{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "tts_an4.ipynb", "version": "0.3.2", "provenance": [], "collapsed_sections": [] }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "accelerator": "GPU" }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "wA10eXyHIv8X", "colab_type": "text" }, "source": [ "# Text-to-Speech (Recipe)\n", "\n", "This is the example notebook of how-to-run the ESPnet TTS recipe using an4 dataset. \n", "You can understand the overview of TTS recipe through this notebook within an hour!\n", "\n", "See also:\n", "- Documentaion: [https://espnet.github.io/espnet](https://espnet.github.io/espnet)\n", "- Github: [https://github.com/espnet](https://github.com/espnet)\n", "\n", "Author: [Tomoki Hayashi](https://github.com/kan-bayashi)\n", "\n", "Last update: 2019/07/25" ] }, { "cell_type": "markdown", "metadata": { "id": "8xWr9FADn0Rf", "colab_type": "text" }, "source": [ "## Setup envrionment\n", "\n", "First, let's setup the environmet to run the recipe. \n", "It take around 10 minues. Please keep waiting for a while.\n" ] }, { "cell_type": "code", "metadata": { "id": "J6k0hPgzjxA5", "colab_type": "code", "collapsed": true, "colab": {} }, "source": [ "# OS setup\n", "!sudo apt-get install bc tree\n", "!cat /etc/os-release\n", "\n", "# espnet setup\n", "!git clone https://github.com/espnet/espnet\n", "!cd espnet; pip install -e .\n", "\n", "# warp ctc setup\n", "!git clone https://github.com/espnet/warp-ctc -b pytorch-1.1\n", "!cd warp-ctc && mkdir build && cd build && cmake .. && make -j\n", "!cd warp-ctc/pytorch_binding && python setup.py install \n", "\n", "# kaldi setup\n", "!cd /content/espnet/tools; git clone https://github.com/kaldi-asr/kaldi\n", "!echo \"\" > ./espnet/tools/kaldi/tools/extras/check_dependencies.sh # ignore check\n", "!chmod +x ./espnet/tools/kaldi/tools/extras/check_dependencies.sh\n", "!cd ./espnet/tools/kaldi/tools; make sph2pipe sclite\n", "!rm -rf espnet/tools/kaldi/tools/python\n", "!wget https://18-198329952-gh.circle-artifacts.com/0/home/circleci/repo/ubuntu16-featbin.tar.gz\n", "!tar -xf ./ubuntu16-featbin.tar.gz # take a few minutes\n", "!cp featbin/* espnet/tools/kaldi/src/featbin/\n", "\n", "# make dummy activate\n", "!mkdir -p espnet/tools/venv/bin\n", "!touch espnet/tools/venv/bin/activate" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "QhW6ey37Kj7y", "colab_type": "text" }, "source": [ "## Run the recipe\n", "\n", "Now ready to run the recipe! \n", "We use the most simplest recipe `egs/an4/tts1` as an example. \n", "> Unfortunately, `egs/an4/tts1` is too small to generate reasonable speech. \n", "> But you can understand the flow or TTS recipe through this recipe since all of the TTS recipes has the exactly same flow.\n" ] }, { "cell_type": "code", "metadata": { "id": "z2G_kA8VpM8X", "colab_type": "code", "colab": {} }, "source": [ "# Let's go to an4 recipe!\n", "import os\n", "os.chdir(\"/content/espnet/egs/an4/tts1\")" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "kK-qFOFupi-I", "colab_type": "text" }, "source": [ "Before running the recipe, let us check the recipe structure." ] }, { "cell_type": "code", "metadata": { "id": "X6T2rNlSpVMV", "colab_type": "code", "outputId": "2cc5b211-b004-43b8-afae-d15672464f90", "colab": { "base_uri": "https://localhost:8080/", "height": 295 } }, "source": [ "!tree -L 1" ], "execution_count": 3, "outputs": [ { "output_type": "stream", "text": [ ".\n", "├── cmd.sh\n", "├── conf\n", "├── data\n", "├── downloads\n", "├── dump\n", "├── exp\n", "├── fbank\n", "├── local -> ../asr1/local\n", "├── path.sh\n", "├── run.sh\n", "├── steps -> ../../../tools/kaldi/egs/wsj/s5/steps\n", "├── tensorboard\n", "└── utils -> ../../../tools/kaldi/egs/wsj/s5/utils\n", "\n", "10 directories, 3 files\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "aGX_y4RMpqIK", "colab_type": "text" }, "source": [ "Each recipe has the same structure and files. \n", "\n", "- **run.sh**: Main script of the recipe. Once you run this script, all of the processing will be conducted from data download, preparation, feature extraction, training, and decoding.\n", "- **cmd.sh**: Command configuration source file about how-to-run each processing. You can modify this script if you want to run the script through job control system e.g. Slurm or Torque.\n", "- **path.sh**: Path configuration source file. Basically, we do not have to touch.\n", "- **conf/**: Directory containing configuration files.\n", "- **local/**: Directory containing the recipe-specific scripts e.g. data preparation.\n", "- **steps/** and **utils/**: Directory containing kaldi tools.\n", "\n", "Main script **run.sh** consists of several stages:\n", "\n", "- **stage -1**: Download data if the data is available online.\n", "- **stage 0**: Prepare data to make kaldi-stype data directory.\n", "- **stage 1**: Extract feature vector, calculate statistics, and perform normalization.\n", "- **stage 2**: Prepare a dictionary and make json files for training.\n", "- **stage 3**: Train the E2E-TTS network.\n", "- **stage 4**: Decode mel-spectrogram using the trained network.\n", "- **stage 5**: Generate a waveform from a generated mel-spectrogram using Griffin-Lim.\n", "\n", "Currently, we support the following networks:\n", "- Tacotron2: [Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions](https://arxiv.org/abs/1712.05884)\n", "- Transformer: [Neural Speech Synthesis with Transformer Network](https://arxiv.org/pdf/1809.08895.pdf)\n", "- FastSpeech: [FastSpeech: Fast, Robust and Controllable Text to Speech](https://arxiv.org/pdf/1905.09263.pdf)\n", "\n", "Let us check each stage step-by-step via **--stage** and **--stop_stage** options!\n" ] }, { "cell_type": "markdown", "metadata": { "id": "l6Zhc4iKLKeC", "colab_type": "text" }, "source": [ "### Stage -1: Data download\n", "\n", "This stage downloads dataset if the dataset is available online." ] }, { "cell_type": "code", "metadata": { "id": "8CNFWXETAeYC", "colab_type": "code", "outputId": "e6559ffa-c534-4cfe-9295-f5a82924f8d7", "collapsed": true, "colab": { "base_uri": "https://localhost:8080/", "height": 69 } }, "source": [ "!./run.sh --stage -1 --stop_stage -1" ], "execution_count": 4, "outputs": [ { "output_type": "stream", "text": [ "stage -1: Data Download\n", "local/download_and_untar.sh: an4 directory already exists in ./downloads\n", "dictionary: data/lang_1char/train_nodev_units.txt\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "Y3FTgWO7w0Rf", "colab_type": "code", "outputId": "250de278-54c6-4e28-fd85-64d8f84c2942", "colab": { "base_uri": "https://localhost:8080/", "height": 312 } }, "source": [ "!tree -L 1\n", "!ls downloads/" ], "execution_count": 5, "outputs": [ { "output_type": "stream", "text": [ ".