Extent: 23.37124173150314\n",
"Rate: 6.622516556291391, Extent: 21.18483812001532\n",
" 57% 250/442 [14:09<15:58, 4.99s/it]Rate: 5.136986301369864, Extent: 31.221002127617005\n",
"Rate: 6.4102564102564115, Extent: 23.567739326419723\n",
" 57% 251/442 [14:21<20:53, 6.56s/it]Rate: 5.232558139534884, Extent: 28.54637371402719\n",
" 59% 261/442 [14:54<10:01, 3.32s/it]Rate: 4.545454545454546, Extent: 18.127771546472466\n",
" 60% 265/442 [15:09<10:58, 3.72s/it]Rate: 3.733333333333334, Extent: 44.01749871662014\n",
" 60% 267/442 [15:22<15:04, 5.17s/it]Rate: 4.3478260869565215, Extent: 58.64114934744066\n",
"Rate: 6.11353711790393, Extent: 34.768889313561495\n",
" 61% 268/442 [15:38<23:55, 8.25s/it]Rate: 5.952380952380952, Extent: 51.73044675437059\n",
" 61% 270/442 [15:42<15:30, 5.41s/it]Rate: 4.823747680890538, Extent: 33.96749064773058\n",
" 61% 271/442 [15:46<14:32, 5.10s/it]Rate: 6.306306306306306, Extent: 20.095913435100556\n",
"Rate: 6.11353711790393, Extent: 22.90380775916687\n",
" 62% 274/442 [15:56<10:47, 3.86s/it]Rate: 4.8076923076923075, Extent: 18.14268503989815\n",
" 63% 278/442 [16:07<08:00, 2.93s/it]Rate: 5.291005291005291, Extent: 19.011103528077637\n",
" 64% 281/442 [16:14<06:40, 2.49s/it]Rate: 6.081081081081082, Extent: 25.350874930307377\n",
" 64% 282/442 [16:18<07:58, 2.99s/it]Rate: 5.338078291814946, Extent: 27.323396294195117\n",
" 64% 283/442 [16:23<09:23, 3.54s/it]Rate: 4.794520547945205, Extent: 31.125137957459497\n",
" 65% 287/442 [16:34<07:12, 2.79s/it]Rate: 5.676855895196506, Extent: 60.85254647238186\n",
" 67% 298/442 [16:58<04:50, 2.02s/it]Rate: 4.878048780487805, Extent: 31.662797030423967\n",
" 68% 299/442 [17:00<04:34, 1.92s/it]Rate: 3.674540682414698, Extent: 33.63852061073346\n",
" 68% 301/442 [17:04<04:23, 1.87s/it]Rate: 5.170975813177648, Extent: 28.840593969924758\n",
" 69% 305/442 [17:12<04:15, 1.86s/it]Rate: 5.88235294117647, Extent: 31.79831993961049\n",
" 69% 306/442 [17:16<05:20, 2.35s/it]Rate: 6.516290726817042, Extent: 22.569097190732673\n",
" 72% 320/442 [17:52<04:45, 2.34s/it]Rate: 4.961832061068702, Extent: 29.046815674502586\n",
"Rate: 5.029013539651838, Extent: 30.34517762896065\n",
"Rate: 5.05050505050505, Extent: 32.0784026772295\n",
" 73% 321/442 [18:16<18:13, 9.04s/it]Rate: 3.233256351039261, Extent: 41.43678556945497\n",
"Rate: 3.733333333333334, Extent: 27.582907311292665\n",
"Rate: 5.555555555555555, Extent: 31.465973447190454\n",
" 74% 325/442 [18:43<13:56, 7.15s/it]Rate: 5.009633911368016, Extent: 40.01028216823603\n",
" 74% 326/442 [18:49<13:26, 6.95s/it]Rate: 6.622516556291391, Extent: 29.142918310824463\n",
" 74% 327/442 [18:51<10:54, 5.69s/it]Rate: 4.201680672268907, Extent: 42.846474014897105\n",
"Rate: 5.128205128205128, Extent: 42.96715451142709\n",
" 74% 328/442 [19:01<12:53, 6.79s/it]Rate: 4.587155963302751, Extent: 46.852239020968774\n",
" 75% 330/442 [19:06<09:22, 5.02s/it]Rate: 4.25531914893617, Extent: 35.30064216767751\n",
" 77% 341/442 [19:35<04:13, 2.51s/it]Rate: 5.577689243027889, Extent: 26.19059161077218\n",
"Rate: 5.291005291005291, Extent: 24.555491490999522\n",
"Rate: 5.384615384615384, Extent: 62.89874508897901\n",
" 77% 342/442 [19:48<09:15, 5.55s/it]Rate: 5.590062111801243, Extent: 41.15224790276898\n",
" 80% 353/442 [20:14<03:22, 2.27s/it]Rate: 4.0983606557377055, Extent: 21.091375662907193\n",
" 82% 361/442 [20:32<02:43, 2.02s/it]Rate: 4.034582132564841, Extent: 22.4812797148307\n",
" 82% 362/442 [20:36<03:13, 2.42s/it]Rate: 5.38243626062323, Extent: 20.391705851254372\n",
" 82% 364/442 [20:40<03:03, 2.35s/it]Rate: 4.0669856459330145, Extent: 49.53662841988476\n",
" 83% 368/442 [20:49<02:51, 2.32s/it]Rate: 5.405405405405405, Extent: 35.64798266406667\n",
"Rate: 5.405405405405405, Extent: 30.441422104565937\n",
"Rate: 6.167400881057268, Extent: 27.060494106362448\n",
" 86% 381/442 [21:40<03:35, 3.54s/it]Rate: 5.5865921787709505, Extent: 38.79071425169004\n",
" 87% 385/442 [21:52<03:08, 3.30s/it]Rate: 4.784688995215311, Extent: 24.416665972340162\n",
" 88% 388/442 [22:04<03:32, 3.93s/it]Rate: 5.232558139534884, Extent: 42.20363863349404\n",
" 88% 389/442 [22:07<03:09, 3.57s/it]Rate: 5.185185185185186, Extent: 24.44616400067954\n",
" 89% 392/442 [22:19<03:23, 4.08s/it]Rate: 5.017921146953405, Extent: 17.913362506015087\n",
"Rate: 7.042253521126761, Extent: 30.79030120064099\n",
"Rate: 4.62962962962963, Extent: 30.758174783686353\n",
"Rate: 5.37190082644628, Extent: 37.898860779101774\n",
" 92% 407/442 [23:14<01:36, 2.77s/it]Rate: 4.854368932038835, Extent: 24.01577646274427\n",
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" 93% 411/442 [23:23<01:12, 2.35s/it]Rate: 4.291845493562231, Extent: 21.51810899192069\n",
"Rate: 4.98220640569395, Extent: 23.231887201048785\n",
" 93% 412/442 [23:26<01:12, 2.43s/it]Rate: 4.9504950495049505, Extent: 28.184729294502358\n",
" 95% 420/442 [23:44<00:53, 2.44s/it]Rate: 5.128205128205128, Extent: 27.104048771717224\n",
" 96% 423/442 [23:49<00:39, 2.07s/it]Rate: 5.434782608695652, Extent: 19.394653805559482\n",
"Rate: 5.434782608695652, Extent: 53.725617913333096\n",
"Rate: 5.10204081632653, Extent: 16.963427241103545\n",
"Rate: 5.524861878453039, Extent: 19.232709550501205\n",
" 96% 424/442 [24:05<01:50, 6.15s/it]Rate: 5.940594059405941, Extent: 30.643588310875707\n",
"Rate: 6.451612903225806, Extent: 28.835688816026895\n",
" 96% 426/442 [24:21<01:52, 7.03s/it]Rate: 4.9504950495049505, Extent: 47.514966610283714\n",
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"Rate: 5.622489959839358, Extent: 42.39851718169699\n",
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"Rate: 5.583756345177664, Extent: 41.363200078485825\n",
" 98% 431/442 [24:53<01:08, 6.21s/it]Rate: 5.080831408775981, Extent: 26.790833198143982\n",
"Rate: 5.952380952380952, Extent: 19.552556267586986\n",
" 98% 434/442 [25:09<00:42, 5.33s/it]Rate: 4.1039671682626535, Extent: 33.65569561969896\n",
"100% 442/442 [25:33<00:00, 3.47s/it]\n",
"++ set +x\n",
"++ nnsvs-prepare-features utt_list=data/list/dev.list out_dir=dump/oniku_kurumi/org/dev/ question_path=../../_common/hed/jp_dev.hed timelag=defaults duration=defaults acoustic=static_deltadelta_sinevib acoustic.sample_rate=48000 acoustic.trajectory_smoothing=false acoustic.trajectory_smoothing_cutoff=50\n",
"/usr/local/lib/python3.7/dist-packages/resampy/interpn.py:114: NumbaWarning: The TBB threading layer requires TBB version 2019.5 or later i.e., TBB_INTERFACE_VERSION >= 11005. Found TBB_INTERFACE_VERSION = 9107. The TBB threading layer is disabled.\n",
" _resample_loop_p(x, t_out, interp_win, interp_delta, num_table, scale, y)\n",
"[\u001b[36m2022-07-17 07:52:44,112\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - timelag:\n",
" enabled: true\n",
" question_path: null\n",
" label_phone_score_dir: data/timelag/label_phone_score\n",
" label_phone_align_dir: data/timelag/label_phone_align\n",
"duration:\n",
" enabled: true\n",
" question_path: null\n",
" label_dir: data/duration/label_phone_align\n",
"acoustic:\n",
" enabled: true\n",
" question_path: null\n",
" wav_dir: data/acoustic/wav\n",
" label_dir: data/acoustic/label_phone_align\n",
" sample_rate: 48000\n",
" subphone_features: coarse_coding\n",
" f0_floor: 150\n",
" f0_ceil: 700\n",
" use_harvest: true\n",
" d4c_threshold: 0.85\n",
" frame_period: 5\n",
" mgc_order: 59\n",
" num_windows: 3\n",
" relative_f0: false\n",
" interp_unvoiced_aperiodicity: true\n",
" vibrato_mode: sine\n",
" trajectory_smoothing: false\n",
" trajectory_smoothing_cutoff: 50\n",
" correct_vuv: false\n",
"verbose: 100\n",
"utt_list: data/list/dev.list\n",
"out_dir: dump/oniku_kurumi/org/dev/\n",
"max_workers: null\n",
"question_path: ../../_common/hed/jp_dev.hed\n",
"log_f0_conditioning: true\n",
"\u001b[0m\n",
"[\u001b[36m2022-07-17 07:52:44,140\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - mkdirs: /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/dump/oniku_kurumi/org/dev/in_timelag\u001b[0m\n",
"[\u001b[36m2022-07-17 07:52:44,141\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - mkdirs: /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/dump/oniku_kurumi/org/dev/out_timelag\u001b[0m\n",
"[\u001b[36m2022-07-17 07:52:44,141\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - mkdirs: /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/dump/oniku_kurumi/org/dev/in_duration\u001b[0m\n",
"[\u001b[36m2022-07-17 07:52:44,141\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - mkdirs: /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/dump/oniku_kurumi/org/dev/out_duration\u001b[0m\n",
"[\u001b[36m2022-07-17 07:52:44,141\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - mkdirs: /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/dump/oniku_kurumi/org/dev/in_acoustic\u001b[0m\n",
"[\u001b[36m2022-07-17 07:52:44,142\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - mkdirs: /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/dump/oniku_kurumi/org/dev/out_acoustic\u001b[0m\n",
"[\u001b[36m2022-07-17 07:52:44,150\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Timelag linguistic feature dim: 337\u001b[0m\n",
"[\u001b[36m2022-07-17 07:52:44,151\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Timelag feature dim: 1\u001b[0m\n",
"100% 3/3 [00:00<00:00, 42.09it/s]\n",
"[\u001b[36m2022-07-17 07:52:44,282\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Duration linguistic feature dim: 337\u001b[0m\n",
"[\u001b[36m2022-07-17 07:52:44,282\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Duration feature dim: 1\u001b[0m\n",
"100% 3/3 [00:00<00:00, 24.23it/s]\n",
"[\u001b[36m2022-07-17 07:52:44,461\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Acoustic linguistic feature dim: 341\u001b[0m\n",
"[\u001b[36m2022-07-17 07:52:46,445\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Acoustic feature dim: 206\u001b[0m\n",
" 33% 1/3 [00:03<00:06, 3.42s/it]Rate: 5.844155844155844, Extent: 43.09901340452365\n",
"100% 3/3 [00:08<00:00, 2.78s/it]\n",
"++ set +x\n",
"++ nnsvs-prepare-features utt_list=data/list/eval.list out_dir=dump/oniku_kurumi/org/eval/ question_path=../../_common/hed/jp_dev.hed timelag=defaults duration=defaults acoustic=static_deltadelta_sinevib acoustic.sample_rate=48000 acoustic.trajectory_smoothing=false acoustic.trajectory_smoothing_cutoff=50\n",
"/usr/local/lib/python3.7/dist-packages/resampy/interpn.py:114: NumbaWarning: The TBB threading layer requires TBB version 2019.5 or later i.e., TBB_INTERFACE_VERSION >= 11005. Found TBB_INTERFACE_VERSION = 9107. The TBB threading layer is disabled.\n",
" _resample_loop_p(x, t_out, interp_win, interp_delta, num_table, scale, y)\n",
"[\u001b[36m2022-07-17 07:53:09,059\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - timelag:\n",
" enabled: true\n",
" question_path: null\n",
" label_phone_score_dir: data/timelag/label_phone_score\n",
" label_phone_align_dir: data/timelag/label_phone_align\n",
"duration:\n",
" enabled: true\n",
" question_path: null\n",
" label_dir: data/duration/label_phone_align\n",
"acoustic:\n",
" enabled: true\n",
" question_path: null\n",
" wav_dir: data/acoustic/wav\n",
" label_dir: data/acoustic/label_phone_align\n",
" sample_rate: 48000\n",
" subphone_features: coarse_coding\n",
" f0_floor: 150\n",
" f0_ceil: 700\n",
" use_harvest: true\n",
" d4c_threshold: 0.85\n",
" frame_period: 5\n",
" mgc_order: 59\n",
" num_windows: 3\n",
" relative_f0: false\n",
" interp_unvoiced_aperiodicity: true\n",
" vibrato_mode: sine\n",
" trajectory_smoothing: false\n",
" trajectory_smoothing_cutoff: 50\n",
" correct_vuv: false\n",
"verbose: 100\n",
"utt_list: data/list/eval.list\n",
"out_dir: dump/oniku_kurumi/org/eval/\n",
"max_workers: null\n",
"question_path: ../../_common/hed/jp_dev.hed\n",
"log_f0_conditioning: true\n",
"\u001b[0m\n",
"[\u001b[36m2022-07-17 07:53:09,087\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - mkdirs: /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/dump/oniku_kurumi/org/eval/in_timelag\u001b[0m\n",
"[\u001b[36m2022-07-17 07:53:09,087\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - mkdirs: /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/dump/oniku_kurumi/org/eval/out_timelag\u001b[0m\n",
"[\u001b[36m2022-07-17 07:53:09,088\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - mkdirs: /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/dump/oniku_kurumi/org/eval/in_duration\u001b[0m\n",
"[\u001b[36m2022-07-17 07:53:09,088\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - mkdirs: /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/dump/oniku_kurumi/org/eval/out_duration\u001b[0m\n",
"[\u001b[36m2022-07-17 07:53:09,088\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - mkdirs: /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/dump/oniku_kurumi/org/eval/in_acoustic\u001b[0m\n",
"[\u001b[36m2022-07-17 07:53:09,088\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - mkdirs: /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/dump/oniku_kurumi/org/eval/out_acoustic\u001b[0m\n",
"[\u001b[36m2022-07-17 07:53:09,106\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Timelag linguistic feature dim: 337\u001b[0m\n",
"[\u001b[36m2022-07-17 07:53:09,108\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Timelag feature dim: 1\u001b[0m\n",
"100% 6/6 [00:00<00:00, 52.62it/s]\n",
"[\u001b[36m2022-07-17 07:53:09,289\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Duration linguistic feature dim: 337\u001b[0m\n",
"[\u001b[36m2022-07-17 07:53:09,290\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Duration feature dim: 1\u001b[0m\n",
"100% 6/6 [00:00<00:00, 25.31it/s]\n",
"[\u001b[36m2022-07-17 07:53:09,605\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Acoustic linguistic feature dim: 341\u001b[0m\n",
"[\u001b[36m2022-07-17 07:53:14,269\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Acoustic feature dim: 206\u001b[0m\n",
"100% 6/6 [00:19<00:00, 3.17s/it]\n",
"++ set +x\n",
"++ nnsvs-fit-scaler list_path=train_list.txt scaler._target_=sklearn.preprocessing.MinMaxScaler out_path=dump/oniku_kurumi/org/in_timelag_scaler.joblib\n",
"[2022-07-17 07:53:35,655][nnsvs][INFO] - verbose: 100\n",
"scaler:\n",
" _target_: sklearn.preprocessing.MinMaxScaler\n",
"list_path: train_list.txt\n",
"out_path: dump/oniku_kurumi/org/in_timelag_scaler.joblib\n",
"external_scaler: null\n",
"\n",
"[2022-07-17 07:53:36,317][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. 5.278103 5.278103 5.278103\n",
" -1. -1. -1. 1. 1. -1.\n",
" -1. -1. 1. -1. -1. -1.\n",
" 1. 1. -1. -1. 1. 5.\n",
" 3. -1. -1. -1. -1. -1.\n",
" -1. 1. 1. 0. 0. 0.\n",
" 3. 0. 4. -1. -1. -1.\n",
" -1. -1. -1. -1. -1. -12.\n",
" -12. ]\n",
"[2022-07-17 07:53:36,321][nnsvs][INFO] - data max:\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. 0. 1. 1.\n",
" 1. 1. 1. 1. 0. 0.\n",
" 1. 0. 0. 0. 0. 0.\n",
" 1. 0. 0. 0. 0. 0.\n",
" 1. 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0. 0.\n",
" 1. 1. 0. 1. 1. 0.\n",
" 1. 0. 0. 1. 0. 0.\n",
" 1. 0. 0. 0. 0. 0.\n",
" 1. 0. 0. 0. 0. 0.\n",
" 0. 0. 1. 0. 1. 0.\n",
" 0. 0. 0. 0. 0. 0.\n",
" 0. 0. 1. 0. 0. 0.\n",
" 0. 0. 0. 1. 1. 1.\n",
" 1. 0. 1. 1. 0. 1.\n",
" 1. 0. 1. 1. 0. 1.\n",
" 1. 0. 1. 0. 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. 0.\n",
" 0. 1. 0. 0. 1. 1.\n",
" 0. 1. 1. 0. 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. 1. 1.\n",
" 1. 0. 0. 1. 1. 1.\n",
" 1. 1. 1. 1. 1. 1.\n",
" 1. 1. 1. 1. 1. 1.\n",
" 1. 0. 1. 0. 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. 0.\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. 1. 1.\n",
" 1. 1. 1. 1. 1. 1.\n",
" 1. 1. 1. 1. 0. 1.\n",
" 1. 1. 1. 1. 1. 1.\n",
" 1. 0. 0. 6.548873 6.548873 6.548873\n",
" 11. 11. 11. 1. 3. -1.\n",
" 2. 3. 3. 3. 1. 1.\n",
" 1. 1. 1. 1. 1. 346.\n",
" 96. 1. 318. 90. 1. 346.\n",
" 96. 10. 10. 43. 46. 93.\n",
" 96. 96. 100. 30. 30. 76.\n",
" 80. 336. 363. 96. 100. 12.\n",
" 12. ]\n",
"++ set +x\n",
"'dump/oniku_kurumi/org/in_timelag_scaler.joblib' -> 'dump/oniku_kurumi/norm/in_timelag_scaler.joblib'\n",
"++ nnsvs-fit-scaler list_path=train_list.txt scaler._target_=sklearn.preprocessing.MinMaxScaler out_path=dump/oniku_kurumi/org/in_duration_scaler.joblib\n",
"[2022-07-17 07:53:38,386][nnsvs][INFO] - verbose: 100\n",
"scaler:\n",
" _target_: sklearn.preprocessing.MinMaxScaler\n",
"list_path: train_list.txt\n",
"out_path: dump/oniku_kurumi/org/in_duration_scaler.joblib\n",
"external_scaler: null\n",
"\n",
"[2022-07-17 07:53:39,119][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. 5.278103 5.278103 5.278103\n",
" -1. -1. -1. 1. 1. -1.\n",
" -1. -1. 1. -1. -1. -1.\n",
" 1. 1. -1. -1. 1. 5.\n",
" 3. -1. -1. -1. -1. -1.\n",
" -1. 1. 1. 0. 0. 0.\n",
" 3. 0. 4. -1. -1. -1.\n",
" -1. -1. -1. -1. -1. -12.\n",
" -12. ]\n",
"[2022-07-17 07:53:39,123][nnsvs][INFO] - data max:\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. 0. 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. 0.\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. 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. 0. 0. 1. 1. 1.\n",
" 1. 1. 1. 1. 1. 1.\n",
" 1. 1. 1. 1. 1. 1.\n",
" 1. 0. 1. 0. 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. 0.\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. 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. 0. 0. 1. 1. 1.\n",
" 1. 1. 1. 1. 1. 1.\n",
" 1. 1. 1. 1. 1. 1.\n",
" 1. 0. 1. 0. 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. 0.\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. 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. 0. 0. 6.548873 6.548873 6.548873\n",
" 11. 11. 11. 3. 3. -1.\n",
" 2. 3. 3. 3. 1. 1.\n",
" 1. 1. 1. 1. 1. 499.\n",
" 189. 1. 346. 90. 1. 346.\n",
" 96. 10. 10. 43. 46. 93.\n",
" 96. 96. 100. 30. 30. 76.\n",
" 80. 336. 363. 96. 100. 12.\n",
" 12. ]\n",
"++ set +x\n",
"'dump/oniku_kurumi/org/in_duration_scaler.joblib' -> 'dump/oniku_kurumi/norm/in_duration_scaler.joblib'\n",
"++ nnsvs-fit-scaler list_path=train_list.txt scaler._target_=sklearn.preprocessing.MinMaxScaler out_path=dump/oniku_kurumi/org/in_acoustic_scaler.joblib\n",
"[2022-07-17 07:53:41,122][nnsvs][INFO] - verbose: 100\n",
"scaler:\n",
" _target_: sklearn.preprocessing.MinMaxScaler\n",
"list_path: train_list.txt\n",
"out_path: dump/oniku_kurumi/org/in_acoustic_scaler.joblib\n",
"external_scaler: null\n",
"\n",
"[2022-07-17 07:53:43,347][nnsvs][INFO] - data min:\n",
"[ 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0.\n",
" 0. 5.278103 5.278103 5.278103 -1.\n",
" -1. -1. 1. 1. -1.\n",
" -1. -1. 1. -1. -1.\n",
" -1. 1. 1. -1. -1.\n",
" 1. 5. 3. -1. -1.\n",
" -1. -1. -1. -1. 1.\n",
" 1. 0. 0. 0. 3.\n",
" 0. 4. -1. -1. -1.\n",
" -1. -1. -1. -1. -1.\n",
" -12. -12. 0.04404987 0.45900714 0.04404987\n",
" 1. ]\n",
"[2022-07-17 07:53:43,351][nnsvs][INFO] - data max:\n",
"[ 1. 1. 1. 1. 1.\n",
" 1. 1. 1. 1. 1.\n",
" 1. 1. 1. 1. 1.\n",
" 1. 1. 1. 1. 0.\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. 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. 0. 1.\n",
" 1. 1. 1. 1. 1.\n",
" 1. 1. 1. 1. 1.\n",
" 1. 1. 1. 1. 1.\n",
" 0. 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. 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. 0.\n",
" 1. 1. 1. 1. 1.\n",
" 1. 1. 1. 1. 1.\n",
" 1. 1. 1. 1. 1.\n",
" 1. 0. 1. 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. 0.\n",
" 0. 1. 1. 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. 1. 0.\n",
" 0. 6.548873 6.548873 6.548873 11.\n",
" 11. 11. 3. 3. -1.\n",
" 2. 3. 3. 3. 1.\n",
" 1. 1. 1. 1. 1.\n",
" 1. 499. 189. 1. 346.\n",
" 90. 1. 346. 96. 10.\n",
" 10. 43. 46. 93. 96.\n",
" 96. 100. 30. 30. 76.\n",
" 80. 336. 363. 96. 100.\n",
" 12. 12. 0.99733615 0.99733615 0.99733615\n",
" 994. ]\n",
"++ set +x\n",
"'dump/oniku_kurumi/org/in_acoustic_scaler.joblib' -> 'dump/oniku_kurumi/norm/in_acoustic_scaler.joblib'\n",
"++ nnsvs-fit-scaler list_path=train_list.txt scaler._target_=sklearn.preprocessing.StandardScaler out_path=dump/oniku_kurumi/org/out_timelag_scaler.joblib\n",
"[2022-07-17 07:53:45,417][nnsvs][INFO] - verbose: 100\n",
"scaler:\n",
" _target_: sklearn.preprocessing.StandardScaler\n",
"list_path: train_list.txt\n",
"out_path: dump/oniku_kurumi/org/out_timelag_scaler.joblib\n",
"external_scaler: null\n",
"\n",
"[2022-07-17 07:53:45,816][nnsvs][INFO] - mean:\n",
"[0.04060533]\n",
"[2022-07-17 07:53:45,816][nnsvs][INFO] - std:\n",
"[7.86408743]\n",
"++ set +x\n",
"'dump/oniku_kurumi/org/out_timelag_scaler.joblib' -> 'dump/oniku_kurumi/norm/out_timelag_scaler.joblib'\n",
"++ nnsvs-fit-scaler list_path=train_list.txt scaler._target_=sklearn.preprocessing.StandardScaler out_path=dump/oniku_kurumi/org/out_duration_scaler.joblib\n",
"[2022-07-17 07:53:47,827][nnsvs][INFO] - verbose: 100\n",
"scaler:\n",
" _target_: sklearn.preprocessing.StandardScaler\n",
"list_path: train_list.txt\n",
"out_path: dump/oniku_kurumi/org/out_duration_scaler.joblib\n",
"external_scaler: null\n",
"\n",
"[2022-07-17 07:53:48,223][nnsvs][INFO] - mean:\n",
"[67.54582921]\n",
"[2022-07-17 07:53:48,224][nnsvs][INFO] - std:\n",
"[83.79628159]\n",
"++ set +x\n",
"'dump/oniku_kurumi/org/out_duration_scaler.joblib' -> 'dump/oniku_kurumi/norm/out_duration_scaler.joblib'\n",
"++ nnsvs-fit-scaler list_path=train_list.txt scaler._target_=sklearn.preprocessing.StandardScaler out_path=dump/oniku_kurumi/org/out_acoustic_scaler.joblib\n",
"[2022-07-17 07:53:50,229][nnsvs][INFO] - verbose: 100\n",
"scaler:\n",
" _target_: sklearn.preprocessing.StandardScaler\n",
"list_path: train_list.txt\n",
"out_path: dump/oniku_kurumi/org/out_acoustic_scaler.joblib\n",
"external_scaler: null\n",
"\n",
"[2022-07-17 07:53:52,591][nnsvs][INFO] - mean:\n",
"[ 5.49873193e+00 2.79285910e+00 -5.01513442e-02 1.17711517e+00\n",
" -3.47282078e-01 6.05280490e-01 -1.98770619e-01 1.40398269e-01\n",
" 1.31985139e-02 2.49454598e-01 -3.02786195e-01 7.49993681e-02\n",
" -8.82376305e-02 5.36799456e-02 -1.52716982e-01 2.45845901e-02\n",
" -4.46831553e-02 1.22847371e-01 -9.32973897e-02 3.85733540e-02\n",
" -9.56805647e-02 1.16027954e-01 -1.01388473e-01 1.01111904e-02\n",
" -8.97941557e-03 4.69483959e-02 -9.01483038e-02 9.08089374e-02\n",
" -5.27809836e-02 1.54450302e-02 -9.69261906e-04 1.80645343e-02\n",
" -2.69604677e-02 2.34432906e-02 -3.06497410e-02 4.52638547e-02\n",
" -4.97031840e-02 4.69590400e-02 -3.54833539e-02 2.58911599e-02\n",
" -1.01325077e-02 -3.03222427e-03 8.16489318e-03 -3.34169376e-03\n",
" -5.72475529e-03 1.51845406e-02 -1.87894060e-02 1.77525692e-02\n",
" -1.06907893e-02 -1.91050400e-04 1.21008839e-02 -2.03289555e-02\n",
" 2.05802309e-02 -1.47389486e-02 4.72417565e-03 2.13571603e-03\n",
" -5.14919986e-03 2.83096350e-03 2.25690094e-03 -8.26157301e-03\n",
" 6.02055408e-04 1.19346086e-04 2.40774129e-05 1.14796845e-04\n",
" -6.38460015e-05 -1.76331160e-05 -2.33780114e-05 7.90004150e-07\n",
" 1.90879769e-05 2.26233447e-05 -4.51131578e-05 3.65937101e-05\n",
" 1.50199535e-05 -1.78283669e-05 -1.16085592e-05 -3.24499318e-06\n",
" 5.61835167e-06 1.47193168e-05 -1.31278330e-06 -7.21901445e-06\n",
" -7.43739582e-06 8.51234249e-06 -1.46614732e-05 -9.47149645e-06\n",
" 2.27456039e-07 8.04044952e-06 -1.13062059e-05 1.04180638e-05\n",
" -1.22867611e-06 -6.15567413e-06 9.14884648e-06 -6.03742368e-06\n",
" 6.02868319e-06 -2.54211074e-06 -7.03441164e-06 1.67028325e-05\n",
" -1.35066411e-05 5.37039642e-06 4.00526124e-06 -5.15274661e-06\n",
" 3.75736826e-06 -3.33577573e-06 4.15953414e-06 -3.97708793e-06\n",
" 4.47285846e-07 6.21439007e-06 -7.70447599e-06 4.60343933e-06\n",
" -1.95694885e-07 -5.03985362e-06 7.90043598e-06 -7.56113981e-06\n",
" 5.15553013e-06 -2.33025807e-06 -4.10049106e-07 1.16224317e-06\n",
" -2.25817547e-06 1.63547359e-06 -1.24301979e-07 -1.11748037e-06\n",
" -1.34517716e-03 -1.96375522e-03 -2.72930929e-04 -9.48991582e-04\n",
" 1.12669080e-04 -3.38082906e-04 -5.21951988e-05 -1.16726037e-04\n",
" -9.08443623e-05 -1.10097527e-04 5.77542725e-05 -8.88083299e-05\n",
" -1.03090477e-05 1.40656113e-05 1.11646022e-04 6.70580194e-06\n",
" 3.57722312e-05 -4.57306528e-05 4.89431094e-05 4.17861508e-06\n",
