<filename>DeepLearningExamples/CUDA-Optimized/FastSpeech/fastspeech/align_tacotron2.py
# Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.

# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#     * Redistributions of source code must retain the above copyright
#       notice, this list of conditions and the following disclaimer.
#     * Redistributions in binary form must reproduce the above copyright
#       notice, this list of conditions and the following disclaimer in the
#       documentation and/or other materials provided with the distribution.
#     * Neither the name of the NVIDIA CORPORATION nor the
#       names of its contributors may be used to endorse or promote products
#       derived from this software without specific prior written permission.

# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

import pathlib

import fire
import torch
from tqdm import tqdm
from fastspeech.data_load import PadDataLoader
from fastspeech.dataset.ljspeech_dataset import LJSpeechDataset
import tacotron2.train
import tacotron2.hparams
from fastspeech import hparam as hp, DEFAULT_DEVICE
import os
import numpy as np

from fastspeech.utils.logging import tprint
from fastspeech.utils.pytorch import to_device_async, to_cpu_numpy


def get_tacotron2(device, is_training=False):
    hparams = tacotron2.hparams.create_hparams()
    model = tacotron2.train.load_model(hparams)
    model.load_state_dict(torch.load(
        hp.tacotron2_path, map_location=torch.device(device))["state_dict"])
    if is_training:
        model.train()
    else:
        model.eval()
    return model


def get_duration(texts, text_lens, mels, mel_lens, tacotron2, device):
    texts = to_device_async(texts, device)
    text_lens = to_device_async(text_lens, device)
    mels = to_device_async(mels, device)
    mel_lens = to_device_async(mel_lens, device)

    _, _, _, aligns = tacotron2.forward(
        (texts, text_lens, mels, None, mel_lens))

    aligns = to_cpu_numpy(aligns)
    durs = torch.FloatTensor([compute_duration(align) for align in aligns])

    return durs


def compute_duration(align):
    """
    Warning. This code assumes the attention is monotonic.
    """
    d_mel, d_text = align.shape
    dur = np.array([0 for _ in range(d_text)])

    for i in range(d_mel):
        idx = np.argmax(align[i])
        dur[idx] += 1

    return dur


def preprocess_aligns(
        hparam="base.yaml",
        device=DEFAULT_DEVICE):
    """ The script for preprocessing alignments.

    By default, this script assumes to load parameters in the default config file, fastspeech/hparams/base.yaml.

    --dataset_path=DATASET_PATH
        Path to dataset directory.
    --tacotron2_path=TACOTRON2_PATH
        Path to tacotron2 checkpoint file.
    --aligns_path=ALIGNS_PATH
        Path to output preprocessed alignments directory.

    Refer to fastspeech/hparams/base.yaml to see more parameters.

    Args:
        hparam (str, optional): Path to default config file. Defaults to "base.yaml".
        device (str, optional): Device to use. Defaults to "cuda" if avaiable, or "cpu".
    """

    hp.set_hparam(hparam)

    pathlib.Path(hp.aligns_path).mkdir(parents=True, exist_ok=True)

    dataset = LJSpeechDataset(hp.dataset_path)
    dataloader = PadDataLoader(
        dataset, batch_size=1, shuffle=False, num_workers=32, drop_last=True)

    tacotron2 = get_tacotron2(device, is_training=True)
    to_device_async(tacotron2, device)

    for batched in tqdm(dataloader):
        names = batched['name']
        texts = batched['text_encoded']
        text_lens = batched['text_len']
        mels = batched['mel']
        mel_lens = batched['mel_len']

        tprint("Processing {}.".format(', '.join(names)))
        durs = get_duration(texts, text_lens, mels,
                            mel_lens, tacotron2, device)

        for i, (name, dur) in enumerate(zip(names, durs)):
            save_path = os.path.join(hp.aligns_path, name + ".align.npy")

            if os.path.exists(save_path):
                continue

            np.save(save_path, dur)
            # assert sum(duration) == len(align)


if __name__ == '__main__':
    fire.Fire(preprocess_aligns)

<filename>DeepLearningExamples/CUDA-Optimized/FastSpeech/fastspeech/align_tacotron2.py
# Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.

# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#     * Redistributions of source code must retain the above copyright
#       notice, this list of conditions and the following disclaimer.
#     * Redistributions in binary form must reproduce the above copyright
#       notice, this list of conditions and the following disclaimer in the
#       documentation and/or other materials provided with the distribution.
#     * Neither the name of the NVIDIA CORPORATION nor the
#       names of its contributors may be used to endorse or promote products
#       derived from this software without specific prior written permission.

# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

import pathlib

import fire
import torch
from tqdm import tqdm
from fastspeech.data_load import PadDataLoader
from fastspeech.dataset.ljspeech_dataset import LJSpeechDataset
import tacotron2.train
import tacotron2.hparams
from fastspeech import hparam as hp, DEFAULT_DEVICE
import os
import numpy as np

from fastspeech.utils.logging import tprint
from fastspeech.utils.pytorch import to_device_async, to_cpu_numpy


def get_tacotron2(device, is_training=False):
    hparams = tacotron2.hparams.create_hparams()
    model = tacotron2.train.load_model(hparams)
    model.load_state_dict(torch.load(
        hp.tacotron2_path, map_location=torch.device(device))["state_dict"])
    if is_training:
        model.train()
    else:
        model.eval()
    return model


def get_duration(texts, text_lens, mels, mel_lens, tacotron2, device):
    texts = to_device_async(texts, device)
    text_lens = to_device_async(text_lens, device)
    mels = to_device_async(mels, device)
    mel_lens = to_device_async(mel_lens, device)

    _, _, _, aligns = tacotron2.forward(
        (texts, text_lens, mels, None, mel_lens))

    aligns = to_cpu_numpy(aligns)
    durs = torch.FloatTensor([compute_duration(align) for align in aligns])

    return durs


def compute_duration(align):
    """
    Warning. This code assumes the attention is monotonic.
    """
    d_mel, d_text = align.shape
    dur = np.array([0 for _ in range(d_text)])

    for i in range(d_mel):
        idx = np.argmax(align[i])
        dur[idx] += 1

    return dur


def preprocess_aligns(
        hparam="base.yaml",
        device=DEFAULT_DEVICE):
    """ The script for preprocessing alignments.

    By default, this script assumes to load parameters in the default config file, fastspeech/hparams/base.yaml.

    --dataset_path=DATASET_PATH
        Path to dataset directory.
    --tacotron2_path=TACOTRON2_PATH
        Path to tacotron2 checkpoint file.
    --aligns_path=ALIGNS_PATH
        Path to output preprocessed alignments directory.

    Refer to fastspeech/hparams/base.yaml to see more parameters.

    Args:
        hparam (str, optional): Path to default config file. Defaults to "base.yaml".
        device (str, optional): Device to use. Defaults to "cuda" if avaiable, or "cpu".
    """

    hp.set_hparam(hparam)

    pathlib.Path(hp.aligns_path).mkdir(parents=True, exist_ok=True)

    dataset = LJSpeechDataset(hp.dataset_path)
    dataloader = PadDataLoader(
        dataset, batch_size=1, shuffle=False, num_workers=32, drop_last=True)

    tacotron2 = get_tacotron2(device, is_training=True)
    to_device_async(tacotron2, device)

    for batched in tqdm(dataloader):
        names = batched['name']
        texts = batched['text_encoded']
        text_lens = batched['text_len']
        mels = batched['mel']
        mel_lens = batched['mel_len']

        tprint("Processing {}.".format(', '.join(names)))
        durs = get_duration(texts, text_lens, mels,
                            mel_lens, tacotron2, device)

        for i, (name, dur) in enumerate(zip(names, durs)):
            save_path = os.path.join(hp.aligns_path, name + ".align.npy")

            if os.path.exists(save_path):
                continue

            np.save(save_path, dur)
            # assert sum(duration) == len(align)


if __name__ == '__main__':
    fire.Fire(preprocess_aligns)

# Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved. # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: #     * Redistributions of source code must retain the above copyright #       notice, this list of conditions and the following disclaimer. #     * Redistributions in binary form must reproduce the above copyright #       notice, this list of conditions and the following disclaimer in the #       documentation and/or other materials provided with the distribution. #     * Neither the name of the NVIDIA CORPORATION nor the #       names of its contributors may be used to endorse or promote products #       derived from this software without specific prior written permission. # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND # ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED # WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE # DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY # DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES # (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; # LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND # ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS # SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. Warning. This code assumes the attention is monotonic. The script for preprocessing alignments.      By default, this script assumes to load parameters in the default config file, fastspeech/hparams/base.yaml.      --dataset_path=DATASET_PATH         Path to dataset directory.     --tacotron2_path=TACOTRON2_PATH         Path to tacotron2 checkpoint file.     --aligns_path=ALIGNS_PATH         Path to output preprocessed alignments directory.      Refer to fastspeech/hparams/base.yaml to see more parameters.      Args:         hparam (str, optional): Path to default config file. Defaults to "base.yaml".         device (str, optional): Device to use. Defaults to "cuda" if avaiable, or "cpu". # assert sum(duration) == len(align)