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add bert

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Stardust·减
2023-09-05 10:02:50 +08:00
committed by GitHub
parent b5e7aefeef
commit 5b5c79dd16
1 changed files with 5 additions and 4 deletions
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9
train_ms.py
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@@ -205,7 +205,7 @@ def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loade
net_d.train() net_d.train()
if net_dur_disc is not None: if net_dur_disc is not None:
net_dur_disc.train() 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: 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 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) 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) tone = tone.cuda(rank, non_blocking=True)
language = language.cuda(rank, non_blocking=True) language = language.cuda(rank, non_blocking=True)
bert = bert.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): with autocast(enabled=hps.train.fp16_run):
y_hat, l_length, attn, ids_slice, x_mask, z_mask, \ 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( mel = spec_to_mel_torch(
spec, spec,
hps.data.filter_length, hps.data.filter_length,
@@ -342,7 +343,7 @@ def evaluate(hps, generator, eval_loader, writer_eval):
audio_dict = {} audio_dict = {}
print("Evaluating ...") print("Evaluating ...")
with torch.no_grad(): 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() x, x_lengths = x.cuda(), x_lengths.cuda()
spec, spec_lengths = spec.cuda(), spec_lengths.cuda() spec, spec_lengths = spec.cuda(), spec_lengths.cuda()
y, y_lengths = y.cuda(), y_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() tone = tone.cuda()
language = language.cuda() language = language.cuda()
for use_sdp in [True, False]: 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 y_hat_lengths = mask.sum([1, 2]).long() * hps.data.hop_length
mel = spec_to_mel_torch( mel = spec_to_mel_torch(