" 5.95253792e-05 -2.61535125e-05 4.00676966e-05 1.28776411e-05\n",
" 1.76044321e-05 -3.67483506e-05 6.29429474e-05 -5.36145507e-05\n",
" 7.70939041e-06 1.92148610e-05 -8.91618149e-06 -9.13069382e-06\n",
" 3.70944452e-06 4.37761044e-06 8.56768740e-06 -2.70012071e-05\n",
" 2.62940972e-05 -1.42282615e-05 -5.49274295e-07 -1.34928752e-06\n",
" -9.47301361e-07 4.43418802e-06 -1.33310947e-05 1.29081996e-05\n",
" -6.51251051e-06 -1.25851681e-06 6.91685831e-06 -7.13062943e-06\n",
" 3.39428175e-06 2.39909712e-06 -1.12644564e-05 1.59330628e-05\n",
" -1.36263350e-05 1.10506511e-05 -7.30436749e-06 3.31855475e-06\n",
" -2.02568090e-06 3.75163710e-06 -6.03309106e-06 8.07737156e-06\n",
" 5.95574602e+00 2.79746354e-05 -6.31500265e-03 8.50978358e-01\n",
" -1.22313270e+01 -6.00319415e+00 -5.02522520e+00 -4.42111617e+00\n",
" -4.82046449e+00 7.16883010e-05 1.00564101e-04 1.92632695e-04\n",
" 1.04171463e-04 3.66133264e-05 3.13952290e-03 2.54342471e-03\n",
" 2.82957314e-03 2.76333271e-03 2.75560720e-03 1.02798336e+01\n",
" 1.38599859e+00 -1.29596487e-06 -1.56977765e-04 -8.56894759e-03\n",
" -1.17059109e-03 3.18285265e-02]\n",
"[2022-07-17 07:53:52,595][nnsvs][INFO] - std:\n",
"[ 3.58852364 0.81067101 0.44808498 0.39205262 0.4269118 0.42300183\n",
" 0.26935385 0.22298686 0.23350039 0.21607543 0.24271653 0.18138476\n",
" 0.1905842 0.1756037 0.17160178 0.1758263 0.1504915 0.14565346\n",
" 0.13568746 0.14421589 0.1341389 0.1442119 0.12562144 0.11830068\n",
" 0.1103623 0.11361267 0.10734197 0.10063382 0.09964095 0.09384366\n",
" 0.09095013 0.08948297 0.09156362 0.08709025 0.08260055 0.08298885\n",
" 0.07987614 0.07430049 0.07078514 0.06971043 0.06746124 0.0659499\n",
" 0.06422584 0.06270514 0.06272968 0.06063294 0.05891477 0.05787714\n",
" 0.05672406 0.05539665 0.05348583 0.05251927 0.05107118 0.05095226\n",
" 0.04995176 0.04793379 0.04715625 0.04691922 0.04635664 0.04512491\n",
" 0.49934697 0.1169164 0.09252099 0.07958563 0.07352537 0.06796686\n",
" 0.06583541 0.06347534 0.06158371 0.06033155 0.06037576 0.05690075\n",
" 0.05608686 0.05460099 0.05362493 0.05262118 0.05079233 0.04997983\n",
" 0.0484497 0.04739849 0.04626865 0.04572679 0.04380771 0.0427875\n",
" 0.04169523 0.04104104 0.04017855 0.0388459 0.03797473 0.03712971\n",
" 0.03646936 0.03556425 0.0348069 0.03401327 0.03357933 0.03300973\n",
" 0.03234282 0.03166029 0.03095844 0.03047948 0.02995142 0.02932588\n",
" 0.0287466 0.02830987 0.02791404 0.02735056 0.02680212 0.02628612\n",
" 0.02580579 0.02538248 0.02500461 0.02462908 0.02404237 0.0237884\n",
" 0.0235065 0.02299117 0.02273197 0.02242645 0.02200169 0.02165305\n",
" 1.36938284 0.2591011 0.23957078 0.21451416 0.20757336 0.19523865\n",
" 0.19390307 0.19082002 0.18809043 0.1834755 0.17919126 0.17633514\n",
" 0.17412201 0.16976234 0.16742225 0.16341285 0.15895587 0.15621585\n",
" 0.15199692 0.14866959 0.14523711 0.14267463 0.13804255 0.13531494\n",
" 0.13186772 0.13034788 0.12822317 0.12444159 0.12144343 0.11922404\n",
" 0.1174302 0.11468523 0.11243703 0.11014507 0.1085183 0.10663619\n",
" 0.10467993 0.10252378 0.10060598 0.09914662 0.09742231 0.09540352\n",
" 0.09376949 0.09234256 0.09108733 0.08934451 0.08762939 0.08615946\n",
" 0.08476882 0.08328305 0.08202246 0.0808677 0.07917646 0.07834844\n",
" 0.07726584 0.0755854 0.07467847 0.07368287 0.0723944 0.0713071\n",
" 0.23926882 0.09696564 0.19246538 0.3561098 6.66990362 4.23287167\n",
" 3.47057995 3.04949449 3.20527081 1.87832658 1.8383517 1.88743868\n",
" 1.75587312 1.86342771 5.59818727 5.89628452 6.27970731 5.87762423\n",
" 6.28913881 15.17360356 2.12066371 0.36691177 0.04054283 0.52482162\n",
" 0.07375618 0.17554336]\n",
"++ set +x\n",
"'dump/oniku_kurumi/org/out_acoustic_scaler.joblib' -> 'dump/oniku_kurumi/norm/out_acoustic_scaler.joblib'\n",
"++ nnsvs-preprocess-normalize in_dir=dump/oniku_kurumi/org/train_no_dev/in_timelag/ scaler_path=dump/oniku_kurumi/org/in_timelag_scaler.joblib out_dir=dump/oniku_kurumi/norm/train_no_dev/in_timelag/\n",
"[\u001b[36m2022-07-17 07:53:54,349\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - verbose: 100\n",
"in_dir: dump/oniku_kurumi/org/train_no_dev/in_timelag/\n",
"out_dir: dump/oniku_kurumi/norm/train_no_dev/in_timelag/\n",
"scaler_path: dump/oniku_kurumi/org/in_timelag_scaler.joblib\n",
"inverse: false\n",
"num_workers: 4\n",
"\u001b[0m\n",
"100% 442/442 [00:00<00:00, 1117.36it/s]\n",
"++ set +x\n",
"++ nnsvs-preprocess-normalize in_dir=dump/oniku_kurumi/org/train_no_dev/in_duration/ scaler_path=dump/oniku_kurumi/org/in_duration_scaler.joblib out_dir=dump/oniku_kurumi/norm/train_no_dev/in_duration/\n",
"[\u001b[36m2022-07-17 07:53:56,852\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - verbose: 100\n",
"in_dir: dump/oniku_kurumi/org/train_no_dev/in_duration/\n",
"out_dir: dump/oniku_kurumi/norm/train_no_dev/in_duration/\n",
"scaler_path: dump/oniku_kurumi/org/in_duration_scaler.joblib\n",
"inverse: false\n",
"num_workers: 4\n",
"\u001b[0m\n",
"100% 442/442 [00:00<00:00, 1086.94it/s]\n",
"++ set +x\n",
"++ nnsvs-preprocess-normalize in_dir=dump/oniku_kurumi/org/train_no_dev/in_acoustic/ scaler_path=dump/oniku_kurumi/org/in_acoustic_scaler.joblib out_dir=dump/oniku_kurumi/norm/train_no_dev/in_acoustic/\n",
"[\u001b[36m2022-07-17 07:53:59,353\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - verbose: 100\n",
"in_dir: dump/oniku_kurumi/org/train_no_dev/in_acoustic/\n",
"out_dir: dump/oniku_kurumi/norm/train_no_dev/in_acoustic/\n",
"scaler_path: dump/oniku_kurumi/org/in_acoustic_scaler.joblib\n",
"inverse: false\n",
"num_workers: 4\n",
"\u001b[0m\n",
"100% 442/442 [00:03<00:00, 111.35it/s]\n",
"++ set +x\n",
"++ nnsvs-preprocess-normalize in_dir=dump/oniku_kurumi/org/train_no_dev/out_timelag/ scaler_path=dump/oniku_kurumi/org/out_timelag_scaler.joblib out_dir=dump/oniku_kurumi/norm/train_no_dev/out_timelag/\n",
"[\u001b[36m2022-07-17 07:54:05,588\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - verbose: 100\n",
"in_dir: dump/oniku_kurumi/org/train_no_dev/out_timelag/\n",
"out_dir: dump/oniku_kurumi/norm/train_no_dev/out_timelag/\n",
"scaler_path: dump/oniku_kurumi/org/out_timelag_scaler.joblib\n",
"inverse: false\n",
"num_workers: 4\n",
"\u001b[0m\n",
"100% 442/442 [00:00<00:00, 1377.41it/s]\n",
"++ set +x\n",
"++ nnsvs-preprocess-normalize in_dir=dump/oniku_kurumi/org/train_no_dev/out_duration/ scaler_path=dump/oniku_kurumi/org/out_duration_scaler.joblib out_dir=dump/oniku_kurumi/norm/train_no_dev/out_duration/\n",
"[\u001b[36m2022-07-17 07:54:08,053\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - verbose: 100\n",
"in_dir: dump/oniku_kurumi/org/train_no_dev/out_duration/\n",
"out_dir: dump/oniku_kurumi/norm/train_no_dev/out_duration/\n",
"scaler_path: dump/oniku_kurumi/org/out_duration_scaler.joblib\n",
"inverse: false\n",
"num_workers: 4\n",
"\u001b[0m\n",
"100% 442/442 [00:00<00:00, 1321.14it/s]\n",
"++ set +x\n",
"++ nnsvs-preprocess-normalize in_dir=dump/oniku_kurumi/org/train_no_dev/out_acoustic/ scaler_path=dump/oniku_kurumi/org/out_acoustic_scaler.joblib out_dir=dump/oniku_kurumi/norm/train_no_dev/out_acoustic/\n",
"[\u001b[36m2022-07-17 07:54:10,523\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - verbose: 100\n",
"in_dir: dump/oniku_kurumi/org/train_no_dev/out_acoustic/\n",
"out_dir: dump/oniku_kurumi/norm/train_no_dev/out_acoustic/\n",
"scaler_path: dump/oniku_kurumi/org/out_acoustic_scaler.joblib\n",
"inverse: false\n",
"num_workers: 4\n",
"\u001b[0m\n",
"100% 442/442 [00:05<00:00, 79.72it/s]\n",
"++ set +x\n",
"++ nnsvs-preprocess-normalize in_dir=dump/oniku_kurumi/org/dev/in_timelag/ scaler_path=dump/oniku_kurumi/org/in_timelag_scaler.joblib out_dir=dump/oniku_kurumi/norm/dev/in_timelag/\n",
"[\u001b[36m2022-07-17 07:54:19,178\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - verbose: 100\n",
"in_dir: dump/oniku_kurumi/org/dev/in_timelag/\n",
"out_dir: dump/oniku_kurumi/norm/dev/in_timelag/\n",
"scaler_path: dump/oniku_kurumi/org/in_timelag_scaler.joblib\n",
"inverse: false\n",
"num_workers: 4\n",
"\u001b[0m\n",
"100% 3/3 [00:00<00:00, 161.61it/s]\n",
"++ set +x\n",
"++ nnsvs-preprocess-normalize in_dir=dump/oniku_kurumi/org/dev/in_duration/ scaler_path=dump/oniku_kurumi/org/in_duration_scaler.joblib out_dir=dump/oniku_kurumi/norm/dev/in_duration/\n",
"[\u001b[36m2022-07-17 07:54:21,731\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - verbose: 100\n",
"in_dir: dump/oniku_kurumi/org/dev/in_duration/\n",
"out_dir: dump/oniku_kurumi/norm/dev/in_duration/\n",
"scaler_path: dump/oniku_kurumi/org/in_duration_scaler.joblib\n",
"inverse: false\n",
"num_workers: 4\n",
"\u001b[0m\n",
"100% 3/3 [00:00<00:00, 255.37it/s]\n",
"++ set +x\n",
"++ nnsvs-preprocess-normalize in_dir=dump/oniku_kurumi/org/dev/in_acoustic/ scaler_path=dump/oniku_kurumi/org/in_acoustic_scaler.joblib out_dir=dump/oniku_kurumi/norm/dev/in_acoustic/\n",
"[\u001b[36m2022-07-17 07:54:23,823\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - verbose: 100\n",
"in_dir: dump/oniku_kurumi/org/dev/in_acoustic/\n",
"out_dir: dump/oniku_kurumi/norm/dev/in_acoustic/\n",
"scaler_path: dump/oniku_kurumi/org/in_acoustic_scaler.joblib\n",
"inverse: false\n",
"num_workers: 4\n",
"\u001b[0m\n",
"100% 3/3 [00:00<00:00, 107.82it/s]\n",
"++ set +x\n",
"++ nnsvs-preprocess-normalize in_dir=dump/oniku_kurumi/org/dev/out_timelag/ scaler_path=dump/oniku_kurumi/org/out_timelag_scaler.joblib out_dir=dump/oniku_kurumi/norm/dev/out_timelag/\n",
"[\u001b[36m2022-07-17 07:54:25,942\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - verbose: 100\n",
"in_dir: dump/oniku_kurumi/org/dev/out_timelag/\n",
"out_dir: dump/oniku_kurumi/norm/dev/out_timelag/\n",
"scaler_path: dump/oniku_kurumi/org/out_timelag_scaler.joblib\n",
"inverse: false\n",
"num_workers: 4\n",
"\u001b[0m\n",
"100% 3/3 [00:00<00:00, 350.78it/s]\n",
"++ set +x\n",
"++ nnsvs-preprocess-normalize in_dir=dump/oniku_kurumi/org/dev/out_duration/ scaler_path=dump/oniku_kurumi/org/out_duration_scaler.joblib out_dir=dump/oniku_kurumi/norm/dev/out_duration/\n",
"[\u001b[36m2022-07-17 07:54:28,071\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - verbose: 100\n",
"in_dir: dump/oniku_kurumi/org/dev/out_duration/\n",
"out_dir: dump/oniku_kurumi/norm/dev/out_duration/\n",
"scaler_path: dump/oniku_kurumi/org/out_duration_scaler.joblib\n",
"inverse: false\n",
"num_workers: 4\n",
"\u001b[0m\n",
"100% 3/3 [00:00<00:00, 264.28it/s]\n",
"++ set +x\n",
"++ nnsvs-preprocess-normalize in_dir=dump/oniku_kurumi/org/dev/out_acoustic/ scaler_path=dump/oniku_kurumi/org/out_acoustic_scaler.joblib out_dir=dump/oniku_kurumi/norm/dev/out_acoustic/\n",
"[\u001b[36m2022-07-17 07:54:30,154\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - verbose: 100\n",
"in_dir: dump/oniku_kurumi/org/dev/out_acoustic/\n",
"out_dir: dump/oniku_kurumi/norm/dev/out_acoustic/\n",
"scaler_path: dump/oniku_kurumi/org/out_acoustic_scaler.joblib\n",
"inverse: false\n",
"num_workers: 4\n",
"\u001b[0m\n",
"100% 3/3 [00:00<00:00, 103.43it/s]\n",
"++ set +x\n",
"++ nnsvs-preprocess-normalize in_dir=dump/oniku_kurumi/org/eval/in_timelag/ scaler_path=dump/oniku_kurumi/org/in_timelag_scaler.joblib out_dir=dump/oniku_kurumi/norm/eval/in_timelag/\n",
"[\u001b[36m2022-07-17 07:54:32,268\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - verbose: 100\n",
"in_dir: dump/oniku_kurumi/org/eval/in_timelag/\n",
"out_dir: dump/oniku_kurumi/norm/eval/in_timelag/\n",
"scaler_path: dump/oniku_kurumi/org/in_timelag_scaler.joblib\n",
"inverse: false\n",
"num_workers: 4\n",
"\u001b[0m\n",
"100% 6/6 [00:00<00:00, 370.59it/s]\n",
"++ set +x\n",
"++ nnsvs-preprocess-normalize in_dir=dump/oniku_kurumi/org/eval/in_duration/ scaler_path=dump/oniku_kurumi/org/in_duration_scaler.joblib out_dir=dump/oniku_kurumi/norm/eval/in_duration/\n",
"[\u001b[36m2022-07-17 07:54:34,368\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - verbose: 100\n",
"in_dir: dump/oniku_kurumi/org/eval/in_duration/\n",
"out_dir: dump/oniku_kurumi/norm/eval/in_duration/\n",
"scaler_path: dump/oniku_kurumi/org/in_duration_scaler.joblib\n",
"inverse: false\n",
"num_workers: 4\n",
"\u001b[0m\n",
"100% 6/6 [00:00<00:00, 440.89it/s]\n",
"++ set +x\n",
"++ nnsvs-preprocess-normalize in_dir=dump/oniku_kurumi/org/eval/in_acoustic/ scaler_path=dump/oniku_kurumi/org/in_acoustic_scaler.joblib out_dir=dump/oniku_kurumi/norm/eval/in_acoustic/\n",
"[\u001b[36m2022-07-17 07:54:36,492\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - verbose: 100\n",
"in_dir: dump/oniku_kurumi/org/eval/in_acoustic/\n",
"out_dir: dump/oniku_kurumi/norm/eval/in_acoustic/\n",
"scaler_path: dump/oniku_kurumi/org/in_acoustic_scaler.joblib\n",
"inverse: false\n",
"num_workers: 4\n",
"\u001b[0m\n",
"100% 6/6 [00:00<00:00, 120.55it/s]\n",
"++ set +x\n",
"++ nnsvs-preprocess-normalize in_dir=dump/oniku_kurumi/org/eval/out_timelag/ scaler_path=dump/oniku_kurumi/org/out_timelag_scaler.joblib out_dir=dump/oniku_kurumi/norm/eval/out_timelag/\n",
"[\u001b[36m2022-07-17 07:54:38,623\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - verbose: 100\n",
"in_dir: dump/oniku_kurumi/org/eval/out_timelag/\n",
"out_dir: dump/oniku_kurumi/norm/eval/out_timelag/\n",
"scaler_path: dump/oniku_kurumi/org/out_timelag_scaler.joblib\n",
"inverse: false\n",
"num_workers: 4\n",
"\u001b[0m\n",
"100% 6/6 [00:00<00:00, 12312.05it/s]\n",
"++ set +x\n",
"++ nnsvs-preprocess-normalize in_dir=dump/oniku_kurumi/org/eval/out_duration/ scaler_path=dump/oniku_kurumi/org/out_duration_scaler.joblib out_dir=dump/oniku_kurumi/norm/eval/out_duration/\n",
"[\u001b[36m2022-07-17 07:54:40,725\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - verbose: 100\n",
"in_dir: dump/oniku_kurumi/org/eval/out_duration/\n",
"out_dir: dump/oniku_kurumi/norm/eval/out_duration/\n",
"scaler_path: dump/oniku_kurumi/org/out_duration_scaler.joblib\n",
"inverse: false\n",
"num_workers: 4\n",
"\u001b[0m\n",
"100% 6/6 [00:00<00:00, 541.77it/s]\n",
"++ set +x\n",
"++ nnsvs-preprocess-normalize in_dir=dump/oniku_kurumi/org/eval/out_acoustic/ scaler_path=dump/oniku_kurumi/org/out_acoustic_scaler.joblib out_dir=dump/oniku_kurumi/norm/eval/out_acoustic/\n",
"[\u001b[36m2022-07-17 07:54:42,821\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - verbose: 100\n",
"in_dir: dump/oniku_kurumi/org/eval/out_acoustic/\n",
"out_dir: dump/oniku_kurumi/norm/eval/out_acoustic/\n",
"scaler_path: dump/oniku_kurumi/org/out_acoustic_scaler.joblib\n",
"inverse: false\n",
"num_workers: 4\n",
"\u001b[0m\n",
"100% 6/6 [00:00<00:00, 138.35it/s]\n",
"++ set +x\n"
]
}
],
"source": [
"#! cd $RECIPE_ROOT && bash run.sh --stage 1 --stop-stage 1"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "HH4AVfZlXa07"
},
"source": [
"# Save extract data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "SVsg4Xn8XIee",
"outputId": "86ce41bd-4218-49f4-f48b-321a17484a4a"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tar: Removing leading `/' from member names\n"
]
}
],
"source": [
" #! tar zcf /content/gdrive/nnsvs_dev20220117_oniku_kurumi_utagoe_db_dev_latest_extracted_data_20220717.tgz $RECIPE_ROOT/dump $RECIPE_ROOT/data $RECIPE_ROOT/outputs"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "GrbNM2Qy4-ok"
},
"outputs": [],
"source": [
"! tar zxf /content/gdrive/nnsvs_dev20220117_oniku_kurumi_utagoe_db_dev_latest_extracted_data_20220717.tgz -C /"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "8EFgAz7v8Ee6"
},
"source": [
"# Training\n",
"## Timelag model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "andN-LWzC9-l",
"outputId": "6513ba71-cfe4-4d3d-c4be-6422c67cd7f9"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"stage 2: Training time-lag model\n",
"++ nnsvs-train --config-dir conf/train/timelag model=timelag_mdn train=myconfig data=myconfig data.train_no_dev.in_dir=dump/oniku_kurumi/norm/train_no_dev/in_timelag/ data.train_no_dev.out_dir=dump/oniku_kurumi/norm/train_no_dev/out_timelag/ data.dev.in_dir=dump/oniku_kurumi/norm/dev/in_timelag/ data.dev.out_dir=dump/oniku_kurumi/norm/dev/out_timelag/ data.in_scaler_path=dump/oniku_kurumi/norm/in_timelag_scaler.joblib data.out_scaler_path=dump/oniku_kurumi/norm/out_timelag_scaler.joblib train.out_dir=exp/oniku_kurumi/timelag_mdn train.log_dir=tensorboard/oniku_kurumi_timelag_mdn train.resume.checkpoint=\n",
"/usr/local/lib/python3.7/dist-packages/resampy/interpn.py:114: NumbaWarning: The TBB threading layer requires TBB version 2019.5 or later i.e., TBB_INTERFACE_VERSION >= 11005. Found TBB_INTERFACE_VERSION = 9107. The TBB threading layer is disabled.\n",
" _resample_loop_p(x, t_out, interp_win, interp_delta, num_table, scale, y)\n",
"[\u001b[36m2022-07-17 10:06:53,124\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - model:\n",
" stream_sizes:\n",
" - 1\n",
" has_dynamic_features:\n",
" - false\n",
" stream_weights:\n",
" - 1\n",
" netG:\n",
" _target_: nnsvs.model.MDNv2\n",
" in_dim: 337\n",
" out_dim: 1\n",
" hidden_dim: 32\n",
" dropout: 0.5\n",
" num_layers: 3\n",
" num_gaussians: 1\n",
" init_type: kaiming_normal\n",
"train:\n",
" out_dir: exp/oniku_kurumi/timelag_mdn\n",
" log_dir: tensorboard/oniku_kurumi_timelag_mdn\n",
" use_amp: false\n",
" max_train_steps: -1\n",
" nepochs: 100\n",
" checkpoint_epoch_interval: 50\n",
" feats_criterion: mse\n",
" stream_wise_loss: false\n",
" use_detect_anomaly: false\n",
" optim:\n",
" optimizer:\n",
" name: Adam\n",
" params:\n",
" lr: 0.001\n",
" betas:\n",
" - 0.9\n",
" - 0.999\n",
" weight_decay: 0.0\n",
" lr_scheduler:\n",
" name: StepLR\n",
" params:\n",
" step_size: 20\n",
" gamma: 0.5\n",
" resume:\n",
" checkpoint: ''\n",
" load_optimizer: false\n",
" cudnn:\n",
" benchmark: false\n",
" deterministic: true\n",
"data:\n",
" train_no_dev:\n",
" in_dir: dump/oniku_kurumi/norm/train_no_dev/in_timelag/\n",
" out_dir: dump/oniku_kurumi/norm/train_no_dev/out_timelag/\n",
" dev:\n",
" in_dir: dump/oniku_kurumi/norm/dev/in_timelag/\n",
" out_dir: dump/oniku_kurumi/norm/dev/out_timelag/\n",
" num_workers: 2\n",
" batch_size: 8\n",
" pin_memory: true\n",
" filter_long_segments: false\n",
" filter_num_frames: 6000\n",
" max_time_frames: -1\n",
" in_scaler_path: dump/oniku_kurumi/norm/in_timelag_scaler.joblib\n",
" out_scaler_path: dump/oniku_kurumi/norm/out_timelag_scaler.joblib\n",
"mlflow:\n",
" enabled: false\n",
" experiment: test\n",
"verbose: 100\n",
"seed: 773\n",
"data_parallel: false\n",
"\u001b[0m\n",
"[\u001b[36m2022-07-17 10:06:53,124\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - PyTorch version: 1.12.0+cu113\u001b[0m\n",
"[\u001b[36m2022-07-17 10:06:53,124\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - cudnn.deterministic: True\u001b[0m\n",
"[\u001b[36m2022-07-17 10:06:53,124\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - cudnn.benchmark: False\u001b[0m\n",
"[\u001b[36m2022-07-17 10:06:53,151\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - cuDNN version: 8302\u001b[0m\n",
"[\u001b[36m2022-07-17 10:06:53,151\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Random seed: 773\u001b[0m\n",
"[\u001b[36m2022-07-17 10:06:57,692\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Number of trainable params: 0.013 million\u001b[0m\n",
"[\u001b[36m2022-07-17 10:06:57,693\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - MDNv2(\n",
" (model): Sequential(\n",
" (0): Linear(in_features=337, out_features=32, bias=True)\n",
" (1): ReLU()\n",
" (2): Dropout(p=0.5, inplace=False)\n",
" (3): Linear(in_features=32, out_features=32, bias=True)\n",
" (4): ReLU()\n",
" (5): Dropout(p=0.5, inplace=False)\n",
" (6): Linear(in_features=32, out_features=32, bias=True)\n",
" (7): ReLU()\n",
" (8): Dropout(p=0.5, inplace=False)\n",
" (9): MDNLayer(\n",
" (log_pi): Linear(in_features=32, out_features=1, bias=True)\n",
" (log_sigma): Linear(in_features=32, out_features=1, bias=True)\n",
" (mu): Linear(in_features=32, out_features=1, bias=True)\n",
" )\n",
" )\n",
")\u001b[0m\n",
"[\u001b[36m2022-07-17 10:06:57,698\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Number of iterations per epoch: 56\u001b[0m\n",
"[\u001b[36m2022-07-17 10:06:57,699\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Number of max_train_steps is set based on nepochs: 5600\u001b[0m\n",
"[\u001b[36m2022-07-17 10:06:57,699\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Number of epochs: 100\u001b[0m\n",
"[\u001b[36m2022-07-17 10:06:57,699\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Number of iterations: 5600\u001b[0m\n",
" 0% 0/100 [00:00<?, ?it/s][\u001b[36m2022-07-17 10:07:11,202\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 1]: loss 1.8819881273167474\u001b[0m\n",
"[\u001b[36m2022-07-17 10:07:11,343\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 1]: loss 1.6762841939926147\u001b[0m\n",
"[\u001b[36m2022-07-17 10:07:11,354\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/timelag_mdn/best_loss.pth\u001b[0m\n",
" 1% 1/100 [00:03<06:17, 3.82s/it][\u001b[36m2022-07-17 10:07:12,604\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 2]: loss 1.4346154566322054\u001b[0m\n",
"[\u001b[36m2022-07-17 10:07:12,739\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 2]: loss 1.6870020627975464\u001b[0m\n",
" 2% 2/100 [00:05<03:53, 2.39s/it][\u001b[36m2022-07-17 10:07:13,685\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 3]: loss 1.4222220608166285\u001b[0m\n",
"[\u001b[36m2022-07-17 10:07:13,821\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 3]: loss 1.6922587156295776\u001b[0m\n",
" 3% 3/100 [00:06<02:53, 1.79s/it][\u001b[36m2022-07-17 10:07:14,771\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 4]: loss 1.414461727653231\u001b[0m\n",
"[\u001b[36m2022-07-17 10:07:14,913\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 4]: loss 1.6921724081039429\u001b[0m\n",
" 4% 4/100 [00:07<02:25, 1.52s/it][\u001b[36m2022-07-17 10:07:15,858\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 5]: loss 1.4051858867917741\u001b[0m\n",
"[\u001b[36m2022-07-17 10:07:16,008\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 5]: loss 1.6801979541778564\u001b[0m\n",
" 5% 5/100 [00:08<02:09, 1.36s/it][\u001b[36m2022-07-17 10:07:16,944\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 6]: loss 1.3823946246079035\u001b[0m\n",
"[\u001b[36m2022-07-17 10:07:17,084\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 6]: loss 1.6527228355407715\u001b[0m\n",
"[\u001b[36m2022-07-17 10:07:17,090\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/timelag_mdn/best_loss.pth\u001b[0m\n",
" 6% 6/100 [00:09<01:59, 1.27s/it][\u001b[36m2022-07-17 10:07:18,008\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 7]: loss 1.3785658542598997\u001b[0m\n",
"[\u001b[36m2022-07-17 10:07:18,158\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 7]: loss 1.6392133235931396\u001b[0m\n",
"[\u001b[36m2022-07-17 10:07:18,165\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/timelag_mdn/best_loss.pth\u001b[0m\n",
" 7% 7/100 [00:10<01:52, 1.20s/it][\u001b[36m2022-07-17 10:07:19,122\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 8]: loss 1.360454071845327\u001b[0m\n",
"[\u001b[36m2022-07-17 10:07:19,259\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 8]: loss 1.6255580186843872\u001b[0m\n",
"[\u001b[36m2022-07-17 10:07:19,266\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/timelag_mdn/best_loss.pth\u001b[0m\n",
" 8% 8/100 [00:11<01:47, 1.17s/it][\u001b[36m2022-07-17 10:07:20,246\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 9]: loss 1.3490146803004401\u001b[0m\n",
"[\u001b[36m2022-07-17 10:07:20,385\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 9]: loss 1.5783262252807617\u001b[0m\n",
"[\u001b[36m2022-07-17 10:07:20,392\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/timelag_mdn/best_loss.pth\u001b[0m\n",
" 9% 9/100 [00:12<01:45, 1.16s/it][\u001b[36m2022-07-17 10:07:21,348\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 10]: loss 1.3311207847935813\u001b[0m\n",
"[\u001b[36m2022-07-17 10:07:21,485\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 10]: loss 1.5792049169540405\u001b[0m\n",
