bacow/GPT-SoVITS
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Refactor: Format Code with Ruff and Update Deprecated G2PW Link (#2255)

Browse Source 
* ruff check --fix

* ruff format --line-length 120 --target-version py39

* Change the link for G2PW Model

* update pytorch version and colab
This commit is contained in:
XXXXRT666
2025-04-07 09:42:47 +01:00
committed by GitHub
parent 9da7e17efe
commit 53cac93589
132 changed files with 8185 additions and 6648 deletions
Download Patch File Download Diff File Expand all files Collapse all files

123
GPT_SoVITS/BigVGAN/train.py
View File

@@ -77,24 +77,18 @@ def train(rank, a, h):
# Define additional discriminators. BigVGAN-v1 uses UnivNet's MRD as default
# New in BigVGAN-v2: option to switch to new discriminators: MultiBandDiscriminator / MultiScaleSubbandCQTDiscriminator
if h.get("use_mbd_instead_of_mrd", False): # Switch to MBD
print(
"[INFO] using MultiBandDiscriminator of BigVGAN-v2 instead of MultiResolutionDiscriminator"
)
print("[INFO] using MultiBandDiscriminator of BigVGAN-v2 instead of MultiResolutionDiscriminator")
# Variable name is kept as "mrd" for backward compatibility & minimal code change
mrd = MultiBandDiscriminator(h).to(device)
elif h.get("use_cqtd_instead_of_mrd", False): # Switch to CQTD
print(
"[INFO] using MultiScaleSubbandCQTDiscriminator of BigVGAN-v2 instead of MultiResolutionDiscriminator"
)
print("[INFO] using MultiScaleSubbandCQTDiscriminator of BigVGAN-v2 instead of MultiResolutionDiscriminator")
mrd = MultiScaleSubbandCQTDiscriminator(h).to(device)
else: # Fallback to original MRD in BigVGAN-v1
mrd = MultiResolutionDiscriminator(h).to(device)
# New in BigVGAN-v2: option to switch to multi-scale L1 mel loss
if h.get("use_multiscale_melloss", False):
print(
"[INFO] using multi-scale Mel l1 loss of BigVGAN-v2 instead of the original single-scale loss"
)
print("[INFO] using multi-scale Mel l1 loss of BigVGAN-v2 instead of the original single-scale loss")
fn_mel_loss_multiscale = MultiScaleMelSpectrogramLoss(
sampling_rate=h.sampling_rate
) # NOTE: accepts waveform as input
@@ -114,9 +108,7 @@ def train(rank, a, h):
if os.path.isdir(a.checkpoint_path):
# New in v2.1: If the step prefix pattern-based checkpoints are not found, also check for renamed files in Hugging Face Hub to resume training
cp_g = scan_checkpoint(
a.checkpoint_path, prefix="g_", renamed_file="bigvgan_generator.pt"
)
cp_g = scan_checkpoint(a.checkpoint_path, prefix="g_", renamed_file="bigvgan_generator.pt")
cp_do = scan_checkpoint(
a.checkpoint_path,
prefix="do_",
@@ -143,9 +135,7 @@ def train(rank, a, h):
mpd = DistributedDataParallel(mpd, device_ids=[rank]).to(device)
mrd = DistributedDataParallel(mrd, device_ids=[rank]).to(device)
optim_g = torch.optim.AdamW(
generator.parameters(), h.learning_rate, betas=[h.adam_b1, h.adam_b2]
)
optim_g = torch.optim.AdamW(generator.parameters(), h.learning_rate, betas=[h.adam_b1, h.adam_b2])
optim_d = torch.optim.AdamW(
itertools.chain(mrd.parameters(), mpd.parameters()),
h.learning_rate,
@@ -156,12 +146,8 @@ def train(rank, a, h):
optim_g.load_state_dict(state_dict_do["optim_g"])
optim_d.load_state_dict(state_dict_do["optim_d"])
scheduler_g = torch.optim.lr_scheduler.ExponentialLR(
optim_g, gamma=h.lr_decay, last_epoch=last_epoch
)
scheduler_d = torch.optim.lr_scheduler.ExponentialLR(
optim_d, gamma=h.lr_decay, last_epoch=last_epoch
)
scheduler_g = torch.optim.lr_scheduler.ExponentialLR(optim_g, gamma=h.lr_decay, last_epoch=last_epoch)
scheduler_d = torch.optim.lr_scheduler.ExponentialLR(optim_d, gamma=h.lr_decay, last_epoch=last_epoch)