\n", "├── cmd.sh\n", "├── conf\n", "├── data\n", "├── downloads\n", "├── dump\n", "├── exp\n", "├── fbank\n", "├── local -> ../asr1/local\n", "├── path.sh\n", "├── run.sh\n", "├── steps -> ../../../tools/kaldi/egs/wsj/s5/steps\n", "├── tensorboard\n", "└── utils -> ../../../tools/kaldi/egs/wsj/s5/utils\n", "\n", "10 directories, 3 files\n", "an4 an4_sphere.tar.gz\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "lzqGzOqpw741", "colab_type": "text" }, "source": [ "You can see **downloads** directory is cretead, which containing donwloaded an4 dataset." ] }, { "cell_type": "markdown", "metadata": { "id": "wa1OkxN3xSRx", "colab_type": "text" }, "source": [ "### Stage 0: Data preparation\n", "\n", "This stage creates kaldi-style data directories." ] }, { "cell_type": "code", "metadata": { "id": "ozLaRF1LA3Aq", "colab_type": "code", "outputId": "5ac935b7-2fc7-415c-ccff-1b45e308adab", "colab": { "base_uri": "https://localhost:8080/", "height": 52 } }, "source": [ "!./run.sh --stage 0 --stop_stage 0" ], "execution_count": 6, "outputs": [ { "output_type": "stream", "text": [ "stage 0: Data preparation\n", "dictionary: data/lang_1char/train_nodev_units.txt\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "5Ch0fonjxep5", "colab_type": "code", "outputId": "48bc5bd7-80e5-4794-9925-ae67c3fc51cb", "colab": { "base_uri": "https://localhost:8080/", "height": 156 } }, "source": [ "!tree -L 1 data" ], "execution_count": 7, "outputs": [ { "output_type": "stream", "text": [ "data\n", "├── lang_1char\n", "├── test\n", "├── train\n", "├── train_dev\n", "└── train_nodev\n", "\n", "5 directories, 0 files\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "bF6wu5n3x9gD", "colab_type": "text" }, "source": [ "Through the data preparation stage, kaldi-style data directories will be created. \n", "Here, **data/train/** is corresponding to training set, and **data/test** is corresponding to evaluation set. \n", "Each directory has the same following files:" ] }, { "cell_type": "code", "metadata": { "id": "G8goZ26ExwMw", "colab_type": "code", "outputId": "75f31c83-f9a3-484a-83b8-fac3972e9137", "colab": { "base_uri": "https://localhost:8080/", "height": 260 } }, "source": [ "!ls data/*" ], "execution_count": 8, "outputs": [ { "output_type": "stream", "text": [ "data/lang_1char:\n", "train_nodev_units.txt\n", "\n", "data/test:\n", "feats.scp filetype spk2utt text utt2num_frames utt2spk wav.scp\n", "\n", "data/train:\n", "feats.scp filetype spk2utt text utt2num_frames utt2spk wav.scp\n", "\n", "data/train_dev:\n", "feats.scp spk2utt text utt2num_frames utt2spk wav.scp\n", "\n", "data/train_nodev:\n", "cmvn.ark feats.scp spk2utt text utt2num_frames utt2spk wav.scp\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "mrxmV_Ntx3gk", "colab_type": "text" }, "source": [ "The above four files are all we have to prepare to create new recipes. \n", "Let's check each file." ] }, { "cell_type": "code", "metadata": { "id": "S5PJH9zxyqNg", "colab_type": "code", "outputId": "88f99c71-9309-494c-ec36-82e3fd29c93d", "colab": { "base_uri": "https://localhost:8080/", "height": 347 } }, "source": [ "!head -n 3 data/train/{wav.scp,text,utt2spk,spk2utt}" ], "execution_count": 9, "outputs": [ { "output_type": "stream", "text": [ "==> data/train/wav.scp <==\n", "fash-an251-b /content/espnet/egs/an4/tts1/../../../tools/kaldi/tools/sph2pipe_v2.5/sph2pipe -f wav -p -c 1 ./downloads/an4/wav/an4_clstk/fash/an251-fash-b.sph |\n", "fash-an253-b /content/espnet/egs/an4/tts1/../../../tools/kaldi/tools/sph2pipe_v2.5/sph2pipe -f wav -p -c 1 ./downloads/an4/wav/an4_clstk/fash/an253-fash-b.sph |\n", "fash-an254-b /content/espnet/egs/an4/tts1/../../../tools/kaldi/tools/sph2pipe_v2.5/sph2pipe -f wav -p -c 1 ./downloads/an4/wav/an4_clstk/fash/an254-fash-b.sph |\n", "\n", "==> data/train/text <==\n", "fash-an251-b YES\n", "fash-an253-b GO\n", "fash-an254-b YES\n", "\n", "==> data/train/utt2spk <==\n", "fash-an251-b fash\n", "fash-an253-b fash\n", "fash-an254-b fash\n", "\n", "==> data/train/spk2utt <==\n", "fash fash-an251-b fash-an253-b fash-an254-b fash-an255-b fash-cen1-b fash-cen2-b fash-cen4-b fash-cen5-b fash-cen7-b\n", "fbbh fbbh-an86-b fbbh-an87-b fbbh-an88-b fbbh-an89-b fbbh-an90-b fbbh-cen1-b fbbh-cen2-b fbbh-cen3-b fbbh-cen4-b fbbh-cen5-b fbbh-cen6-b fbbh-cen7-b fbbh-cen8-b\n", "fclc fclc-an146-b fclc-an147-b fclc-an148-b fclc-an149-b fclc-an150-b fclc-cen1-b fclc-cen2-b fclc-cen3-b fclc-cen4-b fclc-cen5-b fclc-cen6-b fclc-cen7-b fclc-cen8-b\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "EwPydMzvxbcA", "colab_type": "text" }, "source": [ "Each file contains the following information:\n", "- **wav.scp**: List of audio path. Each line has ` `. `` must be unique.\n", "- **text**: List of transcriptions. Each line has ` `. In the case of TTS, we assume that `` is cleaned.\n", "- **utt2spk**: List of correspondence table between utterances and speakers. Each line has ` `.\n", "- **spk2utt**: List of correspondence table between speakers and utterances. Each lien has ` ... `. This file can be automatically created from **utt2spk**. \n", "\n", "In the ESPnet, speaker information is not used for any processing. \n", "Therefore, **utt2spk** and **spk2utt** can be a dummy." ] }, { "cell_type": "markdown", "metadata": { "id": "XEgfec6u1KWA", "colab_type": "text" }, "source": [ "### Stage 1: Feature extration\n", "\n", "This stage performs the following processing:\n", "1. Mel-spectrogram extraction\n", "2. Data split into training and validation set\n", "2. Statistics (mean and variance) calculation\n", "3. Normalization" ] }, { "cell_type": "code", "metadata": { "id": "znHW0IwbBX0o", "colab_type": "code", "outputId": "957f9dce-c181-433a-812b-3c79d2933653", "colab": { "base_uri": "https://localhost:8080/", "height": 364 } }, "source": [ "!./run.sh --stage 1 --stop_stage 1 --nj 4" ], "execution_count": 10, "outputs": [ { "output_type": "stream", "text": [ "stage 1: Feature Generation\n", "/content/espnet/egs/an4/tts1/../../../utils/make_fbank.sh --cmd run.pl --nj 4 --fs 16000 --fmax --fmin --n_fft 1024 --n_shift 256 --win_length --n_mels 80 data/train exp/make_fbank/train fbank\n", "/content/espnet/egs/an4/tts1/../../../utils/make_fbank.sh: moving data/train/feats.scp to data/train/.backup\n", "utils/validate_data_dir.sh: Successfully validated data-directory data/train\n", "/content/espnet/egs/an4/tts1/../../../utils/make_fbank.sh: [info]: no segments file exists: assuming pcm.scp indexed by utterance.