" 10% 10/100 [00:13<01:42, 1.14s/it][\u001b[36m2022-07-17 10:07:22,455\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 11]: loss 1.3108925563948495\u001b[0m\n",
"[\u001b[36m2022-07-17 10:07:22,593\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 11]: loss 1.5398895740509033\u001b[0m\n",
"[\u001b[36m2022-07-17 10:07:22,600\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/timelag_mdn/best_loss.pth\u001b[0m\n",
" 11% 11/100 [00:15<01:40, 1.13s/it][\u001b[36m2022-07-17 10:07:23,560\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 12]: loss 1.297024782214846\u001b[0m\n",
"[\u001b[36m2022-07-17 10:07:23,698\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 12]: loss 1.555284023284912\u001b[0m\n",
" 12% 12/100 [00:16<01:38, 1.12s/it][\u001b[36m2022-07-17 10:07:24,668\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 13]: loss 1.2973887622356415\u001b[0m\n",
"[\u001b[36m2022-07-17 10:07:24,808\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 13]: loss 1.5208743810653687\u001b[0m\n",
"[\u001b[36m2022-07-17 10:07:24,815\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/timelag_mdn/best_loss.pth\u001b[0m\n",
" 13% 13/100 [00:17<01:37, 1.12s/it][\u001b[36m2022-07-17 10:07:25,767\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 14]: loss 1.2847191670111247\u001b[0m\n",
"[\u001b[36m2022-07-17 10:07:25,923\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 14]: loss 1.470308780670166\u001b[0m\n",
"[\u001b[36m2022-07-17 10:07:25,930\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/timelag_mdn/best_loss.pth\u001b[0m\n",
" 14% 14/100 [00:18<01:36, 1.12s/it][\u001b[36m2022-07-17 10:07:26,887\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 15]: loss 1.2732906746012824\u001b[0m\n",
"[\u001b[36m2022-07-17 10:07:27,027\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 15]: loss 1.4988665580749512\u001b[0m\n",
" 15% 15/100 [00:19<01:34, 1.11s/it][\u001b[36m2022-07-17 10:07:28,002\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 16]: loss 1.2673123329877853\u001b[0m\n",
"[\u001b[36m2022-07-17 10:07:28,142\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 16]: loss 1.4675227403640747\u001b[0m\n",
"[\u001b[36m2022-07-17 10:07:28,149\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/timelag_mdn/best_loss.pth\u001b[0m\n",
" 16% 16/100 [00:20<01:33, 1.11s/it][\u001b[36m2022-07-17 10:07:29,119\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 17]: loss 1.2675847496305193\u001b[0m\n",
"[\u001b[36m2022-07-17 10:07:29,258\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 17]: loss 1.4546257257461548\u001b[0m\n",
"[\u001b[36m2022-07-17 10:07:29,265\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/timelag_mdn/best_loss.pth\u001b[0m\n",
" 17% 17/100 [00:21<01:32, 1.12s/it][\u001b[36m2022-07-17 10:07:30,211\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 18]: loss 1.2555948730025972\u001b[0m\n",
"[\u001b[36m2022-07-17 10:07:30,357\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 18]: loss 1.4367355108261108\u001b[0m\n",
"[\u001b[36m2022-07-17 10:07:30,363\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/timelag_mdn/best_loss.pth\u001b[0m\n",
" 18% 18/100 [00:22<01:31, 1.11s/it][\u001b[36m2022-07-17 10:07:31,299\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 19]: loss 1.2603563559906823\u001b[0m\n",
"[\u001b[36m2022-07-17 10:07:31,447\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 19]: loss 1.4432473182678223\u001b[0m\n",
" 19% 19/100 [00:23<01:29, 1.10s/it][\u001b[36m2022-07-17 10:07:32,405\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 20]: loss 1.2492340079375677\u001b[0m\n",
"[\u001b[36m2022-07-17 10:07:32,543\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 20]: loss 1.3973333835601807\u001b[0m\n",
"[\u001b[36m2022-07-17 10:07:32,549\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/timelag_mdn/best_loss.pth\u001b[0m\n",
" 20% 20/100 [00:25<01:28, 1.10s/it][\u001b[36m2022-07-17 10:07:33,507\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 21]: loss 1.2582063866513116\u001b[0m\n",
"[\u001b[36m2022-07-17 10:07:33,644\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 21]: loss 1.4053072929382324\u001b[0m\n",
" 21% 21/100 [00:26<01:26, 1.10s/it][\u001b[36m2022-07-17 10:07:34,587\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 22]: loss 1.2503448597022466\u001b[0m\n",
"[\u001b[36m2022-07-17 10:07:34,722\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 22]: loss 1.3700276613235474\u001b[0m\n",
"[\u001b[36m2022-07-17 10:07:34,729\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/timelag_mdn/best_loss.pth\u001b[0m\n",
" 22% 22/100 [00:27<01:25, 1.10s/it][\u001b[36m2022-07-17 10:07:35,681\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 23]: loss 1.2313607356378011\u001b[0m\n",
"[\u001b[36m2022-07-17 10:07:35,816\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 23]: loss 1.3798669576644897\u001b[0m\n",
" 23% 23/100 [00:28<01:24, 1.09s/it][\u001b[36m2022-07-17 10:07:36,786\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 24]: loss 1.2365308701992035\u001b[0m\n",
"[\u001b[36m2022-07-17 10:07:36,923\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 24]: loss 1.397169589996338\u001b[0m\n",
" 24% 24/100 [00:29<01:23, 1.10s/it][\u001b[36m2022-07-17 10:07:37,876\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 25]: loss 1.2282583607094628\u001b[0m\n",
"[\u001b[36m2022-07-17 10:07:38,018\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 25]: loss 1.4225527048110962\u001b[0m\n",
" 25% 25/100 [00:30<01:22, 1.10s/it][\u001b[36m2022-07-17 10:07:38,962\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 26]: loss 1.2438751884869166\u001b[0m\n",
"[\u001b[36m2022-07-17 10:07:39,104\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 26]: loss 1.404800295829773\u001b[0m\n",
" 26% 26/100 [00:31<01:20, 1.09s/it][\u001b[36m2022-07-17 10:07:40,061\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 27]: loss 1.2204200497695379\u001b[0m\n",
"[\u001b[36m2022-07-17 10:07:40,203\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 27]: loss 1.4030007123947144\u001b[0m\n",
" 27% 27/100 [00:32<01:19, 1.10s/it][\u001b[36m2022-07-17 10:07:41,174\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 28]: loss 1.2322909129517419\u001b[0m\n",
"[\u001b[36m2022-07-17 10:07:41,309\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 28]: loss 1.3938043117523193\u001b[0m\n",
" 28% 28/100 [00:33<01:19, 1.10s/it][\u001b[36m2022-07-17 10:07:42,272\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 29]: loss 1.2293992723737444\u001b[0m\n",
"[\u001b[36m2022-07-17 10:07:42,413\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 29]: loss 1.4028812646865845\u001b[0m\n",
" 29% 29/100 [00:34<01:18, 1.10s/it][\u001b[36m2022-07-17 10:07:43,376\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 30]: loss 1.2276505891765868\u001b[0m\n",
"[\u001b[36m2022-07-17 10:07:43,514\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 30]: loss 1.4006688594818115\u001b[0m\n",
" 30% 30/100 [00:35<01:17, 1.10s/it][\u001b[36m2022-07-17 10:07:44,705\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 31]: loss 1.221539597426142\u001b[0m\n",
"[\u001b[36m2022-07-17 10:07:44,921\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 31]: loss 1.4004688262939453\u001b[0m\n",
" 31% 31/100 [00:37<01:22, 1.19s/it][\u001b[36m2022-07-17 10:07:46,826\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 32]: loss 1.2294603905507497\u001b[0m\n",
"[\u001b[36m2022-07-17 10:07:47,038\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 32]: loss 1.425377368927002\u001b[0m\n",
" 32% 32/100 [00:39<01:39, 1.47s/it][\u001b[36m2022-07-17 10:07:48,094\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 33]: loss 1.2243278324604034\u001b[0m\n",
"[\u001b[36m2022-07-17 10:07:48,232\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 33]: loss 1.393580436706543\u001b[0m\n",
" 33% 33/100 [00:40<01:32, 1.39s/it][\u001b[36m2022-07-17 10:07:49,196\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 34]: loss 1.2102085032633372\u001b[0m\n",
"[\u001b[36m2022-07-17 10:07:49,333\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 34]: loss 1.3994009494781494\u001b[0m\n",
" 34% 34/100 [00:41<01:25, 1.30s/it][\u001b[36m2022-07-17 10:07:50,299\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 35]: loss 1.2173914217523165\u001b[0m\n",
"[\u001b[36m2022-07-17 10:07:50,439\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 35]: loss 1.4046440124511719\u001b[0m\n",
" 35% 35/100 [00:42<01:20, 1.24s/it][\u001b[36m2022-07-17 10:07:51,393\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 36]: loss 1.2015983100448335\u001b[0m\n",
"[\u001b[36m2022-07-17 10:07:51,532\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 36]: loss 1.4012508392333984\u001b[0m\n",
" 36% 36/100 [00:43<01:16, 1.20s/it][\u001b[36m2022-07-17 10:07:52,492\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 37]: loss 1.2112089863845281\u001b[0m\n",
"[\u001b[36m2022-07-17 10:07:52,632\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 37]: loss 1.3985404968261719\u001b[0m\n",
" 37% 37/100 [00:45<01:13, 1.17s/it][\u001b[36m2022-07-17 10:07:53,596\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 38]: loss 1.2096826774733407\u001b[0m\n",
"[\u001b[36m2022-07-17 10:07:53,731\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 38]: loss 1.3710758686065674\u001b[0m\n",
" 38% 38/100 [00:46<01:11, 1.15s/it][\u001b[36m2022-07-17 10:07:54,705\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 39]: loss 1.2203282075268882\u001b[0m\n",
"[\u001b[36m2022-07-17 10:07:54,843\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 39]: loss 1.4091509580612183\u001b[0m\n",
" 39% 39/100 [00:47<01:09, 1.14s/it][\u001b[36m2022-07-17 10:07:55,814\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 40]: loss 1.2156899943947792\u001b[0m\n",
"[\u001b[36m2022-07-17 10:07:55,958\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 40]: loss 1.3908467292785645\u001b[0m\n",
" 40% 40/100 [00:48<01:07, 1.13s/it][\u001b[36m2022-07-17 10:07:56,915\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 41]: loss 1.2126226318734032\u001b[0m\n",
"[\u001b[36m2022-07-17 10:07:57,060\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 41]: loss 1.3834714889526367\u001b[0m\n",
" 41% 41/100 [00:49<01:06, 1.12s/it][\u001b[36m2022-07-17 10:07:58,026\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 42]: loss 1.204583348972457\u001b[0m\n",
"[\u001b[36m2022-07-17 10:07:58,165\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 42]: loss 1.3826287984848022\u001b[0m\n",
" 42% 42/100 [00:50<01:04, 1.12s/it][\u001b[36m2022-07-17 10:07:59,121\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 43]: loss 1.208104093159948\u001b[0m\n",
"[\u001b[36m2022-07-17 10:07:59,258\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 43]: loss 1.3686766624450684\u001b[0m\n",
"[\u001b[36m2022-07-17 10:07:59,264\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/timelag_mdn/best_loss.pth\u001b[0m\n",
" 43% 43/100 [00:51<01:03, 1.11s/it][\u001b[36m2022-07-17 10:08:00,217\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 44]: loss 1.2028741793973106\u001b[0m\n",
"[\u001b[36m2022-07-17 10:08:00,356\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 44]: loss 1.3773776292800903\u001b[0m\n",
" 44% 44/100 [00:52<01:01, 1.11s/it][\u001b[36m2022-07-17 10:08:01,338\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 45]: loss 1.208822491977896\u001b[0m\n",
"[\u001b[36m2022-07-17 10:08:01,474\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 45]: loss 1.382139801979065\u001b[0m\n",
" 45% 45/100 [00:53<01:01, 1.11s/it][\u001b[36m2022-07-17 10:08:02,430\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 46]: loss 1.1898920174155916\u001b[0m\n",
"[\u001b[36m2022-07-17 10:08:02,571\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 46]: loss 1.381348967552185\u001b[0m\n",
" 46% 46/100 [00:55<00:59, 1.11s/it][\u001b[36m2022-07-17 10:08:03,528\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 47]: loss 1.207710170320102\u001b[0m\n",
"[\u001b[36m2022-07-17 10:08:03,669\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 47]: loss 1.3773185014724731\u001b[0m\n",
" 47% 47/100 [00:56<00:58, 1.10s/it][\u001b[36m2022-07-17 10:08:04,638\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 48]: loss 1.194554397038051\u001b[0m\n",
"[\u001b[36m2022-07-17 10:08:04,774\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 48]: loss 1.3533848524093628\u001b[0m\n",
"[\u001b[36m2022-07-17 10:08:04,781\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/timelag_mdn/best_loss.pth\u001b[0m\n",
" 48% 48/100 [00:57<00:57, 1.11s/it][\u001b[36m2022-07-17 10:08:05,749\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 49]: loss 1.193785873906953\u001b[0m\n",
"[\u001b[36m2022-07-17 10:08:05,890\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 49]: loss 1.3594461679458618\u001b[0m\n",
" 49% 49/100 [00:58<00:56, 1.11s/it][\u001b[36m2022-07-17 10:08:06,848\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 50]: loss 1.2037718721798487\u001b[0m\n",
"[\u001b[36m2022-07-17 10:08:06,993\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 50]: loss 1.3727071285247803\u001b[0m\n",
"[\u001b[36m2022-07-17 10:08:07,000\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/timelag_mdn/epoch0050.pth\u001b[0m\n",
" 50% 50/100 [00:59<00:55, 1.11s/it][\u001b[36m2022-07-17 10:08:07,960\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 51]: loss 1.205870847616877\u001b[0m\n",
"[\u001b[36m2022-07-17 10:08:08,100\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 51]: loss 1.381665825843811\u001b[0m\n",
" 51% 51/100 [01:00<00:54, 1.11s/it][\u001b[36m2022-07-17 10:08:09,049\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 52]: loss 1.1960929493818964\u001b[0m\n",
"[\u001b[36m2022-07-17 10:08:09,191\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 52]: loss 1.3618594408035278\u001b[0m\n",
" 52% 52/100 [01:01<00:52, 1.10s/it][\u001b[36m2022-07-17 10:08:10,130\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 53]: loss 1.1948177175862449\u001b[0m\n",
"[\u001b[36m2022-07-17 10:08:10,273\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 53]: loss 1.3768517971038818\u001b[0m\n",
" 53% 53/100 [01:02<00:51, 1.10s/it][\u001b[36m2022-07-17 10:08:11,234\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 54]: loss 1.1909563349825996\u001b[0m\n",
"[\u001b[36m2022-07-17 10:08:11,382\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 54]: loss 1.369380235671997\u001b[0m\n",
" 54% 54/100 [01:03<00:50, 1.10s/it][\u001b[36m2022-07-17 10:08:12,354\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 55]: loss 1.1971642949751444\u001b[0m\n",
"[\u001b[36m2022-07-17 10:08:12,492\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 55]: loss 1.3680601119995117\u001b[0m\n",
" 55% 55/100 [01:04<00:49, 1.10s/it][\u001b[36m2022-07-17 10:08:13,440\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 56]: loss 1.1962766157729285\u001b[0m\n",
"[\u001b[36m2022-07-17 10:08:13,577\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 56]: loss 1.38181734085083\u001b[0m\n",
" 56% 56/100 [01:06<00:48, 1.10s/it][\u001b[36m2022-07-17 10:08:14,530\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 57]: loss 1.2036537357739039\u001b[0m\n",
"[\u001b[36m2022-07-17 10:08:14,671\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 57]: loss 1.366079330444336\u001b[0m\n",
" 57% 57/100 [01:07<00:47, 1.10s/it][\u001b[36m2022-07-17 10:08:15,641\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 58]: loss 1.1818362410579408\u001b[0m\n",
"[\u001b[36m2022-07-17 10:08:15,777\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 58]: loss 1.3730212450027466\u001b[0m\n",
" 58% 58/100 [01:08<00:46, 1.10s/it][\u001b[36m2022-07-17 10:08:16,736\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 59]: loss 1.1919769389288766\u001b[0m\n",
"[\u001b[36m2022-07-17 10:08:16,876\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 59]: loss 1.3613793849945068\u001b[0m\n",
" 59% 59/100 [01:09<00:45, 1.10s/it][\u001b[36m2022-07-17 10:08:17,840\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 60]: loss 1.1981405743530817\u001b[0m\n",
"[\u001b[36m2022-07-17 10:08:17,979\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 60]: loss 1.3548054695129395\u001b[0m\n",
" 60% 60/100 [01:10<00:44, 1.10s/it][\u001b[36m2022-07-17 10:08:18,934\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 61]: loss 1.1900953469531876\u001b[0m\n",
"[\u001b[36m2022-07-17 10:08:19,071\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 61]: loss 1.3693116903305054\u001b[0m\n",
" 61% 61/100 [01:11<00:42, 1.10s/it][\u001b[36m2022-07-17 10:08:20,029\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 62]: loss 1.1915076545306615\u001b[0m\n",
"[\u001b[36m2022-07-17 10:08:20,165\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 62]: loss 1.3568015098571777\u001b[0m\n",
" 62% 62/100 [01:12<00:41, 1.10s/it][\u001b[36m2022-07-17 10:08:21,129\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 63]: loss 1.194013563649995\u001b[0m\n",
"[\u001b[36m2022-07-17 10:08:21,266\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 63]: loss 1.3605660200119019\u001b[0m\n",
" 63% 63/100 [01:13<00:40, 1.10s/it][\u001b[36m2022-07-17 10:08:22,231\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 64]: loss 1.1811128184199333\u001b[0m\n",
"[\u001b[36m2022-07-17 10:08:22,373\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 64]: loss 1.3594967126846313\u001b[0m\n",
" 64% 64/100 [01:14<00:39, 1.10s/it][\u001b[36m2022-07-17 10:08:23,332\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 65]: loss 1.1920101855482375\u001b[0m\n",
"[\u001b[36m2022-07-17 10:08:23,469\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 65]: loss 1.3670623302459717\u001b[0m\n",
" 65% 65/100 [01:15<00:38, 1.10s/it][\u001b[36m2022-07-17 10:08:24,424\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 66]: loss 1.177561925990241\u001b[0m\n",
"[\u001b[36m2022-07-17 10:08:24,560\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 66]: loss 1.37412691116333\u001b[0m\n",
" 66% 66/100 [01:17<00:37, 1.10s/it][\u001b[36m2022-07-17 10:08:25,526\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 67]: loss 1.1914606860705785\u001b[0m\n",
"[\u001b[36m2022-07-17 10:08:25,672\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 67]: loss 1.3615418672561646\u001b[0m\n",
" 67% 67/100 [01:18<00:36, 1.10s/it][\u001b[36m2022-07-17 10:08:26,640\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 68]: loss 1.1831135654023714\u001b[0m\n",
"[\u001b[36m2022-07-17 10:08:26,785\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 68]: loss 1.3603196144104004\u001b[0m\n",
" 68% 68/100 [01:19<00:35, 1.10s/it][\u001b[36m2022-07-17 10:08:27,740\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 69]: loss 1.19382886162826\u001b[0m\n",
"[\u001b[36m2022-07-17 10:08:27,881\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 69]: loss 1.3596338033676147\u001b[0m\n",
" 69% 69/100 [01:20<00:34, 1.10s/it][\u001b[36m2022-07-17 10:08:28,853\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 70]: loss 1.1916385771972793\u001b[0m\n",
"[\u001b[36m2022-07-17 10:08:28,992\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 70]: loss 1.3565598726272583\u001b[0m\n",
" 70% 70/100 [01:21<00:33, 1.10s/it][\u001b[36m2022-07-17 10:08:29,956\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 71]: loss 1.1937362200447492\u001b[0m\n",
"[\u001b[36m2022-07-17 10:08:30,093\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 71]: loss 1.3534560203552246\u001b[0m\n",
" 71% 71/100 [01:22<00:32, 1.10s/it][\u001b[36m2022-07-17 10:08:31,047\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 72]: loss 1.196092086178916\u001b[0m\n",
"[\u001b[36m2022-07-17 10:08:31,187\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 72]: loss 1.3560974597930908\u001b[0m\n",
" 72% 72/100 [01:23<00:30, 1.10s/it][\u001b[36m2022-07-17 10:08:32,149\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 73]: loss 1.1909968044076646\u001b[0m\n",
"[\u001b[36m2022-07-17 10:08:32,289\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 73]: loss 1.3485839366912842\u001b[0m\n",
"[\u001b[36m2022-07-17 10:08:32,296\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/timelag_mdn/best_loss.pth\u001b[0m\n",
" 73% 73/100 [01:24<00:29, 1.10s/it][\u001b[36m2022-07-17 10:08:33,249\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 74]: loss 1.1849516278931074\u001b[0m\n",
"[\u001b[36m2022-07-17 10:08:33,391\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 74]: loss 1.3505277633666992\u001b[0m\n",
" 74% 74/100 [01:25<00:28, 1.10s/it][\u001b[36m2022-07-17 10:08:34,340\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 75]: loss 1.1905866989067622\u001b[0m\n",
"[\u001b[36m2022-07-17 10:08:34,480\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 75]: loss 1.3630480766296387\u001b[0m\n",
" 75% 75/100 [01:26<00:27, 1.10s/it][\u001b[36m2022-07-17 10:08:35,447\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 76]: loss 1.1933424366371972\u001b[0m\n",
"[\u001b[36m2022-07-17 10:08:35,585\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 76]: loss 1.3531477451324463\u001b[0m\n",
" 76% 76/100 [01:28<00:26, 1.10s/it][\u001b[36m2022-07-17 10:08:36,561\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 77]: loss 1.1786022537520953\u001b[0m\n",
"[\u001b[36m2022-07-17 10:08:36,697\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 77]: loss 1.357223629951477\u001b[0m\n",
" 77% 77/100 [01:29<00:25, 1.10s/it][\u001b[36m2022-07-17 10:08:37,694\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 78]: loss 1.1793673144919532\u001b[0m\n",
"[\u001b[36m2022-07-17 10:08:37,834\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 78]: loss 1.349335789680481\u001b[0m\n",
" 78% 78/100 [01:30<00:24, 1.11s/it][\u001b[36m2022-07-17 10:08:38,813\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 79]: loss 1.1900251465184348\u001b[0m\n",
"[\u001b[36m2022-07-17 10:08:38,955\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 79]: loss 1.3574063777923584\u001b[0m\n",
" 79% 79/100 [01:31<00:23, 1.12s/it][\u001b[36m2022-07-17 10:08:39,912\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 80]: loss 1.1780395720686232\u001b[0m\n",
"[\u001b[36m2022-07-17 10:08:40,057\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 80]: loss 1.3572275638580322\u001b[0m\n",
" 80% 80/100 [01:32<00:22, 1.11s/it][\u001b[36m2022-07-17 10:08:41,015\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 81]: loss 1.1859238158379282\u001b[0m\n",
"[\u001b[36m2022-07-17 10:08:41,159\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 81]: loss 1.3555094003677368\u001b[0m\n",
" 81% 81/100 [01:33<00:21, 1.11s/it][\u001b[36m2022-07-17 10:08:42,123\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 82]: loss 1.187451344515596\u001b[0m\n",
"[\u001b[36m2022-07-17 10:08:42,264\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 82]: loss 1.3579833507537842\u001b[0m\n",
" 82% 82/100 [01:34<00:19, 1.11s/it][\u001b[36m2022-07-17 10:08:43,237\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 83]: loss 1.186842798122338\u001b[0m\n",
"[\u001b[36m2022-07-17 10:08:43,376\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 83]: loss 1.351947546005249\u001b[0m\n",
" 83% 83/100 [01:35<00:18, 1.11s/it][\u001b[36m2022-07-17 10:08:44,346\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 84]: loss 1.1898308715650014\u001b[0m\n",
"[\u001b[36m2022-07-17 10:08:44,486\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 84]: loss 1.3537472486495972\u001b[0m\n",
" 84% 84/100 [01:36<00:17, 1.11s/it][\u001b[36m2022-07-17 10:08:45,481\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 85]: loss 1.1769450051443917\u001b[0m\n",
"[\u001b[36m2022-07-17 10:08:45,622\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 85]: loss 1.3547794818878174\u001b[0m\n",