# Define training and validation datasets
@@ -169,9 +155,7 @@ def train(rank, a, h):
unseen_validation_filelist will contain sample filepaths outside the seen training & validation dataset
Example: trained on LibriTTS, validate on VCTK
"""
training_filelist, validation_filelist, list_unseen_validation_filelist = (
get_dataset_filelist(a)
)
training_filelist, validation_filelist, list_unseen_validation_filelist = get_dataset_filelist(a)
trainset = MelDataset(
training_filelist,
@@ -324,33 +308,26 @@ def train(rank, a, h):
h.fmax_for_loss,
)
min_t = min(y_mel.size(-1), y_g_hat_mel.size(-1))
val_err_tot += F.l1_loss(y_mel[...,:min_t], y_g_hat_mel[...,:min_t]).item()
val_err_tot += F.l1_loss(y_mel[..., :min_t], y_g_hat_mel[..., :min_t]).item()
# PESQ calculation. only evaluate PESQ if it's speech signal (nonspeech PESQ will error out)
if (
not "nonspeech" in mode
): # Skips if the name of dataset (in mode string) contains "nonspeech"
if "nonspeech" not in mode: # Skips if the name of dataset (in mode string) contains "nonspeech"
# Resample to 16000 for pesq
y_16k = pesq_resampler(y)
y_g_hat_16k = pesq_resampler(y_g_hat.squeeze(1))
y_int_16k = (y_16k[0] * MAX_WAV_VALUE).short().cpu().numpy()
y_g_hat_int_16k = (
(y_g_hat_16k[0] * MAX_WAV_VALUE).short().cpu().numpy()
)
y_g_hat_int_16k = (y_g_hat_16k[0] * MAX_WAV_VALUE).short().cpu().numpy()
val_pesq_tot += pesq(16000, y_int_16k, y_g_hat_int_16k, "wb")
# MRSTFT calculation
min_t = min(y.size(-1), y_g_hat.size(-1))
val_mrstft_tot += loss_mrstft(y_g_hat[...,:min_t], y[...,:min_t]).item()
val_mrstft_tot += loss_mrstft(y_g_hat[..., :min_t], y[..., :min_t]).item()
# Log audio and figures to Tensorboard
if j % a.eval_subsample == 0: # Subsample every nth from validation set
if steps >= 0:
sw.add_audio(f"gt_{mode}/y_{j}", y[0], steps, h.sampling_rate)
if (
a.save_audio
): # Also save audio to disk if --save_audio is set to True
if a.save_audio: # Also save audio to disk if --save_audio is set to True
save_audio(
y[0],
os.path.join(
@@ -373,9 +350,7 @@ def train(rank, a, h):
steps,
h.sampling_rate,
)
if (
a.save_audio
): # Also save audio to disk if --save_audio is set to True
if a.save_audio: # Also save audio to disk if --save_audio is set to True
save_audio(
y_g_hat[0, 0],
os.path.join(
@@ -487,15 +462,11 @@ def train(rank, a, h):
# MPD
y_df_hat_r, y_df_hat_g, _, _ = mpd(y, y_g_hat.detach())
loss_disc_f, losses_disc_f_r, losses_disc_f_g = discriminator_loss(
y_df_hat_r, y_df_hat_g
)
loss_disc_f, losses_disc_f_r, losses_disc_f_g = discriminator_loss(y_df_hat_r, y_df_hat_g)
# MRD
y_ds_hat_r, y_ds_hat_g, _, _ = mrd(y, y_g_hat.detach())
loss_disc_s, losses_disc_s_r, losses_disc_s_g = discriminator_loss(
y_ds_hat_r, y_ds_hat_g
)
loss_disc_s, losses_disc_s_r, losses_disc_s_g = discriminator_loss(y_ds_hat_r, y_ds_hat_g)
loss_disc_all = loss_disc_s + loss_disc_f
@@ -505,17 +476,11 @@ def train(rank, a, h):
# Whether to freeze D for initial training steps
if steps >= a.freeze_step:
loss_disc_all.backward()
grad_norm_mpd = torch.nn.utils.clip_grad_norm_(
mpd.parameters(), clip_grad_norm
)
grad_norm_mrd = torch.nn.utils.clip_grad_norm_(
mrd.parameters(), clip_grad_norm
)
grad_norm_mpd = torch.nn.utils.clip_grad_norm_(mpd.parameters(), clip_grad_norm)
grad_norm_mrd = torch.nn.utils.clip_grad_norm_(mrd.parameters(), clip_grad_norm)
optim_d.step()
else:
print(
f"[WARNING] skipping D training for the first {a.freeze_step} steps"
)
print(f"[WARNING] skipping D training for the first {a.freeze_step} steps")
grad_norm_mpd = 0.0
grad_norm_mrd = 0.0
@@ -523,9 +488,7 @@ def train(rank, a, h):
optim_g.zero_grad()
# L1 Mel-Spectrogram Loss
lambda_melloss = h.get(
"lambda_melloss", 45.0
