dan
/
fastspeech_squeezewave


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								# from tacotron2.layers import TacotronSTFT

								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

								sys.path.insert(0, 'tacotron2')


								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, 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)

								#         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()

								#     data_config = json.loads(data)["data_config"]

								#     mel2samp = Mel2Samp(**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)