fastspeech_squeezewave/SqueezeWave/mel2samp.py

# We retain the copyright notice by NVIDIA from the original code. However, we
# we reserve our rights on the modifications based on the original code.
#
# *****************************************************************************
#  Copyright (c) 2018, 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 os
import random
import argparse
import json
import torch
import torch.utils.data
import sys
from scipy.io.wavfile import read

# We're using the audio processing from TacoTron2 to make sure it matches
from TacotronSTFT import TacotronSTFT

MAX_WAV_VALUE = 32768.0

def files_to_list(filename):
    """
    Takes a text file of filenames and makes a list of filenames
    """
    with open(filename, encoding='utf-8') as f:
        files = f.readlines()

    files = [f.rstrip() for f in files]
    return files

def load_wav_to_torch(full_path):
    """
    Loads wavdata into torch array
    """
    sampling_rate, data = read(full_path)
    return torch.from_numpy(data).float(), sampling_rate


class Mel2Samp(torch.utils.data.Dataset):
    """
    This is the main class that calculates the spectrogram and returns the
    spectrogram, audio pair.
    """
    def __init__(self, n_audio_channel, training_files, segment_length,
                 filter_length, hop_length, win_length, sampling_rate, mel_fmin,
                 mel_fmax):
        self.audio_files = files_to_list(training_files)
        random.seed(1234)
        random.shuffle(self.audio_files)
        self.stft = TacotronSTFT(filter_length=filter_length,
                                 hop_length=hop_length,
                                 win_length=win_length,
                                 sampling_rate=sampling_rate,
                                 mel_fmin=mel_fmin, mel_fmax=mel_fmax,
                                 n_group=n_audio_channel)
        self.segment_length = segment_length
        self.sampling_rate = sampling_rate

    def get_mel(self, audio):
        audio_norm = audio / MAX_WAV_VALUE
        audio_norm = audio_norm.unsqueeze(0)
        audio_norm = torch.autograd.Variable(audio_norm, requires_grad=False)
        melspec = self.stft.mel_spectrogram(audio_norm)
        melspec = torch.squeeze(melspec, 0)
        return melspec

    def __getitem__(self, index):
        # Read audio
        filename = self.audio_files[index]
        audio, sampling_rate = load_wav_to_torch(filename)
        if sampling_rate != self.sampling_rate:
            raise ValueError("{} SR doesn't match target {} SR".format(
                sampling_rate, self.sampling_rate))

        # Take segment
        if audio.size(0) >= self.segment_length:
            max_audio_start = audio.size(0) - self.segment_length
            audio_start = random.randint(0, max_audio_start)
            audio = audio[audio_start:audio_start+self.segment_length]
        else:
            audio = torch.nn.functional.pad(
                audio, (0, self.segment_length - audio.size(0)),
                'constant').data

        mel = self.get_mel(audio)
        audio = audio / MAX_WAV_VALUE

        return (mel, audio)

    def __len__(self):
        return len(self.audio_files)

# ===================================================================
# Takes directory of clean audio and makes directory of spectrograms
# Useful for making test sets
# ===================================================================
if __name__ == "__main__":
    # Get defaults so it can work with no Sacred
    parser = argparse.ArgumentParser()
    parser.add_argument('-f', "--filelist_path", required=True)
    parser.add_argument('-c', '--config', type=str,
                        help='JSON file for configuration')
    parser.add_argument('-o', '--output_dir', type=str,
                        help='Output directory')
    args = parser.parse_args()

    with open(args.config) as f:
        data = f.read()
    config = json.loads(data)
    data_config = config["data_config"]
    squeezewave_config = config["squeezewave_config"]
    mel2samp = Mel2Samp(squeezewave_config['n_audio_channel'], **data_config)

    filepaths = files_to_list(args.filelist_path)

    # Make directory if it doesn't exist
    if not os.path.isdir(args.output_dir):
        os.makedirs(args.output_dir)
        os.chmod(args.output_dir, 0o775)

    for filepath in filepaths:
        audio, sr = load_wav_to_torch(filepath)
        melspectrogram = mel2samp.get_mel(audio)
        filename = os.path.basename(filepath)
        new_filepath = args.output_dir + '/' + filename + '.pt'
        print(new_filepath)
        torch.save(melspectrogram, new_filepath)