colab-train-zh-cn.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oxl2bna8ztl2"
      },
      "source": [
        "**2022/08/12 更新和汉化，排除tensorflow版本问题，使用 https://github.com/CjangCjengh/tacotron2-japanese 库中的cleaner省去罗马音环节**\n",
        "**2022/08/12 Updates and use Chinese instead. Tensorflow version problem solved.**\n",
        "**Use cleaners in repo https://github.com/CjangCjengh/tacotron2-japanese to auto-generate roman words.**\n",
        "<br >\n",
        "\n",
        "**Updated 2022/03/14 and the unpickling error is solved. The training part works as of 2022/03/14**\n",
        "\n",
        "**2022/03/15 Speech synsthesis with HiFi-GAN works** \n",
        "\n",
        "**2022/03/16 Speech synsthesis with Waveglow should work again now (tested)** \n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Tacotron 2 Training and Synthesis Notebook**\n",
        "originally based on the following notebooks\n",
        "https://github.com/NVIDIA/tacotron2,\n",
        "https://bit.ly/3F4DkH2\n",
        "and those presented in Adam is cool and stuff (https://youtu.be/LQAOCXdU8p8 and https://youtu.be/XLt_K_692Mc)\n",
        "\n",
        "<br >\n",
        "\n",
        "**Tacotron 2 训练和语音合成笔记本**\n",
        "基于\n",
        "https://github.com/NVIDIA/tacotron2,\n",
        "https://bit.ly/3F4DkH2,\n",
        "https://colab.research.google.com/drive/1VAuIqEAnrmCig3Edt5zFgQdckY9TDi3N\n",
        "\n",
        "还要感谢Adam is cool and stuff频道的视频 (https://youtu.be/LQAOCXdU8p8 and https://youtu.be/XLt_K_692Mc)\n",
        "\n",
        "感谢CjangCjengh的cleaner支持和视频灵感 (https://www.bilibili.com/video/BV1rV4y177Z7)"
      ],
      "metadata": {
        "id": "2CuJ5rIAIv1H"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M5rkhiBCXbMY"
      },
      "source": [
        "**使用LJ Speech数据集预先训练权重的WaveGlow模型可以从这个地址下载：https://catalog.ngc.nvidia.com/orgs/nvidia/models/waveglow_ljs_256channels**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r2D2Mt80bamF"
      },
      "source": [
        "# 准备数据 "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "execution": {
          "iopub.execute_input": "2022-08-12T04:13:05.594388Z",
          "iopub.status.busy": "2022-08-12T04:13:05.593773Z",
          "iopub.status.idle": "2022-08-12T04:13:11.760149Z",
          "shell.execute_reply": "2022-08-12T04:13:11.758823Z",
          "shell.execute_reply.started": "2022-08-12T04:13:05.594273Z"
        },
        "id": "67u1nnaJcyPt",
        "trusted": true
      },
      "outputs": [],
      "source": [
        "#@title 下载 Tacotron 2\n",
        "!git clone https://github.com/CjangCjengh/tacotron2-japanese tacotron2\n",
        "!git submodule init\n",
        "!git submodule update"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-08-12T04:13:20.371238Z",
          "iopub.status.busy": "2022-08-12T04:13:20.370604Z",
          "iopub.status.idle": "2022-08-12T04:18:18.664088Z",
          "shell.execute_reply": "2022-08-12T04:18:18.662898Z",
          "shell.execute_reply.started": "2022-08-12T04:13:20.371200Z"
        },
        "trusted": true,
        "cellView": "form",
        "id": "ujgAmihAIZg1"
      },
      "outputs": [],
      "source": [
        "#@title 安装依赖\n",
        "!pip install -U tensorflow==1.15.2\n",
        "!pip install -q unidecode tensorboardX\n",
        "!pip install librosa==0.8.0\n",
        "!pip install pysoundfile==0.9.0.post1\n",
        "!pip install unidecode==1.3.4\n",
        "!pip install pyopenjtalk==0.2.0\n",
        "!pip install inflect==5.6.2\n",
        "!pip install janome==0.4.2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true,
        "cellView": "form",
        "id": "TAouEAY7IZg7"
      },
      "outputs": [],
      "source": [
        "#@title 加载Google云端硬盘\n",
        "from google.colab import drive\n",
        "drive.mount('drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title 创建文件夹和下载预训练模型\n",
        "import os\n",
        "if os.getcwd() != '/content/tacotron2':\n",
        "    os.chdir('/content/tacotron2')\n",
        "! gdown --id 1c5ZTuT7J08wLUoVZ2KkUs_VdZuJ86ZqA\n",
        "if not os.path.isdir(\"wavs\"):\n",
        "    os.mkdir('wavs')\n",
        "if not os.path.isdir(\"outdir\"):\n",
        "    os.mkdir(\"outdir\")"
      ],
      "metadata": {
        "cellView": "form",
        "id": "-tH9g-WrJ0Ro"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pMt_D2p7c7Dl"
      },
      "source": [
        "### 上传数据\n",
        "\n",
        "`text file` 是音频文件列表\n",
        "\n",
        "`audio files` 是音频文件"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
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      ],
      "metadata": {
        "id": "6tKrpYzpg8t0"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IOETvfJdbfYO"
      },
      "source": [
        "# 准备模型"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-08-12T04:22:26.512807Z",
          "iopub.status.busy": "2022-08-12T04:22:26.512405Z",
          "iopub.status.idle": "2022-08-12T04:22:48.851514Z",
          "shell.execute_reply": "2022-08-12T04:22:48.850448Z",
          "shell.execute_reply.started": "2022-08-12T04:22:26.512767Z"
        },
        "trusted": true,
        "cellView": "form",
        "id": "VKFIe_5pIZg9"
      },
      "outputs": [],
      "source": [
        "#@title 训练模型的代码\n",
        "%matplotlib inline\n",
        "\n",
        "import time\n",
        "import argparse\n",
        "import math\n",
        "from numpy import finfo\n",
        "\n",
        "import torch\n",
        "from distributed import apply_gradient_allreduce\n",
        "import torch.distributed as dist\n",
        "from torch.utils.data.distributed import DistributedSampler\n",
        "from torch.utils.data import DataLoader\n",
        "\n",
        "from model import Tacotron2\n",
        "from data_utils import TextMelLoader, TextMelCollate\n",
        "from loss_function import Tacotron2Loss\n",
        "from logger import Tacotron2Logger\n",
        "from hparams import create_hparams\n",
        " \n",
        "import random\n",
        "import numpy as np\n",
        "\n",
        "import layers\n",
        "from utils import load_wav_to_torch, load_filepaths_and_text\n",
        "from text import text_to_sequence\n",
        "from math import e\n",
        "#from tqdm import tqdm # Terminal\n",
        "#from tqdm import tqdm_notebook as tqdm # Legacy Notebook TQDM\n",
        "from tqdm.notebook import tqdm # Modern Notebook TQDM\n",
        "from distutils.dir_util import copy_tree\n",
        "import matplotlib.pylab as plt\n",
        "\n",
        "def download_from_google_drive(file_id, file_name):\n",