" 85% 85/100 [01:38<00:16, 1.12s/it][\u001b[36m2022-07-17 10:08:46,585\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 86]: loss 1.1785075717738696\u001b[0m\n",
"[\u001b[36m2022-07-17 10:08:46,725\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 86]: loss 1.3576562404632568\u001b[0m\n",
" 86% 86/100 [01:39<00:15, 1.11s/it][\u001b[36m2022-07-17 10:08:47,692\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 87]: loss 1.1843251490167208\u001b[0m\n",
"[\u001b[36m2022-07-17 10:08:47,833\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 87]: loss 1.354170799255371\u001b[0m\n",
" 87% 87/100 [01:40<00:14, 1.11s/it][\u001b[36m2022-07-17 10:08:48,793\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 88]: loss 1.1782695140157426\u001b[0m\n",
"[\u001b[36m2022-07-17 10:08:48,930\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 88]: loss 1.3504612445831299\u001b[0m\n",
" 88% 88/100 [01:41<00:13, 1.11s/it][\u001b[36m2022-07-17 10:08:49,883\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 89]: loss 1.1847187323229653\u001b[0m\n",
"[\u001b[36m2022-07-17 10:08:50,022\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 89]: loss 1.3531585931777954\u001b[0m\n",
" 89% 89/100 [01:42<00:12, 1.10s/it][\u001b[36m2022-07-17 10:08:50,988\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 90]: loss 1.175314833010946\u001b[0m\n",
"[\u001b[36m2022-07-17 10:08:51,130\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 90]: loss 1.3554811477661133\u001b[0m\n",
" 90% 90/100 [01:43<00:11, 1.10s/it][\u001b[36m2022-07-17 10:08:52,088\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 91]: loss 1.1783251336642675\u001b[0m\n",
"[\u001b[36m2022-07-17 10:08:52,230\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 91]: loss 1.3600915670394897\u001b[0m\n",
" 91% 91/100 [01:44<00:09, 1.10s/it][\u001b[36m2022-07-17 10:08:53,176\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 92]: loss 1.1813702136278152\u001b[0m\n",
"[\u001b[36m2022-07-17 10:08:53,324\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 92]: loss 1.3536114692687988\u001b[0m\n",
" 92% 92/100 [01:45<00:08, 1.10s/it][\u001b[36m2022-07-17 10:08:54,266\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 93]: loss 1.168422163597175\u001b[0m\n",
"[\u001b[36m2022-07-17 10:08:54,412\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 93]: loss 1.3576297760009766\u001b[0m\n",
" 93% 93/100 [01:46<00:07, 1.10s/it][\u001b[36m2022-07-17 10:08:55,361\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 94]: loss 1.189123347401619\u001b[0m\n",
"[\u001b[36m2022-07-17 10:08:55,497\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 94]: loss 1.3584293127059937\u001b[0m\n",
" 94% 94/100 [01:47<00:06, 1.09s/it][\u001b[36m2022-07-17 10:08:56,475\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 95]: loss 1.173099541238376\u001b[0m\n",
"[\u001b[36m2022-07-17 10:08:56,622\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 95]: loss 1.354727029800415\u001b[0m\n",
" 95% 95/100 [01:49<00:05, 1.10s/it][\u001b[36m2022-07-17 10:08:57,586\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 96]: loss 1.172777214220592\u001b[0m\n",
"[\u001b[36m2022-07-17 10:08:57,723\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 96]: loss 1.3576377630233765\u001b[0m\n",
" 96% 96/100 [01:50<00:04, 1.10s/it][\u001b[36m2022-07-17 10:08:58,674\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 97]: loss 1.1693728970629829\u001b[0m\n",
"[\u001b[36m2022-07-17 10:08:58,811\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 97]: loss 1.3562281131744385\u001b[0m\n",
" 97% 97/100 [01:51<00:03, 1.10s/it][\u001b[36m2022-07-17 10:08:59,777\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 98]: loss 1.1812683418393135\u001b[0m\n",
"[\u001b[36m2022-07-17 10:08:59,917\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 98]: loss 1.3529962301254272\u001b[0m\n",
" 98% 98/100 [01:52<00:02, 1.10s/it][\u001b[36m2022-07-17 10:09:00,871\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 99]: loss 1.1907714158296585\u001b[0m\n",
"[\u001b[36m2022-07-17 10:09:01,012\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 99]: loss 1.3531525135040283\u001b[0m\n",
" 99% 99/100 [01:53<00:01, 1.10s/it][\u001b[36m2022-07-17 10:09:01,976\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 100]: loss 1.1850884833506174\u001b[0m\n",
"[\u001b[36m2022-07-17 10:09:02,115\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 100]: loss 1.3584741353988647\u001b[0m\n",
"[\u001b[36m2022-07-17 10:09:02,121\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/timelag_mdn/epoch0100.pth\u001b[0m\n",
"100% 100/100 [01:54<00:00, 1.15s/it]\n",
"[\u001b[36m2022-07-17 10:09:02,127\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/timelag_mdn/epoch0100.pth\u001b[0m\n",
"[\u001b[36m2022-07-17 10:09:02,128\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - The best loss was 1.3485839366912842\u001b[0m\n",
"++ set +x\n"
]
}
],
"source": [
"! cd $RECIPE_ROOT && bash run.sh --stage 2 --stop-stage 2"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "DyPXU3RJ8MFp"
},
"source": [
"## Duration model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "js0FLYInDEGL",
"outputId": "7ae7a51e-36f6-4fda-8649-a7ed6e2e9ad9"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"stage 3: Training duration model\n",
"++ nnsvs-train --config-dir conf/train/duration model=duration_vp_mdn train=myconfig data=myconfig data.train_no_dev.in_dir=dump/oniku_kurumi/norm/train_no_dev/in_duration/ data.train_no_dev.out_dir=dump/oniku_kurumi/norm/train_no_dev/out_duration/ data.dev.in_dir=dump/oniku_kurumi/norm/dev/in_duration/ data.dev.out_dir=dump/oniku_kurumi/norm/dev/out_duration/ data.in_scaler_path=dump/oniku_kurumi/norm/in_duration_scaler.joblib data.out_scaler_path=dump/oniku_kurumi/norm/out_duration_scaler.joblib train.out_dir=exp/oniku_kurumi/duration_vp_mdn train.log_dir=tensorboard/oniku_kurumi_duration_vp_mdn train.resume.checkpoint=\n",
"/usr/local/lib/python3.7/dist-packages/resampy/interpn.py:114: NumbaWarning: The TBB threading layer requires TBB version 2019.5 or later i.e., TBB_INTERFACE_VERSION >= 11005. Found TBB_INTERFACE_VERSION = 9107. The TBB threading layer is disabled.\n",
" _resample_loop_p(x, t_out, interp_win, interp_delta, num_table, scale, y)\n",
"[\u001b[36m2022-07-17 10:09:15,476\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - model:\n",
" stream_sizes:\n",
" - 1\n",
" has_dynamic_features:\n",
" - false\n",
" stream_weights:\n",
" - 1\n",
" netG:\n",
" _target_: nnsvs.model.VariancePredictor\n",
" in_dim: 337\n",
" out_dim: 1\n",
" hidden_dim: 256\n",
" num_layers: 5\n",
" kernel_size: 5\n",
" dropout: 0.5\n",
" use_mdn: true\n",
" num_gaussians: 4\n",
" init_type: kaiming_normal\n",
"train:\n",
" out_dir: exp/oniku_kurumi/duration_vp_mdn\n",
" log_dir: tensorboard/oniku_kurumi_duration_vp_mdn\n",
" use_amp: false\n",
" max_train_steps: -1\n",
" nepochs: 100\n",
" checkpoint_epoch_interval: 50\n",
" feats_criterion: mse\n",
" stream_wise_loss: false\n",
" use_detect_anomaly: false\n",
" optim:\n",
" optimizer:\n",
" name: Adam\n",
" params:\n",
" lr: 0.001\n",
" betas:\n",
" - 0.9\n",
" - 0.999\n",
" weight_decay: 0.0\n",
" lr_scheduler:\n",
" name: StepLR\n",
" params:\n",
" step_size: 20\n",
" gamma: 0.5\n",
" resume:\n",
" checkpoint: ''\n",
" load_optimizer: false\n",
" cudnn:\n",
" benchmark: false\n",
" deterministic: true\n",
"data:\n",
" train_no_dev:\n",
" in_dir: dump/oniku_kurumi/norm/train_no_dev/in_duration/\n",
" out_dir: dump/oniku_kurumi/norm/train_no_dev/out_duration/\n",
" dev:\n",
" in_dir: dump/oniku_kurumi/norm/dev/in_duration/\n",
" out_dir: dump/oniku_kurumi/norm/dev/out_duration/\n",
" num_workers: 2\n",
" batch_size: 8\n",
" pin_memory: true\n",
" filter_long_segments: false\n",
" filter_num_frames: 6000\n",
" max_time_frames: -1\n",
" in_scaler_path: dump/oniku_kurumi/norm/in_duration_scaler.joblib\n",
" out_scaler_path: dump/oniku_kurumi/norm/out_duration_scaler.joblib\n",
"mlflow:\n",
" enabled: false\n",
" experiment: test\n",
"verbose: 100\n",
"seed: 773\n",
"data_parallel: false\n",
"\u001b[0m\n",
"[\u001b[36m2022-07-17 10:09:15,477\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - PyTorch version: 1.12.0+cu113\u001b[0m\n",
"[\u001b[36m2022-07-17 10:09:15,477\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - cudnn.deterministic: True\u001b[0m\n",
"[\u001b[36m2022-07-17 10:09:15,477\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - cudnn.benchmark: False\u001b[0m\n",
"[\u001b[36m2022-07-17 10:09:15,478\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - cuDNN version: 8302\u001b[0m\n",
"[\u001b[36m2022-07-17 10:09:15,478\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Random seed: 773\u001b[0m\n",
"[\u001b[36m2022-07-17 10:09:17,075\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Number of trainable params: 1.749 million\u001b[0m\n",
"[\u001b[36m2022-07-17 10:09:17,076\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - VariancePredictor(\n",
" (conv): Sequential(\n",
" (0): Sequential(\n",
" (0): Conv1d(337, 256, kernel_size=(5,), stride=(1,), padding=(2,))\n",
" (1): ReLU()\n",
" (2): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
" (3): Dropout(p=0.5, inplace=False)\n",
" )\n",
" (1): Sequential(\n",
" (0): Conv1d(256, 256, kernel_size=(5,), stride=(1,), padding=(2,))\n",
" (1): ReLU()\n",
" (2): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
" (3): Dropout(p=0.5, inplace=False)\n",
" )\n",
" (2): Sequential(\n",
" (0): Conv1d(256, 256, kernel_size=(5,), stride=(1,), padding=(2,))\n",
" (1): ReLU()\n",
" (2): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
" (3): Dropout(p=0.5, inplace=False)\n",
" )\n",
" (3): Sequential(\n",
" (0): Conv1d(256, 256, kernel_size=(5,), stride=(1,), padding=(2,))\n",
" (1): ReLU()\n",
" (2): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
" (3): Dropout(p=0.5, inplace=False)\n",
" )\n",
" (4): Sequential(\n",
" (0): Conv1d(256, 256, kernel_size=(5,), stride=(1,), padding=(2,))\n",
" (1): ReLU()\n",
" (2): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
" (3): Dropout(p=0.5, inplace=False)\n",
" )\n",
" )\n",
" (mdn_layer): MDNLayer(\n",
" (log_pi): Linear(in_features=256, out_features=4, bias=True)\n",
" (log_sigma): Linear(in_features=256, out_features=4, bias=True)\n",
" (mu): Linear(in_features=256, out_features=4, bias=True)\n",
" )\n",
")\u001b[0m\n",
"[\u001b[36m2022-07-17 10:09:17,082\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Number of iterations per epoch: 56\u001b[0m\n",
"[\u001b[36m2022-07-17 10:09:17,082\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Number of max_train_steps is set based on nepochs: 5600\u001b[0m\n",
"[\u001b[36m2022-07-17 10:09:17,082\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Number of epochs: 100\u001b[0m\n",
"[\u001b[36m2022-07-17 10:09:17,082\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Number of iterations: 5600\u001b[0m\n",
" 0% 0/100 [00:00<?, ?it/s][\u001b[36m2022-07-17 10:09:25,658\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 1]: loss 2.646130996091025\u001b[0m\n",
"[\u001b[36m2022-07-17 10:09:25,824\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 1]: loss 1.203948736190796\u001b[0m\n",
"[\u001b[36m2022-07-17 10:09:25,867\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/duration_vp_mdn/best_loss.pth\u001b[0m\n",
" 1% 1/100 [00:06<10:38, 6.45s/it][\u001b[36m2022-07-17 10:09:27,155\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 2]: loss 1.6544350130217416\u001b[0m\n",
"[\u001b[36m2022-07-17 10:09:27,320\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 2]: loss 0.9663150906562805\u001b[0m\n",
"[\u001b[36m2022-07-17 10:09:27,382\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/duration_vp_mdn/best_loss.pth\u001b[0m\n",
" 2% 2/100 [00:07<05:47, 3.55s/it][\u001b[36m2022-07-17 10:09:28,688\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 3]: loss 1.2918789110013418\u001b[0m\n",
"[\u001b[36m2022-07-17 10:09:28,854\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 3]: loss 0.8338977694511414\u001b[0m\n",
"[\u001b[36m2022-07-17 10:09:28,911\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/duration_vp_mdn/best_loss.pth\u001b[0m\n",
" 3% 3/100 [00:09<04:14, 2.63s/it][\u001b[36m2022-07-17 10:09:30,202\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 4]: loss 1.0935186190264565\u001b[0m\n",
"[\u001b[36m2022-07-17 10:09:30,364\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 4]: loss 0.67059326171875\u001b[0m\n",
"[\u001b[36m2022-07-17 10:09:30,425\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/duration_vp_mdn/best_loss.pth\u001b[0m\n",
" 4% 4/100 [00:11<03:29, 2.19s/it][\u001b[36m2022-07-17 10:09:31,702\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 5]: loss 0.9474198424390384\u001b[0m\n",
"[\u001b[36m2022-07-17 10:09:31,864\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 5]: loss 0.5264842510223389\u001b[0m\n",
"[\u001b[36m2022-07-17 10:09:31,924\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/duration_vp_mdn/best_loss.pth\u001b[0m\n",
" 5% 5/100 [00:12<03:04, 1.94s/it][\u001b[36m2022-07-17 10:09:33,214\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 6]: loss 0.8302079055990491\u001b[0m\n",
"[\u001b[36m2022-07-17 10:09:33,378\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 6]: loss 0.4467860162258148\u001b[0m\n",
"[\u001b[36m2022-07-17 10:09:33,437\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/duration_vp_mdn/best_loss.pth\u001b[0m\n",
" 6% 6/100 [00:14<02:48, 1.79s/it][\u001b[36m2022-07-17 10:09:34,713\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 7]: loss 0.6902472084122044\u001b[0m\n",
"[\u001b[36m2022-07-17 10:09:34,880\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 7]: loss 0.3139960765838623\u001b[0m\n",
"[\u001b[36m2022-07-17 10:09:34,939\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/duration_vp_mdn/best_loss.pth\u001b[0m\n",
" 7% 7/100 [00:15<02:37, 1.70s/it][\u001b[36m2022-07-17 10:09:36,247\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 8]: loss 0.5727573484182358\u001b[0m\n",
"[\u001b[36m2022-07-17 10:09:36,412\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 8]: loss 0.17164303362369537\u001b[0m\n",
"[\u001b[36m2022-07-17 10:09:36,469\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/duration_vp_mdn/best_loss.pth\u001b[0m\n",
" 8% 8/100 [00:17<02:31, 1.64s/it][\u001b[36m2022-07-17 10:09:37,740\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 9]: loss 0.46696473604866434\u001b[0m\n",
"[\u001b[36m2022-07-17 10:09:37,901\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 9]: loss 0.0374319851398468\u001b[0m\n",
"[\u001b[36m2022-07-17 10:09:37,962\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/duration_vp_mdn/best_loss.pth\u001b[0m\n",
" 9% 9/100 [00:18<02:25, 1.60s/it][\u001b[36m2022-07-17 10:09:39,221\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 10]: loss 0.3993323313604508\u001b[0m\n",
"[\u001b[36m2022-07-17 10:09:39,393\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 10]: loss -0.011698705144226551\u001b[0m\n",
"[\u001b[36m2022-07-17 10:09:39,454\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/duration_vp_mdn/best_loss.pth\u001b[0m\n",
" 10% 10/100 [00:20<02:20, 1.56s/it][\u001b[36m2022-07-17 10:09:40,754\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 11]: loss 0.35606072656810284\u001b[0m\n",
"[\u001b[36m2022-07-17 10:09:40,919\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 11]: loss -0.0706024318933487\u001b[0m\n",
"[\u001b[36m2022-07-17 10:09:40,975\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/duration_vp_mdn/best_loss.pth\u001b[0m\n",
" 11% 11/100 [00:21<02:18, 1.55s/it][\u001b[36m2022-07-17 10:09:42,240\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 12]: loss 0.27311379043385386\u001b[0m\n",
"[\u001b[36m2022-07-17 10:09:42,416\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 12]: loss -0.1980307251214981\u001b[0m\n",
"[\u001b[36m2022-07-17 10:09:42,474\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/duration_vp_mdn/best_loss.pth\u001b[0m\n",
" 12% 12/100 [00:23<02:15, 1.54s/it][\u001b[36m2022-07-17 10:09:43,743\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 13]: loss 0.22759593351344978\u001b[0m\n",
"[\u001b[36m2022-07-17 10:09:43,907\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 13]: loss -0.1017477810382843\u001b[0m\n",
" 13% 13/100 [00:24<02:10, 1.50s/it][\u001b[36m2022-07-17 10:09:45,170\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 14]: loss 0.19322720664890117\u001b[0m\n",
"[\u001b[36m2022-07-17 10:09:45,337\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 14]: loss -0.33786001801490784\u001b[0m\n",
"[\u001b[36m2022-07-17 10:09:45,397\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/duration_vp_mdn/best_loss.pth\u001b[0m\n",
" 14% 14/100 [00:25<02:09, 1.50s/it][\u001b[36m2022-07-17 10:09:46,694\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 15]: loss 0.17225695159452567\u001b[0m\n",
"[\u001b[36m2022-07-17 10:09:46,861\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 15]: loss -0.1908777803182602\u001b[0m\n",
" 15% 15/100 [00:27<02:06, 1.49s/it][\u001b[36m2022-07-17 10:09:48,117\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 16]: loss 0.14636153994797496\u001b[0m\n",
"[\u001b[36m2022-07-17 10:09:48,281\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 16]: loss -0.2748127579689026\u001b[0m\n",
" 16% 16/100 [00:28<02:03, 1.47s/it][\u001b[36m2022-07-17 10:09:49,555\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 17]: loss 0.059930981685673554\u001b[0m\n",
"[\u001b[36m2022-07-17 10:09:49,720\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 17]: loss -0.45613664388656616\u001b[0m\n",
"[\u001b[36m2022-07-17 10:09:49,776\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/duration_vp_mdn/best_loss.pth\u001b[0m\n",
" 17% 17/100 [00:30<02:02, 1.48s/it][\u001b[36m2022-07-17 10:09:51,041\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 18]: loss 0.021219341464789716\u001b[0m\n",
"[\u001b[36m2022-07-17 10:09:51,208\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 18]: loss -0.3722762167453766\u001b[0m\n",
" 18% 18/100 [00:31<01:59, 1.46s/it][\u001b[36m2022-07-17 10:09:52,478\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 19]: loss 0.029514004133358997\u001b[0m\n",
"[\u001b[36m2022-07-17 10:09:52,647\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 19]: loss -0.28012770414352417\u001b[0m\n",
" 19% 19/100 [00:33<01:57, 1.46s/it][\u001b[36m2022-07-17 10:09:53,908\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 20]: loss -0.025722671158811345\u001b[0m\n",
"[\u001b[36m2022-07-17 10:09:54,069\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 20]: loss -0.4051469564437866\u001b[0m\n",
" 20% 20/100 [00:34<01:55, 1.45s/it][\u001b[36m2022-07-17 10:09:55,327\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 21]: loss -0.06291283656797272\u001b[0m\n",
"[\u001b[36m2022-07-17 10:09:55,492\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 21]: loss -0.5141132473945618\u001b[0m\n",
"[\u001b[36m2022-07-17 10:09:55,551\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/duration_vp_mdn/best_loss.pth\u001b[0m\n",
" 21% 21/100 [00:36<01:55, 1.46s/it][\u001b[36m2022-07-17 10:09:56,858\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 22]: loss -0.09359925656878788\u001b[0m\n",
"[\u001b[36m2022-07-17 10:09:57,023\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 22]: loss -0.4922477602958679\u001b[0m\n",
" 22% 22/100 [00:37<01:53, 1.46s/it][\u001b[36m2022-07-17 10:09:58,288\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 23]: loss -0.12880641564593784\u001b[0m\n",
"[\u001b[36m2022-07-17 10:09:58,456\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 23]: loss -0.4485433101654053\u001b[0m\n",
" 23% 23/100 [00:39<01:51, 1.45s/it][\u001b[36m2022-07-17 10:09:59,708\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 24]: loss -0.16451156953865262\u001b[0m\n",
"[\u001b[36m2022-07-17 10:09:59,877\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 24]: loss -0.5072503089904785\u001b[0m\n",
" 24% 24/100 [00:40<01:49, 1.44s/it][\u001b[36m2022-07-17 10:10:01,154\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 25]: loss -0.18365980247576122\u001b[0m\n",
"[\u001b[36m2022-07-17 10:10:01,322\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 25]: loss -0.56026291847229\u001b[0m\n",
"[\u001b[36m2022-07-17 10:10:01,379\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/duration_vp_mdn/best_loss.pth\u001b[0m\n",
" 25% 25/100 [00:41<01:49, 1.46s/it][\u001b[36m2022-07-17 10:10:02,673\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 26]: loss -0.19590835855342448\u001b[0m\n",
"[\u001b[36m2022-07-17 10:10:02,843\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 26]: loss -0.5753440856933594\u001b[0m\n",
"[\u001b[36m2022-07-17 10:10:02,902\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/duration_vp_mdn/best_loss.pth\u001b[0m\n",
" 26% 26/100 [00:43<01:49, 1.48s/it][\u001b[36m2022-07-17 10:10:04,204\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 27]: loss -0.21971262105840392\u001b[0m\n",
"[\u001b[36m2022-07-17 10:10:04,373\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 27]: loss -0.5647346377372742\u001b[0m\n",
" 27% 27/100 [00:44<01:47, 1.48s/it][\u001b[36m2022-07-17 10:10:05,679\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 28]: loss -0.2480518006071049\u001b[0m\n",
"[\u001b[36m2022-07-17 10:10:05,845\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 28]: loss -0.6179808974266052\u001b[0m\n",
"[\u001b[36m2022-07-17 10:10:05,911\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/duration_vp_mdn/best_loss.pth\u001b[0m\n",
" 28% 28/100 [00:46<01:47, 1.50s/it][\u001b[36m2022-07-17 10:10:07,202\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 29]: loss -0.2669170773796005\u001b[0m\n",
"[\u001b[36m2022-07-17 10:10:07,371\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 29]: loss -0.5531905889511108\u001b[0m\n",
" 29% 29/100 [00:47<01:45, 1.48s/it][\u001b[36m2022-07-17 10:10:08,667\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 30]: loss -0.27828110308785525\u001b[0m\n",
"[\u001b[36m2022-07-17 10:10:08,832\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 30]: loss -0.6768397092819214\u001b[0m\n",
"[\u001b[36m2022-07-17 10:10:08,889\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/duration_vp_mdn/best_loss.pth\u001b[0m\n",
" 30% 30/100 [00:49<01:44, 1.49s/it][\u001b[36m2022-07-17 10:10:10,210\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 31]: loss -0.3202305613750858\u001b[0m\n",
"[\u001b[36m2022-07-17 10:10:10,378\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 31]: loss -0.6766812801361084\u001b[0m\n",
" 31% 31/100 [00:50<01:43, 1.49s/it][\u001b[36m2022-07-17 10:10:11,662\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 32]: loss -0.3064615372235754\u001b[0m\n",
"[\u001b[36m2022-07-17 10:10:11,828\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 32]: loss -0.6734722256660461\u001b[0m\n",
" 32% 32/100 [00:52<01:40, 1.48s/it][\u001b[36m2022-07-17 10:10:13,130\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 33]: loss -0.33031766715326477\u001b[0m\n",
"[\u001b[36m2022-07-17 10:10:13,296\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 33]: loss -0.641183078289032\u001b[0m\n",
" 33% 33/100 [00:53<01:38, 1.48s/it][\u001b[36m2022-07-17 10:10:14,587\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 34]: loss -0.34684657039386885\u001b[0m\n",
"[\u001b[36m2022-07-17 10:10:14,751\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 34]: loss -0.5572686195373535\u001b[0m\n",
" 34% 34/100 [00:55<01:37, 1.47s/it][\u001b[36m2022-07-17 10:10:16,028\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 35]: loss -0.37725233632539\u001b[0m\n",
"[\u001b[36m2022-07-17 10:10:16,200\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 35]: loss -0.698361337184906\u001b[0m\n",
