From 70c299faa7c6c26323a1c72fff0c4d49154d4d95 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Stardust=C2=B7=E5=87=8F?= <2225664821@qq.com> Date: Tue, 29 Aug 2023 20:01:48 +0800 Subject: [PATCH] =?UTF-8?q?=E4=BF=AE=E6=AD=A3Dur=20GAN?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- train_ms.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/train_ms.py b/train_ms.py index d2a4dd8..c795956 100644 --- a/train_ms.py +++ b/train_ms.py @@ -227,7 +227,7 @@ def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loade with autocast(enabled=hps.train.fp16_run): y_hat, l_length, attn, ids_slice, x_mask, z_mask, \ - (z, z_p, m_p, logs_p, m_q, logs_q), (hidden_x, logw, logw_) = net_g(x, x_lengths, spec, spec_lengths, speakers, tone, language, bert) + (z, z_p, m_p, logs_p, m_q, logs_q), (hidden_x, logw_, logw) = net_g(x, x_lengths, spec, spec_lengths, speakers, tone, language, bert) mel = spec_to_mel_torch( spec, hps.data.filter_length, @@ -255,7 +255,7 @@ 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.detach(), x_mask.detach(), logw.detach(), 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) @@ -276,7 +276,7 @@ def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loade # Generator y_d_hat_r, y_d_hat_g, fmap_r, fmap_g = net_d(y, y_hat) if net_dur_disc is not None: - y_dur_hat_r, y_dur_hat_g = net_dur_disc(hidden_x, x_mask, logw, logw_) + y_dur_hat_r, y_dur_hat_g = net_dur_disc(hidden_x, x_mask, logw_, logw) with autocast(enabled=False): loss_dur = torch.sum(l_length.float()) loss_mel = F.l1_loss(y_mel, y_hat_mel) * hps.train.c_mel