        "  # download a file from the Google Drive link\n",
        "  !rm -f ./cookie\n",
        "  !curl -c ./cookie -s -L \"https://drive.google.com/uc?export=download&id={file_id}\" > /dev/null\n",
        "  confirm_text = !awk '/download/ {print $NF}' ./cookie\n",
        "  confirm_text = confirm_text[0]\n",
        "  !curl -Lb ./cookie \"https://drive.google.com/uc?export=download&confirm={confirm_text}&id={file_id}\" -o {file_name}\n",
        "\n",
        "def create_mels():\n",
        "    print(\"Generating Mels\")\n",
        "    stft = layers.TacotronSTFT(\n",
        "                hparams.filter_length, hparams.hop_length, hparams.win_length,\n",
        "                hparams.n_mel_channels, hparams.sampling_rate, hparams.mel_fmin,\n",
        "                hparams.mel_fmax)\n",
        "    def save_mel(filename):\n",
        "        audio, sampling_rate = load_wav_to_torch(filename)\n",
        "        if sampling_rate != stft.sampling_rate:\n",
        "            raise ValueError(\"{} {} SR doesn't match target {} SR\".format(filename, \n",
        "                sampling_rate, stft.sampling_rate))\n",
        "        audio_norm = audio / hparams.max_wav_value\n",
        "        audio_norm = audio_norm.unsqueeze(0)\n",
        "        audio_norm = torch.autograd.Variable(audio_norm, requires_grad=False)\n",
        "        melspec = stft.mel_spectrogram(audio_norm)\n",
        "        melspec = torch.squeeze(melspec, 0).cpu().numpy()\n",
        "        np.save(filename.replace('.wav', ''), melspec)\n",
        "\n",
        "    import glob\n",
        "    wavs = glob.glob('wavs/*.wav')\n",
        "    for i in tqdm(wavs):\n",
        "        save_mel(i)\n",
        "\n",
        "\n",
        "def reduce_tensor(tensor, n_gpus):\n",
        "    rt = tensor.clone()\n",
        "    dist.all_reduce(rt, op=dist.reduce_op.SUM)\n",
        "    rt /= n_gpus\n",
        "    return rt\n",
        "\n",
        "\n",
        "def init_distributed(hparams, n_gpus, rank, group_name):\n",
        "    assert torch.cuda.is_available(), \"Distributed mode requires CUDA.\"\n",
        "    print(\"Initializing Distributed\")\n",
        "\n",
        "    # Set cuda device so everything is done on the right GPU.\n",
        "    torch.cuda.set_device(rank % torch.cuda.device_count())\n",
        "\n",
        "    # Initialize distributed communication\n",
        "    dist.init_process_group(\n",
        "        backend=hparams.dist_backend, init_method=hparams.dist_url,\n",
        "        world_size=n_gpus, rank=rank, group_name=group_name)\n",
        "\n",
        "    print(\"Done initializing distributed\")\n",
        "\n",
        "\n",
        "def prepare_dataloaders(hparams):\n",
        "    # Get data, data loaders and collate function ready\n",
        "    trainset = TextMelLoader(hparams.training_files, hparams)\n",
        "    valset = TextMelLoader(hparams.validation_files, hparams)\n",
        "    collate_fn = TextMelCollate(hparams.n_frames_per_step)\n",
        "\n",
        "    if hparams.distributed_run:\n",
        "        train_sampler = DistributedSampler(trainset)\n",
        "        shuffle = False\n",
        "    else:\n",
        "        train_sampler = None\n",
        "        shuffle = True\n",
        "\n",
        "    train_loader = DataLoader(trainset, num_workers=1, shuffle=shuffle,\n",
        "                              sampler=train_sampler,\n",
        "                              batch_size=hparams.batch_size, pin_memory=False,\n",
        "                              drop_last=True, collate_fn=collate_fn)\n",
        "    return train_loader, valset, collate_fn\n",
        "\n",
        "\n",
        "def prepare_directories_and_logger(output_directory, log_directory, rank):\n",
        "    if rank == 0:\n",
        "        if not os.path.isdir(output_directory):\n",
        "            os.makedirs(output_directory)\n",
        "            os.chmod(output_directory, 0o775)\n",
        "        logger = Tacotron2Logger(os.path.join(output_directory, log_directory))\n",
        "    else:\n",
        "        logger = None\n",
        "    return logger\n",
        "\n",
        "\n",
        "def load_model(hparams):\n",
        "    model = Tacotron2(hparams).cuda()\n",
        "    if hparams.fp16_run:\n",
        "        model.decoder.attention_layer.score_mask_value = finfo('float16').min\n",
        "\n",
        "    if hparams.distributed_run:\n",
        "        model = apply_gradient_allreduce(model)\n",
        "\n",
        "    return model\n",
        "\n",
        "\n",
        "def warm_start_model(checkpoint_path, model, ignore_layers):\n",
        "    assert os.path.isfile(checkpoint_path)\n",
        "    print(\"Warm starting model from checkpoint '{}'\".format(checkpoint_path))\n",
        "    checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')\n",
        "    model_dict = checkpoint_dict['state_dict']\n",
        "    if len(ignore_layers) > 0:\n",
        "        model_dict = {k: v for k, v in model_dict.items()\n",
        "                      if k not in ignore_layers}\n",
        "        dummy_dict = model.state_dict()\n",
        "        dummy_dict.update(model_dict)\n",
        "        model_dict = dummy_dict\n",
        "    model.load_state_dict(model_dict)\n",
        "    return model\n",
        "\n",
        "\n",
        "def load_checkpoint(checkpoint_path, model, optimizer):\n",
        "    assert os.path.isfile(checkpoint_path)\n",
        "    print(\"Loading checkpoint '{}'\".format(checkpoint_path))\n",
        "    checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')\n",
        "    model.load_state_dict(checkpoint_dict['state_dict'])\n",
        "    optimizer.load_state_dict(checkpoint_dict['optimizer'])\n",
        "    learning_rate = checkpoint_dict['learning_rate']\n",
        "    iteration = checkpoint_dict['iteration']\n",
        "    print(\"Loaded checkpoint '{}' from iteration {}\" .format(\n",
        "        checkpoint_path, iteration))\n",
        "    return model, optimizer, learning_rate, iteration\n",
        "\n",
        "\n",
        "def save_checkpoint(model, optimizer, learning_rate, iteration, filepath):\n",
        "    print(\"Saving model and optimizer state at iteration {} to {}\".format(\n",
        "        iteration, filepath))\n",
        "    try:\n",
        "        torch.save({'iteration': iteration,\n",
        "                'state_dict': model.state_dict(),\n",
        "                'optimizer': optimizer.state_dict(),\n",
        "                'learning_rate': learning_rate}, filepath)\n",