\n", "Succeeded creating filterbank features for train\n", "/content/espnet/egs/an4/tts1/../../../utils/make_fbank.sh --cmd run.pl --nj 4 --fs 16000 --fmax --fmin --n_fft 1024 --n_shift 256 --win_length --n_mels 80 data/test exp/make_fbank/test fbank\n", "/content/espnet/egs/an4/tts1/../../../utils/make_fbank.sh: moving data/test/feats.scp to data/test/.backup\n", "utils/validate_data_dir.sh: Successfully validated data-directory data/test\n", "/content/espnet/egs/an4/tts1/../../../utils/make_fbank.sh: [info]: no segments file exists: assuming pcm.scp indexed by utterance.\n", "Succeeded creating filterbank features for test\n", "utils/subset_data_dir.sh: reducing #utt from 948 to 100\n", "utils/subset_data_dir.sh: reducing #utt from 948 to 848\n", "compute-cmvn-stats scp:data/train_nodev/feats.scp data/train_nodev/cmvn.ark \n", "LOG (compute-cmvn-stats[5.5.428~1-29b3]:main():compute-cmvn-stats.cc:168) Wrote global CMVN stats to data/train_nodev/cmvn.ark\n", "LOG (compute-cmvn-stats[5.5.428~1-29b3]:main():compute-cmvn-stats.cc:171) Done accumulating CMVN stats for 848 utterances; 0 had errors.\n", "/content/espnet/egs/an4/tts1/../../../utils/dump.sh --cmd run.pl --nj 4 --do_delta false data/train_nodev/feats.scp data/train_nodev/cmvn.ark exp/dump_feats/train dump/train_nodev\n", "/content/espnet/egs/an4/tts1/../../../utils/dump.sh --cmd run.pl --nj 4 --do_delta false data/train_dev/feats.scp data/train_nodev/cmvn.ark exp/dump_feats/dev dump/train_dev\n", "/content/espnet/egs/an4/tts1/../../../utils/dump.sh --cmd run.pl --nj 4 --do_delta false data/test/feats.scp data/train_nodev/cmvn.ark exp/dump_feats/eval dump/test\n", "dictionary: data/lang_1char/train_nodev_units.txt\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "ONBddj474CwB", "colab_type": "text" }, "source": [ "Raw filterbanks are saved in **fbank/** directory with ark/scp format." ] }, { "cell_type": "code", "metadata": { "id": "udo85LqA19Yq", "colab_type": "code", "outputId": "73535451-9620-4439-ad63-4d498608e01e", "colab": { "base_uri": "https://localhost:8080/", "height": 121 } }, "source": [ "!ls fbank" ], "execution_count": 11, "outputs": [ { "output_type": "stream", "text": [ "raw_fbank_test.1.ark raw_fbank_test.4.ark raw_fbank_train.3.ark\n", "raw_fbank_test.1.scp raw_fbank_test.4.scp raw_fbank_train.3.scp\n", "raw_fbank_test.2.ark raw_fbank_train.1.ark raw_fbank_train.4.ark\n", "raw_fbank_test.2.scp raw_fbank_train.1.scp raw_fbank_train.4.scp\n", "raw_fbank_test.3.ark raw_fbank_train.2.ark\n", "raw_fbank_test.3.scp raw_fbank_train.2.scp\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "e9OS6JIp474Z", "colab_type": "text" }, "source": [ "**.ark** is binary file and **.scp** contain the correspondence between `` and ``. \n", "Since feature extraction can be performed for split small sets in parallel, raw_fbank is split into `raw_fbank_*.{1..N}.{scp,ark}.`" ] }, { "cell_type": "code", "metadata": { "id": "tuPfH0ly46Ko", "colab_type": "code", "outputId": "76de2cad-96b9-4781-afd3-7509960de58c", "colab": { "base_uri": "https://localhost:8080/", "height": 69 } }, "source": [ "!head -n 3 fbank/raw_fbank_train.1.scp" ], "execution_count": 12, "outputs": [ { "output_type": "stream", "text": [ "fash-an251-b /content/espnet/egs/an4/tts1/fbank/raw_fbank_train.1.ark:13\n", "fash-an253-b /content/espnet/egs/an4/tts1/fbank/raw_fbank_train.1.ark:5727\n", "fash-an254-b /content/espnet/egs/an4/tts1/fbank/raw_fbank_train.1.ark:9921\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "oj1bx25q5bl_", "colab_type": "text" }, "source": [ "These files can be loaded in python via **kaldiio** as follows:" ] }, { "cell_type": "code", "metadata": { "id": "3K0uiQgB5aYK", "colab_type": "code", "outputId": "d7797bdd-820b-45e4-ca91-ea821d5e7b36", "colab": { "base_uri": "https://localhost:8080/", "height": 545 } }, "source": [ "import kaldiio\n", "import matplotlib.pyplot as plt\n", "\n", "# load scp file\n", "scp_dict = kaldiio.load_scp(\"fbank/raw_fbank_train.1.scp\")\n", "for key in scp_dict:\n", " plt.imshow(scp_dict[key].T[::-1])\n", " plt.title(key)\n", " plt.colorbar()\n", " plt.show()\n", " break\n", " \n", "# load ark file\n", "ark_generator = kaldiio.load_ark(\"fbank/raw_fbank_train.1.ark\")\n", "for key, array in ark_generator:\n", " plt.imshow(array.T[::-1])\n", " plt.title(key)\n", " plt.colorbar()\n", " plt.show()\n", " break" ], "execution_count": 13, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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VWV31PAWyBLJcoZrNobzirLIS8WQ4OK3UWyMUhIVisP3piwH0JiAYIyL/FsA/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\n/f2kX7ctoosRETEuKHZDlNq2N+d2zXvhBKRfttv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VWV31PAWyBLJcoZrNobzirLIS8WQ4OK3UWyMUhIVisP3piwH0JiAYIyL/FsA/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\n/wEbwryhhGAG1gAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "markdown", "metadata": { "id": "-Au70T7i8ctz", "colab_type": "text" }, "source": [ "After raw mel-spectrogram extraction, some files are added in **data/train/**. \n", "**feats.scp** is concatenated scp file of **fbank/raw_fbank_train.{1..N}.scp**. \n", "**utt2num_frames** has the number of feature frames of each ``." ] }, { "cell_type": "code", "metadata": { "id": "ySykrx407Tek", "colab_type": "code", "outputId": "a0829cb1-f291-4d51-cd17-e7da04ec3d53", "colab": { "base_uri": "https://localhost:8080/", "height": 191 } }, "source": [ "!ls data/train\n", "!head -n 3 data/train/{feats.scp,utt2num_frames}" ], "execution_count": 14, "outputs": [ { "output_type": "stream", "text": [ "feats.scp filetype spk2utt text utt2num_frames utt2spk wav.scp\n", "==> data/train/feats.scp <==\n", "fash-an251-b /content/espnet/egs/an4/tts1/fbank/raw_fbank_train.1.ark:13\n", "fash-an253-b /content/espnet/egs/an4/tts1/fbank/raw_fbank_train.1.ark:5727\n", "fash-an254-b /content/espnet/egs/an4/tts1/fbank/raw_fbank_train.1.ark:9921\n", "\n", "==> data/train/utt2num_frames <==\n", "fash-an251-b 63\n", "fash-an253-b 44\n", "fash-an254-b 57\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "qt4s7lkl9miZ", "colab_type": "text" }, "source": [ "And **data/train/** directory is split into two directory:\n", "- **data/train_nodev/**: data directory for training\n", "- **data/train_dev/**: data directory for validation\n" ] }, { "cell_type": "code", "metadata": { "id": "Yq6WNznw9P4z", "colab_type": "code", "outputId": "be2c96cc-c9fb-4068-b1d0-3367a15cbb8f", "colab": { "base_uri": "https://localhost:8080/", "height": 121 } }, "source": [ "!ls data\n", "!ls data/train_*" ], "execution_count": 15, "outputs": [ { "output_type": "stream", "text": [ "lang_1char test train train_dev train_nodev\n", "data/train_dev:\n", "feats.scp spk2utt text utt2num_frames utt2spk wav.scp\n", "\n", "data/train_nodev:\n", "cmvn.ark feats.scp spk2utt text utt2num_frames utt2spk wav.scp\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "Fw_KBzQCBN0E", "colab_type": "text" }, "source": [ "You can find **cmvn.ark** in **data/train_nodev**, which is the calculated statistics file. \n", "This file also can be loaded in python via kaldiio.