"[\u001b[36m2022-07-17 10:10:16,254\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/duration_vp_mdn/best_loss.pth\u001b[0m\n",
" 35% 35/100 [00:56<01:36, 1.48s/it][\u001b[36m2022-07-17 10:10:17,528\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 36]: loss -0.37269368794347557\u001b[0m\n",
"[\u001b[36m2022-07-17 10:10:17,694\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 36]: loss -0.7457754015922546\u001b[0m\n",
"[\u001b[36m2022-07-17 10:10:17,750\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/duration_vp_mdn/best_loss.pth\u001b[0m\n",
" 36% 36/100 [00:58<01:35, 1.48s/it][\u001b[36m2022-07-17 10:10:19,013\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 37]: loss -0.41645987278648783\u001b[0m\n",
"[\u001b[36m2022-07-17 10:10:19,187\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 37]: loss -0.7094877362251282\u001b[0m\n",
" 37% 37/100 [00:59<01:32, 1.47s/it][\u001b[36m2022-07-17 10:10:20,451\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 38]: loss -0.4115079451086266\u001b[0m\n",
"[\u001b[36m2022-07-17 10:10:20,617\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 38]: loss -0.736817479133606\u001b[0m\n",
" 38% 38/100 [01:01<01:30, 1.46s/it][\u001b[36m2022-07-17 10:10:21,894\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 39]: loss -0.42684950599712984\u001b[0m\n",
"[\u001b[36m2022-07-17 10:10:22,058\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 39]: loss -0.7087638974189758\u001b[0m\n",
" 39% 39/100 [01:02<01:28, 1.45s/it][\u001b[36m2022-07-17 10:10:23,332\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 40]: loss -0.45433733612298965\u001b[0m\n",
"[\u001b[36m2022-07-17 10:10:23,499\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 40]: loss -0.7333222031593323\u001b[0m\n",
" 40% 40/100 [01:04<01:26, 1.45s/it][\u001b[36m2022-07-17 10:10:24,766\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 41]: loss -0.4852020437163966\u001b[0m\n",
"[\u001b[36m2022-07-17 10:10:24,935\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 41]: loss -0.787286102771759\u001b[0m\n",
"[\u001b[36m2022-07-17 10:10:24,991\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/duration_vp_mdn/best_loss.pth\u001b[0m\n",
" 41% 41/100 [01:05<01:26, 1.46s/it][\u001b[36m2022-07-17 10:10:26,255\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 42]: loss -0.48664177155920435\u001b[0m\n",
"[\u001b[36m2022-07-17 10:10:26,426\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 42]: loss -0.7723602652549744\u001b[0m\n",
" 42% 42/100 [01:07<01:24, 1.45s/it][\u001b[36m2022-07-17 10:10:27,708\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 43]: loss -0.49229344193424496\u001b[0m\n",
"[\u001b[36m2022-07-17 10:10:27,874\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 43]: loss -0.763935923576355\u001b[0m\n",
" 43% 43/100 [01:08<01:22, 1.45s/it][\u001b[36m2022-07-17 10:10:29,181\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 44]: loss -0.5161033416432994\u001b[0m\n",
"[\u001b[36m2022-07-17 10:10:29,348\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 44]: loss -0.8521526455879211\u001b[0m\n",
"[\u001b[36m2022-07-17 10:10:29,406\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/duration_vp_mdn/best_loss.pth\u001b[0m\n",
" 44% 44/100 [01:09<01:22, 1.48s/it][\u001b[36m2022-07-17 10:10:30,690\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 45]: loss -0.5170773525855371\u001b[0m\n",
"[\u001b[36m2022-07-17 10:10:30,854\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 45]: loss -0.7480339407920837\u001b[0m\n",
" 45% 45/100 [01:11<01:20, 1.47s/it][\u001b[36m2022-07-17 10:10:32,131\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 46]: loss -0.5433308114962918\u001b[0m\n",
"[\u001b[36m2022-07-17 10:10:32,297\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 46]: loss -0.8403776288032532\u001b[0m\n",
" 46% 46/100 [01:12<01:18, 1.46s/it][\u001b[36m2022-07-17 10:10:33,577\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 47]: loss -0.5440053423600537\u001b[0m\n",
"[\u001b[36m2022-07-17 10:10:33,742\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 47]: loss -0.8958118557929993\u001b[0m\n",
"[\u001b[36m2022-07-17 10:10:33,797\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/duration_vp_mdn/best_loss.pth\u001b[0m\n",
" 47% 47/100 [01:14<01:18, 1.47s/it][\u001b[36m2022-07-17 10:10:35,080\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 48]: loss -0.5546370537153312\u001b[0m\n",
"[\u001b[36m2022-07-17 10:10:35,247\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 48]: loss -0.9153844118118286\u001b[0m\n",
"[\u001b[36m2022-07-17 10:10:35,304\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/duration_vp_mdn/best_loss.pth\u001b[0m\n",
" 48% 48/100 [01:15<01:17, 1.48s/it][\u001b[36m2022-07-17 10:10:37,581\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 49]: loss -0.5551863653319222\u001b[0m\n",
"[\u001b[36m2022-07-17 10:10:37,827\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 49]: loss -0.934801459312439\u001b[0m\n",
"[\u001b[36m2022-07-17 10:10:37,907\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/duration_vp_mdn/best_loss.pth\u001b[0m\n",
" 49% 49/100 [01:18<01:32, 1.82s/it][\u001b[36m2022-07-17 10:10:39,499\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 50]: loss -0.5647800059190818\u001b[0m\n",
"[\u001b[36m2022-07-17 10:10:39,663\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 50]: loss -0.8609095811843872\u001b[0m\n",
"[\u001b[36m2022-07-17 10:10:39,706\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/duration_vp_mdn/epoch0050.pth\u001b[0m\n",
" 50% 50/100 [01:20<01:31, 1.82s/it][\u001b[36m2022-07-17 10:10:41,028\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 51]: loss -0.5730561361249004\u001b[0m\n",
"[\u001b[36m2022-07-17 10:10:41,195\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 51]: loss -0.9109963178634644\u001b[0m\n",
" 51% 51/100 [01:21<01:23, 1.71s/it][\u001b[36m2022-07-17 10:10:42,473\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 52]: loss -0.5596560418073621\u001b[0m\n",
"[\u001b[36m2022-07-17 10:10:42,639\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 52]: loss -0.9276463985443115\u001b[0m\n",
" 52% 52/100 [01:23<01:18, 1.63s/it][\u001b[36m2022-07-17 10:10:43,918\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 53]: loss -0.5740387296038014\u001b[0m\n",
"[\u001b[36m2022-07-17 10:10:44,079\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 53]: loss -0.8782821297645569\u001b[0m\n",
" 53% 53/100 [01:24<01:14, 1.57s/it][\u001b[36m2022-07-17 10:10:45,374\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 54]: loss -0.5896020675344127\u001b[0m\n",
"[\u001b[36m2022-07-17 10:10:45,541\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 54]: loss -0.9022565484046936\u001b[0m\n",
" 54% 54/100 [01:26<01:10, 1.54s/it][\u001b[36m2022-07-17 10:10:46,812\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 55]: loss -0.592544966510364\u001b[0m\n",
"[\u001b[36m2022-07-17 10:10:46,987\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 55]: loss -0.9553582072257996\u001b[0m\n",
"[\u001b[36m2022-07-17 10:10:47,043\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/duration_vp_mdn/best_loss.pth\u001b[0m\n",
" 55% 55/100 [01:27<01:08, 1.53s/it][\u001b[36m2022-07-17 10:10:48,311\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 56]: loss -0.6044099357511316\u001b[0m\n",
"[\u001b[36m2022-07-17 10:10:48,482\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 56]: loss -0.9447216987609863\u001b[0m\n",
" 56% 56/100 [01:29<01:06, 1.50s/it][\u001b[36m2022-07-17 10:10:49,741\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 57]: loss -0.6034813360976321\u001b[0m\n",
"[\u001b[36m2022-07-17 10:10:49,906\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 57]: loss -0.9116730690002441\u001b[0m\n",
" 57% 57/100 [01:30<01:03, 1.48s/it][\u001b[36m2022-07-17 10:10:51,201\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 58]: loss -0.6117524887834277\u001b[0m\n",
"[\u001b[36m2022-07-17 10:10:51,371\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 58]: loss -0.9214063286781311\u001b[0m\n",
" 58% 58/100 [01:31<01:01, 1.47s/it][\u001b[36m2022-07-17 10:10:52,650\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 59]: loss -0.6256100394363914\u001b[0m\n",
"[\u001b[36m2022-07-17 10:10:52,816\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 59]: loss -0.8875522017478943\u001b[0m\n",
" 59% 59/100 [01:33<01:00, 1.47s/it][\u001b[36m2022-07-17 10:10:54,090\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 60]: loss -0.6370019827570234\u001b[0m\n",
"[\u001b[36m2022-07-17 10:10:54,258\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 60]: loss -0.9835578799247742\u001b[0m\n",
"[\u001b[36m2022-07-17 10:10:54,318\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/duration_vp_mdn/best_loss.pth\u001b[0m\n",
" 60% 60/100 [01:34<00:59, 1.48s/it][\u001b[36m2022-07-17 10:10:55,603\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 61]: loss -0.6537156296627862\u001b[0m\n",
"[\u001b[36m2022-07-17 10:10:55,770\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 61]: loss -0.9208745956420898\u001b[0m\n",
" 61% 61/100 [01:36<00:57, 1.47s/it][\u001b[36m2022-07-17 10:10:57,054\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 62]: loss -0.6528667211532593\u001b[0m\n",
"[\u001b[36m2022-07-17 10:10:57,227\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 62]: loss -0.9610403776168823\u001b[0m\n",
" 62% 62/100 [01:37<00:55, 1.47s/it][\u001b[36m2022-07-17 10:10:58,506\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 63]: loss -0.6682727959539209\u001b[0m\n",
"[\u001b[36m2022-07-17 10:10:58,672\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 63]: loss -1.013179063796997\u001b[0m\n",
"[\u001b[36m2022-07-17 10:10:58,729\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/duration_vp_mdn/best_loss.pth\u001b[0m\n",
" 63% 63/100 [01:39<00:54, 1.48s/it][\u001b[36m2022-07-17 10:10:59,998\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 64]: loss -0.6713237640048776\u001b[0m\n",
"[\u001b[36m2022-07-17 10:11:00,167\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 64]: loss -0.9975940585136414\u001b[0m\n",
" 64% 64/100 [01:40<00:52, 1.47s/it][\u001b[36m2022-07-17 10:11:01,463\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 65]: loss -0.683530619101865\u001b[0m\n",
"[\u001b[36m2022-07-17 10:11:01,628\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 65]: loss -0.9877912998199463\u001b[0m\n",
" 65% 65/100 [01:42<00:51, 1.46s/it][\u001b[36m2022-07-17 10:11:02,896\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 66]: loss -0.6761179614279952\u001b[0m\n",
"[\u001b[36m2022-07-17 10:11:03,059\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 66]: loss -0.9822624921798706\u001b[0m\n",
" 66% 66/100 [01:43<00:49, 1.45s/it][\u001b[36m2022-07-17 10:11:04,324\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 67]: loss -0.6593792491725513\u001b[0m\n",
"[\u001b[36m2022-07-17 10:11:04,488\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 67]: loss -0.9803711175918579\u001b[0m\n",
" 67% 67/100 [01:45<00:47, 1.45s/it][\u001b[36m2022-07-17 10:11:05,768\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 68]: loss -0.6856220008007118\u001b[0m\n",
"[\u001b[36m2022-07-17 10:11:05,936\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 68]: loss -0.9910953640937805\u001b[0m\n",
" 68% 68/100 [01:46<00:46, 1.45s/it][\u001b[36m2022-07-17 10:11:07,209\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 69]: loss -0.6668249131845576\u001b[0m\n",
"[\u001b[36m2022-07-17 10:11:07,377\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 69]: loss -1.0208240747451782\u001b[0m\n",
"[\u001b[36m2022-07-17 10:11:07,436\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/duration_vp_mdn/best_loss.pth\u001b[0m\n",
" 69% 69/100 [01:48<00:45, 1.46s/it][\u001b[36m2022-07-17 10:11:08,709\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 70]: loss -0.6797444724610874\u001b[0m\n",
"[\u001b[36m2022-07-17 10:11:08,873\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 70]: loss -0.999022901058197\u001b[0m\n",
" 70% 70/100 [01:49<00:43, 1.46s/it][\u001b[36m2022-07-17 10:11:10,148\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 71]: loss -0.6848522547100272\u001b[0m\n",
"[\u001b[36m2022-07-17 10:11:10,312\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 71]: loss -0.9515711665153503\u001b[0m\n",
" 71% 71/100 [01:50<00:42, 1.45s/it][\u001b[36m2022-07-17 10:11:11,612\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 72]: loss -0.679444860134806\u001b[0m\n",
"[\u001b[36m2022-07-17 10:11:11,775\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 72]: loss -0.9506444931030273\u001b[0m\n",
" 72% 72/100 [01:52<00:40, 1.45s/it][\u001b[36m2022-07-17 10:11:13,068\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 73]: loss -0.707762096609388\u001b[0m\n",
"[\u001b[36m2022-07-17 10:11:13,233\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 73]: loss -0.9728472828865051\u001b[0m\n",
" 73% 73/100 [01:53<00:39, 1.46s/it][\u001b[36m2022-07-17 10:11:14,500\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 74]: loss -0.6858134620956012\u001b[0m\n",
"[\u001b[36m2022-07-17 10:11:14,674\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 74]: loss -0.9921979904174805\u001b[0m\n",
" 74% 74/100 [01:55<00:37, 1.45s/it][\u001b[36m2022-07-17 10:11:15,946\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 75]: loss -0.6946281061640808\u001b[0m\n",
"[\u001b[36m2022-07-17 10:11:16,113\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 75]: loss -1.0241451263427734\u001b[0m\n",
"[\u001b[36m2022-07-17 10:11:16,168\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/duration_vp_mdn/best_loss.pth\u001b[0m\n",
" 75% 75/100 [01:56<00:36, 1.46s/it][\u001b[36m2022-07-17 10:11:17,435\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 76]: loss -0.7066321255905288\u001b[0m\n",
"[\u001b[36m2022-07-17 10:11:17,600\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 76]: loss -0.9968053698539734\u001b[0m\n",
" 76% 76/100 [01:58<00:34, 1.45s/it][\u001b[36m2022-07-17 10:11:18,880\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 77]: loss -0.7101610771247319\u001b[0m\n",
"[\u001b[36m2022-07-17 10:11:19,047\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 77]: loss -1.0077271461486816\u001b[0m\n",
" 77% 77/100 [01:59<00:33, 1.45s/it][\u001b[36m2022-07-17 10:11:20,318\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 78]: loss -0.7212433101875442\u001b[0m\n",
"[\u001b[36m2022-07-17 10:11:20,486\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 78]: loss -0.9972227215766907\u001b[0m\n",
" 78% 78/100 [02:01<00:31, 1.45s/it][\u001b[36m2022-07-17 10:11:21,771\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 79]: loss -0.7370136720793588\u001b[0m\n",
"[\u001b[36m2022-07-17 10:11:21,937\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 79]: loss -1.004882574081421\u001b[0m\n",
" 79% 79/100 [02:02<00:30, 1.45s/it][\u001b[36m2022-07-17 10:11:23,215\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 80]: loss -0.7220803940934795\u001b[0m\n",
"[\u001b[36m2022-07-17 10:11:23,383\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 80]: loss -1.0090211629867554\u001b[0m\n",
" 80% 80/100 [02:03<00:28, 1.45s/it][\u001b[36m2022-07-17 10:11:24,683\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 81]: loss -0.7345373013189861\u001b[0m\n",
"[\u001b[36m2022-07-17 10:11:24,853\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 81]: loss -1.019540548324585\u001b[0m\n",
" 81% 81/100 [02:05<00:27, 1.45s/it][\u001b[36m2022-07-17 10:11:26,158\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 82]: loss -0.7382955907710961\u001b[0m\n",
"[\u001b[36m2022-07-17 10:11:26,323\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 82]: loss -1.0310771465301514\u001b[0m\n",
"[\u001b[36m2022-07-17 10:11:26,378\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/duration_vp_mdn/best_loss.pth\u001b[0m\n",
" 82% 82/100 [02:06<00:26, 1.48s/it][\u001b[36m2022-07-17 10:11:27,670\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 83]: loss -0.7458089872130326\u001b[0m\n",
"[\u001b[36m2022-07-17 10:11:27,839\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 83]: loss -1.0007559061050415\u001b[0m\n",
" 83% 83/100 [02:08<00:25, 1.47s/it][\u001b[36m2022-07-17 10:11:29,159\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 84]: loss -0.7506302137460027\u001b[0m\n",
"[\u001b[36m2022-07-17 10:11:29,326\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 84]: loss -0.9950738549232483\u001b[0m\n",
" 84% 84/100 [02:09<00:23, 1.48s/it][\u001b[36m2022-07-17 10:11:30,610\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 85]: loss -0.7398620322346687\u001b[0m\n",
"[\u001b[36m2022-07-17 10:11:30,775\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 85]: loss -1.0522781610488892\u001b[0m\n",
"[\u001b[36m2022-07-17 10:11:30,835\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/duration_vp_mdn/best_loss.pth\u001b[0m\n",
" 85% 85/100 [02:11<00:22, 1.49s/it][\u001b[36m2022-07-17 10:11:32,138\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 86]: loss -0.7560706346162728\u001b[0m\n",
"[\u001b[36m2022-07-17 10:11:32,309\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 86]: loss -1.040703535079956\u001b[0m\n",
" 86% 86/100 [02:12<00:20, 1.48s/it][\u001b[36m2022-07-17 10:11:33,583\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 87]: loss -0.7568161732384137\u001b[0m\n",
"[\u001b[36m2022-07-17 10:11:33,749\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 87]: loss -1.0542638301849365\u001b[0m\n",
"[\u001b[36m2022-07-17 10:11:33,807\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/duration_vp_mdn/best_loss.pth\u001b[0m\n",
" 87% 87/100 [02:14<00:19, 1.49s/it][\u001b[36m2022-07-17 10:11:35,109\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 88]: loss -0.754905070577349\u001b[0m\n",
"[\u001b[36m2022-07-17 10:11:35,275\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 88]: loss -1.049107313156128\u001b[0m\n",
" 88% 88/100 [02:15<00:17, 1.48s/it][\u001b[36m2022-07-17 10:11:36,563\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 89]: loss -0.7436193488538265\u001b[0m\n",
"[\u001b[36m2022-07-17 10:11:36,727\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 89]: loss -1.0251551866531372\u001b[0m\n",
" 89% 89/100 [02:17<00:16, 1.47s/it][\u001b[36m2022-07-17 10:11:37,997\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 90]: loss -0.7486272432974407\u001b[0m\n",
"[\u001b[36m2022-07-17 10:11:38,163\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 90]: loss -1.0428483486175537\u001b[0m\n",
" 90% 90/100 [02:18<00:14, 1.46s/it][\u001b[36m2022-07-17 10:11:39,436\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 91]: loss -0.7445180022290775\u001b[0m\n",
"[\u001b[36m2022-07-17 10:11:39,605\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 91]: loss -1.0031347274780273\u001b[0m\n",
" 91% 91/100 [02:20<00:13, 1.46s/it][\u001b[36m2022-07-17 10:11:40,862\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 92]: loss -0.7624630119119372\u001b[0m\n",
"[\u001b[36m2022-07-17 10:11:41,030\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 92]: loss -1.0290493965148926\u001b[0m\n",
" 92% 92/100 [02:21<00:11, 1.45s/it][\u001b[36m2022-07-17 10:11:42,321\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 93]: loss -0.7602659304227147\u001b[0m\n",
"[\u001b[36m2022-07-17 10:11:42,488\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 93]: loss -0.9918602108955383\u001b[0m\n",
" 93% 93/100 [02:23<00:10, 1.45s/it][\u001b[36m2022-07-17 10:11:43,755\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 94]: loss -0.7633764089218208\u001b[0m\n",
"[\u001b[36m2022-07-17 10:11:43,921\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 94]: loss -1.0161821842193604\u001b[0m\n",
" 94% 94/100 [02:24<00:08, 1.44s/it][\u001b[36m2022-07-17 10:11:45,190\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 95]: loss -0.7753122076392174\u001b[0m\n",
"[\u001b[36m2022-07-17 10:11:45,365\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 95]: loss -1.0200101137161255\u001b[0m\n",
" 95% 95/100 [02:25<00:07, 1.44s/it][\u001b[36m2022-07-17 10:11:46,640\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 96]: loss -0.7702007793954441\u001b[0m\n",
"[\u001b[36m2022-07-17 10:11:46,808\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 96]: loss -0.9887881875038147\u001b[0m\n",
" 96% 96/100 [02:27<00:05, 1.44s/it][\u001b[36m2022-07-17 10:11:48,086\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 97]: loss -0.7734773675245898\u001b[0m\n",
"[\u001b[36m2022-07-17 10:11:48,248\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 97]: loss -1.038788080215454\u001b[0m\n",
" 97% 97/100 [02:28<00:04, 1.44s/it][\u001b[36m2022-07-17 10:11:49,530\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 98]: loss -0.7749269551464489\u001b[0m\n",
"[\u001b[36m2022-07-17 10:11:49,694\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 98]: loss -1.0306273698806763\u001b[0m\n",
" 98% 98/100 [02:30<00:02, 1.44s/it][\u001b[36m2022-07-17 10:11:50,938\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 99]: loss -0.7656064895646912\u001b[0m\n",
"[\u001b[36m2022-07-17 10:11:51,106\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 99]: loss -1.036591649055481\u001b[0m\n",
" 99% 99/100 [02:31<00:01, 1.43s/it][\u001b[36m2022-07-17 10:11:52,372\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 100]: loss -0.771382677767958\u001b[0m\n",
"[\u001b[36m2022-07-17 10:11:52,541\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 100]: loss -1.0373291969299316\u001b[0m\n",
"[\u001b[36m2022-07-17 10:11:52,583\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/duration_vp_mdn/epoch0100.pth\u001b[0m\n",
"100% 100/100 [02:33<00:00, 1.53s/it]\n",
"[\u001b[36m2022-07-17 10:11:52,670\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/duration_vp_mdn/epoch0100.pth\u001b[0m\n",
"[\u001b[36m2022-07-17 10:11:52,703\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - The best loss was -1.0542638301849365\u001b[0m\n",
"++ set +x\n"
]
}
],
"source": [
"! cd $RECIPE_ROOT && bash run.sh --stage 3 --stop-stage 3"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"background_save": true,
"base_uri": "https://localhost:8080/"
},
"id": "KQ1u_vYdDFwm",
"outputId": "8ec867c2-f49d-4053-f9ac-01a4ec499e9e"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"stage 4: Training acoustic model\n",
"++ nnsvs-train-resf0 --config-dir conf/train_resf0/acoustic model=acoustic_resf0convlstm train=myconfig data=myconfig data.train_no_dev.in_dir=dump/oniku_kurumi/norm/train_no_dev/in_acoustic/ data.train_no_dev.out_dir=dump/oniku_kurumi/norm/train_no_dev/out_acoustic/ data.dev.in_dir=dump/oniku_kurumi/norm/dev/in_acoustic/ data.dev.out_dir=dump/oniku_kurumi/norm/dev/out_acoustic/ data.in_scaler_path=dump/oniku_kurumi/norm/in_acoustic_scaler.joblib data.out_scaler_path=dump/oniku_kurumi/norm/out_acoustic_scaler.joblib ++data.sample_rate=48000 train.out_dir=exp/oniku_kurumi/acoustic_resf0convlstm train.log_dir=tensorboard/oniku_kurumi_acoustic_resf0convlstm train.resume.checkpoint=\n",
"/usr/local/lib/python3.7/dist-packages/resampy/interpn.py:114: NumbaWarning: The TBB threading layer requires TBB version 2019.5 or later i.e., TBB_INTERFACE_VERSION >= 11005. Found TBB_INTERFACE_VERSION = 9107. The TBB threading layer is disabled.\n",
" _resample_loop_p(x, t_out, interp_win, interp_delta, num_table, scale, y)\n",
"[\u001b[36m2022-07-17 10:12:06,060\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - model:\n",
" stream_sizes:\n",
" - 180\n",
" - 3\n",
" - 1\n",
" - 15\n",
" - 6\n",
" - 1\n",
" has_dynamic_features:\n",
" - true\n",
" - true\n",
" - false\n",
" - true\n",
" - true\n",
" - false\n",
" num_windows: 3\n",
" stream_weights: null\n",
" netG:\n",
" _target_: nnsvs.model.ResSkipF0FFConvLSTM\n",
" in_dim: 341\n",
" out_dim: 206\n",