        "    except KeyboardInterrupt:\n",
        "        print(\"interrupt received while saving, waiting for save to complete.\")\n",
        "        torch.save({'iteration': iteration,'state_dict': model.state_dict(),'optimizer': optimizer.state_dict(),'learning_rate': learning_rate}, filepath)\n",
        "    print(\"Model Saved\")\n",
        "\n",
        "def plot_alignment(alignment, info=None):\n",
        "    %matplotlib inline\n",
        "    fig, ax = plt.subplots(figsize=(int(alignment_graph_width/100), int(alignment_graph_height/100)))\n",
        "    im = ax.imshow(alignment, cmap='inferno', aspect='auto', origin='lower',\n",
        "                   interpolation='none')\n",
        "    ax.autoscale(enable=True, axis=\"y\", tight=True)\n",
        "    fig.colorbar(im, ax=ax)\n",
        "    xlabel = 'Decoder timestep'\n",
        "    if info is not None:\n",
        "        xlabel += '\\n\\n' + info\n",
        "    plt.xlabel(xlabel)\n",
        "    plt.ylabel('Encoder timestep')\n",
        "    plt.tight_layout()\n",
        "    fig.canvas.draw()\n",
        "    plt.show()\n",
        "\n",
        "def validate(model, criterion, valset, iteration, batch_size, n_gpus,\n",
        "             collate_fn, logger, distributed_run, rank, epoch, start_eposh, learning_rate):\n",
        "    \"\"\"Handles all the validation scoring and printing\"\"\"\n",
        "    model.eval()\n",
        "    with torch.no_grad():\n",
        "        val_sampler = DistributedSampler(valset) if distributed_run else None\n",
        "        val_loader = DataLoader(valset, sampler=val_sampler, num_workers=1,\n",
        "                                shuffle=False, batch_size=batch_size,\n",
        "                                pin_memory=False, collate_fn=collate_fn)\n",
        "\n",
        "        val_loss = 0.0\n",
        "        for i, batch in enumerate(val_loader):\n",
        "            x, y = model.parse_batch(batch)\n",
        "            y_pred = model(x)\n",
        "            loss = criterion(y_pred, y)\n",
        "            if distributed_run:\n",
        "                reduced_val_loss = reduce_tensor(loss.data, n_gpus).item()\n",
        "            else:\n",
        "                reduced_val_loss = loss.item()\n",
        "            val_loss += reduced_val_loss\n",
        "        val_loss = val_loss / (i + 1)\n",
        "\n",
        "    model.train()\n",
        "    if rank == 0:\n",
        "        print(\"Epoch: {} Validation loss {}: {:9f}  Time: {:.1f}m LR: {:.6f}\".format(epoch, iteration, val_loss,(time.perf_counter()-start_eposh)/60, learning_rate))\n",
        "        logger.log_validation(val_loss, model, y, y_pred, iteration)\n",
        "        if hparams.show_alignments:\n",
        "            %matplotlib inline\n",
        "            _, mel_outputs, gate_outputs, alignments = y_pred\n",
        "            idx = random.randint(0, alignments.size(0) - 1)\n",
        "            plot_alignment(alignments[idx].data.cpu().numpy().T)\n",
        "\n",
        "def train(output_directory, log_directory, checkpoint_path, warm_start, n_gpus,\n",
        "          rank, group_name, hparams, log_directory2):\n",
        "    \"\"\"Training and validation logging results to tensorboard and stdout\n",
        "\n",
        "    Params\n",
        "    ------\n",
        "    output_directory (string): directory to save checkpoints\n",
        "    log_directory (string) directory to save tensorboard logs\n",
        "    checkpoint_path(string): checkpoint path\n",
        "    n_gpus (int): number of gpus\n",
        "    rank (int): rank of current gpu\n",
        "    hparams (object): comma separated list of \"name=value\" pairs.\n",
        "    \"\"\"\n",
        "    if hparams.distributed_run:\n",
        "        init_distributed(hparams, n_gpus, rank, group_name)\n",
        "\n",
        "    torch.manual_seed(hparams.seed)\n",
        "    torch.cuda.manual_seed(hparams.seed)\n",
        "\n",
        "    model = load_model(hparams)\n",
        "    learning_rate = hparams.learning_rate\n",
        "    optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate,\n",
        "                                 weight_decay=hparams.weight_decay)\n",
        "\n",
        "    if hparams.fp16_run:\n",
        "        from apex import amp\n",
        "        model, optimizer = amp.initialize(\n",
        "            model, optimizer, opt_level='O2')\n",
        "\n",
        "    if hparams.distributed_run:\n",
        "        model = apply_gradient_allreduce(model)\n",
        "\n",
        "    criterion = Tacotron2Loss()\n",
        "\n",
        "    logger = prepare_directories_and_logger(\n",
        "        output_directory, log_directory, rank)\n",
        "\n",
        "    train_loader, valset, collate_fn = prepare_dataloaders(hparams)\n",
        "\n",
        "    # Load checkpoint if one exists\n",
        "    iteration = 0\n",
        "    epoch_offset = 0\n",
        "    if checkpoint_path is not None and os.path.isfile(checkpoint_path):\n",
        "        if warm_start:\n",
        "            model = warm_start_model(\n",
        "                checkpoint_path, model, hparams.ignore_layers)\n",
        "        else:\n",
        "            model, optimizer, _learning_rate, iteration = load_checkpoint(\n",
        "                checkpoint_path, model, optimizer)\n",
        "            if hparams.use_saved_learning_rate:\n",
        "                learning_rate = _learning_rate\n",
        "            iteration += 1  # next iteration is iteration + 1\n",
        "            epoch_offset = max(0, int(iteration / len(train_loader)))\n",
        "    else:\n",
        "      os.path.isfile(\"tacotron2_statedict.pt\")\n",
        "      model = warm_start_model(\"tacotron2_statedict.pt\", model, hparams.ignore_layers)\n",
        "      # download LJSpeech pretrained model if no checkpoint already exists\n",
        "    \n",
        "    start_eposh = time.perf_counter()\n",
        "    learning_rate = 0.0\n",
        "    model.train()\n",
        "    is_overflow = False\n",
        "    # ================ MAIN TRAINNIG LOOP! ===================\n",
        "    for epoch in tqdm(range(epoch_offset, hparams.epochs)):\n",
        "        print(\"\\nStarting Epoch: {} Iteration: {}\".format(epoch, iteration))\n",
        "        start_eposh = time.perf_counter() # eposh is russian, not a typo\n",
        "        for i, batch in tqdm(enumerate(train_loader), total=len(train_loader)):\n",
        "            start = time.perf_counter()\n",