\n" ] }, { "cell_type": "code", "metadata": { "id": "s5YxfvNwBIlG", "colab_type": "code", "outputId": "76ce0685-53e6-4ff3-9195-8dcdeb35b3cf", "colab": { "base_uri": "https://localhost:8080/", "height": 521 } }, "source": [ "# load cmvn.ark file (Be careful not load_ark, but load_mat)\n", "cmvn = kaldiio.load_mat(\"data/train_nodev/cmvn.ark\")\n", "\n", "# cmvn consists of mean and variance, the last dimension of mean represents the number of frames.\n", "print(\"cmvn shape = \"+ str(cmvn.shape))\n", "\n", "# calculate mean and variance\n", "mu = cmvn[0, :-1] / cmvn[0, -1]\n", "var = cmvn[1, :-1] / cmvn[0, -1]\n", "\n", "# show mean\n", "print(\"mean = \" + str(mu))\n", "print(\"variance = \" + str(var))" ], "execution_count": 16, "outputs": [ { "output_type": "stream", "text": [ "cmvn shape = (2, 81)\n", "mean = [-2.3015275 -2.1957324 -1.9654763 -1.9376634 -1.7633141 -1.6846672\n", " -1.7875645 -1.9486219 -1.9248276 -1.8872185 -1.9270604 -1.991474\n", " -1.9778731 -2.09246 -2.1971824 -2.209918 -2.3019788 -2.2964242\n", " -2.32575 -2.3705876 -2.40271 -2.449803 -2.4300003 -2.466036\n", " -2.5255735 -2.5386114 -2.582323 -2.5250685 -2.6118424 -2.632455\n", " -2.6633208 -2.672028 -2.6356308 -2.6361263 -2.6829064 -2.691491\n", " -2.694131 -2.675015 -2.6734142 -2.665589 -2.6630545 -2.6658235\n", " -2.657909 -2.6691167 -2.6635575 -2.6643658 -2.6674545 -2.661883\n", " -2.6606252 -2.657067 -2.6489155 -2.6527998 -2.6650834 -2.678152\n", " -2.698867 -2.708589 -2.7201533 -2.7215238 -2.7254083 -2.7388637\n", " -2.7713299 -2.8122768 -2.8621979 -2.9113584 -2.9572158 -3.0068166\n", " -3.0537205 -3.1046875 -3.160605 -3.2164888 -3.2550604 -3.2886407\n", " -3.3207698 -3.3445303 -3.3530545 -3.3561647 -3.357716 -3.3631625\n", " -3.3077478 -3.2325494]\n", "variance = [ 5.478512 5.1941466 4.6532855 4.389601 3.6336286 3.4551063\n", " 3.7337823 4.3736997 4.2437925 4.1938186 4.4535246 4.6856284\n", " 4.5197277 4.960779 5.486998 5.5303926 5.9634395 5.9297132\n", " 6.0229506 6.2188787 6.402854 6.6038113 6.456947 6.6182714\n", " 6.9164844 6.9951644 7.2030034 6.832003 7.333239 7.455782\n", " 7.639241 7.6680694 7.402422 7.3907475 7.693637 7.768753\n", " 7.773008 7.671266 7.682944 7.639572 7.626395 7.6473846\n", " 7.611816 7.679605 7.6519523 7.658576 7.6860723 7.670902\n", " 7.6756134 7.674951 7.639459 7.6657314 7.7366185 7.789132\n", " 7.8906317 7.934334 8.0020485 8.016449 8.038348 8.104283\n", " 8.278971 8.490983 8.759398 9.036307 9.297286 9.589112\n", " 9.866017 10.173562 10.518697 10.873265 11.115793 11.32935\n", " 11.530077 11.672476 11.716665 11.724409 11.7235985 11.751684\n", " 11.366631 10.894848 ]\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "pTCG1CPRDN5H", "colab_type": "text" }, "source": [ "Normalzed features for training, validation and evaluation set are dumped in **dump/{train_nodev,train_dev,test}/**. \n", "There ark and scp can be loaded as the same as the above procedure.\n", "\n" ] }, { "cell_type": "code", "metadata": { "id": "JazwdIVbDNFs", "colab_type": "code", "outputId": "0ac7485e-7ea7-4519-a7e4-347d041e9389", "colab": { "base_uri": "https://localhost:8080/", "height": 260 } }, "source": [ "!ls dump/*" ], "execution_count": 17, "outputs": [ { "output_type": "stream", "text": [ "dump/test:\n", "data.json feats.2.ark feats.3.scp feats.scp utt2num_frames\n", "feats.1.ark feats.2.scp feats.4.ark filetype\n", "feats.1.scp feats.3.ark feats.4.scp log\n", "\n", "dump/train_dev:\n", "data.json feats.2.ark feats.3.scp feats.scp utt2num_frames\n", "feats.1.ark feats.2.scp feats.4.ark filetype\n", "feats.1.scp feats.3.ark feats.4.scp log\n", "\n", "dump/train_nodev:\n", "data.json feats.2.ark feats.3.scp feats.scp utt2num_frames\n", "feats.1.ark feats.2.scp feats.4.ark filetype\n", "feats.1.scp feats.3.ark feats.4.scp log\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "eYtmQCDdDk0d", "colab_type": "text" }, "source": [ "### Stage 2: Dictionary and json preparation\n", "\n", "This stage creates dictrionary from **data/train_nodev/text** and makes json file for training." ] }, { "cell_type": "code", "metadata": { "id": "fgWDdveUBctQ", "colab_type": "code", "outputId": "1f32337d-5e62-4462-d908-224b277ee647", "colab": { "base_uri": "https://localhost:8080/", "height": 173 } }, "source": [ "!./run.sh --stage 2 --stop_stage 2" ], "execution_count": 18, "outputs": [ { "output_type": "stream", "text": [ "dictionary: data/lang_1char/train_nodev_units.txt\n", "stage 2: Dictionary and Json Data Preparation\n", "28 data/lang_1char/train_nodev_units.txt\n", "/content/espnet/egs/an4/tts1/../../../utils/data2json.sh --feat dump/train_nodev/feats.scp data/train_nodev data/lang_1char/train_nodev_units.txt\n", "/content/espnet/egs/an4/tts1/../../../utils/feat_to_shape.sh --cmd run.pl --nj 1 --filetype --preprocess-conf --verbose 0 dump/train_nodev/feats.scp data/train_nodev/tmp-6VLm9/input/shape.scp\n", "/content/espnet/egs/an4/tts1/../../../utils/data2json.sh --feat dump/train_dev/feats.scp data/train_dev data/lang_1char/train_nodev_units.txt\n", "/content/espnet/egs/an4/tts1/../../../utils/feat_to_shape.sh --cmd run.pl --nj 1 --filetype --preprocess-conf --verbose 0 dump/train_dev/feats.scp data/train_dev/tmp-g5AHB/input/shape.scp\n", "/content/espnet/egs/an4/tts1/../../../utils/data2json.sh --feat dump/test/feats.scp data/test data/lang_1char/train_nodev_units.txt\n", "/content/espnet/egs/an4/tts1/../../../utils/feat_to_shape.sh --cmd run.pl --nj 1 --filetype --preprocess-conf --verbose 0 dump/test/feats.scp data/test/tmp-U7FtO/input/shape.scp\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "IB-eU-W1FNLk", "colab_type": "text" }, "source": [ "Dictrionary file will be created in **data/lang_1char/**. \n", "Dictionary file consists of `` ``. \n", "Here, `` starts from 1 because 0 is used as padding index.