dan
/
fastspeech_squeezewave


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								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)