" ff_hidden_dim: 1024\n",
" conv_hidden_dim: 512\n",
" lstm_hidden_dim: 256\n",
" dropout: 0.0\n",
" num_lstm_layers: 2\n",
" bidirectional: true\n",
" init_type: kaiming_normal\n",
" use_mdn: false\n",
" num_gaussians: 8\n",
" dim_wise: true\n",
" in_lf0_idx: 292\n",
" out_lf0_idx: 180\n",
" in_lf0_min: null\n",
" in_lf0_max: null\n",
" out_lf0_mean: null\n",
" out_lf0_scale: null\n",
"train:\n",
" out_dir: exp/oniku_kurumi/acoustic_resf0convlstm\n",
" log_dir: tensorboard/oniku_kurumi_acoustic_resf0convlstm\n",
" use_amp: false\n",
" max_train_steps: -1\n",
" nepochs: 100\n",
" checkpoint_epoch_interval: 50\n",
" feats_criterion: l1\n",
" pitch_reg_weight: 1.0\n",
" stream_wise_loss: false\n",
" use_detect_anomaly: false\n",
" optim:\n",
" optimizer:\n",
" name: Adam\n",
" params:\n",
" lr: 0.001\n",
" betas:\n",
" - 0.9\n",
" - 0.999\n",
" weight_decay: 0.0\n",
" lr_scheduler:\n",
" name: StepLR\n",
" params:\n",
" step_size: 20\n",
" gamma: 0.5\n",
" resume:\n",
" checkpoint: ''\n",
" load_optimizer: false\n",
" cudnn:\n",
" benchmark: false\n",
" deterministic: true\n",
"data:\n",
" train_no_dev:\n",
" in_dir: dump/oniku_kurumi/norm/train_no_dev/in_acoustic/\n",
" out_dir: dump/oniku_kurumi/norm/train_no_dev/out_acoustic/\n",
" dev:\n",
" in_dir: dump/oniku_kurumi/norm/dev/in_acoustic/\n",
" out_dir: dump/oniku_kurumi/norm/dev/out_acoustic/\n",
" num_workers: 2\n",
" batch_size: 8\n",
" pin_memory: true\n",
" sample_rate: 48000\n",
" filter_long_segments: false\n",
" filter_num_frames: 6000\n",
" max_time_frames: -1\n",
" in_scaler_path: dump/oniku_kurumi/norm/in_acoustic_scaler.joblib\n",
" out_scaler_path: dump/oniku_kurumi/norm/out_acoustic_scaler.joblib\n",
" in_lf0_idx: 292\n",
" in_rest_idx: 1\n",
" out_lf0_idx: 180\n",
"mlflow:\n",
" enabled: false\n",
" experiment: test\n",
"verbose: 100\n",
"seed: 773\n",
"data_parallel: false\n",
"\u001b[0m\n",
"[\u001b[36m2022-07-17 10:12:06,061\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - PyTorch version: 1.12.0+cu113\u001b[0m\n",
"[\u001b[36m2022-07-17 10:12:06,061\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - cudnn.deterministic: True\u001b[0m\n",
"[\u001b[36m2022-07-17 10:12:06,061\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - cudnn.benchmark: False\u001b[0m\n",
"[\u001b[36m2022-07-17 10:12:06,062\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - cuDNN version: 8302\u001b[0m\n",
"[\u001b[36m2022-07-17 10:12:06,063\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Random seed: 773\u001b[0m\n",
"[\u001b[36m2022-07-17 10:12:08,344\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Number of trainable params: 13.057 million\u001b[0m\n",
"[\u001b[36m2022-07-17 10:12:08,344\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - ResSkipF0FFConvLSTM(\n",
" (ff): Sequential(\n",
" (0): Linear(in_features=341, out_features=1024, bias=True)\n",
" (1): ReLU()\n",
" (2): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (3): ReLU()\n",
" (4): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (5): ReLU()\n",
" )\n",
" (conv): Sequential(\n",
" (0): ReflectionPad1d((3, 3))\n",
" (1): Conv1d(1025, 512, kernel_size=(7,), stride=(1,))\n",
" (2): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (3): ReLU()\n",
" (4): ReflectionPad1d((3, 3))\n",
" (5): Conv1d(512, 512, kernel_size=(7,), stride=(1,))\n",
" (6): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (7): ReLU()\n",
" (8): ReflectionPad1d((3, 3))\n",
" (9): Conv1d(512, 512, kernel_size=(7,), stride=(1,))\n",
" (10): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (11): ReLU()\n",
" )\n",
" (lstm): LSTM(512, 256, num_layers=2, batch_first=True, bidirectional=True)\n",
" (fc): Linear(in_features=512, out_features=206, bias=True)\n",
")\u001b[0m\n",
"[\u001b[36m2022-07-17 10:12:08,351\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Number of iterations per epoch: 56\u001b[0m\n",
"[\u001b[36m2022-07-17 10:12:08,351\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Number of max_train_steps is set based on nepochs: 5600\u001b[0m\n",
"[\u001b[36m2022-07-17 10:12:08,352\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Number of epochs: 100\u001b[0m\n",
"[\u001b[36m2022-07-17 10:12:08,352\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Number of iterations: 5600\u001b[0m\n",
"[\u001b[36m2022-07-17 10:12:10,133\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Checking model configs for residual F0 prediction\u001b[0m\n",
"[\u001b[36m2022-07-17 10:12:10,133\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - in_lf0_idx: 292\u001b[0m\n",
"[\u001b[36m2022-07-17 10:12:10,134\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - in_rest_idx: 1\u001b[0m\n",
"[\u001b[36m2022-07-17 10:12:10,134\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - out_lf0_idx: 180\u001b[0m\n",
"[\u001b[36m2022-07-17 10:12:10,134\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - in_lf0_min: 5.278103\u001b[0m\n",
"[\u001b[36m2022-07-17 10:12:10,134\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - in_lf0_max: 6.548873\u001b[0m\n",
"[\u001b[36m2022-07-17 10:12:10,135\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - model.out_lf0_mean: 5.955746018850272\u001b[0m\n",
"[\u001b[36m2022-07-17 10:12:10,135\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - model.out_lf0_scale: 0.23926882447957243\u001b[0m\n",
" 0% 0/100 [00:00<?, ?it/s][\u001b[36m2022-07-17 10:13:54,788\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 1]: loss 0.7485061905213765\u001b[0m\n",
"[\u001b[36m2022-07-17 10:15:14,137\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 1]: loss 0.7355414032936096\u001b[0m\n",
"[\u001b[36m2022-07-17 10:15:14,447\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/acoustic_resf0convlstm/best_loss.pth\u001b[0m\n",
" 1% 1/100 [03:03<5:03:10, 183.74s/it][\u001b[36m2022-07-17 10:16:59,426\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 2]: loss 0.7233402079769543\u001b[0m\n",
"[\u001b[36m2022-07-17 10:18:17,600\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 2]: loss 0.728647768497467\u001b[0m\n",
"[\u001b[36m2022-07-17 10:18:17,943\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/acoustic_resf0convlstm/best_loss.pth\u001b[0m\n",
" 2% 2/100 [06:07<4:59:52, 183.60s/it][\u001b[36m2022-07-17 10:19:59,201\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 3]: loss 0.7188248112797737\u001b[0m\n",
"[\u001b[36m2022-07-17 10:21:18,649\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 3]: loss 0.7257325649261475\u001b[0m\n",
"[\u001b[36m2022-07-17 10:21:18,990\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/acoustic_resf0convlstm/best_loss.pth\u001b[0m\n",
" 3% 3/100 [09:08<4:54:55, 182.43s/it][\u001b[36m2022-07-17 10:23:00,957\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 4]: loss 0.7154404031378883\u001b[0m\n",
"[\u001b[36m2022-07-17 10:24:20,210\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 4]: loss 0.723335862159729\u001b[0m\n",
"[\u001b[36m2022-07-17 10:24:20,582\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/acoustic_resf0convlstm/best_loss.pth\u001b[0m\n",
" 4% 4/100 [12:09<4:51:21, 182.10s/it][\u001b[36m2022-07-17 10:26:03,442\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 5]: loss 0.7123271067227636\u001b[0m\n",
"[\u001b[36m2022-07-17 10:27:22,570\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 5]: loss 0.7236455082893372\u001b[0m\n",
" 5% 5/100 [15:11<4:48:15, 182.06s/it][\u001b[36m2022-07-17 10:29:03,561\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 6]: loss 0.7102760172316006\u001b[0m\n",
"[\u001b[36m2022-07-17 10:30:23,243\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 6]: loss 0.7210119962692261\u001b[0m\n",
"[\u001b[36m2022-07-17 10:30:23,607\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/acoustic_resf0convlstm/best_loss.pth\u001b[0m\n",
" 6% 6/100 [18:12<4:44:40, 181.71s/it][\u001b[36m2022-07-17 10:32:03,606\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 7]: loss 0.7093882550086293\u001b[0m\n",
"[\u001b[36m2022-07-17 10:33:22,665\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 7]: loss 0.7188901305198669\u001b[0m\n",
"[\u001b[36m2022-07-17 10:33:23,020\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/acoustic_resf0convlstm/best_loss.pth\u001b[0m\n",
" 7% 7/100 [21:12<4:40:29, 180.96s/it][\u001b[36m2022-07-17 10:35:04,301\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 8]: loss 0.7078418625252587\u001b[0m\n",
"[\u001b[36m2022-07-17 10:36:23,438\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 8]: loss 0.7195698618888855\u001b[0m\n",
" 8% 8/100 [24:12<4:37:12, 180.79s/it][\u001b[36m2022-07-17 10:38:05,065\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 9]: loss 0.7088339243616376\u001b[0m\n",
"[\u001b[36m2022-07-17 10:39:24,032\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 9]: loss 0.7183623313903809\u001b[0m\n",
"[\u001b[36m2022-07-17 10:39:24,392\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/acoustic_resf0convlstm/best_loss.pth\u001b[0m\n",
" 9% 9/100 [27:13<4:34:16, 180.84s/it][\u001b[36m2022-07-17 10:41:06,375\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 10]: loss 0.7067193314433098\u001b[0m\n",
"[\u001b[36m2022-07-17 10:42:25,411\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 10]: loss 0.716911792755127\u001b[0m\n",
"[\u001b[36m2022-07-17 10:42:25,782\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/acoustic_resf0convlstm/best_loss.pth\u001b[0m\n",
" 10% 10/100 [30:15<4:31:30, 181.01s/it][\u001b[36m2022-07-17 10:44:06,576\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 11]: loss 0.7071770621197564\u001b[0m\n",
"[\u001b[36m2022-07-17 10:45:25,664\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 11]: loss 0.7159714102745056\u001b[0m\n",
"[\u001b[36m2022-07-17 10:45:26,012\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/acoustic_resf0convlstm/best_loss.pth\u001b[0m\n",
" 11% 11/100 [33:15<4:28:08, 180.77s/it][\u001b[36m2022-07-17 10:47:06,442\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 12]: loss 0.7055068090558052\u001b[0m\n",
"[\u001b[36m2022-07-17 10:48:26,811\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 12]: loss 0.7153160572052002\u001b[0m\n",
"[\u001b[36m2022-07-17 10:48:27,170\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/acoustic_resf0convlstm/best_loss.pth\u001b[0m\n",
" 12% 12/100 [36:16<4:25:18, 180.89s/it][\u001b[36m2022-07-17 10:50:10,967\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 13]: loss 0.705994827406747\u001b[0m\n",
"[\u001b[36m2022-07-17 10:51:31,539\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 13]: loss 0.7154873013496399\u001b[0m\n",
" 13% 13/100 [39:20<4:23:49, 181.94s/it][\u001b[36m2022-07-17 10:53:11,121\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 14]: loss 0.7049205228686333\u001b[0m\n",
"[\u001b[36m2022-07-17 10:54:31,552\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 14]: loss 0.7196761965751648\u001b[0m\n",
" 14% 14/100 [42:20<4:19:56, 181.36s/it][\u001b[36m2022-07-17 10:56:11,308\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 15]: loss 0.7046166456171444\u001b[0m\n",
"[\u001b[36m2022-07-17 10:57:31,558\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 15]: loss 0.7227178812026978\u001b[0m\n",
" 15% 15/100 [45:20<4:16:20, 180.95s/it][\u001b[36m2022-07-17 10:59:13,086\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 16]: loss 0.7069553828665188\u001b[0m\n",
"[\u001b[36m2022-07-17 11:00:33,678\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 16]: loss 0.7181958556175232\u001b[0m\n",
" 16% 16/100 [48:22<4:13:49, 181.30s/it][\u001b[36m2022-07-17 11:02:14,300\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 17]: loss 0.7039565742015839\u001b[0m\n",
"[\u001b[36m2022-07-17 11:03:33,385\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 17]: loss 0.7158979177474976\u001b[0m\n",
" 17% 17/100 [51:22<4:10:08, 180.82s/it][\u001b[36m2022-07-17 11:05:16,972\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 18]: loss 0.7039417092289243\u001b[0m\n",
"[\u001b[36m2022-07-17 11:06:35,975\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 18]: loss 0.7223993539810181\u001b[0m\n",
" 18% 18/100 [54:25<4:07:51, 181.35s/it][\u001b[36m2022-07-17 11:08:17,100\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 19]: loss 0.7029211765953473\u001b[0m\n",
"[\u001b[36m2022-07-17 11:09:36,049\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 19]: loss 0.715058445930481\u001b[0m\n",
"[\u001b[36m2022-07-17 11:09:36,382\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/acoustic_resf0convlstm/best_loss.pth\u001b[0m\n",
" 19% 19/100 [57:25<4:04:26, 181.07s/it][\u001b[36m2022-07-17 11:11:18,369\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 20]: loss 0.7029803714581898\u001b[0m\n",
"[\u001b[36m2022-07-17 11:12:37,252\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 20]: loss 0.7143490314483643\u001b[0m\n",
"[\u001b[36m2022-07-17 11:12:37,601\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/acoustic_resf0convlstm/best_loss.pth\u001b[0m\n",
" 20% 20/100 [1:00:26<4:01:29, 181.11s/it][\u001b[36m2022-07-17 11:14:16,964\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 21]: loss 0.7018776653068406\u001b[0m\n",
"[\u001b[36m2022-07-17 11:15:36,634\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 21]: loss 0.7142007946968079\u001b[0m\n",
"[\u001b[36m2022-07-17 11:15:36,993\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/acoustic_resf0convlstm/best_loss.pth\u001b[0m\n",
" 21% 21/100 [1:03:26<3:57:47, 180.60s/it][\u001b[36m2022-07-17 11:17:17,938\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 22]: loss 0.7008141194071088\u001b[0m\n",
"[\u001b[36m2022-07-17 11:18:38,203\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 22]: loss 0.7133501768112183\u001b[0m\n",
"[\u001b[36m2022-07-17 11:18:38,549\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/acoustic_resf0convlstm/best_loss.pth\u001b[0m\n",
" 22% 22/100 [1:06:27<3:55:09, 180.89s/it][\u001b[36m2022-07-17 11:20:19,249\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 23]: loss 0.7001892126032284\u001b[0m\n",
"[\u001b[36m2022-07-17 11:21:39,436\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 23]: loss 0.7130545973777771\u001b[0m\n",
"[\u001b[36m2022-07-17 11:21:39,801\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/acoustic_resf0convlstm/best_loss.pth\u001b[0m\n",
" 23% 23/100 [1:09:29<3:52:16, 181.00s/it][\u001b[36m2022-07-17 11:23:19,577\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 24]: loss 0.700861031455653\u001b[0m\n",
"[\u001b[36m2022-07-17 11:24:39,384\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 24]: loss 0.7130870819091797\u001b[0m\n",
" 24% 24/100 [1:12:28<3:48:43, 180.57s/it][\u001b[36m2022-07-17 11:26:19,768\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 25]: loss 0.6997266805597714\u001b[0m\n",
"[\u001b[36m2022-07-17 11:27:39,271\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 25]: loss 0.7146977186203003\u001b[0m\n",
" 25% 25/100 [1:15:28<3:45:27, 180.37s/it][\u001b[36m2022-07-17 11:29:20,077\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 26]: loss 0.6997107341885567\u001b[0m\n",
"[\u001b[36m2022-07-17 11:30:40,812\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 26]: loss 0.7146811485290527\u001b[0m\n",
" 26% 26/100 [1:18:30<3:42:53, 180.72s/it][\u001b[36m2022-07-17 11:32:22,831\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 27]: loss 0.7002198951584953\u001b[0m\n",
"[\u001b[36m2022-07-17 11:33:43,315\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 27]: loss 0.7148009538650513\u001b[0m\n",
" 27% 27/100 [1:21:32<3:40:31, 181.25s/it][\u001b[36m2022-07-17 11:35:25,198\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 28]: loss 0.6994968269552503\u001b[0m\n",
"[\u001b[36m2022-07-17 11:36:46,170\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 28]: loss 0.7151042819023132\u001b[0m\n",
" 28% 28/100 [1:24:35<3:38:04, 181.73s/it][\u001b[36m2022-07-17 11:38:27,157\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 29]: loss 0.6993393759642329\u001b[0m\n",
"[\u001b[36m2022-07-17 11:39:46,463\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 29]: loss 0.7143082618713379\u001b[0m\n",
" 29% 29/100 [1:27:35<3:34:32, 181.30s/it][\u001b[36m2022-07-17 11:41:29,620\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 30]: loss 0.6981957416449275\u001b[0m\n",
"[\u001b[36m2022-07-17 11:42:48,948\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 30]: loss 0.7173887491226196\u001b[0m\n",
" 30% 30/100 [1:30:38<3:31:55, 181.66s/it][\u001b[36m2022-07-17 11:44:29,839\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 31]: loss 0.6982003354600498\u001b[0m\n",
"[\u001b[36m2022-07-17 11:45:50,328\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 31]: loss 0.7138604521751404\u001b[0m\n",
" 31% 31/100 [1:33:39<3:28:48, 181.57s/it][\u001b[36m2022-07-17 11:47:31,317\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 32]: loss 0.6976277679204941\u001b[0m\n",
"[\u001b[36m2022-07-17 11:48:51,566\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 32]: loss 0.7137477397918701\u001b[0m\n",
" 32% 32/100 [1:36:40<3:25:40, 181.47s/it][\u001b[36m2022-07-17 11:50:31,860\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 33]: loss 0.6984797673566001\u001b[0m\n",
"[\u001b[36m2022-07-17 11:51:52,467\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 33]: loss 0.7185724377632141\u001b[0m\n",
" 33% 33/100 [1:39:41<3:22:27, 181.30s/it][\u001b[36m2022-07-17 11:53:33,886\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 34]: loss 0.6976582620825086\u001b[0m\n",
"[\u001b[36m2022-07-17 11:54:53,266\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 34]: loss 0.7129064798355103\u001b[0m\n",
"[\u001b[36m2022-07-17 11:54:53,622\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/acoustic_resf0convlstm/best_loss.pth\u001b[0m\n",
" 34% 34/100 [1:42:42<3:19:22, 181.26s/it][\u001b[36m2022-07-17 11:56:36,077\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 35]: loss 0.6969552550997052\u001b[0m\n",
"[\u001b[36m2022-07-17 11:57:55,451\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 35]: loss 0.7141364216804504\u001b[0m\n",
" 35% 35/100 [1:45:44<3:16:32, 181.43s/it][\u001b[36m2022-07-17 11:59:35,358\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 36]: loss 0.6969359272292682\u001b[0m\n",
"[\u001b[36m2022-07-17 12:00:54,624\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 36]: loss 0.7161743640899658\u001b[0m\n",
" 36% 36/100 [1:48:43<3:12:48, 180.75s/it][\u001b[36m2022-07-17 12:02:36,531\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 37]: loss 0.6961122740592275\u001b[0m\n",
"[\u001b[36m2022-07-17 12:03:57,014\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 37]: loss 0.7141920328140259\u001b[0m\n",
" 37% 37/100 [1:51:46<3:10:18, 181.24s/it][\u001b[36m2022-07-17 12:05:37,173\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 38]: loss 0.6954616201775414\u001b[0m\n",
"[\u001b[36m2022-07-17 12:06:57,773\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 38]: loss 0.7139496207237244\u001b[0m\n",
" 38% 38/100 [1:54:47<3:07:08, 181.10s/it][\u001b[36m2022-07-17 12:08:38,245\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 39]: loss 0.6963980974895614\u001b[0m\n",
"[\u001b[36m2022-07-17 12:09:58,728\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 39]: loss 0.7128743529319763\u001b[0m\n",
"[\u001b[36m2022-07-17 12:09:59,087\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/acoustic_resf0convlstm/best_loss.pth\u001b[0m\n",
" 39% 39/100 [1:57:48<3:04:10, 181.16s/it][\u001b[36m2022-07-17 12:11:39,390\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 40]: loss 0.6957339314477784\u001b[0m\n",
"[\u001b[36m2022-07-17 12:13:00,044\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 40]: loss 0.713630199432373\u001b[0m\n",
" 40% 40/100 [2:00:49<3:01:06, 181.10s/it][\u001b[36m2022-07-17 12:14:42,420\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 41]: loss 0.694326211299215\u001b[0m\n",
"[\u001b[36m2022-07-17 12:16:01,974\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 41]: loss 0.7149229049682617\u001b[0m\n",
" 41% 41/100 [2:03:51<2:58:19, 181.35s/it][\u001b[36m2022-07-17 12:17:43,013\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 42]: loss 0.6933759280613491\u001b[0m\n",
"[\u001b[36m2022-07-17 12:19:02,334\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 42]: loss 0.7120000123977661\u001b[0m\n",
"[\u001b[36m2022-07-17 12:19:02,660\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/acoustic_resf0convlstm/best_loss.pth\u001b[0m\n",
" 42% 42/100 [2:06:51<2:55:06, 181.15s/it][\u001b[36m2022-07-17 12:20:45,114\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 43]: loss 0.6938385495117733\u001b[0m\n",
"[\u001b[36m2022-07-17 12:22:04,353\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 43]: loss 0.7137588262557983\u001b[0m\n",
" 43% 43/100 [2:09:53<2:52:14, 181.31s/it][\u001b[36m2022-07-17 12:23:46,232\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 44]: loss 0.6938110845429557\u001b[0m\n",
"[\u001b[36m2022-07-17 12:25:06,843\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 44]: loss 0.7133674025535583\u001b[0m\n",
" 44% 44/100 [2:12:56<2:49:33, 181.67s/it][\u001b[36m2022-07-17 12:26:47,735\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 45]: loss 0.6928358365382467\u001b[0m\n",
"[\u001b[36m2022-07-17 12:28:08,671\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 45]: loss 0.714301347732544\u001b[0m\n",
" 45% 45/100 [2:15:57<2:46:34, 181.71s/it][\u001b[36m2022-07-17 12:29:49,906\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 46]: loss 0.6926273182034492\u001b[0m\n",
"[\u001b[36m2022-07-17 12:31:10,618\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 46]: loss 0.7121370434761047\u001b[0m\n",
" 46% 46/100 [2:18:59<2:43:36, 181.78s/it][\u001b[36m2022-07-17 12:32:52,933\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 47]: loss 0.6924645858151572\u001b[0m\n",
"[\u001b[36m2022-07-17 12:34:13,547\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 47]: loss 0.7137022018432617\u001b[0m\n",
" 47% 47/100 [2:22:02<2:40:52, 182.13s/it][\u001b[36m2022-07-17 12:35:54,560\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 48]: loss 0.6917717052357537\u001b[0m\n",
"[\u001b[36m2022-07-17 12:37:15,386\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 48]: loss 0.7129413485527039\u001b[0m\n",
" 48% 48/100 [2:25:04<2:37:46, 182.04s/it][\u001b[36m2022-07-17 12:38:55,902\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 49]: loss 0.6912635479654584\u001b[0m\n",
"[\u001b[36m2022-07-17 12:40:16,071\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 49]: loss 0.7159643769264221\u001b[0m\n",
" 49% 49/100 [2:28:05<2:34:23, 181.63s/it][\u001b[36m2022-07-17 12:41:57,577\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 50]: loss 0.6916937093649592\u001b[0m\n",
"[\u001b[36m2022-07-17 12:43:18,107\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 50]: loss 0.7141079306602478\u001b[0m\n",
"[\u001b[36m2022-07-17 12:43:18,418\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/acoustic_resf0convlstm/epoch0050.pth\u001b[0m\n",
" 50% 50/100 [2:31:08<2:31:37, 181.95s/it][\u001b[36m2022-07-17 12:44:58,594\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 51]: loss 0.6905666877116475\u001b[0m\n",
"[\u001b[36m2022-07-17 12:46:19,141\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 51]: loss 0.7159185409545898\u001b[0m\n",
" 51% 51/100 [2:34:08<2:28:12, 181.48s/it][\u001b[36m2022-07-17 12:47:59,679\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 52]: loss 0.690307797065803\u001b[0m\n",
"[\u001b[36m2022-07-17 12:49:19,973\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 52]: loss 0.7156856060028076\u001b[0m\n",
" 52% 52/100 [2:37:09<2:25:01, 181.29s/it][\u001b[36m2022-07-17 12:51:00,051\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 53]: loss 0.6904569800410952\u001b[0m\n",