\n" ] }, { "cell_type": "code", "metadata": { "id": "q8S74SjdFXcF", "colab_type": "code", "outputId": "02f0fd30-a05f-4804-b6a8-5938782269d6", "colab": { "base_uri": "https://localhost:8080/", "height": 521 } }, "source": [ "!ls data\n", "!cat data/lang_1char/train_nodev_units.txt" ], "execution_count": 19, "outputs": [ { "output_type": "stream", "text": [ "lang_1char test train train_dev train_nodev\n", " 1\n", " 2\n", "A 3\n", "B 4\n", "C 5\n", "D 6\n", "E 7\n", "F 8\n", "G 9\n", "H 10\n", "I 11\n", "J 12\n", "K 13\n", "L 14\n", "M 15\n", "N 16\n", "O 17\n", "P 18\n", "Q 19\n", "R 20\n", "S 21\n", "T 22\n", "U 23\n", "V 24\n", "W 25\n", "X 26\n", "Y 27\n", "Z 28\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "b-QO0MLGGaBO", "colab_type": "text" }, "source": [ "Json file will be created for training / validation /evaludation sets and they are saved as **dump/{train_nodev,train_dev,test}/data.json**." ] }, { "cell_type": "code", "metadata": { "id": "BrYKpYVUGh6K", "colab_type": "code", "outputId": "1f168c10-6407-411e-b617-eafc5b1dae24", "colab": { "base_uri": "https://localhost:8080/", "height": 34 } }, "source": [ "!ls dump/*/*.json" ], "execution_count": 20, "outputs": [ { "output_type": "stream", "text": [ "dump/test/data.json dump/train_dev/data.json dump/train_nodev/data.json\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "CoRMOHDnG1Xk", "colab_type": "text" }, "source": [ "Each json file contains all of the information in the data directory." ] }, { "cell_type": "code", "metadata": { "id": "O_My36NFHAVI", "colab_type": "code", "outputId": "02642af2-6ae1-4c64-ab74-3b9ee2ada422", "colab": { "base_uri": "https://localhost:8080/", "height": 486 } }, "source": [ "!head -n 27 dump/train_nodev/data.json" ], "execution_count": 21, "outputs": [ { "output_type": "stream", "text": [ "{\n", " \"utts\": {\n", " \"fash-an251-b\": {\n", " \"input\": [\n", " {\n", " \"feat\": \"/content/espnet/egs/an4/tts1/dump/train_nodev/feats.1.ark:13\",\n", " \"name\": \"input1\",\n", " \"shape\": [\n", " 63,\n", " 80\n", " ]\n", " }\n", " ],\n", " \"output\": [\n", " {\n", " \"name\": \"target1\",\n", " \"shape\": [\n", " 3,\n", " 30\n", " ],\n", " \"text\": \"YES\",\n", " \"token\": \"Y E S\",\n", " \"tokenid\": \"27 7 21\"\n", " }\n", " ],\n", " \"utt2spk\": \"fash\"\n", " },\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "O88CpXHtHnZA", "colab_type": "text" }, "source": [ "- \"shape\": Shape of the input or output sequence. Here input shape [63, 80] represents the number of frames = 63 and the dimension of mel-spectrogram = 80.\n", "- \"text\": Original transcription.\n", "- \"token\": Token sequence of original transcription.\n", "- \"tokenid\" Token id sequence of original transcription, which is converted using the dictionary.\n", "\n", "Now ready to start training!" ] }, { "cell_type": "markdown", "metadata": { "id": "QUkLphZbIw51", "colab_type": "text" }, "source": [ "### Stage 3: Network training\n", "\n", "This stage performs training of the network. \n", "Network training configurations are written as **.yaml** format file. \n", "Let us check the default cofiguration **conf/train_pytroch_tacotron2.yaml**." ] }, { "cell_type": "code", "metadata": { "id": "b-f5K2bYOaVY", "colab_type": "code", "outputId": "22c2ec06-6a8a-4440-8ab3-e3eb877fdb68", "colab": { "base_uri": "https://localhost:8080/", "height": 816 } }, "source": [ "!cat conf/train_pytorch_tacotron2.yaml" ], "execution_count": 22, "outputs": [ { "output_type": "stream", "text": [ "# This is the basic tactron2 training settting\n", "\n", "# encoder related\n", "embed-dim: 512\n", "elayers: 1\n", "eunits: 512\n", "econv-layers: 3 # if set 0, no conv layer is used\n", "econv-chans: 512\n", "econv-filts: 5\n", "\n", "# decoder related\n", "dlayers: 2\n", "dunits: 1024\n", "prenet-layers: 2 # if set 0, no prenet is used\n", "prenet-units: 256\n", "postnet-layers: 5 # if set 0, no postnet is used\n", "postnet-chans: 512\n", "postnet-filts: 5\n", "\n", "# attention related\n", "atype: location\n", "adim: 128\n", "aconv-chans: 32\n", "aconv-filts: 15 # resulting in filter-size = aconv-filts * 2 + 1\n", "cumulate-att-w: true # whether to cumulate attetion weight\n", "use-batch-norm: true # whether to use batch normalization in conv layer\n", "use-concate: true # whether to concatenate encoder embedding with decoder lstm outputs\n", "use-residual: false # whether to use residual connection in encoder convolution\n", "use-masking: true # whether to mask the padded part in loss calculation\n", "bce-pos-weight: 1.0 # weight for positive samples of stop token in cross-entropy calculation\n", "reduction-factor: 2\n", "\n", "# minibatch related\n", "batch-size: 32\n", "batch-sort-key: shuffle # shuffle or input or output\n", "maxlen-in: 150 # if input length > maxlen-in, batchsize is reduced (if use \"shuffle\", not effect)\n", "maxlen-out: 400 # if output length > maxlen-out, batchsize is reduced (if use \"shuffle\", not effect)\n", "\n", "# optimization related\n", "lr: 1e-3\n", "eps: 1e-6\n", "weight-decay: 0.0\n", "dropout-rate: 0.5\n", "zoneout-rate: 0.1\n", "epochs: 50\n", "patience: 5\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "6_QmCAS1OlD2", "colab_type": "text" }, "source": [ "You can modify this configuration file to change the hyperparameters. \n", "Here, let's change the number of epochs for this demonstration." ] }, { "cell_type": "code", "metadata": { "id": "f2Q9sYVxOjzm", "colab_type": "code", "outputId": "e32d1ff5-dd58-41b0-8475-0eeb8ca65f16", "colab": { "base_uri": "https://localhost:8080/", "height": 816 } }, "source": [ "# TODO(kan-bayashi): Change here to use change_yaml.py\n", "!cat conf/train_pytorch_tacotron2.yaml | sed -e \"s/epochs: 50/epochs: 3/g\" > conf/train_pytorch_tacotron2_sample.yaml\n", "!cat conf/train_pytorch_tacotron2_sample.yaml" ], "execution_count": 23, "outputs": [ { "output_type": "stream", "text": [ "# This is the basic tactron2 training settting\n", "\n", "# encoder related\n", "embed-dim: 512\n", "elayers: 1\n", "eunits: 512\n", "econv-layers: 3 # if set 0, no conv layer is used\n", "econv-chans: 512\n", "econv-filts: 5\n", "\n", "# decoder related\n", "dlayers: 2\n", "dunits: 1024\n", "prenet-layers: 2 # if set 0, no prenet is used\n", "prenet-units: 256\n", "postnet-layers: 5 # if set 0, no postnet is used\n", "postnet-chans: 512\n", "postnet-filts: 5\n", "\n", "# attention related\n", "atype: location\n", "adim: 128\n", "aconv-chans: 32\n", "aconv-filts: 15 # resulting in filter-size = aconv-filts * 2 + 1\n", "cumulate-att-w: true # whether to cumulate attetion weight\n", "use-batch-norm: true # whether to use batch normalization in conv layer\n", "use-concate: true # whether to concatenate encoder embedding with decoder lstm outputs\n", "use-residual: false # whether to use residual connection in encoder convolution\n", "use-masking: true # whether to mask the padded part in loss calculation\n", "bce-pos-weight: 1.0 # weight for positive samples of stop token in cross-entropy calculation\n", "reduction-factor: 2\n", "\n", "# minibatch related\n", "batch-size: 32\n", "batch-sort-key: shuffle # shuffle or input or output\n", "maxlen-in: 150 # if input length > maxlen-in, batchsize is reduced (if use \"shuffle\", not effect)\n", "maxlen-out: 400 # if output length > maxlen-out, batchsize is reduced (if use \"shuffle\", not effect)\n", "\n", "# optimization related\n", "lr: 1e-3\n", "eps: 1e-6\n", "weight-decay: 0.0\n", "dropout-rate: 0.5\n", "zoneout-rate: 0.1\n", "epochs: 3\n", "patience: 5\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "MlkD_x4wRD80", "colab_type": "text" }, "source": [ "Let's train the network. \n", "You can specify the config file via **--train_config** option.\n", "It takes several minutes.\n" ] }, { "cell_type": "code", "metadata": { "id": "cgdY6vp0Bhy4", "colab_type": "code", "outputId": "73a6fc3a-5e44-4dd7-c229-42037276ba44", "colab": { "base_uri": "https://localhost:8080/", "height": 69 } }, "source": [ "!./run.sh --stage 3 --stop_stage 3 --train_config conf/train_pytorch_tacotron2_sample.yaml --verbose 1" ], "execution_count": 24, "outputs": [ { "output_type": "stream", "text": [ "dictionary: data/lang_1char/train_nodev_units.txt\n", "stage 3: Text-to-speech model training\n", "^C\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "HsN8qD1gKnTc", "colab_type": "text" }, "source": [ "You can see the training log in `exp/train_*/train.log`." ] }, { "cell_type": "markdown", "metadata": { "id": "wy0rXqBNTAtk", "colab_type": "text" }, "source": [ "The models are saved in `exp/train_*/results/` directory." ] }, { "cell_type": "code", "metadata": { "id": "YRxIFjnWS_t9", "colab_type": "code", "outputId": "5408227c-5118-4b6f-f36f-e6a72e3ddc58", "colab": { "base_uri": "https://localhost:8080/", "height": 208 } }, "source": [ "!ls exp/train_nodev_pytorch_train_pytorch_tacotron2_sample/{results,results/att_ws}" ], "execution_count": 26, "outputs": [ { "output_type": "stream", "text": [ "exp/train_nodev_pytorch_train_pytorch_tacotron2_sample/results:\n", "all_loss.png l1_loss.png model.loss.best snapshot.ep.2\n", "att_ws\t loss.png\t mse_loss.png snapshot.ep.3\n", "bce_loss.png model.json snapshot.ep.1\n", "\n", "exp/train_nodev_pytorch_train_pytorch_tacotron2_sample/results/att_ws:\n", "fash-an251-b.ep.1.png fash-an253-b.ep.3.png fash-an255-b.ep.2.png\n", "fash-an251-b.ep.2.png fash-an254-b.ep.1.png fash-an255-b.ep.3.png\n", "fash-an251-b.ep.3.png fash-an254-b.ep.2.png fash-cen1-b.ep.1.png\n", "fash-an253-b.ep.1.png fash-an254-b.ep.3.png fash-cen1-b.ep.2.png\n", "fash-an253-b.ep.2.png fash-an255-b.ep.1.png fash-cen1-b.ep.3.png\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "_vyKmHl8T5uj", "colab_type": "text" }, "source": [ "`exp/train_*/results/*.png` are the figures of training curve." ] }, { "cell_type": "code", "metadata": { "id": "Dwee4a8NUOBg", "colab_type": "code", "outputId": "ea2a6c3f-4296-4ab7-f918-2d378227aec8", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 } }, "source": [ "from IPython.display import Image, display_png\n", "print(\"all loss curve\")\n", "display_png(Image(\"exp/train_nodev_pytorch_train_pytorch_tacotron2_sample/results/all_loss.png\"))\n", "print(\"l1 loss curve\")\n", "display_png(Image(\"exp/train_nodev_pytorch_train_pytorch_tacotron2_sample/results/l1_loss.png\"))\n", "print(\"mse loss curve\")\n", "display_png(Image(\"exp/train_nodev_pytorch_train_pytorch_tacotron2_sample/results/mse_loss.png\"))\n", "print(\"bce loss curve\")\n", "display_png(Image(\"exp/train_nodev_pytorch_train_pytorch_tacotron2_sample/results/bce_loss.png\"))" ], "execution_count": 27, "outputs": [ { "output_type": "stream", "text": [ "all loss curve\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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wmCH/kvW48Fs17VBaAxUHQ8kUrn4E5J6Ei3eA7+3g5tzyys7QqpUEerkQ6OXi1H0WiwVd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\nGRnZNn0W7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}, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "l1 loss curve\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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Yhy/eZtr643wffpKXa5Wie4MyFHOxt3RUERERkTypSJEiFClSxNIx0i3NX7Hf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}, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "mse loss curve\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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rq6umZIe5uTl//PEHp06dom7duowbN45vvvkmU5v+/fvTsWNHunTpQuPGjXnw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}, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "bce loss curve\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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njt1U43w+CzJ/cxx4zhyGoivUl/cNL3t2p3qNyxYquKnG+RrSOFcNjXPVqayx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\nCjQiIiIiIuKy/n+uVE86e+ZaawAAAABJRU5ErkJggg==\n" }, "metadata": { "tags": [] } } ] }, { "cell_type": "markdown", "metadata": { "id": "Ui57IrR4VCpv", "colab_type": "text" }, "source": [ "`exp/train_*/results/att_ws/.png` are the figures of attention weights in each epoch." ] }, { "cell_type": "code", "metadata": { "id": "NyMW2tx0VKjC", "colab_type": "code", "outputId": "d301ec6f-1a0a-4aa5-efb7-c69fd978d4ae", "colab": { "base_uri": "https://localhost:8080/", "height": 514 } }, "source": [ "print(\"Attention weights of initial epoch\")\n", "display_png(Image(\"exp/train_nodev_pytorch_train_pytorch_tacotron2_sample/results/att_ws/fash-cen1-b.ep.1.png\"))" ], "execution_count": 28, "outputs": [ { "output_type": "stream", "text": [ "Attention weights of initial