        "            if iteration < hparams.decay_start: learning_rate = hparams.A_\n",
        "            else: iteration_adjusted = iteration - hparams.decay_start; learning_rate = (hparams.A_*(e**(-iteration_adjusted/hparams.B_))) + hparams.C_\n",
        "            learning_rate = max(hparams.min_learning_rate, learning_rate) # output the largest number\n",
        "            for param_group in optimizer.param_groups:\n",
        "                param_group['lr'] = learning_rate\n",
        "\n",
        "            model.zero_grad()\n",
        "            x, y = model.parse_batch(batch)\n",
        "            y_pred = model(x)\n",
        "\n",
        "            loss = criterion(y_pred, y)\n",
        "            if hparams.distributed_run:\n",
        "                reduced_loss = reduce_tensor(loss.data, n_gpus).item()\n",
        "            else:\n",
        "                reduced_loss = loss.item()\n",
        "            if hparams.fp16_run:\n",
        "                with amp.scale_loss(loss, optimizer) as scaled_loss:\n",
        "                    scaled_loss.backward()\n",
        "            else:\n",
        "                loss.backward()\n",
        "\n",
        "            if hparams.fp16_run:\n",
        "                grad_norm = torch.nn.utils.clip_grad_norm_(\n",
        "                    amp.master_params(optimizer), hparams.grad_clip_thresh)\n",
        "                is_overflow = math.isnan(grad_norm)\n",
        "            else:\n",
        "                grad_norm = torch.nn.utils.clip_grad_norm_(\n",
        "                    model.parameters(), hparams.grad_clip_thresh)\n",
        "\n",
        "            optimizer.step()\n",
        "\n",
        "            if not is_overflow and rank == 0:\n",
        "                duration = time.perf_counter() - start\n",
        "                logger.log_training(\n",
        "                    reduced_loss, grad_norm, learning_rate, duration, iteration)\n",
        "                #print(\"Batch {} loss {:.6f} Grad Norm {:.6f} Time {:.6f}\".format(iteration, reduced_loss, grad_norm, duration), end='\\r', flush=True)\n",
        "\n",
        "            iteration += 1\n",
        "        validate(model, criterion, valset, iteration,\n",
        "                 hparams.batch_size, n_gpus, collate_fn, logger,\n",
        "                 hparams.distributed_run, rank, epoch, start_eposh, learning_rate)\n",
        "        save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path)\n",
        "        if log_directory2 != None:\n",
        "            copy_tree(log_directory, log_directory2)\n",
        "def check_dataset(hparams):\n",
        "    from utils import load_wav_to_torch, load_filepaths_and_text\n",
        "    import os\n",
        "    import numpy as np\n",
        "    def check_arr(filelist_arr):\n",
        "        for i, file in enumerate(filelist_arr):\n",
        "            if len(file) > 2:\n",
        "                print(\"|\".join(file), \"\\nhas multiple '|', this may not be an error.\")\n",
        "            if hparams.load_mel_from_disk and '.wav' in file[0]:\n",
        "                print(\"[WARNING]\", file[0], \" in filelist while expecting .npy .\")\n",
        "            else:\n",
        "                if not hparams.load_mel_from_disk and '.npy' in file[0]:\n",
        "                    print(\"[WARNING]\", file[0], \" in filelist while expecting .wav .\")\n",
        "            if (not os.path.exists(file[0])):\n",
        "                print(\"|\".join(file), \"\\n[WARNING] does not exist.\")\n",
        "            if len(file[1]) < 3:\n",
        "                print(\"|\".join(file), \"\\n[info] has no/very little text.\")\n",
        "            if not ((file[1].strip())[-1] in r\"!?,.;:\"):\n",
        "                print(\"|\".join(file), \"\\n[info] has no ending punctuation.\")\n",
        "            mel_length = 1\n",
        "            if hparams.load_mel_from_disk and '.npy' in file[0]:\n",
        "                melspec = torch.from_numpy(np.load(file[0], allow_pickle=True))\n",
        "                mel_length = melspec.shape[1]\n",
        "            if mel_length == 0:\n",
        "                print(\"|\".join(file), \"\\n[WARNING] has 0 duration.\")\n",
        "    print(\"Checking Training Files\")\n",
        "    audiopaths_and_text = load_filepaths_and_text(hparams.training_files) # get split lines from training_files text file.\n",
        "    check_arr(audiopaths_and_text)\n",
        "    print(\"Checking Validation Files\")\n",
        "    audiopaths_and_text = load_filepaths_and_text(hparams.validation_files) # get split lines from validation_files text file.\n",
        "    check_arr(audiopaths_and_text)\n",
        "    print(\"Finished Checking\")\n",
        "\n",
        "warm_start=False #sorry about that\n",
        "n_gpus=1\n",
        "rank=0\n",
        "group_name=None\n",
        "\n",
        "# ---- 这是定义的默认参数，可以不用管 ----\n",
        "hparams = create_hparams()\n",
        "model_filename = 'current_model'\n",
        "hparams.training_files = \"filelists/clipper_train_filelist.txt\"\n",
        "hparams.validation_files = \"filelists/clipper_val_filelist.txt\"\n",
        "#hparams.use_mmi=True,          # not used in this notebook\n",
        "#hparams.use_gaf=True,          # not used in this notebook\n",
        "#hparams.max_gaf=0.5,           # not used in this notebook\n",
        "#hparams.drop_frame_rate = 0.2  # not used in this notebook\n",
        "hparams.p_attention_dropout=0.1\n",
        "hparams.p_decoder_dropout=0.1\n",
        "hparams.decay_start = 15000\n",
        "hparams.A_ = 5e-4\n",
        "hparams.B_ = 8000\n",
        "hparams.C_ = 0\n",
        "hparams.min_learning_rate = 1e-5\n",
        "generate_mels = True\n",
        "hparams.show_alignments = True\n",
        "alignment_graph_height = 600\n",
        "alignment_graph_width = 1000\n",
        "hparams.batch_size = 32\n",
        "hparams.load_mel_from_disk = True\n",
        "hparams.ignore_layers = []\n",
        "hparams.epochs = 10000\n",
        "\n",
        "torch.backends.cudnn.enabled = hparams.cudnn_enabled\n",
        "torch.backends.cudnn.benchmark = hparams.cudnn_benchmark\n",
        "output_directory = '/content/drive/MyDrive/colab/outdir' # Location to save Checkpoints\n",
        "log_directory = '/content/tacotron2/logs' # Location to save Log files locally\n",
        "log_directory2 = '/content/drive/MyDrive/colab/logs' # Location to copy log files (done at the end of each epoch to cut down on I/O)e\n",