"[\u001b[36m2022-07-17 12:52:20,528\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 53]: loss 0.7143575549125671\u001b[0m\n",
" 53% 53/100 [2:40:09<2:21:50, 181.07s/it][\u001b[36m2022-07-17 12:54:00,405\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 54]: loss 0.6901836203677314\u001b[0m\n",
"[\u001b[36m2022-07-17 12:55:21,286\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 54]: loss 0.7152528762817383\u001b[0m\n",
" 54% 54/100 [2:43:10<2:18:44, 180.97s/it][\u001b[36m2022-07-17 12:57:01,551\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 55]: loss 0.6894809337598937\u001b[0m\n",
"[\u001b[36m2022-07-17 12:58:22,162\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 55]: loss 0.7143320441246033\u001b[0m\n",
" 55% 55/100 [2:46:11<2:15:42, 180.94s/it][\u001b[36m2022-07-17 13:00:02,432\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 56]: loss 0.6901884557945388\u001b[0m\n",
"[\u001b[36m2022-07-17 13:01:22,972\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 56]: loss 0.7137412428855896\u001b[0m\n",
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"[\u001b[36m2022-07-17 13:04:25,007\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 57]: loss 0.7139521241188049\u001b[0m\n",
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"[\u001b[36m2022-07-17 13:07:25,886\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 58]: loss 0.7158541083335876\u001b[0m\n",
" 58% 58/100 [2:55:15<2:06:47, 181.13s/it][\u001b[36m2022-07-17 13:09:09,473\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 59]: loss 0.6880351038915771\u001b[0m\n",
"[\u001b[36m2022-07-17 13:10:29,124\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 59]: loss 0.7146214246749878\u001b[0m\n",
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"[\u001b[36m2022-07-17 13:13:30,552\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 60]: loss 0.7147162556648254\u001b[0m\n",
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"[\u001b[36m2022-07-17 13:16:33,471\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 61]: loss 0.7143681645393372\u001b[0m\n",
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"[\u001b[36m2022-07-17 13:19:34,257\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 62]: loss 0.7142163515090942\u001b[0m\n",
" 62% 62/100 [3:07:23<1:55:03, 181.66s/it][\u001b[36m2022-07-17 13:21:15,095\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 63]: loss 0.6863915334854808\u001b[0m\n",
"[\u001b[36m2022-07-17 13:22:34,403\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 63]: loss 0.7146042585372925\u001b[0m\n",
" 63% 63/100 [3:10:23<1:51:44, 181.21s/it][\u001b[36m2022-07-17 13:24:12,443\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 64]: loss 0.685406183557851\u001b[0m\n",
"[\u001b[36m2022-07-17 13:25:32,678\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 64]: loss 0.7144904136657715\u001b[0m\n",
" 64% 64/100 [3:13:21<1:48:11, 180.33s/it][\u001b[36m2022-07-17 13:27:15,647\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 65]: loss 0.6850105236683574\u001b[0m\n",
"[\u001b[36m2022-07-17 13:28:35,971\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 65]: loss 0.7153890132904053\u001b[0m\n",
" 65% 65/100 [3:16:25<1:45:42, 181.22s/it][\u001b[36m2022-07-17 13:30:14,657\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 66]: loss 0.6845316120556423\u001b[0m\n",
"[\u001b[36m2022-07-17 13:31:34,431\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 66]: loss 0.7146962881088257\u001b[0m\n",
" 66% 66/100 [3:19:23<1:42:13, 180.39s/it][\u001b[36m2022-07-17 13:33:14,994\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 67]: loss 0.6846008609448161\u001b[0m\n",
"[\u001b[36m2022-07-17 13:34:35,627\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 67]: loss 0.7150234580039978\u001b[0m\n",
" 67% 67/100 [3:22:24<1:39:20, 180.63s/it][\u001b[36m2022-07-17 13:36:15,200\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 68]: loss 0.6836963689752987\u001b[0m\n",
"[\u001b[36m2022-07-17 13:37:36,177\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 68]: loss 0.7146798968315125\u001b[0m\n",
" 68% 68/100 [3:25:25<1:36:19, 180.61s/it][\u001b[36m2022-07-17 13:39:19,431\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 69]: loss 0.6847038471273014\u001b[0m\n",
"[\u001b[36m2022-07-17 13:40:38,599\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 69]: loss 0.7159401774406433\u001b[0m\n",
" 69% 69/100 [3:28:27<1:33:35, 181.15s/it][\u001b[36m2022-07-17 13:42:21,242\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 70]: loss 0.6844303054468972\u001b[0m\n",
"[\u001b[36m2022-07-17 13:43:41,555\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 70]: loss 0.7156570553779602\u001b[0m\n",
" 70% 70/100 [3:31:30<1:30:50, 181.69s/it][\u001b[36m2022-07-17 13:45:23,068\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 71]: loss 0.6833482384681702\u001b[0m\n",
"[\u001b[36m2022-07-17 13:46:44,081\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 71]: loss 0.7150239944458008\u001b[0m\n",
" 71% 71/100 [3:34:33<1:27:56, 181.94s/it][\u001b[36m2022-07-17 13:48:25,042\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 72]: loss 0.683627975838525\u001b[0m\n",
"[\u001b[36m2022-07-17 13:49:45,378\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 72]: loss 0.7163045406341553\u001b[0m\n",
" 72% 72/100 [3:37:34<1:24:48, 181.75s/it][\u001b[36m2022-07-17 13:51:24,393\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 73]: loss 0.6835322241697993\u001b[0m\n",
"[\u001b[36m2022-07-17 13:52:44,774\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 73]: loss 0.716058611869812\u001b[0m\n",
" 73% 73/100 [3:40:34<1:21:28, 181.04s/it][\u001b[36m2022-07-17 13:54:25,668\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 74]: loss 0.6832559161952564\u001b[0m\n",
"[\u001b[36m2022-07-17 13:55:45,879\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 74]: loss 0.7156367301940918\u001b[0m\n",
" 74% 74/100 [3:43:35<1:18:27, 181.06s/it][\u001b[36m2022-07-17 13:57:29,007\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 75]: loss 0.683108488363879\u001b[0m\n",
"[\u001b[36m2022-07-17 13:58:49,068\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 75]: loss 0.7162330746650696\u001b[0m\n",
" 75% 75/100 [3:46:38<1:15:42, 181.70s/it][\u001b[36m2022-07-17 14:00:31,106\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 76]: loss 0.6823414234178407\u001b[0m\n",
"[\u001b[36m2022-07-17 14:01:50,232\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 76]: loss 0.7157100439071655\u001b[0m\n",
" 76% 76/100 [3:49:39<1:12:36, 181.54s/it][\u001b[36m2022-07-17 14:03:30,815\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 77]: loss 0.6829887990440641\u001b[0m\n",
"[\u001b[36m2022-07-17 14:04:50,999\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 77]: loss 0.7159550786018372\u001b[0m\n",
" 77% 77/100 [3:52:40<1:09:30, 181.31s/it][\u001b[36m2022-07-17 14:06:30,295\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 78]: loss 0.6833684231553759\u001b[0m\n",
"[\u001b[36m2022-07-17 14:07:50,916\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 78]: loss 0.7161213755607605\u001b[0m\n",
" 78% 78/100 [3:55:40<1:06:19, 180.89s/it][\u001b[36m2022-07-17 14:09:33,512\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 79]: loss 0.6814240291714668\u001b[0m\n",
"[\u001b[36m2022-07-17 14:10:53,748\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 79]: loss 0.7164033651351929\u001b[0m\n",
" 79% 79/100 [3:58:43<1:03:30, 181.47s/it][\u001b[36m2022-07-17 14:12:35,500\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 80]: loss 0.6820910988109452\u001b[0m\n",
"[\u001b[36m2022-07-17 14:13:55,691\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 80]: loss 0.7166481614112854\u001b[0m\n",
" 80% 80/100 [4:01:44<1:00:32, 181.61s/it][\u001b[36m2022-07-17 14:15:36,889\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 81]: loss 0.681204514844077\u001b[0m\n",
"[\u001b[36m2022-07-17 14:16:56,067\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 81]: loss 0.7168768048286438\u001b[0m\n",
" 81% 81/100 [4:04:45<57:23, 181.24s/it] [\u001b[36m2022-07-17 14:18:36,969\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 82]: loss 0.6807073535663741\u001b[0m\n",
"[\u001b[36m2022-07-17 14:19:57,199\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 82]: loss 0.7162447571754456\u001b[0m\n",
" 82% 82/100 [4:07:46<54:21, 181.21s/it][\u001b[36m2022-07-17 14:21:39,839\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 83]: loss 0.6808460386736053\u001b[0m\n",
"[\u001b[36m2022-07-17 14:22:58,969\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 83]: loss 0.7161741852760315\u001b[0m\n",
" 83% 83/100 [4:10:48<51:23, 181.38s/it][\u001b[36m2022-07-17 14:24:40,744\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 84]: loss 0.6808644416076797\u001b[0m\n",
"[\u001b[36m2022-07-17 14:26:00,901\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 84]: loss 0.7165154218673706\u001b[0m\n",
" 84% 84/100 [4:13:50<48:24, 181.54s/it][\u001b[36m2022-07-17 14:27:41,784\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 85]: loss 0.6795940612043653\u001b[0m\n",
"[\u001b[36m2022-07-17 14:29:02,458\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 85]: loss 0.7165151834487915\u001b[0m\n",
" 85% 85/100 [4:16:51<45:23, 181.55s/it][\u001b[36m2022-07-17 14:30:43,630\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 86]: loss 0.6798787659832409\u001b[0m\n",
"[\u001b[36m2022-07-17 14:32:02,798\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 86]: loss 0.7167169451713562\u001b[0m\n",
" 86% 86/100 [4:19:52<42:16, 181.19s/it][\u001b[36m2022-07-17 14:33:45,403\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 87]: loss 0.67948075064591\u001b[0m\n",
"[\u001b[36m2022-07-17 14:35:05,947\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 87]: loss 0.7173956632614136\u001b[0m\n",
" 87% 87/100 [4:22:55<39:23, 181.77s/it][\u001b[36m2022-07-17 14:36:46,788\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 88]: loss 0.6788927103791919\u001b[0m\n",
"[\u001b[36m2022-07-17 14:38:07,510\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 88]: loss 0.7166451215744019\u001b[0m\n",
" 88% 88/100 [4:25:56<36:20, 181.71s/it][\u001b[36m2022-07-17 14:39:48,722\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 89]: loss 0.6793763701404844\u001b[0m\n",
"[\u001b[36m2022-07-17 14:41:08,543\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 89]: loss 0.7168745398521423\u001b[0m\n",
" 89% 89/100 [4:28:57<33:16, 181.51s/it][\u001b[36m2022-07-17 14:42:48,179\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 90]: loss 0.6787483330283847\u001b[0m\n",
"[\u001b[36m2022-07-17 14:44:09,151\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 90]: loss 0.7163087725639343\u001b[0m\n",
" 90% 90/100 [4:31:58<30:12, 181.24s/it][\u001b[36m2022-07-17 14:45:51,117\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 91]: loss 0.6787023150495121\u001b[0m\n",
"[\u001b[36m2022-07-17 14:47:11,401\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 91]: loss 0.7174398899078369\u001b[0m\n",
" 91% 91/100 [4:35:00<27:13, 181.54s/it][\u001b[36m2022-07-17 14:48:56,786\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 92]: loss 0.6794648596218654\u001b[0m\n",
"[\u001b[36m2022-07-17 14:50:16,393\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 92]: loss 0.7168363928794861\u001b[0m\n",
" 92% 92/100 [4:38:05<24:20, 182.58s/it][\u001b[36m2022-07-17 14:51:55,513\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 93]: loss 0.6791103439671653\u001b[0m\n",
"[\u001b[36m2022-07-17 14:53:14,785\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 93]: loss 0.7177684307098389\u001b[0m\n",
" 93% 93/100 [4:41:04<21:09, 181.32s/it][\u001b[36m2022-07-17 14:54:57,347\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 94]: loss 0.6785347344619888\u001b[0m\n",
"[\u001b[36m2022-07-17 14:56:17,635\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 94]: loss 0.717737078666687\u001b[0m\n",
" 94% 94/100 [4:44:06<18:10, 181.78s/it][\u001b[36m2022-07-17 14:57:56,724\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 95]: loss 0.6784808859229088\u001b[0m\n",
"[\u001b[36m2022-07-17 14:59:16,966\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 95]: loss 0.7174811363220215\u001b[0m\n",
" 95% 95/100 [4:47:06<15:05, 181.05s/it][\u001b[36m2022-07-17 15:00:57,622\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 96]: loss 0.678301528096199\u001b[0m\n",
"[\u001b[36m2022-07-17 15:02:16,662\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 96]: loss 0.71721351146698\u001b[0m\n",
" 96% 96/100 [4:50:05<12:02, 180.64s/it][\u001b[36m2022-07-17 15:03:57,785\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 97]: loss 0.6783113202878407\u001b[0m\n",
"[\u001b[36m2022-07-17 15:05:16,956\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 97]: loss 0.7174183130264282\u001b[0m\n",
" 97% 97/100 [4:53:06<09:01, 180.54s/it][\u001b[36m2022-07-17 15:06:59,157\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 98]: loss 0.678546133850302\u001b[0m\n",
"[\u001b[36m2022-07-17 15:08:19,358\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 98]: loss 0.7174617052078247\u001b[0m\n",
" 98% 98/100 [4:56:08<06:02, 181.10s/it][\u001b[36m2022-07-17 15:10:01,103\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 99]: loss 0.6781182257192475\u001b[0m\n",
"[\u001b[36m2022-07-17 15:11:21,765\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 99]: loss 0.7176664471626282\u001b[0m\n",
" 99% 99/100 [4:59:11<03:01, 181.49s/it][\u001b[36m2022-07-17 15:13:01,424\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [train_no_dev] [Epoch 100]: loss 0.6776793939726693\u001b[0m\n",
"[\u001b[36m2022-07-17 15:14:21,686\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - [dev] [Epoch 100]: loss 0.7178601026535034\u001b[0m\n",
"[\u001b[36m2022-07-17 15:14:22,022\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/acoustic_resf0convlstm/epoch0100.pth\u001b[0m\n",
"100% 100/100 [5:02:11<00:00, 181.32s/it]\n",
"[\u001b[36m2022-07-17 15:14:23,327\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Saved checkpoint at /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/acoustic_resf0convlstm/epoch0100.pth\u001b[0m\n",
"[\u001b[36m2022-07-17 15:14:23,719\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - The best loss was 0.7120000123977661\u001b[0m\n",
"++ set +x\n"
]
}
],
"source": [
"! cd $RECIPE_ROOT && bash run.sh --stage 4 --stop-stage 4"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ELcT-dlGRTNp",
"outputId": "5b0ae53e-2698-49df-b164-4f814895b023"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"remote: Enumerating objects: 5, done.\u001b[K\n",
"remote: Counting objects: 20% (1/5)\u001b[K\rremote: Counting objects: 40% (2/5)\u001b[K\rremote: Counting objects: 60% (3/5)\u001b[K\rremote: Counting objects: 80% (4/5)\u001b[K\rremote: Counting objects: 100% (5/5)\u001b[K\rremote: Counting objects: 100% (5/5), done.\u001b[K\n",
"remote: Compressing objects: 50% (1/2)\u001b[K\rremote: Compressing objects: 100% (2/2)\u001b[K\rremote: Compressing objects: 100% (2/2), done.\u001b[K\n",
"remote: Total 5 (delta 3), reused 5 (delta 3), pack-reused 0\u001b[K\n",
"Unpacking objects: 20% (1/5) \rUnpacking objects: 40% (2/5) \rUnpacking objects: 60% (3/5) \rUnpacking objects: 80% (4/5) \rUnpacking objects: 100% (5/5) \rUnpacking objects: 100% (5/5), done.\n",
"From https://github.com/taroushirani/nnsvs\n",
" * branch dev2_local_mlpg -> FETCH_HEAD\n",
" d97b916..d156baa dev2_local_mlpg -> origin/dev2_local_mlpg\n",
"Updating d97b916..d156baa\n",
"Fast-forward\n",
" nnsvs/data/data_source.py | 1 \u001b[32m+\u001b[m\n",
" 1 file changed, 1 insertion(+)\n",
" Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
" Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
" Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n",
" Building wheel for nnsvs (PEP 517) ... \u001b[?25l\u001b[?25hdone\n"
]
}
],
"source": [
"! cd nnsvs && git checkout $RECIPE_ROOT/config.yaml\n",
"! cd nnsvs && git pull origin dev2_local_mlpg\n",
"! cd nnsvs && pip install -q . --use-feature=in-tree-build\n",
"! sed -i 's#\\~\\/data#\\/content\\/gdrive#g' $RECIPE_ROOT/config.yaml "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"background_save": true
},
"id": "sVU6ra7TDHEZ",
"outputId": "3215b565-42ba-454a-d61e-98f81ae44882"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"stage 5: Generate features from timelag/duration/acoustic models\n",
"++ nnsvs-generate model.checkpoint=exp/oniku_kurumi/timelag_mdn/latest.pth model.model_yaml=exp/oniku_kurumi/timelag_mdn/model.yaml out_scaler_path=dump/oniku_kurumi/norm/out_timelag_scaler.joblib in_dir=dump/oniku_kurumi/norm/dev/in_timelag/ out_dir=exp/oniku_kurumi/timelag_mdn/predicted/dev/latest/\n",
"/usr/local/lib/python3.7/dist-packages/resampy/interpn.py:114: NumbaWarning: The TBB threading layer requires TBB version 2019.5 or later i.e., TBB_INTERFACE_VERSION >= 11005. Found TBB_INTERFACE_VERSION = 9107. The TBB threading layer is disabled.\n",
" _resample_loop_p(x, t_out, interp_win, interp_delta, num_table, scale, y)\n",
"[\u001b[36m2022-07-17 15:14:47,147\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - verbose: 100\n",
"in_dir: dump/oniku_kurumi/norm/dev/in_timelag/\n",
"out_dir: exp/oniku_kurumi/timelag_mdn/predicted/dev/latest/\n",
"out_scaler_path: dump/oniku_kurumi/norm/out_timelag_scaler.joblib\n",
"model:\n",
" checkpoint: exp/oniku_kurumi/timelag_mdn/latest.pth\n",
" model_yaml: exp/oniku_kurumi/timelag_mdn/model.yaml\n",
"\u001b[0m\n",
"100% 3/3 [00:00<00:00, 5.46it/s]\n",
"++ set +x\n",
"++ nnsvs-generate model.checkpoint=exp/oniku_kurumi/duration_vp_mdn/latest.pth model.model_yaml=exp/oniku_kurumi/duration_vp_mdn/model.yaml out_scaler_path=dump/oniku_kurumi/norm/out_duration_scaler.joblib in_dir=dump/oniku_kurumi/norm/dev/in_duration/ out_dir=exp/oniku_kurumi/duration_vp_mdn/predicted/dev/latest/\n",
"/usr/local/lib/python3.7/dist-packages/resampy/interpn.py:114: NumbaWarning: The TBB threading layer requires TBB version 2019.5 or later i.e., TBB_INTERFACE_VERSION >= 11005. Found TBB_INTERFACE_VERSION = 9107. The TBB threading layer is disabled.\n",
" _resample_loop_p(x, t_out, interp_win, interp_delta, num_table, scale, y)\n",
"[\u001b[36m2022-07-17 15:15:04,143\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - verbose: 100\n",
"in_dir: dump/oniku_kurumi/norm/dev/in_duration/\n",
"out_dir: exp/oniku_kurumi/duration_vp_mdn/predicted/dev/latest/\n",
"out_scaler_path: dump/oniku_kurumi/norm/out_duration_scaler.joblib\n",
"model:\n",
" checkpoint: exp/oniku_kurumi/duration_vp_mdn/latest.pth\n",
" model_yaml: exp/oniku_kurumi/duration_vp_mdn/model.yaml\n",
"\u001b[0m\n",
"100% 3/3 [00:03<00:00, 1.25s/it]\n",
"++ set +x\n",
"++ nnsvs-generate model.checkpoint=exp/oniku_kurumi/acoustic_resf0convlstm/latest.pth model.model_yaml=exp/oniku_kurumi/acoustic_resf0convlstm/model.yaml out_scaler_path=dump/oniku_kurumi/norm/out_acoustic_scaler.joblib in_dir=dump/oniku_kurumi/norm/dev/in_acoustic/ out_dir=exp/oniku_kurumi/acoustic_resf0convlstm/predicted/dev/latest/\n",
"/usr/local/lib/python3.7/dist-packages/resampy/interpn.py:114: NumbaWarning: The TBB threading layer requires TBB version 2019.5 or later i.e., TBB_INTERFACE_VERSION >= 11005. Found TBB_INTERFACE_VERSION = 9107. The TBB threading layer is disabled.\n",
" _resample_loop_p(x, t_out, interp_win, interp_delta, num_table, scale, y)\n",
"[\u001b[36m2022-07-17 15:15:22,092\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - verbose: 100\n",
"in_dir: dump/oniku_kurumi/norm/dev/in_acoustic/\n",
"out_dir: exp/oniku_kurumi/acoustic_resf0convlstm/predicted/dev/latest/\n",
"out_scaler_path: dump/oniku_kurumi/norm/out_acoustic_scaler.joblib\n",
"model:\n",
" checkpoint: exp/oniku_kurumi/acoustic_resf0convlstm/latest.pth\n",
" model_yaml: exp/oniku_kurumi/acoustic_resf0convlstm/model.yaml\n",
"\u001b[0m\n",
"100% 3/3 [00:01<00:00, 2.59it/s]\n",
"++ set +x\n",
"++ nnsvs-generate model.checkpoint=exp/oniku_kurumi/timelag_mdn/latest.pth model.model_yaml=exp/oniku_kurumi/timelag_mdn/model.yaml out_scaler_path=dump/oniku_kurumi/norm/out_timelag_scaler.joblib in_dir=dump/oniku_kurumi/norm/eval/in_timelag/ out_dir=exp/oniku_kurumi/timelag_mdn/predicted/eval/latest/\n",
"/usr/local/lib/python3.7/dist-packages/resampy/interpn.py:114: NumbaWarning: The TBB threading layer requires TBB version 2019.5 or later i.e., TBB_INTERFACE_VERSION >= 11005. Found TBB_INTERFACE_VERSION = 9107. The TBB threading layer is disabled.\n",
" _resample_loop_p(x, t_out, interp_win, interp_delta, num_table, scale, y)\n",
"[\u001b[36m2022-07-17 15:15:39,087\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - verbose: 100\n",
"in_dir: dump/oniku_kurumi/norm/eval/in_timelag/\n",
"out_dir: exp/oniku_kurumi/timelag_mdn/predicted/eval/latest/\n",
"out_scaler_path: dump/oniku_kurumi/norm/out_timelag_scaler.joblib\n",
"model:\n",
" checkpoint: exp/oniku_kurumi/timelag_mdn/latest.pth\n",
" model_yaml: exp/oniku_kurumi/timelag_mdn/model.yaml\n",
"\u001b[0m\n",
"100% 6/6 [00:00<00:00, 16.24it/s]\n",
"++ set +x\n",
"++ nnsvs-generate model.checkpoint=exp/oniku_kurumi/duration_vp_mdn/latest.pth model.model_yaml=exp/oniku_kurumi/duration_vp_mdn/model.yaml out_scaler_path=dump/oniku_kurumi/norm/out_duration_scaler.joblib in_dir=dump/oniku_kurumi/norm/eval/in_duration/ out_dir=exp/oniku_kurumi/duration_vp_mdn/predicted/eval/latest/\n",
"/usr/local/lib/python3.7/dist-packages/resampy/interpn.py:114: NumbaWarning: The TBB threading layer requires TBB version 2019.5 or later i.e., TBB_INTERFACE_VERSION >= 11005. Found TBB_INTERFACE_VERSION = 9107. The TBB threading layer is disabled.\n",
" _resample_loop_p(x, t_out, interp_win, interp_delta, num_table, scale, y)\n",
"[\u001b[36m2022-07-17 15:15:53,411\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - verbose: 100\n",
"in_dir: dump/oniku_kurumi/norm/eval/in_duration/\n",
"out_dir: exp/oniku_kurumi/duration_vp_mdn/predicted/eval/latest/\n",
"out_scaler_path: dump/oniku_kurumi/norm/out_duration_scaler.joblib\n",
"model:\n",
" checkpoint: exp/oniku_kurumi/duration_vp_mdn/latest.pth\n",
" model_yaml: exp/oniku_kurumi/duration_vp_mdn/model.yaml\n",
"\u001b[0m\n",
"100% 6/6 [00:00<00:00, 6.05it/s]\n",
"++ set +x\n",
"++ nnsvs-generate model.checkpoint=exp/oniku_kurumi/acoustic_resf0convlstm/latest.pth model.model_yaml=exp/oniku_kurumi/acoustic_resf0convlstm/model.yaml out_scaler_path=dump/oniku_kurumi/norm/out_acoustic_scaler.joblib in_dir=dump/oniku_kurumi/norm/eval/in_acoustic/ out_dir=exp/oniku_kurumi/acoustic_resf0convlstm/predicted/eval/latest/\n",
"/usr/local/lib/python3.7/dist-packages/resampy/interpn.py:114: NumbaWarning: The TBB threading layer requires TBB version 2019.5 or later i.e., TBB_INTERFACE_VERSION >= 11005. Found TBB_INTERFACE_VERSION = 9107. The TBB threading layer is disabled.\n",
" _resample_loop_p(x, t_out, interp_win, interp_delta, num_table, scale, y)\n",
"[\u001b[36m2022-07-17 15:16:08,585\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - verbose: 100\n",
"in_dir: dump/oniku_kurumi/norm/eval/in_acoustic/\n",
"out_dir: exp/oniku_kurumi/acoustic_resf0convlstm/predicted/eval/latest/\n",
"out_scaler_path: dump/oniku_kurumi/norm/out_acoustic_scaler.joblib\n",
"model:\n",
" checkpoint: exp/oniku_kurumi/acoustic_resf0convlstm/latest.pth\n",
" model_yaml: exp/oniku_kurumi/acoustic_resf0convlstm/model.yaml\n",
"\u001b[0m\n",
"100% 6/6 [00:01<00:00, 4.06it/s]\n",
"++ set +x\n"
]
}
],
"source": [
"! cd $RECIPE_ROOT && bash run.sh --stage 5 --stop-stage 5"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"background_save": true
},
"id": "_GuQN0zEGXjA",
"outputId": "e4cfbb41-5514-45e5-bf4f-947c2446e48b"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"stage 6: Synthesis waveforms\n",
"++ nnsvs-synthesis sample_rate=48000 question_path=../../_common/hed/jp_dev.hed timelag=defaults duration=defaults acoustic=defaults acoustic.relative_f0=false timelag.checkpoint=exp/oniku_kurumi/timelag_mdn/latest.pth timelag.in_scaler_path=dump/oniku_kurumi/norm/in_timelag_scaler.joblib timelag.out_scaler_path=dump/oniku_kurumi/norm/out_timelag_scaler.joblib timelag.model_yaml=exp/oniku_kurumi/timelag_mdn/model.yaml duration.checkpoint=exp/oniku_kurumi/duration_vp_mdn/latest.pth duration.in_scaler_path=dump/oniku_kurumi/norm/in_duration_scaler.joblib duration.out_scaler_path=dump/oniku_kurumi/norm/out_duration_scaler.joblib duration.model_yaml=exp/oniku_kurumi/duration_vp_mdn/model.yaml acoustic.checkpoint=exp/oniku_kurumi/acoustic_resf0convlstm/latest.pth acoustic.in_scaler_path=dump/oniku_kurumi/norm/in_acoustic_scaler.joblib acoustic.out_scaler_path=dump/oniku_kurumi/norm/out_acoustic_scaler.joblib acoustic.model_yaml=exp/oniku_kurumi/acoustic_resf0convlstm/model.yaml utt_list=./data/list/dev.list in_dir=data/acoustic/label_phone_score/ out_dir=exp/oniku_kurumi/synthesis_timelag_mdn_duration_vp_mdn_acoustic_resf0convlstm/dev/latest/label_phone_score ground_truth_duration=false\n",
"/usr/local/lib/python3.7/dist-packages/resampy/interpn.py:114: NumbaWarning: The TBB threading layer requires TBB version 2019.5 or later i.e., TBB_INTERFACE_VERSION >= 11005. Found TBB_INTERFACE_VERSION = 9107. The TBB threading layer is disabled.\n",
" _resample_loop_p(x, t_out, interp_win, interp_delta, num_table, scale, y)\n",
"[\u001b[36m2022-07-17 15:16:24,305\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - timelag:\n",
" question_path: null\n",
" checkpoint: exp/oniku_kurumi/timelag_mdn/latest.pth\n",
" in_scaler_path: dump/oniku_kurumi/norm/in_timelag_scaler.joblib\n",
" out_scaler_path: dump/oniku_kurumi/norm/out_timelag_scaler.joblib\n",
" model_yaml: exp/oniku_kurumi/timelag_mdn/model.yaml\n",
" allowed_range:\n",
" - -20\n",
" - 20\n",
" allowed_range_rest:\n",
" - -40\n",
" - 40\n",
" force_clip_input_features: true\n",
"duration:\n",
" question_path: null\n",
" checkpoint: exp/oniku_kurumi/duration_vp_mdn/latest.pth\n",
" in_scaler_path: dump/oniku_kurumi/norm/in_duration_scaler.joblib\n",
" out_scaler_path: dump/oniku_kurumi/norm/out_duration_scaler.joblib\n",
" model_yaml: exp/oniku_kurumi/duration_vp_mdn/model.yaml\n",
" force_clip_input_features: true\n",
"acoustic:\n",
" question_path: null\n",
" checkpoint: exp/oniku_kurumi/acoustic_resf0convlstm/latest.pth\n",
" in_scaler_path: dump/oniku_kurumi/norm/in_acoustic_scaler.joblib\n",
" out_scaler_path: dump/oniku_kurumi/norm/out_acoustic_scaler.joblib\n",
" model_yaml: exp/oniku_kurumi/acoustic_resf0convlstm/model.yaml\n",
" subphone_features: coarse_coding\n",
" relative_f0: false\n",
" post_filter: true\n",
" force_clip_input_features: true\n",
"verbose: 100\n",
"device: cuda\n",
"utt_list: ./data/list/dev.list\n",
"in_dir: data/acoustic/label_phone_score/\n",
"out_dir: exp/oniku_kurumi/synthesis_timelag_mdn_duration_vp_mdn_acoustic_resf0convlstm/dev/latest/label_phone_score\n",
"label_path: null\n",
"out_wav_path: null\n",
"sample_rate: 48000\n",
"frame_period: 5\n",
"question_path: ../../_common/hed/jp_dev.hed\n",
"log_f0_conditioning: true\n",
"ground_truth_duration: false\n",
"vibrato_scale: 1.0\n",
"gain_normalize: false\n",
"stats_dir: null\n",
"model_dir: null\n",
"model_checkpoint: latest.pth\n",
"\u001b[0m\n",
"[\u001b[36m2022-07-17 15:16:26,695\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Processes 3 utterances...\u001b[0m\n",
"100% 3/3 [00:12<00:00, 4.10s/it]\n",
"++ set +x\n",
"++ nnsvs-synthesis sample_rate=48000 question_path=../../_common/hed/jp_dev.hed timelag=defaults duration=defaults acoustic=defaults acoustic.relative_f0=false timelag.checkpoint=exp/oniku_kurumi/timelag_mdn/latest.pth timelag.in_scaler_path=dump/oniku_kurumi/norm/in_timelag_scaler.joblib timelag.out_scaler_path=dump/oniku_kurumi/norm/out_timelag_scaler.joblib timelag.model_yaml=exp/oniku_kurumi/timelag_mdn/model.yaml duration.checkpoint=exp/oniku_kurumi/duration_vp_mdn/latest.pth duration.in_scaler_path=dump/oniku_kurumi/norm/in_duration_scaler.joblib duration.out_scaler_path=dump/oniku_kurumi/norm/out_duration_scaler.joblib duration.model_yaml=exp/oniku_kurumi/duration_vp_mdn/model.yaml acoustic.checkpoint=exp/oniku_kurumi/acoustic_resf0convlstm/latest.pth acoustic.in_scaler_path=dump/oniku_kurumi/norm/in_acoustic_scaler.joblib acoustic.out_scaler_path=dump/oniku_kurumi/norm/out_acoustic_scaler.joblib acoustic.model_yaml=exp/oniku_kurumi/acoustic_resf0convlstm/model.yaml utt_list=./data/list/dev.list in_dir=data/acoustic/label_phone_align/ out_dir=exp/oniku_kurumi/synthesis_timelag_mdn_duration_vp_mdn_acoustic_resf0convlstm/dev/latest/label_phone_align ground_truth_duration=true\n",
"/usr/local/lib/python3.7/dist-packages/resampy/interpn.py:114: NumbaWarning: The TBB threading layer requires TBB version 2019.5 or later i.e., TBB_INTERFACE_VERSION >= 11005. Found TBB_INTERFACE_VERSION = 9107. The TBB threading layer is disabled.\n",
" _resample_loop_p(x, t_out, interp_win, interp_delta, num_table, scale, y)\n",
"[\u001b[36m2022-07-17 15:16:50,546\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - timelag:\n",
" question_path: null\n",
" checkpoint: exp/oniku_kurumi/timelag_mdn/latest.pth\n",
" in_scaler_path: dump/oniku_kurumi/norm/in_timelag_scaler.joblib\n",
" out_scaler_path: dump/oniku_kurumi/norm/out_timelag_scaler.joblib\n",
" model_yaml: exp/oniku_kurumi/timelag_mdn/model.yaml\n",
" allowed_range:\n",
" - -20\n",
" - 20\n",
" allowed_range_rest:\n",
" - -40\n",
" - 40\n",
" force_clip_input_features: true\n",
"duration:\n",
" question_path: null\n",
" checkpoint: exp/oniku_kurumi/duration_vp_mdn/latest.pth\n",
" in_scaler_path: dump/oniku_kurumi/norm/in_duration_scaler.joblib\n",
" out_scaler_path: dump/oniku_kurumi/norm/out_duration_scaler.joblib\n",
" model_yaml: exp/oniku_kurumi/duration_vp_mdn/model.yaml\n",
" force_clip_input_features: true\n",
"acoustic:\n",
" question_path: null\n",
" checkpoint: exp/oniku_kurumi/acoustic_resf0convlstm/latest.pth\n",
" in_scaler_path: dump/oniku_kurumi/norm/in_acoustic_scaler.joblib\n",
" out_scaler_path: dump/oniku_kurumi/norm/out_acoustic_scaler.joblib\n",
" model_yaml: exp/oniku_kurumi/acoustic_resf0convlstm/model.yaml\n",
" subphone_features: coarse_coding\n",
" relative_f0: false\n",
" post_filter: true\n",
" force_clip_input_features: true\n",
"verbose: 100\n",
"device: cuda\n",
"utt_list: ./data/list/dev.list\n",
"in_dir: data/acoustic/label_phone_align/\n",
"out_dir: exp/oniku_kurumi/synthesis_timelag_mdn_duration_vp_mdn_acoustic_resf0convlstm/dev/latest/label_phone_align\n",
"label_path: null\n",
"out_wav_path: null\n",
"sample_rate: 48000\n",
"frame_period: 5\n",
"question_path: ../../_common/hed/jp_dev.hed\n",
"log_f0_conditioning: true\n",
"ground_truth_duration: true\n",
"vibrato_scale: 1.0\n",
"gain_normalize: false\n",
"stats_dir: null\n",
"model_dir: null\n",
"model_checkpoint: latest.pth\n",
"\u001b[0m\n",
"[\u001b[36m2022-07-17 15:16:52,934\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Processes 3 utterances...\u001b[0m\n",
"100% 3/3 [00:12<00:00, 4.10s/it]\n",
"++ set +x\n",
"++ nnsvs-synthesis sample_rate=48000 question_path=../../_common/hed/jp_dev.hed timelag=defaults duration=defaults acoustic=defaults acoustic.relative_f0=false timelag.checkpoint=exp/oniku_kurumi/timelag_mdn/latest.pth timelag.in_scaler_path=dump/oniku_kurumi/norm/in_timelag_scaler.joblib timelag.out_scaler_path=dump/oniku_kurumi/norm/out_timelag_scaler.joblib timelag.model_yaml=exp/oniku_kurumi/timelag_mdn/model.yaml duration.checkpoint=exp/oniku_kurumi/duration_vp_mdn/latest.pth duration.in_scaler_path=dump/oniku_kurumi/norm/in_duration_scaler.joblib duration.out_scaler_path=dump/oniku_kurumi/norm/out_duration_scaler.joblib duration.model_yaml=exp/oniku_kurumi/duration_vp_mdn/model.yaml acoustic.checkpoint=exp/oniku_kurumi/acoustic_resf0convlstm/latest.pth acoustic.in_scaler_path=dump/oniku_kurumi/norm/in_acoustic_scaler.joblib acoustic.out_scaler_path=dump/oniku_kurumi/norm/out_acoustic_scaler.joblib acoustic.model_yaml=exp/oniku_kurumi/acoustic_resf0convlstm/model.yaml utt_list=./data/list/eval.list in_dir=data/acoustic/label_phone_score/ out_dir=exp/oniku_kurumi/synthesis_timelag_mdn_duration_vp_mdn_acoustic_resf0convlstm/eval/latest/label_phone_score ground_truth_duration=false\n",
"/usr/local/lib/python3.7/dist-packages/resampy/interpn.py:114: NumbaWarning: The TBB threading layer requires TBB version 2019.5 or later i.e., TBB_INTERFACE_VERSION >= 11005. Found TBB_INTERFACE_VERSION = 9107. The TBB threading layer is disabled.\n",
" _resample_loop_p(x, t_out, interp_win, interp_delta, num_table, scale, y)\n",
"[\u001b[36m2022-07-17 15:17:16,877\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - timelag:\n",
" question_path: null\n",
" checkpoint: exp/oniku_kurumi/timelag_mdn/latest.pth\n",
" in_scaler_path: dump/oniku_kurumi/norm/in_timelag_scaler.joblib\n",
" out_scaler_path: dump/oniku_kurumi/norm/out_timelag_scaler.joblib\n",
" model_yaml: exp/oniku_kurumi/timelag_mdn/model.yaml\n",
" allowed_range:\n",
" - -20\n",
" - 20\n",
" allowed_range_rest:\n",
" - -40\n",
" - 40\n",
" force_clip_input_features: true\n",
"duration:\n",
" question_path: null\n",
" checkpoint: exp/oniku_kurumi/duration_vp_mdn/latest.pth\n",
" in_scaler_path: dump/oniku_kurumi/norm/in_duration_scaler.joblib\n",
" out_scaler_path: dump/oniku_kurumi/norm/out_duration_scaler.joblib\n",
" model_yaml: exp/oniku_kurumi/duration_vp_mdn/model.yaml\n",
" force_clip_input_features: true\n",
"acoustic:\n",
" question_path: null\n",
" checkpoint: exp/oniku_kurumi/acoustic_resf0convlstm/latest.pth\n",
" in_scaler_path: dump/oniku_kurumi/norm/in_acoustic_scaler.joblib\n",
" out_scaler_path: dump/oniku_kurumi/norm/out_acoustic_scaler.joblib\n",
" model_yaml: exp/oniku_kurumi/acoustic_resf0convlstm/model.yaml\n",
" subphone_features: coarse_coding\n",
" relative_f0: false\n",
" post_filter: true\n",
" force_clip_input_features: true\n",
"verbose: 100\n",
"device: cuda\n",
"utt_list: ./data/list/eval.list\n",
"in_dir: data/acoustic/label_phone_score/\n",
"out_dir: exp/oniku_kurumi/synthesis_timelag_mdn_duration_vp_mdn_acoustic_resf0convlstm/eval/latest/label_phone_score\n",
"label_path: null\n",
"out_wav_path: null\n",
"sample_rate: 48000\n",
"frame_period: 5\n",
"question_path: ../../_common/hed/jp_dev.hed\n",
"log_f0_conditioning: true\n",
"ground_truth_duration: false\n",
"vibrato_scale: 1.0\n",
"gain_normalize: false\n",
"stats_dir: null\n",
"model_dir: null\n",
"model_checkpoint: latest.pth\n",
"\u001b[0m\n",
"[\u001b[36m2022-07-17 15:17:19,317\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Processes 6 utterances...\u001b[0m\n",
"100% 6/6 [00:23<00:00, 3.88s/it]\n",
"++ set +x\n",
"++ nnsvs-synthesis sample_rate=48000 question_path=../../_common/hed/jp_dev.hed timelag=defaults duration=defaults acoustic=defaults acoustic.relative_f0=false timelag.checkpoint=exp/oniku_kurumi/timelag_mdn/latest.pth timelag.in_scaler_path=dump/oniku_kurumi/norm/in_timelag_scaler.joblib timelag.out_scaler_path=dump/oniku_kurumi/norm/out_timelag_scaler.joblib timelag.model_yaml=exp/oniku_kurumi/timelag_mdn/model.yaml duration.checkpoint=exp/oniku_kurumi/duration_vp_mdn/latest.pth duration.in_scaler_path=dump/oniku_kurumi/norm/in_duration_scaler.joblib duration.out_scaler_path=dump/oniku_kurumi/norm/out_duration_scaler.joblib duration.model_yaml=exp/oniku_kurumi/duration_vp_mdn/model.yaml acoustic.checkpoint=exp/oniku_kurumi/acoustic_resf0convlstm/latest.pth acoustic.in_scaler_path=dump/oniku_kurumi/norm/in_acoustic_scaler.joblib acoustic.out_scaler_path=dump/oniku_kurumi/norm/out_acoustic_scaler.joblib acoustic.model_yaml=exp/oniku_kurumi/acoustic_resf0convlstm/model.yaml utt_list=./data/list/eval.list in_dir=data/acoustic/label_phone_align/ out_dir=exp/oniku_kurumi/synthesis_timelag_mdn_duration_vp_mdn_acoustic_resf0convlstm/eval/latest/label_phone_align ground_truth_duration=true\n",
"/usr/local/lib/python3.7/dist-packages/resampy/interpn.py:114: NumbaWarning: The TBB threading layer requires TBB version 2019.5 or later i.e., TBB_INTERFACE_VERSION >= 11005. Found TBB_INTERFACE_VERSION = 9107. The TBB threading layer is disabled.\n",
" _resample_loop_p(x, t_out, interp_win, interp_delta, num_table, scale, y)\n",
"[\u001b[36m2022-07-17 15:17:54,285\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - timelag:\n",
" question_path: null\n",
" checkpoint: exp/oniku_kurumi/timelag_mdn/latest.pth\n",
" in_scaler_path: dump/oniku_kurumi/norm/in_timelag_scaler.joblib\n",
" out_scaler_path: dump/oniku_kurumi/norm/out_timelag_scaler.joblib\n",
" model_yaml: exp/oniku_kurumi/timelag_mdn/model.yaml\n",
" allowed_range:\n",
" - -20\n",
" - 20\n",
" allowed_range_rest:\n",
" - -40\n",
" - 40\n",
" force_clip_input_features: true\n",
"duration:\n",
" question_path: null\n",
" checkpoint: exp/oniku_kurumi/duration_vp_mdn/latest.pth\n",
" in_scaler_path: dump/oniku_kurumi/norm/in_duration_scaler.joblib\n",
" out_scaler_path: dump/oniku_kurumi/norm/out_duration_scaler.joblib\n",
" model_yaml: exp/oniku_kurumi/duration_vp_mdn/model.yaml\n",
" force_clip_input_features: true\n",
"acoustic:\n",
" question_path: null\n",
" checkpoint: exp/oniku_kurumi/acoustic_resf0convlstm/latest.pth\n",
" in_scaler_path: dump/oniku_kurumi/norm/in_acoustic_scaler.joblib\n",
" out_scaler_path: dump/oniku_kurumi/norm/out_acoustic_scaler.joblib\n",
" model_yaml: exp/oniku_kurumi/acoustic_resf0convlstm/model.yaml\n",
" subphone_features: coarse_coding\n",
" relative_f0: false\n",
" post_filter: true\n",
" force_clip_input_features: true\n",
"verbose: 100\n",
"device: cuda\n",
"utt_list: ./data/list/eval.list\n",
"in_dir: data/acoustic/label_phone_align/\n",
"out_dir: exp/oniku_kurumi/synthesis_timelag_mdn_duration_vp_mdn_acoustic_resf0convlstm/eval/latest/label_phone_align\n",
"label_path: null\n",
"out_wav_path: null\n",
"sample_rate: 48000\n",
"frame_period: 5\n",
"question_path: ../../_common/hed/jp_dev.hed\n",
"log_f0_conditioning: true\n",
"ground_truth_duration: true\n",
"vibrato_scale: 1.0\n",
"gain_normalize: false\n",
"stats_dir: null\n",
"model_dir: null\n",
"model_checkpoint: latest.pth\n",
"\u001b[0m\n",
"[\u001b[36m2022-07-17 15:17:56,687\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"
]
}
],
"source": [
"! cd $RECIPE_ROOT && bash run.sh --stage 6 --stop-stage 6"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"background_save": true
},
"id": "YQMmSBWEBZ9I",
"outputId": "5a236610-b1fd-43f8-c2e3-ca6303263d71"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tar: Removing leading `/' from member names\n"
]
}
],
"source": [
"! tar zcf /content/gdrive/nnsvs_dev20220717_oniku_kurumi_utagoe_db_dev_latest_trained_data_20220717.tgz $RECIPE_ROOT/dump $RECIPE_ROOT/data $RECIPE_ROOT/exp $RECIPE_ROOT/outputs"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "WX2_fBwPATwg"
},
"outputs": [],
"source": [
"! tar zxf /content/gdrive/nnsvs_dev20220717_oniku_kurumi_utagoe_db_dev_latest_trained_data_20220717.tgz -C /"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"background_save": true
},
"id": "mhbd7HBHpeG6",
"outputId": "ab8ed212-0683-4645-f5c3-e6b1a4094643"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"<re.Match object; span=(0, 61), match='[2022-05-11 14:43:07,288][nnsvs][INFO] - ResSkipF>\n",
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"{'model_name': 'ResSkipF0FFConvLSTM', 'training_type': 'acoustic_resf0convlstm', 'log': dataset ... loss\n",
"0 train_no_dev ... 0.901342\n",
"1 dev ... 0.882815\n",
"2 train_no_dev ... 0.825112\n",
"3 dev ... 0.873575\n",
"4 train_no_dev ... 0.815909\n",
".. ... ... ...\n",
"95 dev ... 0.813792\n",
"96 train_no_dev ... 0.602489\n",
"97 dev ... 0.822459\n",
"98 train_no_dev ... 0.598885\n",
"99 dev ... 0.825190\n",
"\n",
"[100 rows x 3 columns]}\n",
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"<re.Match object; span=(0, 45), match='[2022-05-11 14:40:05,602][nnsvs][INFO] - MDN('>\n",
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"{'model_name': 'MDN', 'training_type': 'timelag_mdn', 'log': dataset ... loss\n",
"0 train_no_dev ... 1.305376\n",
"1 dev ... 1.386335\n",
"2 train_no_dev ... 1.131707\n",
"3 dev ... 1.266746\n",
"4 train_no_dev ... 1.091684\n",
".. ... ... ...\n",
"95 dev ... 1.313723\n",
"96 train_no_dev ... 0.932576\n",
"97 dev ... 1.281018\n",
"98 train_no_dev ... 0.931488\n",
"99 dev ... 1.308918\n",
"\n",
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"{'model_name': 'MDN', 'training_type': 'duration_mdn', 'log': dataset ... loss\n",
"0 train_no_dev ... 0.498712\n",
"1 dev ... 0.200057\n",
"2 train_no_dev ... -0.084001\n",
"3 dev ... -0.417816\n",
"4 train_no_dev ... -0.327031\n",
".. ... ... ...\n",
"95 dev ... -1.022281\n",
"96 train_no_dev ... -1.707457\n",
"97 dev ... -0.945342\n",
"98 train_no_dev ... -1.708459\n",
"99 dev ... -0.831344\n",
"\n",
"[100 rows x 3 columns]}\n"
]
}
],
"source": [
"! cd $RECIPE_ROOT && python /content/nnsvs/utils/make_graph.py ."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"background_save": true
},
"id": "9fxtGe19NSRD"
},
"outputs": [],
"source": [
"import pysinsy\n",
"from os.path import basename, join, splitext\n",
"from glob import glob\n",
"sample_dir=\"/content/gdrive/sample_score_20220326\"\n",
"\n",
"song_list_file=join(sample_dir, \"song_list.txt\")\n",
"\n",
"sinsy = pysinsy.sinsy.Sinsy()\n",
"# Set language to Japanese\n",
"assert sinsy.setLanguages(\"j\", join(RECIPE_ROOT, \"../../_common/no2/dic\"))\n",
"\n",
"song_list = []\n",
"musicxml_files = glob(join(sample_dir, \"*hello*.*xml\"))\n",
"for musicxml_file in musicxml_files:\n",
" assert sinsy.loadScoreFromMusicXML(musicxml_file)\n",
" is_mono = False\n",
" labels = sinsy.createLabelData(is_mono, 1, 1).getData()\n",
" song_name = splitext(basename(musicxml_file))[0]\n",
" song_list.append(song_name)\n",
" lab_file_path = join(sample_dir, song_name + \".lab\")\n",
" with open(lab_file_path, \"w\") as f:\n",
" f.write(\"\\n\".join(labels))\n",
"\n",
" sinsy.clearScore()\n",
"\n",
"with open(song_list_file, \"w\") as f:\n",
" f.write(\"\\n\".join(song_list))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"background_save": true
},
"id": "0fQizP4eOe3b",
"outputId": "408477df-c730-4232-a658-9256ea710d32"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.7/dist-packages/resampy/interpn.py:114: NumbaWarning: The TBB threading layer requires TBB version 2019.5 or later i.e., TBB_INTERFACE_VERSION >= 11005. Found TBB_INTERFACE_VERSION = 9107. The TBB threading layer is disabled.\n",
" _resample_loop_p(x, t_out, interp_win, interp_delta, num_table, scale, y)\n",
"[\u001b[36m2022-07-17 15:25:58,464\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - timelag:\n",
" question_path: null\n",
" checkpoint: /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/timelag_mdn/best_loss.pth\n",
" in_scaler_path: /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/dump/oniku_kurumi/norm/in_timelag_scaler.joblib\n",
" out_scaler_path: /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/dump/oniku_kurumi/norm/out_timelag_scaler.joblib\n",
" model_yaml: /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/timelag_mdn/model.yaml\n",
" allowed_range:\n",
" - -20\n",
" - 20\n",
" allowed_range_rest:\n",
" - -40\n",
" - 40\n",
" force_clip_input_features: true\n",
"duration:\n",
" question_path: null\n",
" checkpoint: /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/duration_vp_mdn/best_loss.pth\n",
" in_scaler_path: /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/dump/oniku_kurumi/norm/in_duration_scaler.joblib\n",
" out_scaler_path: /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/dump/oniku_kurumi/norm/out_duration_scaler.joblib\n",
" model_yaml: /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/duration_vp_mdn/model.yaml\n",
" force_clip_input_features: true\n",
"acoustic:\n",
" question_path: null\n",
" checkpoint: /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/acoustic_resf0convlstm/best_loss.pth\n",
" in_scaler_path: /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/dump/oniku_kurumi/norm/in_acoustic_scaler.joblib\n",
" out_scaler_path: /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/dump/oniku_kurumi/norm/out_acoustic_scaler.joblib\n",
" model_yaml: /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/acoustic_resf0convlstm/model.yaml\n",
" subphone_features: coarse_coding\n",
" relative_f0: false\n",
" post_filter: true\n",
" force_clip_input_features: true\n",
"verbose: 100\n",
"device: cuda\n",
"utt_list: /content/gdrive/sample_score_20220326/song_list.txt\n",
"in_dir: /content/gdrive/sample_score_20220326\n",
"out_dir: /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/exp/oniku_kurumi/synthesis/sample\n",
"label_path: null\n",
"out_wav_path: null\n",
"sample_rate: 48000\n",
"frame_period: 5\n",
"question_path: /content/nnsvs/recipes/oniku_kurumi_utagoe_db/dev-latest/../../_common/hed/jp_dev.hed\n",
"log_f0_conditioning: true\n",
"ground_truth_duration: false\n",
"vibrato_scale: 1.0\n",
"gain_normalize: false\n",
"stats_dir: null\n",
"model_dir: null\n",
"model_checkpoint: latest.pth\n",
"\u001b[0m\n",
"[\u001b[36m2022-07-17 15:26:07,299\u001b[0m][\u001b[34mnnsvs\u001b[0m][\u001b[32mINFO\u001b[0m] - Processes 0 utterances...\u001b[0m\n",
"0it [00:00, ?it/s]\n"
]
}
],
"source": [
"import yaml\n",
"with open(join(RECIPE_ROOT, 'config.yaml'), 'r') as yml:\n",
" config = yaml.load(yml, Loader=yaml.FullLoader)\n",
"\n",
"exp_dir=join(RECIPE_ROOT, \"exp\", config[\"spk\"])\n",
"dump_dir=join(RECIPE_ROOT, \"dump\")\n",
"dump_org_dir=join(dump_dir, config[\"spk\"], \"org\")\n",
"dump_norm_dir=join(dump_dir, config[\"spk\"], \"norm\")\n",
"out_dir=join(exp_dir, \"synthesis/sample\")\n",
"question_path=join(RECIPE_ROOT, config[\"question_path\"])\n",
"timelag_model=config[\"timelag_model\"]\n",
"duration_model=config[\"duration_model\"]\n",
"acoustic_model=config[\"acoustic_model\"]\n",
"\n",
"! nnsvs-synthesis question_path=$question_path \\\n",
"timelag=defaults duration=defaults acoustic=defaults \\\n",
"acoustic.relative_f0=false \\\n",
"timelag.checkpoint=$exp_dir/$timelag_model/best_loss.pth \\\n",
"timelag.in_scaler_path=$dump_norm_dir/in_timelag_scaler.joblib \\\n",
"timelag.out_scaler_path=$dump_norm_dir/out_timelag_scaler.joblib \\\n",
"timelag.model_yaml=$exp_dir/$timelag_model/model.yaml \\\n",
"duration.checkpoint=$exp_dir/$duration_model/best_loss.pth \\\n",
"duration.in_scaler_path=$dump_norm_dir/in_duration_scaler.joblib \\\n",
"duration.out_scaler_path=$dump_norm_dir/out_duration_scaler.joblib \\\n",
"duration.model_yaml=$exp_dir/$duration_model/model.yaml \\\n",
"acoustic.checkpoint=$exp_dir/$acoustic_model/best_loss.pth \\\n",
"acoustic.in_scaler_path=$dump_norm_dir/in_acoustic_scaler.joblib \\\n",
"acoustic.out_scaler_path=$dump_norm_dir/out_acoustic_scaler.joblib \\\n",
"acoustic.model_yaml=$exp_dir/$acoustic_model/model.yaml \\\n",
"utt_list=$song_list_file \\\n",
"in_dir=$sample_dir \\\n",
"out_dir=$out_dir \\\n",
"ground_truth_duration=false"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "NWBiFS-Fg7CH"
},
"outputs": [],
"source": [
"DESTDIR=\"/content/gdrive/nnsvs_dev20220717_oniku_kurumi_utagoe_db_dev_latest_trained_data_20220717\"\n",
"! mkdir -p $DESTDIR"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "9w05y0e24_54"
},
"outputs": [],
"source": [
"! cp $out_dir/*.wav $DESTDIR"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "IEj_agixAfax"
},
"outputs": [],
"source": [
"! cp $RECIPE_ROOT/*.png $DESTDIR"
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"collapsed_sections": [],
"name": "nnsvs_dev20220717_oniku_kurumi_utagoe_db_dev_latest_training_20220717",
"provenance": [],
"mount_file_id": "12HbEBcuG8pRRDY0w9QECR16MVyqlitsN",
"authorship_tag": "ABX9TyPrMNZ+GhpEdXKI3V+B4xeR",
"include_colab_link": true
},
"gpuClass": "standard",
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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