Run through a single background audio file, overlaying with wake words.
        Generates (mfccs, target) where mfccs is a series of mfcc values and
        target is a single integer classification of the target network output for that chunk

def vectors_from_fn(self, fn: str):
        """
        Run through a single background audio file, overlaying with wake words.
        Generates (mfccs, target) where mfccs is a series of mfcc values and
        target is a single integer classification of the target network output for that chunk
        """
        audio = load_audio(fn)
        audio_volume = self.calc_volume(audio)
        audio_volume *= 0.4 + 0.5 * random()
        audio = self.normalize_volume_to(audio, audio_volume)

        self.listener.clear()
        chunked_bg = chunk_audio(audio, self.args.chunk_size)
        chunked_ww = self.chunk_audio_pieces(self.generate_wakeword_pieces(audio_volume), self.args.chunk_size)

        for i, (chunk_bg, (chunk_ww, targets)) in enumerate(zip(chunked_bg, chunked_ww)):
            chunk = self.merge(chunk_bg, chunk_ww, 0.6)
            self.vals_buffer = np.concatenate((self.vals_buffer[len(targets):], targets))
            self.audio_buffer = np.concatenate((self.audio_buffer[len(chunk):], chunk))
            mfccs = self.listener.update_vectors(chunk)
            percent_overlapping = self.max_run_length(self.vals_buffer, 1) / len(self.vals_buffer)

            if self.vals_buffer[-1] == 0 and percent_overlapping > 0.8:
                target = 1
            elif percent_overlapping < 0.5:
                target = 0
            else:
                continue

            if random() > 1.0 - self.args.save_prob:
                name = splitext(basename(fn))[0]
                wav_file = join('debug', 'ww' if target == 1 else 'nww', '{} - {}.wav'.format(name, i))
                save_audio(wav_file, self.audio_buffer)
            yield mfccs, target