        "checkpoint_path = output_directory+(r'/')+model_filename\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "execution": {
          "iopub.execute_input": "2022-08-12T04:23:09.697053Z",
          "iopub.status.busy": "2022-08-12T04:23:09.696148Z",
          "iopub.status.idle": "2022-08-12T04:23:09.701991Z",
          "shell.execute_reply": "2022-08-12T04:23:09.700529Z",
          "shell.execute_reply.started": "2022-08-12T04:23:09.697015Z"
        },
        "id": "GKRvQ1EWiVhn",
        "trusted": true
      },
      "outputs": [],
      "source": [
        "#@title 给你的模型取名（写字母数字）\n",
        "model_filename = \"test\" #@param {type:\"string\"}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-08-12T04:23:14.426817Z",
          "iopub.status.busy": "2022-08-12T04:23:14.426455Z",
          "iopub.status.idle": "2022-08-12T04:23:14.433170Z",
          "shell.execute_reply": "2022-08-12T04:23:14.432204Z",
          "shell.execute_reply.started": "2022-08-12T04:23:14.426786Z"
        },
        "trusted": true,
        "cellView": "form",
        "id": "ew2HzQl2IZhH"
      },
      "outputs": [],
      "source": [
        "#@title 添加之前训练的模型到输出文件夹\n",
        "\n",
        "#@markdown 如果之前训练过**同名**模型: 在Google云端硬盘分享模型，设置为任何人可见，然后把share id放在这里即可（参考gdown用法）\n",
        "\n",
        "#@markdown 如果第一次训练      : 直接跳过\n",
        "os.chdir(\"outdir\")\n",
        "# ! gdown --id \n",
        "os.chdir(\"..\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "execution": {
          "iopub.execute_input": "2022-08-12T04:23:21.495804Z",
          "iopub.status.busy": "2022-08-12T04:23:21.494800Z",
          "iopub.status.idle": "2022-08-12T04:23:21.504471Z",
          "shell.execute_reply": "2022-08-12T04:23:21.503429Z",
          "shell.execute_reply.started": "2022-08-12T04:23:21.495754Z"
        },
        "id": "JzevuoJnkIsi",
        "trusted": true
      },
      "outputs": [],
      "source": [
        "#@title 设置参数\n",
        "\n",
        "#@markdown **这两个参数是最重要的。**\n",
        "\n",
        "#@markdown 这个参数控制模型训练得多快。**不要设置太大，否则显卡会炸。**如果数据集比较大，设置在30左右比较好。\n",
        "\n",
        "#@markdown 如果数据集里音频文件的数量和这个参数差不多，训练会失败。\n",
        "\n",
        "hparams.batch_size = 8 #@param {type:\"integer\"}\n",
        "\n",
        "#@markdown 这个参数控制训练的次数\n",
        "hparams.epochs = 1000 #@param {type:\"integer\"}\n",
        "\n",
        "#The rest aren't that important\n",
        "hparams.p_attention_dropout=0.1\n",
        "hparams.p_decoder_dropout=0.1\n",
        "hparams.decay_start = 15000         # wait till decay_start to start decaying learning rate\n",
        "hparams.A_ = 5e-4                   # Start/Max Learning Rate\n",
        "hparams.B_ = 8000                   # Decay Rate\n",
        "hparams.C_ = 0                      # Shift learning rate equation by this value\n",
        "hparams.min_learning_rate = 1e-5    # Min Learning Rate\n",
        "generate_mels = True # Don't change\n",
        "hparams.show_alignments = True\n",
        "alignment_graph_height = 600\n",
        "alignment_graph_width = 1000\n",
        "hparams.load_mel_from_disk = True\n",
        "hparams.ignore_layers = [] # Layers to reset (None by default, other than foreign languages this param can be ignored)\n",
        "\n",
        "torch.backends.cudnn.enabled = hparams.cudnn_enabled\n",
        "torch.backends.cudnn.benchmark = hparams.cudnn_benchmark\n",
        "output_directory = '/content/drive/MyDrive/colab/outdir' # Location to save Checkpoints\n",
        "log_directory = '/content/tacotron2/logs' # Location to save Log files locally\n",
        "log_directory2 = '/content/drive/MyDrive/colab/logs' # Location to copy log files (done at the end of each epoch to cut down on I/O)\n",
        "checkpoint_path = output_directory+(r'/')+model_filename"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "execution": {
          "iopub.execute_input": "2022-08-12T04:23:25.281471Z",
          "iopub.status.busy": "2022-08-12T04:23:25.280625Z",
          "iopub.status.idle": "2022-08-12T04:23:26.307536Z",
          "shell.execute_reply": "2022-08-12T04:23:26.305920Z",
          "shell.execute_reply.started": "2022-08-12T04:23:25.281432Z"
        },
        "id": "yehA2fOliyUI",
        "trusted": true
      },
      "outputs": [],
      "source": [
        "#@title 数据集文件列表\n",
        "#@markdown 如果要求不高，两个列表用同一个文件即可\n",
        "\n",
        "#@markdown 训练集文件列表\n",
        "training_files_name = \"list.txt\" #@param {type:\"string\"}\n",
        "#@markdown 验证集文件列表\n",
        "validation_files_name = \"list.txt\" #@param {type:\"string\"}\n",
        "#@markdown 预处理文本的cleaner\n",
        "\n",
        "hparams_prefix = \"/content/tacotron2/filelists/\"\n",
        "text_cleaner='japanese_phrase_cleaners' #@param {type:\"string\"}\n",
        "text_cleaners=[text_cleaner]\n",
        "#@markdown ### 各种cleaner的效果示例\n",
        "#@markdown ### 1. 'japanese_cleaners'\n",
        "#@markdown #### 处理前\n",
        "#@markdown 何かあったらいつでも話して下さい。学院のことじゃなく、私事に関することでも何でも\n",
        "#@markdown #### 处理后\n",
        "#@markdown nanikaacltaraitsudemohanashItekudasai.gakuiNnokotojanaku,shijinikaNsurukotodemonanidemo.\n",
        "#@markdown ### 2. 'japanese_tokenization_cleaners'\n",
        "#@markdown #### 处理前\n",
        "#@markdown 何かあったらいつでも話して下さい。学院のことじゃなく、私事に関することでも何でも\n",
        "#@markdown #### 处理后\n",
        "#@markdown nani ka acl tara itsu demo hanashi te kudasai. gakuiN no koto ja naku, shiji nikaNsuru koto de mo naNdemo.\n",
        "#@markdown ### 3. 'japanese_accent_cleaners'\n",
        "#@markdown #### 处理前\n",
        "#@markdown 何かあったらいつでも話して下さい。学院のことじゃなく、私事に関することでも何でも\n",
        "#@markdown #### 处理后\n",
        "#@markdown :na)nika a)cltara i)tsudemo ha(na)shIte ku(dasa)i.:ga(kuiNno ko(to)janaku,:shi)jini ka(Nsu)ru ko(to)demo na)nidemo.\n",
        "#@markdown ### 4. 'japanese_phrase_cleaners'\n",
        "#@markdown #### 处理前\n",
        "#@markdown 何かあったらいつでも話して下さい。学院のことじゃなく、私事に関することでも何でも\n",
        "#@markdown #### 处理后\n",
        "#@markdown nanika acltara itsudemo hanashIte kudasai. gakuiNno kotojanaku, shijini kaNsuru kotodemo nanidemo.\n",
        "\n",
        "training_files = hparams_prefix + training_files_name\n",
        "validation_files = hparams_prefix + validation_files_name\n",
        "\n",
        "hparams.training_files = training_files\n",
        "hparams.validation_files = validation_files\n",
        "hparams.text_cleaners = text_cleaners"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "execution": {
          "iopub.execute_input": "2022-08-12T04:23:32.743603Z",
          "iopub.status.busy": "2022-08-12T04:23:32.743060Z",
