From 5b5c79dd165d43164245aa1db09bffde04422444 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Stardust=C2=B7=E5=87=8F?= Date: Tue, 5 Sep 2023 10:02:50 +0800 Subject: [PATCH] add bert --- train_ms.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/train_ms.py b/train_ms.py index 297fea6..0568aeb 100644 --- a/train_ms.py +++ b/train_ms.py @@ -205,7 +205,7 @@ def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loade net_d.train() if net_dur_disc is not None: net_dur_disc.train() - for batch_idx, (x, x_lengths, spec, spec_lengths, y, y_lengths, speakers, tone, language, bert) in tqdm(enumerate(train_loader)): + for batch_idx, (x, x_lengths, spec, spec_lengths, y, y_lengths, speakers, tone, language, bert, ja_bert) in tqdm(enumerate(train_loader)): if net_g.module.use_noise_scaled_mas: current_mas_noise_scale = net_g.module.mas_noise_scale_initial - net_g.module.noise_scale_delta * global_step net_g.module.current_mas_noise_scale = max(current_mas_noise_scale, 0.0) @@ -216,10 +216,11 @@ def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loade tone = tone.cuda(rank, non_blocking=True) language = language.cuda(rank, non_blocking=True) bert = bert.cuda(rank, non_blocking=True) + ja_bert = ja_bert.cuda(rank, non_blocking=True) 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, ja_bert) mel = spec_to_mel_torch( spec, hps.data.filter_length, @@ -342,7 +343,7 @@ def evaluate(hps, generator, eval_loader, writer_eval): audio_dict = {} print("Evaluating ...") with torch.no_grad(): - for batch_idx, (x, x_lengths, spec, spec_lengths, y, y_lengths, speakers, tone, language, bert) in enumerate(eval_loader): + for batch_idx, (x, x_lengths, spec, spec_lengths, y, y_lengths, speakers, tone, language, bert, ja_bert) in enumerate(eval_loader): x, x_lengths = x.cuda(), x_lengths.cuda() spec, spec_lengths = spec.cuda(), spec_lengths.cuda() y, y_lengths = y.cuda(), y_lengths.cuda() @@ -351,7 +352,7 @@ def evaluate(hps, generator, eval_loader, writer_eval): tone = tone.cuda() language = language.cuda() for use_sdp in [True, False]: - y_hat, attn, mask, *_ = generator.module.infer(x, x_lengths, speakers, tone, language, bert, y=spec, max_len=1000, sdp_ratio=0.0 if not use_sdp else 1.0) + y_hat, attn, mask, *_ = generator.module.infer(x, x_lengths, speakers, tone, language, bert, ja_bert, y=spec, max_len=1000, sdp_ratio=0.0 if not use_sdp else 1.0) y_hat_lengths = mask.sum([1, 2]).long() * hps.data.hop_length mel = spec_to_mel_torch(