(path, audio_dir,
                                       sample_rate, window_size,
                                       window_stride, window,
                                       normalize_audio, truncate=None)
return (examples_iter, num_feats)
Args:
            path (str): location of a src file containing audio paths.
            audio_dir (str): location of source audio files.
            sample_rate (int): sample_rate.
            window_size (float) : window size for spectrogram in seconds.
            window_stride (float): window stride for spectrogram in seconds.
            window (str): window type for spectrogram generation.
            normalize_audio (bool): subtract spectrogram by mean and divide
                by std or not.
            truncate (int): maximum audio length (0 or None for unlimited).

        Returns:
            (example_dict iterator, num_feats) tuple
Args:
            path (str): location of a src file containing audio paths.
            audio_dir (str): location of source audio files.
            sample_rate (int): sample_rate.
            window_size (float) : window size for spectrogram in seconds.
            window_stride (float): window stride for spectrogram in seconds.
            window (str): window type for spectrogram generation.
            normalize_audio (bool): subtract spectrogram by mean and divide
                by std or not.
            truncate (int): maximum audio length (0 or None for unlimited).
Args : path ( str ) : location of a src file containing audio paths . audio_dir ( str ) : location of source audio files . sample_rate ( int ) : sample_rate . window_size ( float ) : window size for spectrogram in seconds . window_stride ( float ) : window stride for spectrogram in seconds . window ( str ) : window type for spectrogram generation . normalize_audio ( bool ) : subtract spectrogram by mean and divide by std or not . truncate ( int ) : maximum audio length ( 0 or None for unlimited ) .
def make_audio_examples_nfeats_tpl(path, audio_dir,
                                       sample_rate, window_size,
                                       window_stride, window,
                                       normalize_audio, truncate=None):
        """
        Args:
            path (str): location of a src file containing audio paths.
            audio_dir (str): location of source audio files.
            sample_rate (int): sample_rate.
            window_size (float) : window size for spectrogram in seconds.
            window_stride (float): window stride for spectrogram in seconds.
            window (str): window type for spectrogram generation.
            normalize_audio (bool): subtract spectrogram by mean and divide
                by std or not.
            truncate (int): maximum audio length (0 or None for unlimited).

        Returns:
            (example_dict iterator, num_feats) tuple
        """
        examples_iter = AudioDataset.read_audio_file(
            path, audio_dir, "src", sample_rate,
            window_size, window_stride, window,
            normalize_audio, truncate)
        num_feats = 0  # Source side(audio) has no features.

        return (examples_iter, num_feats)
def make_audio_examples_nfeats_tpl ( path , audio_dir , sample_rate , window_size , window_stride , window , normalize_audio , truncate = None ) : examples_iter = AudioDataset . read_audio_file ( path , audio_dir , "src" , sample_rate , window_size , window_stride , window , normalize_audio , truncate ) num_feats = 0 # Source side(audio) has no features. return ( examples_iter , num_feats )