          "iopub.status.idle": "2022-08-12T04:24:06.307565Z",
          "shell.execute_reply": "2022-08-12T04:24:06.306350Z",
          "shell.execute_reply.started": "2022-08-12T04:23:32.743559Z"
        },
        "id": "b_xMcYMfkc9L",
        "trusted": true
      },
      "outputs": [],
      "source": [
        "#@title 生成MEL谱\n",
        "# ---- Replace .wav with .npy in filelists ----\n",
        "!sed -i -- 's,.wav|,.npy|,g' filelists/*.txt\n",
        "# ---- Replace .wav with .npy in filelists ----\n",
        "if generate_mels:\n",
        "    create_mels()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "execution": {
          "iopub.execute_input": "2022-08-12T04:24:09.055998Z",
          "iopub.status.busy": "2022-08-12T04:24:09.055604Z",
          "iopub.status.idle": "2022-08-12T04:24:09.263224Z",
          "shell.execute_reply": "2022-08-12T04:24:09.261774Z",
          "shell.execute_reply.started": "2022-08-12T04:24:09.055966Z"
        },
        "id": "oJXxqs6kkgLw",
        "trusted": true
      },
      "outputs": [],
      "source": [
        "#@title 检查数据集\n",
        "#@markdown 没有error就算成功\n",
        "check_dataset(hparams)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "62-cfyIubje_"
      },
      "source": [
        "#训练"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "execution": {
          "iopub.execute_input": "2022-08-12T04:25:58.143443Z",
          "iopub.status.busy": "2022-08-12T04:25:58.143025Z",
          "iopub.status.idle": "2022-08-12T04:26:55.002866Z",
          "shell.execute_reply": "2022-08-12T04:26:55.001342Z",
          "shell.execute_reply.started": "2022-08-12T04:25:58.143406Z"
        },
        "id": "qJTrZhShk8ZR",
        "trusted": true
      },
      "outputs": [],
      "source": [
        "#@title 开始训练\n",
        "#@markdown  Validation loss 越小，拟合效果可能越好\n",
        "print('FP16 Run:', hparams.fp16_run)\n",
        "print('Dynamic Loss Scaling:', hparams.dynamic_loss_scaling)\n",
        "print('Distributed Run:', hparams.distributed_run)\n",
        "print('cuDNN Enabled:', hparams.cudnn_enabled)\n",
        "print('cuDNN Benchmark:', hparams.cudnn_benchmark)\n",
        "train(output_directory, log_directory, checkpoint_path,\n",
        "      warm_start, n_gpus, rank, group_name, hparams, log_directory2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jDGVcS77b25R"
      },
      "source": [
        "#语音合成"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V6pX7t0cVlj9"
      },
      "source": [
        "##用HiFi-GAN转换##"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mwsEA9fP4qfZ",
        "cellView": "form"
      },
      "outputs": [],
      "source": [
        "#@markdown 配置：\n",
        "\n",
        "#@markdown 重新运行即可应用配置的更改\n",
        "\n",
        "#国际 HiFi-GAN 模型(有点机器音): 1qpgI41wNXFcH-iKq1Y42JlBC9j0je8PW\n",
        "#@markdown 你训练好的tacotron2模型的路径填在`Tacotron2_Model`这里\n",
        "Tacotron2_Model = '/content/drive/MyDrive/YOURMODEL'#@param {type:\"string\"}\n",
        "TACOTRON2_ID = Tacotron2_Model\n",
        "HIFIGAN_ID = \"1qpgI41wNXFcH-iKq1Y42JlBC9j0je8PW\"\n",
        "#@markdown 选择预处理文本的cleaner\n",
        "text_cleaner = 'japanese_phrase_cleaners'#@param {type:\"string\"}\n",
        "\n",
        "# Check if Initilized\n",
        "try:\n",
        "    initilized\n",
        "except NameError:\n",
        "    print(\"Setting up, please wait.\\n\")\n",
        "    !pip install tqdm -q\n",
        "    from tqdm.notebook import tqdm\n",
        "    with tqdm(total=5, leave=False) as pbar:\n",
        "        %tensorflow_version 1.x\n",
        "        import os\n",
        "        from os.path import exists, join, basename, splitext\n",
        "        !pip install gdown\n",
        "        git_repo_url = 'https://github.com/CjangCjengh/tacotron2-japanese.git'\n",
        "        project_name = splitext(basename(git_repo_url))[0]\n",
        "        if not exists(project_name):\n",
        "            # clone and install\n",
        "            !git clone -q --recursive {git_repo_url}\n",
        "            !git clone -q --recursive https://github.com/SortAnon/hifi-gan\n",
        "            !pip install -q librosa unidecode\n",
        "        pbar.update(1) # downloaded TT2 and HiFi-GAN\n",
        "        import sys\n",
        "        sys.path.append('hifi-gan')\n",
        "        sys.path.append(project_name)\n",
        "        import time\n",
        "        import matplotlib\n",
        "        import matplotlib.pylab as plt\n",
        "        import gdown\n",
        "        d = 'https://drive.google.com/uc?id='\n",
        "\n",
        "        %matplotlib inline\n",
        "        import IPython.display as ipd\n",
        "        import numpy as np\n",
        "        import torch\n",
        "        import json\n",
        "        from hparams import create_hparams\n",
        "        from model import Tacotron2\n",
        "        from layers import TacotronSTFT\n",
        "        from audio_processing import griffin_lim\n",
        "        from text import text_to_sequence\n",
        "        from env import AttrDict\n",
        "        from meldataset import MAX_WAV_VALUE\n",
        "        from models import Generator\n",
        "\n",
        "        pbar.update(1) # initialized Dependancies\n",
        "\n",
        "        graph_width = 900\n",
        "        graph_height = 360\n",
        "        def plot_data(data, figsize=(int(graph_width/100), int(graph_height/100))):\n",
        "            %matplotlib inline\n",
        "            fig, axes = plt.subplots(1, len(data), figsize=figsize)\n",
        "            for i in range(len(data)):\n",
        "                axes[i].imshow(data[i], aspect='auto', origin='bottom', \n",
        "                            interpolation='none', cmap='inferno')\n",
        "            fig.canvas.draw()\n",
        "            plt.show()\n",
        "\n",
        "        # Setup Pronounciation Dictionary\n",
        "        !gdown --id '1E12g_sREdcH5vuZb44EZYX8JjGWQ9rRp'\n",
        "        thisdict = {}\n",
        "        for line in reversed((open('merged.dict.txt', \"r\").read()).splitlines()):\n",
        "            thisdict[(line.split(\" \",1))[0]] = (line.split(\" \",1))[1].strip()\n",
        "\n",
        "        pbar.update(1) # Downloaded and Set up Pronounciation Dictionary\n",
        "\n",
        "        def ARPA(text, punctuation=r\"!?,.;\", EOS_Token=True):\n",
        "            out = ''\n",
        "            for word_ in text.split(\" \"):\n",
        "                word=word_; end_chars = ''\n",
