about alignment.py about fastspeech HOT 2 CLOSED

hparams = hp_tacotron2.create_hparams()
hparams.sampling_rate = hp.sample_rate

checkpoint_path = os.path.join("Tacotron2", os.path.join(
    "pre_trained_model", "tacotron2_statedict.pt"))

tacotron2 = train_tacotron2.load_model(hparams)
state_dict = torch.load(checkpoint_path)['state_dict']

tacotron2.load_state_dict(state_dict)
_ = tacotron2.cuda().eval().half()

with open(train_text_path, "r", encoding="utf-8") as f:
    inx = 0
    txt = []
    for line in f.readlines():
        cnt = 0
        l = line.strip().split("|")
        cid = l[0]
        n = int(cid.replace(".npy", "").split("-")[-1])
        text_seq = l[-1]
        sequence = np.array(text_to_sequence(text_seq, hp.text_cleaners))[None, :]
        sequence = torch.autograd.Variable(
            torch.from_numpy(sequence)).cuda().long()

        mel, mel_postnet, _, alignment = tacotron2.inference(sequence)
        alignment = alignment.float().data.cpu().numpy()[0]
        alignment = get_D(alignment)

        np.save("alignment_targets/%s.npy" %n, alignment)

    return txt`