def __init__(self, audio_conf, manifest_filepath, labels, normalize=False, speed_volume_perturb=False, spec_augment=False):
    '\n        Dataset that loads tensors via a csv containing file paths to audio files and transcripts separated by\n        a comma. Each new line is a different sample. Example below:\n\n        /path/to/audio.wav,/path/to/audio.txt\n        ...\n\n        :param audio_conf: Dictionary containing the sample rate, window and the window length/stride in seconds\n        :param manifest_filepath: Path to manifest csv as describe above\n        :param labels: String containing all the possible characters to map to\n        :param normalize: Apply standard mean and deviation normalization to audio tensor\n        :param speed_volume_perturb(default False): Apply random tempo and gain perturbations\n        :param spec_augment(default False): Apply simple spectral augmentation to mel spectograms\n        '
    with open(manifest_filepath) as f:
        ids = f.readlines()
    ids = [x.strip().split(',') for x in ids]
    self.ids = ids
    self.size = len(ids)
    self.labels_map = dict([(labels[i], i) for i in range(len(labels))])
    try:
        self.use_jamo = audio_conf['use_jamo']
    except:
        self.use_jamo = False
    super(SpectrogramDataset, self).__init__(audio_conf, normalize, speed_volume_perturb, spec_augment)
Dataset that loads tensors via a csv containing file paths to audio files and transcripts separated by
a comma. Each new line is a different sample. Example below:

/path/to/audio.wav,/path/to/audio.txt
...

:param audio_conf: Dictionary containing the sample rate, window and the window length/stride in seconds
:param manifest_filepath: Path to manifest csv as describe above
:param labels: String containing all the possible characters to map to
:param normalize: Apply standard mean and deviation normalization to audio tensor
:param speed_volume_perturb(default False): Apply random tempo and gain perturbations
:param spec_augment(default False): Apply simple spectral augmentation to mel spectograms
def __init__(self, audio_conf, manifest_filepath, labels, normalize=False, speed_volume_perturb=False, spec_augment=False):
    '\n        Dataset that loads tensors via a csv containing file paths to audio files and transcripts separated by\n        a comma. Each new line is a different sample. Example below:\n\n        /path/to/audio.wav,/path/to/audio.txt\n        ...\n\n        :param audio_conf: Dictionary containing the sample rate, window and the window length/stride in seconds\n        :param manifest_filepath: Path to manifest csv as describe above\n        :param labels: String containing all the possible characters to map to\n        :param normalize: Apply standard mean and deviation normalization to audio tensor\n        :param speed_volume_perturb(default False): Apply random tempo and gain perturbations\n        :param spec_augment(default False): Apply simple spectral augmentation to mel spectograms\n        '
    with open(manifest_filepath) as f:
        ids = f.readlines()
    ids = [x.strip().split(',') for x in ids]
    self.ids = ids
    self.size = len(ids)
    self.labels_map = dict([(labels[i], i) for i in range(len(labels))])
    try:
        self.use_jamo = audio_conf['use_jamo']
    except:
        self.use_jamo = False
    super(SpectrogramDataset, self).__init__(audio_conf, normalize, speed_volume_perturb, spec_augment)