) # Defaults to 45 in BigVGAN-v1 if not set
lambda_melloss = h.get("lambda_melloss", 45.0) # Defaults to 45 in BigVGAN-v1 if not set
if h.get("use_multiscale_melloss", False): # uses wav <y, y_g_hat> for loss
loss_mel = fn_mel_loss_multiscale(y, y_g_hat) * lambda_melloss
else: # Uses mel <y_mel, y_g_hat_mel> for loss
@@ -542,27 +505,19 @@ def train(rank, a, h):
loss_gen_s, losses_gen_s = generator_loss(y_ds_hat_g)
if steps >= a.freeze_step:
loss_gen_all = (
loss_gen_s + loss_gen_f + loss_fm_s + loss_fm_f + loss_mel
)
loss_gen_all = loss_gen_s + loss_gen_f + loss_fm_s + loss_fm_f + loss_mel
else:
print(
f"[WARNING] using regression loss only for G for the first {a.freeze_step} steps"
)
print(f"[WARNING] using regression loss only for G for the first {a.freeze_step} steps")
loss_gen_all = loss_mel
loss_gen_all.backward()
grad_norm_g = torch.nn.utils.clip_grad_norm_(
generator.parameters(), clip_grad_norm
)
grad_norm_g = torch.nn.utils.clip_grad_norm_(generator.parameters(), clip_grad_norm)
optim_g.step()
if rank == 0:
# STDOUT logging
if steps % a.stdout_interval == 0:
mel_error = (
loss_mel.item() / lambda_melloss
) # Log training mel regression loss to stdout
mel_error = loss_mel.item() / lambda_melloss # Log training mel regression loss to stdout
print(
f"Steps: {steps:d}, "
f"Gen Loss Total: {loss_gen_all:4.3f}, "
@@ -577,11 +532,7 @@ def train(rank, a, h):
checkpoint_path = f"{a.checkpoint_path}/g_{steps:08d}"
save_checkpoint(
checkpoint_path,
{
"generator": (
generator.module if h.num_gpus > 1 else generator
).state_dict()
},
{"generator": (generator.module if h.num_gpus > 1 else generator).state_dict()},
)
checkpoint_path = f"{a.checkpoint_path}/do_{steps:08d}"
save_checkpoint(
@@ -598,9 +549,7 @@ def train(rank, a, h):
# Tensorboard summary logging
if steps % a.summary_interval == 0:
mel_error = (
loss_mel.item() / lambda_melloss
) # Log training mel regression loss to tensorboard
mel_error = loss_mel.item() / lambda_melloss # Log training mel regression loss to tensorboard
sw.add_scalar("training/gen_loss_total", loss_gen_all.item(), steps)
sw.add_scalar("training/mel_spec_error", mel_error, steps)
sw.add_scalar("training/fm_loss_mpd", loss_fm_f.item(), steps)
@@ -612,12 +561,8 @@ def train(rank, a, h):
sw.add_scalar("training/disc_loss_mrd", loss_disc_s.item(), steps)
sw.add_scalar("training/grad_norm_mrd", grad_norm_mrd, steps)
sw.add_scalar("training/grad_norm_g", grad_norm_g, steps)
sw.add_scalar(
"training/learning_rate_d", scheduler_d.get_last_lr()[0], steps
)
sw.add_scalar(
"training/learning_rate_g", scheduler_g.get_last_lr()[0], steps
)
sw.add_scalar("training/learning_rate_d", scheduler_d.get_last_lr()[0], steps)
sw.add_scalar("training/learning_rate_g", scheduler_g.get_last_lr()[0], steps)
sw.add_scalar("training/epoch", epoch + 1, steps)
# Validation
@@ -660,9 +605,7 @@ def train(rank, a, h):
scheduler_d.step()
if rank == 0:
print(
f"Time taken for epoch {epoch + 1} is {int(time.time() - start)} sec\n"
)
print(f"Time taken for epoch {epoch + 1} is {int(time.time() - start)} sec\n")
def main():
@@ -674,12 +617,8 @@ def main():
parser.add_argument("--input_wavs_dir", default="LibriTTS")
parser.add_argument("--input_mels_dir", default="ft_dataset")
parser.add_argument(
"--input_training_file", default="tests/LibriTTS/train-full.txt"
)
parser.add_argument(
"--input_validation_file", default="tests/LibriTTS/val-full.txt"
)
parser.add_argument("--input_training_file", default="tests/LibriTTS/train-full.txt")
parser.add_argument("--input_validation_file", default="tests/LibriTTS/val-full.txt")
parser.add_argument(
"--list_input_unseen_wavs_dir",