From fe3768b872620f5f67832d834a983a9128ec7ae1 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Stardust=C2=B7=E5=87=8F?= <2225664821@qq.com> Date: Wed, 23 Aug 2023 16:48:31 +0800 Subject: [PATCH] Update train_ms.py --- train_ms.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/train_ms.py b/train_ms.py index 202a6b1..2eb80e3 100644 --- a/train_ms.py +++ b/train_ms.py @@ -244,7 +244,7 @@ def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loade loss_disc, losses_disc_r, losses_disc_g = discriminator_loss(y_d_hat_r, y_d_hat_g) loss_disc_all = loss_disc if net_dur_disc is not None: - y_dur_hat_r, y_dur_hat_g = net_dur_disc(hidden_x, x_mask, logw, logw_.detach()) + y_dur_hat_r, y_dur_hat_g = net_dur_disc(hidden_x.detach(), x_mask.detach(), logw.detach(), logw_.detach()) with autocast(enabled=False): # TODO: I think need to mean using the mask, but for now, just mean all loss_dur_disc, losses_dur_disc_r, losses_dur_disc_g = discriminator_loss(y_dur_hat_r, y_dur_hat_g) @@ -303,9 +303,10 @@ def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loade scalar_dict.update({"loss/d_r/{}".format(i): v for i, v in enumerate(losses_disc_r)}) scalar_dict.update({"loss/d_g/{}".format(i): v for i, v in enumerate(losses_disc_g)}) if net_dur_disc is not None: - scalar_dict.update({f"loss/dur_disc_r/{losses_dur_disc_r}"}) - scalar_dict.update({f"loss/dur_disc_g/{losses_dur_disc_g}"}) - scalar_dict.update({f"loss/dur_gen/{loss_dur_gen}"}) + scalar_dict.update({"loss/dur_disc_r" f"{losses_dur_disc_r}"}) + scalar_dict.update({"loss/dur_disc_g" : f"{losses_dur_disc_g}"}) + scalar_dict.update({"loss/dur_gen" : f"{loss_dur_gen}"}) + image_dict = { "slice/mel_org": utils.plot_spectrogram_to_numpy(y_mel[0].data.cpu().numpy()), "slice/mel_gen": utils.plot_spectrogram_to_numpy(y_hat_mel[0].data.cpu().numpy()),