        "                while any(elem in word for elem in punctuation) and len(word) > 1:\n",
        "                    if word[-1] in punctuation: end_chars = word[-1] + end_chars; word = word[:-1]\n",
        "                    else: break\n",
        "                try:\n",
        "                    word_arpa = thisdict[word.upper()]\n",
        "                    word = \"{\" + str(word_arpa) + \"}\"\n",
        "                except KeyError: pass\n",
        "                out = (out + \" \" + word + end_chars).strip()\n",
        "            if EOS_Token and out[-1] != \";\": out += \";\"\n",
        "            return out\n",
        "\n",
        "        def get_hifigan(MODEL_ID):\n",
        "            # Download HiFi-GAN\n",
        "            hifigan_pretrained_model = 'hifimodel'\n",
        "            gdown.download(d+MODEL_ID, hifigan_pretrained_model, quiet=False)\n",
        "            if not exists(hifigan_pretrained_model):\n",
        "                raise Exception(\"HiFI-GAN model failed to download!\")\n",
        "\n",
        "            # Load HiFi-GAN\n",
        "            conf = os.path.join(\"hifi-gan\", \"config_v1.json\")\n",
        "            with open(conf) as f:\n",
        "                json_config = json.loads(f.read())\n",
        "            h = AttrDict(json_config)\n",
        "            torch.manual_seed(h.seed)\n",
        "            hifigan = Generator(h).to(torch.device(\"cuda\"))\n",
        "            state_dict_g = torch.load(hifigan_pretrained_model, map_location=torch.device(\"cuda\"))\n",
        "            hifigan.load_state_dict(state_dict_g[\"generator\"])\n",
        "            hifigan.eval()\n",
        "            hifigan.remove_weight_norm()\n",
        "            return hifigan, h\n",
        "\n",
        "        hifigan, h = get_hifigan(HIFIGAN_ID)\n",
        "        pbar.update(1) # Downloaded and Set up HiFi-GAN\n",
        "\n",
        "        def has_MMI(STATE_DICT):\n",
        "            return any(True for x in STATE_DICT.keys() if \"mi.\" in x)\n",
        "\n",
        "        def get_Tactron2(MODEL_ID):\n",
        "            # Download Tacotron2\n",
        "            tacotron2_pretrained_model = TACOTRON2_ID\n",
        "            if not exists(tacotron2_pretrained_model):\n",
        "                raise Exception(\"Tacotron2 model failed to download!\")\n",
        "            # Load Tacotron2 and Config\n",
        "            hparams = create_hparams()\n",
        "            hparams.sampling_rate = 22050\n",
        "            hparams.max_decoder_steps = 3000 # Max Duration\n",
        "            hparams.gate_threshold = 0.25 # Model must be 25% sure the clip is over before ending generation\n",
        "            model = Tacotron2(hparams)\n",
        "            state_dict = torch.load(tacotron2_pretrained_model)['state_dict']\n",
        "            if has_MMI(state_dict):\n",
        "                raise Exception(\"ERROR: This notebook does not currently support MMI models.\")\n",
        "            model.load_state_dict(state_dict)\n",
        "            _ = model.cuda().eval().half()\n",
        "            return model, hparams\n",
        "\n",
        "        model, hparams = get_Tactron2(TACOTRON2_ID)\n",
        "        previous_tt2_id = TACOTRON2_ID\n",
        "\n",
        "        pbar.update(1) # Downloaded and Set up Tacotron2\n",
        "\n",
        "        # Extra Info\n",
        "        def end_to_end_infer(text, pronounciation_dictionary, show_graphs):\n",
        "            for i in [x for x in text.split(\"\\n\") if len(x)]:\n",
        "                if not pronounciation_dictionary:\n",
        "                    if i[-1] != \";\": i=i+\";\" \n",
        "                else: i = ARPA(i)\n",
        "                with torch.no_grad(): # save VRAM by not including gradients\n",
        "                    sequence = np.array(text_to_sequence(i, [text_cleaner]))[None, :]\n",
        "                    sequence = torch.autograd.Variable(torch.from_numpy(sequence)).cuda().long()\n",
        "                    mel_outputs, mel_outputs_postnet, _, alignments = model.inference(sequence)\n",
        "                    if show_graphs:\n",
        "                        plot_data((mel_outputs_postnet.float().data.cpu().numpy()[0],\n",
        "                                alignments.float().data.cpu().numpy()[0].T))\n",
        "                    y_g_hat = hifigan(mel_outputs_postnet.float())\n",
        "                    audio = y_g_hat.squeeze()\n",
        "                    audio = audio * MAX_WAV_VALUE\n",
        "                    print(\"\")\n",
        "                    ipd.display(ipd.Audio(audio.cpu().numpy().astype(\"int16\"), rate=hparams.sampling_rate))\n",
        "    from IPython.display import clear_output\n",
        "    clear_output()\n",
        "    initilized = \"Ready\"\n",
        "\n",
        "if previous_tt2_id != TACOTRON2_ID:\n",
        "    print(\"Updating Models\")\n",
        "    model, hparams = get_Tactron2(TACOTRON2_ID)\n",
        "    hifigan, h = get_hifigan(HIFIGAN_ID)\n",
        "    previous_tt2_id = TACOTRON2_ID\n",
        "\n",
        "pronounciation_dictionary = False #@param {type:\"boolean\"}\n",
        "# disables automatic ARPAbet conversion, useful for inputting your own ARPAbet pronounciations or just for testing\n",
        "show_graphs = True #@param {type:\"boolean\"}\n",
        "max_duration = 25 #this does nothing\n",
        "model.decoder.max_decoder_steps = 1000 #@param {type:\"integer\"}\n",
        "stop_threshold = 0.324 #@param {type:\"number\"}\n",
        "model.decoder.gate_threshold = stop_threshold\n",
        "\n",
        "#@markdown ---\n",
        "\n",
        "print(f\"Current Config:\\npronounciation_dictionary: {pronounciation_dictionary}\\nshow_graphs: {show_graphs}\\nmax_duration (in seconds): {max_duration}\\nstop_threshold: {stop_threshold}\\n\\n\")\n",
        "\n",
        "time.sleep(1)\n",
        "print(\"输入要转换成语音的文本.\")\n",
        "contents = []\n",
        "while True:\n",
        "    try:\n",
        "        print(\"-\"*50)\n",
        "        line = input()\n",
        "        if line == \"\":\n",
        "            continue\n",
        "        end_to_end_infer(line, pronounciation_dictionary, show_graphs)\n",
        "    except EOFError:\n",
        "        break\n",
        "    except KeyboardInterrupt:\n",
        "        print(\"程序终止...\")\n",
        "        break"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Uitul995V0Jw"
      },
      "source": [
        "##用 Waveglow##\n",