epoch\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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AABiGAAgAAGAYAiAAAIBhKrQNDHA+2T7b7RIAAAgLjAACAAAYhgAIAABgGKaA\nTcDUKQAA+AVGAAEAAAxDAAQAADAMARAAAMAwBEAAAADDEAABAAAMQwAEAAAwDAEQAADAMARAAAAA\nwxAAAQAADEMABAAAMAwBEAAAwDAEQAAAAMMQAAEAAAxDAAQAADBMhNsF4DzwWG5X4IwytwsAACA8\nMAIIAABgGAIgAACAYQiAAAAAhiEAAgAAGIYACAAAYBgCIAAAgGEIgAAAAIYhAAIAABiGAAgAAGAY\nAiAAAIBhCIAAAACGIQACAAAYhgAIAABgGAIgAACAYQiAAAAAhgm5AFhWVqYxY8YoLS1NsbGxqlOn\njsaNGyfbtv3X2LatsWPHKiUlRbGxscrIyNDevXtdrBoAACB4hFwAnDx5smbMmKHnn39eu3fv1uTJ\nkzVlyhQ999xz/mumTJmiadOmaebMmdq0aZPi4uLUuXNnnThxwsXKAQAAgkOE2wX8Vh9//LGuvfZa\nXXPNNZKk2rVr65VXXtHmzZslnRr9mzp1qh5++GFde+21kqT58+crKSlJS5YsUb9+/VyrHQAAIBiE\n3AjgpZdeqqysLO3Zs0eS9Mknn2j9+vXq2rWrJOnAgQPKy8tTRkaG/3cSEhLUtm1bbdiw4az3LS4u\nVmFhYcABAAAQjkJuBHDUqFEqLCxU/fr15fV6VVZWpgkTJqh///6SpLy8PElSUlJSwO8lJSX5z53J\npEmT9Nhjj527wgEAAIJEyI0Avv7661qwYIEWLlyobdu2ad68eXryySc1b968Ct139OjRKigo8B+5\nubkOVQwAABBcQm4E8L777tOoUaP8a/maNGmir7/+WpMmTdKAAQOUnJwsScrPz1dKSor/9/Lz89W8\nefOz3jc6OlrR0dHntngAAIAgEHIjgD/88IM8nsCyvV6vfD6fJCktLU3JycnKysryny8sLNSmTZuU\nnp5+XmsFAAAIRiE3AtijRw9NmDBBtWrVUqNGjbR9+3Y9/fTTuvnmmyVJlmVp+PDhGj9+vOrVq6e0\ntDSNGTNGqamp6tWrl8vVAwAAuC/kAuBzzz2nMWPG6K677tKRI0eUmpqq22+/XWPHjvVfc//99+v4\n8eMaPHiwjh07pssuu0wrVqxQTEyMi5UDAAAEB8v+5Ss04FdYWKiEhAR9t+dPiq8ccjPlAbqktXW7\nBEfYJaVul+AMX5nbFQAAQlCpXaIPtFQFBQWKj4+v0L1CO9kAAADgNyMAAgAAGIYACAAAYBgCIAAA\ngGEIgAAAAIYhAAIAABiGAAgAAGAYAiAAAIBhCIAAAACGIQACAAAYhgAIAABgGAIgAACAYQiAAAAA\nhiEAAgAAGIYACAAAYBgCIABe8HFiAAATA0lEQVQAgGEIgAAAAIYhAAIAABiGAAgAAGAYAiAAAIBh\nCIAAAACGIQACAAAYhgAIAABgGAIgAACAYQiAAAAAholwu4BgV2b7VGa7XUXFWJbldgmOCPGvAQCA\noMEIIAAAgGEIgAAAAIYhAAIAABiGAAgAAGAYAiAAAIBhCIAAAACGIQACAAAYhgAIAABgGAIgAACA\nYQiAAAAAhiEAAgAAGIYACAAAYBgCIAAAgGEIgAAAAIYhAAIAABiGAAgAAGAYAiAAAIBhCIAAAACG\nIQACAAAYhgAIAABgGAIgAACAYQiAAAAAhiEAAgAAGIYACAAAYBgCIAAAgGEIgAAAAIYhAAIAABiG\nAAgAAGAYAiAAAIBhItwuINj5ZMsn2+0yKsZDzgcAAP8fyQAAAMAwBEAAAADDEAABAAAMQwAEAAAw\nDAEQAADAMARAAAAAwxAAAQAADBN0AXDdunXq0aOHUlNTZVmWlixZEnDetm2NHTtWKSkpio2NVUZG\nhvbu3RtwzdGjR9W/f3/Fx8erSpUquuWWW1RUVHQ+PwYAAEDQCroAePz4cTVr1kzTp08/4/kpU6Zo\n2rRpmjlzpjZt2qS4uDh17txZJ06c8F/Tv39/ffbZZ1q1apXeeecdrVu3ToMHDz5fHwEAACCoWbZt\nB+1rLizL0uLFi9WrVy9Jp0b/UlNTdc899+jee++VJBUUFCgpKUlz585Vv379tHv3bjVs2FBbtmxR\n69atJUkrVqxQt27d9M9//lOpqam/qu/CwkIlJCToSM5Fiq8cdDn5N+lZ73K3S3CE70Sx2yU4w1fm\ndgUAgBBUapfoAy1VQUGB4uPjK3SvkEo2Bw4cUF5enjIyMvxtCQkJatu2rTZs2CBJ2rBhg6pUqeIP\nf5KUkZEhj8ejTZs2nfXexcXFKiwsDDgAAADCUUgFwLy8PElSUlJSQHtSUpL/XF5enqpXrx5wPiIi\nQlWrVvVfcyaTJk1SQkKC/6hZs6bD1QMAAASHkAqA59Lo0aNVUFDgP3Jzc90uCQAA4JwIqQCYnJws\nScrPzw9oz8/P959LTk7WkSNHAs6Xlpbq6NGj/mvOJDo6WvHx8QEHAABAOAqpAJiWlqbk5GRlZWX5\n2woLC7Vp0yalp6dLktLT03Xs2DFlZ2f7r1m9erV8Pp/atm173msGAAAINhFuF/B/FRUVad++ff6f\nDxw4oB07dqhq1aqqVauWhg8frvHjx6tevXpKS0vTmDFjlJqa6n9SuEGDBurSpYtuu+02zZw5UyUl\nJRoyZIj69ev3q58ABgAACGdBFwC3bt2qDh06+H8eOXKkJGnAgAGaO3eu7r//fh0/flyDBw/WsWPH\ndNlll2nFihWKiYnx/86CBQs0ZMgQderUSR6PR3369NG0adPO+2cBAAAIRkG9D6Cb2Acw+LAPIADA\nZMbuAwgAAICKIwACAAAYhgAIAABgGAIgAACAYQiAAAAAhiEAAgAAGIYACAAAYJig2wg62Pjkk8/t\nIgAAABzECCAAAIBhCIAAAACGYQrYBJbldgUAACCIMAIIAABgGAIgAACAYQiAAAAAhiEAAgAAGIYA\nCAAAYBgCIAAAgGEIgAAAAIYhAAIAABiGAAgAAGAYAiAAAIBhCIAAAACGIQACAAAYhgAIAABgmAi3\nCwhWtm1Lkr4v8rlcScWV2ifdLsERPrvE7RKcYZe5XQEAIASV6tTfwZ8zSkUQAM/i+++/lyTVa3XI\n5Uqc8IrbBQAAAId8//33SkhIqNA9LNuJGBmGfD6fDh8+rMqVK8uyrHPSR2FhoWrWrKnc3FzFx8ef\nkz7w6/F9BBe+j+DC9xFc+D6Cy/n6Pmzb1vfff6/U1FR5PBVbxccI4Fl4PB7VqFHjvPQVHx/Pv8BB\nhO8juPB9BBe+j+DC9xFczsf3UdGRv5/xEAgAAIBhCIAAAACG8T766KOPul2Eybxer6688kpFRDAb\nHwz4PoIL30dw4fsILnwfwSXUvg8eAgEAADAMU8AAAACGIQACAAAYhgAIAABgGAKgS6ZPn67atWsr\nJiZGbdu21ebNm90uyUiTJk1SmzZtVLlyZVWvXl29evVSTk6O22XhJ0888YQsy9Lw4cPdLsVohw4d\n0g033KDExETFxsaqSZMm2rp1q9tlGamsrExjxoxRWlqaYmNjVadOHY0bN86RV4OhfOvWrVOPHj2U\nmpoqy7K0ZMmSgPO2bWvs2LFKSUlRbGysMjIytHfvXpeq/fcIgC547bXXNHLkSD3yyCPatm2bmjVr\nps6dO+vIkSNul2actWvXKjMzUxs3btSqVatUUlKiq6++WsePH3e7NONt2bJFs2bNUtOmTd0uxWjf\nffed2rVrp8jISC1fvlyff/65nnrqKV1wwQVul2akyZMna8aMGXr++ee1e/duTZ48WVOmTNFzzz3n\ndmlGOH78uJo1a6bp06ef8fyUKVM0bdo0zZw5U5s2bVJcXJw6d+6sEydOnOdKy8dTwC5o27at2rRp\no+eff17SqdfO1axZU0OHDtWoUaNcrs5s33zzjapXr661a9fqiiuucLscYxUVFally5Z64YUXNH78\neDVv3lxTp051uywjjRo1Sh999JE+/PBDt0uBpO7duyspKUkvv/yyv61Pnz6KjY3V//zP/7hYmXks\ny9LixYvVq1cvSadG/1JTU3XPPffo3nvvlSQVFBQoKSlJc+fOVb9+/dws9zSMAJ5nJ0+eVHZ2tjIy\nMvxtHo9HGRkZ2rBhg4uVQTr1L6skVa1a1eVKzJaZmalrrrkm4N8TuOPtt99W69at1bdvX1WvXl0t\nWrTQiy++6HZZxrr00kuVlZWlPXv2SJI++eQTrV+/Xl27dnW5Mhw4cEB5eXkB/91KSEhQ27Ztg/Lv\ne2jsVhhGvv32W5WVlSkpKSmgPSkpSV988YVLVUE6NRI7fPhwtWvXTo0bN3a7HGO9+uqr2rZtm7Zs\n2eJ2KZD05ZdfasaMGRo5cqQefPBBbdmyRcOGDVNUVJQGDBjgdnnGGTVqlAoLC1W/fn15vV6VlZVp\nwoQJ6t+/v9ulGS8vL0+Szvj3/edzwYQACPwkMzNTn376qdavX+92KcbKzc3V3XffrVWrVikmJsbt\ncqBT/8eodevWmjhxoiSpRYsW+vTTTzVz5kwCoAtef/11LViwQAsXLlSjRo20Y8cODR8+XKmpqXwf\n+E2YAj7PqlWrJq/Xq/z8/ID2/Px8JScnu1QVhgwZonfeeUdr1qxRjRo13C7HWNnZ2Tpy5Ihatmyp\niIgIRUREaO3atZo2bZoiIiJUVlbmdonGSUlJUcOGDQPaGjRooIMHD7pUkdnuu+8+jRo1Sv369VOT\nJk104403asSIEZo0aZLbpRnv57/hofL3nQB4nkVFRalVq1bKysryt/l8PmVlZSk9Pd3Fysxk27aG\nDBmixYsXa/Xq1UpLS3O7JKN16tRJu3bt0o4dO/xH69at1b9/f+3YsUNer9ftEo3Trl2707ZG2rNn\njy666CKXKjLbDz/8II8n8E+31+uVz+dzqSL8LC0tTcnJyQF/3wsLC7Vp06ag/PvOFLALRo4cqQED\nBqh169a65JJLNHXqVB0/flyDBg1yuzTjZGZmauHChVq6dKkqV67sX6eRkJCg2NhYl6szT+XKlU9b\nfxkXF6fExETWZbpkxIgRuvTSSzVx4kRdd9112rx5s2bPnq3Zs2e7XZqRevTooQkTJqhWrVpq1KiR\ntm/frqefflo333yz26UZoaioSPv27fP/fODAAe3YsUNVq1ZVrVq1NHz4cI0fP1716tVTWlqaxowZ\no9TUVP+TwkHFhiuee+45u1atWnZUVJR9ySWX2Bs3bnS7JCNJOuMxZ84ct0vDT9q3b2/ffffdbpdh\ntGXLltmNGze2o6Oj7fr169uzZ892uyRjFRYW2nfffbddq1YtOyYmxv7Tn/5kP/TQQ3ZxcbHbpRlh\nzZo1Z/ybMWDAANu2bdvn89ljxoyxk5KS7OjoaLtTp052Tk6Ou0WfBfsAAgAAGIY1gAAAAIYhAAIA\nABiGAAgAAGAYAiAAAIBhCIAAAACGIQACAAAYhgAIAABgGAIgAACAYQiAAPA7fPXVV7IsSzt27HC7\nlLOqXbu2pk6d6nYZAIIQARBAUBs4cKAsyzrt6NKli9ulnTOPPvqomjdv7nYZAMJYhNsFAEB5unTp\nojlz5gS0RUdHu1SNc06ePKmoqCi3ywBgIEYAAQS96OhoJScnBxwXXHCB/7xlWXrppZf0H//xH/rD\nH/6gevXq6e233w64x2effabu3bsrPj5elStX1uWXX679+/dLknw+nx5//HHVqFFD0dHRat68uVas\nWBHw+5s3b1aLFi0UExOj1q1ba/v27afV+emnn6pr166qVKmSkpKSdOONN+rbb7/1n7/yyis1ZMgQ\nDR8+XNWqVVPnzp1/1ecfOHCgevXqpSeffFIpKSlKTExUZmamSkpK/NccOXJEPXr0UGxsrNLS0rRg\nwYLT7nPs2DHdeuut+uMf/6j4+Hh17NhRn3zyiSTJtm1lZGSoc+fO+vkV8UePHlWNGjU0duzYX1Un\ngNBBAAQQFh577DFdd9112rlzp7p166b+/fvr6NGjkqRDhw7piiuuUHR0tFavXq3s7GzdfPPNKi0t\nlSQ9++yzeuqpp/Tkk09q586d6ty5s3r27Km9e/dKkoqKitS9e3c1bNhQ2dnZevTRR3XvvfcG9H/s\n2DF17NhRLVq00NatW7VixQrl5+fruuuuC7hu3rx5ioqK0kcffaSZM2f+6s+3Zs0a7d+/X2vWrNG8\nefM0d+5czZ07139+4MCBys3N1Zo1a/Tmm2/qhRde0JEjRwLu0bdvXx05ckTLly9Xdna2WrZsqU6d\nOuno0aOyLEvz5s3Tli1bNG3aNEnSHXfcoQsvvJAACIQjGwCC2IABA2yv12vHxcUFHBMmTPBfI8l+\n+OGH/T8XFRXZkuzly5fbtm3bo0ePttPS0uyTJ0+esY/U1NSA+9m2bbdp08a+6667bNu27VmzZtmJ\niYn2jz/+6D8/Y8YMW5K9fft227Zte9y4cfbVV18dcI/c3Fxbkp2Tk2Pbtm23b9/ebtGiRbmf+ZFH\nHrGbNWsW8L/BRRddZJeWlvrb+vbta19//fW2bdt2Tk6OLcnevHmz//zu3bttSfYzzzxj27Ztf/jh\nh3Z8fLx94sSJgL7q1Kljz5o1y//z66+/bsfExNijRo2y4+Li7D179pRbL4DQwxpAAEGvQ4cOmjFj\nRkBb1apVA35u2rSp/5/j4uIUHx/vHwHbsWOHLr/8ckVGRp5278LCQh0+fFjt2rULaG/Xrp1/enT3\n7t1q2rSpYmJi/OfT09MDrv/kk0+0Zs0aVapU6bQ+9u/fr4svvliS1KpVq3I/75k0atRIXq/X/3NK\nSop27drlry8iIiLg3vXr11eVKlUC6isqKlJiYmLAfX/88Uf/VLh0apRw8eLFeuKJJzRjxgzVq1fv\nd9ULILgRAAEEvbi4ONWtW/ffXvN/w51lWfL5fJKk2NjYc1bbz4qKitSjRw9Nnjz5tHMpKSn+f46L\ni/td9/93n+/X1peSkqIPPvjgtHO/DIo//PCDsrOz5fV6/VPgAMIPawABhL2mTZvqww8/DHho4mfx\n8fFKTU3VRx99FND+0UcfqWHDhpKkBg0aaOfOnTpx4oT//MaNGwOub9mypT777DPVrl1bdevWDTh+\nb+j7terXr6/S0lJlZ2f723JycnTs2LGA+vLy8hQREXFafdWqVfNfd88998jj8Wj58uWaNm2aVq9e\nfU5rB+AOAiCAoFdcXKy8vLyA45dP15ZnyJAhKiwsVL9+/bR161bt3btX//3f/62cnBxJ0n333afJ\nkyfrtddeU05OjkaNGqUdO3bo7rvvliT97W9/k2VZuu222/T555/r3Xff1ZNPPhnQR2Zmpo4ePar/\n/M//1JYtW7R//36tXLlSgwYNUllZmXP/Y5zBn//8Z3Xp0kW33367Nm3apOzsbN16660BI58ZGRlK\nT09Xr1699N577+mrr77Sxx9/rIceekhbt26VJP3jH//Qf/3Xf2nBggW66qqrdN9992nAgAH67rvv\nzmn9AM4/AiCAoLdixQqlpKQEHJdddtmv/v3ExEStXr1aRUVFat++vVq1aqUXX3zRP606bNgwjRw5\nUvfcc4+aNGmiFStW6O233/avf6tUqZKWLVumXbt2qUWLFnrooYdOm+r9eRSxrKxMV199tZo0aaLh\nw4erSpUq8njO/X9q58yZo9TUVLVv3169e/fW4MGDVb16df95y7L07rvv6oorrtCgQYN08cUXq1+/\nfvr666+VlJSkb775RrfccoseffRRtWzZUtKpJ6uTkpJ0xx13nPP6AZxflm3/tOETAAAAjMAIIAAA\ngGEIgAAAAIYhAAIAABiGAAgAAGAYAiAAAIBhCIAAAACGIQACAAAYhgAIAABgGAIgAACAYQiAAAAA\nhiEAAgAAGIYACAAAYBgCIAAAgGEIgAAAAIYhAAIAABiGAAgAAGAYAiAAAIBh/h/iO0L0StzrygAA\nAABJRU5ErkJggg==\n" }, "metadata": { "tags": [] } } ] }, { "cell_type": "markdown", "metadata": { "id": "s6_hIi3OVsvZ", "colab_type": "text" }, "source": [ "`exp/train_*/results/model.loss.best` contains only the model parameters. \n", "On the other hand, `exp/train_*/results/snapshot` contains the model parameters, optimizer states, and iterator states. \n", "So you can restart from the training by specifying the snapshot file with **--resume** option.\n" ] }, { "cell_type": "code", "metadata": { "id": "bYY323h4WVgQ", "colab_type": "code", "outputId": "2767c24d-3d0e-41e8-9cca-147b77813266", "colab": { "base_uri": "https://localhost:8080/", "height": 89 } }, "source": [ "# resume training from snapshot.ep.2\n", "!./run.sh --stage 3 --stop_stage 3 --train_config conf/train_pytorch_tacotron2_sample.yaml --resume exp/train_nodev_pytorch_train_pytorch_tacotron2_sample/results/snapshot.ep.2 --verbose 1" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "dictionary: data/lang_1char/train_nodev_units.txt\n", "stage 3: Text-to-speech model training\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "JNfzDyQRWsf-", "colab_type": "code", "collapsed": true, "colab": {} }, "source": [ "!cat exp/train_nodev_pytorch_train_pytorch_tacotron2_sample/train.log" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "JjkrYF-NeZoM", "colab_type": "text" }, "source": [ "Also, we support tensorboard. \n", "You can see the training log through tensorboard." ] }, { "cell_type": "code", "metadata": { "id": "OqwnbGznmrgb", "colab_type": "code", "colab": {} }, "source": [ "%load_ext tensorboard\n", "%tensorboard --logdir tensorboard/train_nodev_pytorch_train_pytorch_tacotron2_sample/" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "88UvHDgGJSLX", "colab_type": "text" }, "source": [ "### Stage 4: Network decoding\n", "\n", "This stage performs decoding using the trained model to generate mel-spectrogram from a given text. \n" ] }, { "cell_type": "code", "metadata": { "id": "PjVP2QPXBmav", "colab_type": "code", "colab": {} }, "source": [ "!./run.sh --stage 4 --stop_stage 4 --nj 8 --train_config conf/train_pytorch_tacotron2_sample.yaml " ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "I7-8ja9kcdDG", "colab_type": "text" }, "source": [ "Generated features are saved as ark/scp format." ] }, { "cell_type": "code", "metadata": { "id": "8V_g_VbDckjT", "colab_type": "code", "colab": {} }, "source": [ "!ls exp/train_nodev_pytorch_train_pytorch_tacotron2_sample/outputs_model.loss.best_decode/*" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "eLvdP4z9cJKc", "colab_type": "text" }, "source": [ "We can specify the model or snapshot to be used for decoding via **--model**. " ] }, { "cell_type": "code", "metadata": { "id": "_Op5GAW6cM7q", "colab_type": "code", "colab": {} }, "source": [ "!./run.sh --stage 4 --stop_stage 4 --nj 8 --train_config conf/train_pytorch_tacotron2_sample.yaml --model snapshot.ep.2" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "hFowPMOsdvY9", "colab_type": "code", "colab": {} }, "source": [ "!ls exp/train_nodev_pytorch_train_pytorch_tacotron2_sample/outputs_snapshot.ep.2_decode/*" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "9A74Jv8KL_FC", "colab_type": "text" }, "source": [ "### Stage 5: Waveform synthesis\n", "\n", "Finally, in this stage, we generate waveform using Grrifin-Lim algorithm. \n", "First, we perform de-normalization to convert the generated mel-spectrogram into the original scale. \n", "Then we apply Grrifin-Lim algorithm to restore phase components and apply inverse STFT to generate waveforms." ] }, { "cell_type": "code", "metadata": { "id": "wasoVKklc4eS", "colab_type": "code", "colab": {} }, "source": [ "!./run.sh --stage 5 --stop_stage 5 --nj 8 --train_config conf/train_pytorch_tacotron2_sample.yaml --griffin_lim_iters 50" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "E7eoOkRYeIhN", "colab_type": "text" }, "source": [ "Generated wav files are saved in `exp/train_nodev_pytorch_train_pytorch_tacotron2_sample/outputs_model.loss.best_decode_denorm/*/wav`" ] }, { "cell_type": "code", "metadata": { "id": "r-pfSNMheHdf", "colab_type": "code", "colab": {} }, "source": [ "!ls exp/train_nodev_pytorch_train_pytorch_tacotron2_sample/outputs_model.loss.best_decode_denorm/*/wav" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "kp6LcFfih0hJ", "colab_type": "code", "colab": {} }, "source": [ "!tree -L 3" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "ti5PArvce6HU", "colab_type": "text" }, "source": [ "## NEXT step\n", "\n", "- Try pretrained model to generate speech.\n", "- Try a large single speaker dataset recipe **egs/ljspeech/tts1**.\n", "- Try a large multi-speaker recipe **egs/libritts/tts1**.\n", "- Make the original recipe using your own dataset." ] } ] }