        "（个人不建议使用）"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "EXR9p7AeSuL6"
      },
      "outputs": [],
      "source": [
        "#@title 安装 Tacotron 和 Waveglow\n",
        "!pip install -U tensorflow==1.15.2\n",
        "import os\n",
        "from os.path import exists, join, basename, splitext\n",
        "!pip install gdown\n",
        "git_repo_url = 'https://github.com/CjangCjengh/tacotron2-japanese.git'\n",
        "project_name = splitext(basename(git_repo_url))[0]\n",
        "if not exists(project_name):\n",
        "  # clone and install\n",
        "  !git clone -q --recursive {git_repo_url}\n",
        "  !cd {project_name}/waveglow && git checkout 2fd4e63\n",
        "  !pip install -q librosa unidecode\n",
        "  \n",
        "import sys\n",
        "sys.path.append(join(project_name, 'waveglow/'))\n",
        "sys.path.append(project_name)\n",
        "import time\n",
        "import matplotlib\n",
        "import matplotlib.pylab as plt\n",
        "import gdown\n",
        "d = 'https://drive.google.com/uc?id='"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "liDdgta-SyPP"
      },
      "outputs": [],
      "source": [
        "#@title 加载预训练模型\n",
        "force_download_TT2 = True\n",
        "tacotron2_pretrained_model = '/PATH/Your Tactron2 Model'#@param {type:\"string\"}\n",
        "waveglow_pretrained_model = '/PATH/waveglow_256channels_ljs_v3.pt'#@param {type:\"string\"}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Q0BWBVdCS9ty",
        "cellView": "form"
      },
      "outputs": [],
      "source": [
        "#@title 安装 Tacotron 和 Waveglow \n",
        "%matplotlib inline\n",
        "import IPython.display as ipd\n",
        "import numpy as np\n",
        "import torch\n",
        "\n",
        "from hparams import create_hparams\n",
        "from model import Tacotron2\n",
        "from layers import TacotronSTFT\n",
        "from audio_processing import griffin_lim\n",
        "from text import text_to_sequence\n",
        "from denoiser import Denoiser\n",
        "\n",
        "graph_width = 900\n",
        "graph_height = 360\n",
        "def plot_data(data, figsize=(int(graph_width/100), int(graph_height/100))):\n",
        "    %matplotlib inline\n",
        "    fig, axes = plt.subplots(1, len(data), figsize=figsize)\n",
        "    for i in range(len(data)):\n",
        "        axes[i].imshow(data[i], aspect='auto', origin='bottom', \n",
        "                       interpolation='none', cmap='inferno')\n",
        "    fig.canvas.draw()\n",
        "    plt.show()\n",
        "\n",
        "!gdown --id '1E12g_sREdcH5vuZb44EZYX8JjGWQ9rRp'\n",
        "thisdict = {}\n",
        "for line in reversed((open('merged.dict.txt', \"r\").read()).splitlines()):\n",
        "    thisdict[(line.split(\" \",1))[0]] = (line.split(\" \",1))[1].strip()\n",
        "def ARPA(text):\n",
        "    out = ''\n",
        "    for word_ in text.split(\" \"):\n",
        "        word=word_; end_chars = ''\n",
        "        while any(elem in word for elem in r\"!?,.;\") and len(word) > 1:\n",
        "            if word[-1] == '!': end_chars = '!' + end_chars; word = word[:-1]\n",
        "            if word[-1] == '?': end_chars = '?' + end_chars; word = word[:-1]\n",
        "            if word[-1] == ',': end_chars = ',' + end_chars; word = word[:-1]\n",
        "            if word[-1] == '.': end_chars = '.' + end_chars; word = word[:-1]\n",
        "            if word[-1] == ';': end_chars = ';' + end_chars; word = word[:-1]\n",
        "            else: break\n",
        "        try: word_arpa = thisdict[word.upper()]\n",
        "        except: word_arpa = ''\n",
        "        if len(word_arpa)!=0: word = \"{\" + str(word_arpa) + \"}\"\n",
        "        out = (out + \" \" + word + end_chars).strip()\n",
        "    if out[-1] != \";\": out = out + \";\"\n",
        "    return out\n",
        "\n",
        "#torch.set_grad_enabled(False)\n",
        "\n",
        "# initialize Tacotron2 with the pretrained model\n",
        "hparams = create_hparams()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "Sqe-jWarTCw6"
      },
      "outputs": [],
      "source": [
        "#@title 参数\n",
        "# Load Tacotron2 (run this cell every time you change the model)\n",
        "hparams.sampling_rate = 22050 # Don't change this\n",
        "hparams.max_decoder_steps = 1000 # How long the audio will be before it cuts off (1000 is about 11 seconds)\n",
        "hparams.gate_threshold = 0.1 # Model must be 90% sure the clip is over before ending generation (the higher this number is, the more likely that the AI will keep generating until it reaches the Max Decoder Steps)\n",
        "model = Tacotron2(hparams)\n",
        "model.load_state_dict(torch.load(tacotron2_pretrained_model)['state_dict'])\n",
        "_ = model.cuda().eval().half()\n",
        "\n",
        "# Load WaveGlow\n",
        "waveglow = torch.load(waveglow_pretrained_model)['model']\n",
        "waveglow.cuda().eval().half()\n",
        "for k in waveglow.convinv:\n",
        "    k.float()\n",
        "denoiser = Denoiser(waveglow)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "FiRVI-J3TNnc"
      },
      "outputs": [],
      "source": [
        "#@title 开始合成！\n",
        "text = 'Your Text Here'#@param {type:\"string\"}\n",
        "sigma = 0.8\n",
        "denoise_strength = 0.324\n",
        "raw_input = True # disables automatic ARPAbet conversion, useful for inputting your own ARPAbet pronounciations or just for testing.\n",
        "                  # should be True if synthesizing a non-English language\n",
        "\n",
        "for i in text.split(\"\\n\"):\n",
        "    if len(i) < 1: continue;\n",
        "    print(i)\n",
        "    if raw_input:\n",
        "        if i[-1] != \";\": i=i+\";\" \n",
        "    else: i = ARPA(i)\n",
        "    print(i)\n",
        "    with torch.no_grad(): # save VRAM by not including gradients\n",
        "        sequence = np.array(text_to_sequence(i, ['english_cleaners']))[None, :]\n",
        "        sequence = torch.autograd.Variable(torch.from_numpy(sequence)).cuda().long()\n",
        "        mel_outputs, mel_outputs_postnet, _, alignments = model.inference(sequence)\n",
        "        plot_data((mel_outputs_postnet.float().data.cpu().numpy()[0],\n",
        "                alignments.float().data.cpu().numpy()[0].T))\n",
        "        audio = waveglow.infer(mel_outputs_postnet, sigma=sigma); print(\"\"); ipd.display(ipd.Audio(audio[0].data.cpu().numpy(), rate=hparams.sampling_rate))"
      ]
    }
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