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from typing import Any, Callable
import lightning as L
import torch
import torch.nn.functional as F
import wandb
from lightning.pytorch.loggers import TensorBoardLogger, WandbLogger
from matplotlib import pyplot as plt
from torch import nn
from fish_speech.models.vits_decoder.losses import (
discriminator_loss,
feature_loss,
generator_loss,
kl_loss,
)
from fish_speech.models.vqgan.utils import (
avg_with_mask,
plot_mel,
sequence_mask,
slice_segments,
)
class VITSDecoder(L.LightningModule):
def __init__(
self,
optimizer: Callable,
lr_scheduler: Callable,
generator: nn.Module,
discriminator: nn.Module,
mel_transform: nn.Module,
spec_transform: nn.Module,
hop_length: int = 512,
sample_rate: int = 44100,
freeze_discriminator: bool = False,
weight_mel: float = 45,
weight_kl: float = 0.1,
):
super().__init__()
# Model parameters
self.optimizer_builder = optimizer
self.lr_scheduler_builder = lr_scheduler
# Generator and discriminator
self.generator = generator
self.discriminator = discriminator
self.mel_transform = mel_transform
self.spec_transform = spec_transform
self.freeze_discriminator = freeze_discriminator
# Loss weights
self.weight_mel = weight_mel
self.weight_kl = weight_kl
# Other parameters
self.hop_length = hop_length
self.sampling_rate = sample_rate
# Disable automatic optimization
self.automatic_optimization = False
if self.freeze_discriminator:
for p in self.discriminator.parameters():
p.requires_grad = False
def configure_optimizers(self):
# Need two optimizers and two schedulers
optimizer_generator = self.optimizer_builder(self.generator.parameters())
optimizer_discriminator = self.optimizer_builder(
self.discriminator.parameters()
)
lr_scheduler_generator = self.lr_scheduler_builder(optimizer_generator)
lr_scheduler_discriminator = self.lr_scheduler_builder(optimizer_discriminator)
return (
{
"optimizer": optimizer_generator,
"lr_scheduler": {
"scheduler": lr_scheduler_generator,
"interval": "step",
"name": "optimizer/generator",
},
},
{
"optimizer": optimizer_discriminator,
"lr_scheduler": {
"scheduler": lr_scheduler_discriminator,
"interval": "step",
"name": "optimizer/discriminator",
},
},
)
def training_step(self, batch, batch_idx):
optim_g, optim_d = self.optimizers()
audios, audio_lengths = batch["audios"], batch["audio_lengths"]
texts, text_lengths = batch["texts"], batch["text_lengths"]
audios = audios.float()
audios = audios[:, None, :]
with torch.no_grad():
gt_mels = self.mel_transform(audios)
gt_specs = self.spec_transform(audios)
spec_lengths = audio_lengths // self.hop_length
spec_masks = torch.unsqueeze(
sequence_mask(spec_lengths, gt_mels.shape[2]), 1
).to(gt_mels.dtype)
(
fake_audios,
ids_slice,
y_mask,
(z, z_p, m_p, logs_p, m_q, logs_q),
) = self.generator(
audios,
audio_lengths,
gt_specs,
spec_lengths,
texts,
text_lengths,
)
gt_mels = slice_segments(gt_mels, ids_slice, self.generator.segment_size)
spec_masks = slice_segments(spec_masks, ids_slice, self.generator.segment_size)
audios = slice_segments(
audios,
ids_slice * self.hop_length,
self.generator.segment_size * self.hop_length,
)
fake_mels = self.mel_transform(fake_audios.squeeze(1))
assert (
audios.shape == fake_audios.shape
), f"{audios.shape} != {fake_audios.shape}"
# Discriminator
if self.freeze_discriminator is False:
y_d_hat_r, y_d_hat_g, _, _ = self.discriminator(
audios, fake_audios.detach()
)
with torch.autocast(device_type=audios.device.type, enabled=False):
loss_disc, _, _ = discriminator_loss(y_d_hat_r, y_d_hat_g)
self.log(
f"train/discriminator/loss",
loss_disc,
on_step=True,
on_epoch=False,
prog_bar=False,
logger=True,
sync_dist=True,
)
optim_d.zero_grad()
self.manual_backward(loss_disc)
self.clip_gradients(
optim_d, gradient_clip_val=1000.0, gradient_clip_algorithm="norm"
)
optim_d.step()
# Adv Loss
y_d_hat_r, y_d_hat_g, _, _ = self.discriminator(audios, fake_audios)
# Adversarial Loss
with torch.autocast(device_type=audios.device.type, enabled=False):
loss_adv, _ = generator_loss(y_d_hat_g)
self.log(
f"train/generator/adv",
loss_adv,
on_step=True,
on_epoch=False,
prog_bar=False,
logger=True,
sync_dist=True,
)
with torch.autocast(device_type=audios.device.type, enabled=False):
loss_fm = feature_loss(y_d_hat_r, y_d_hat_g)
self.log(
f"train/generator/adv_fm",
loss_fm,
on_step=True,
on_epoch=False,
prog_bar=False,
logger=True,
sync_dist=True,
)
with torch.autocast(device_type=audios.device.type, enabled=False):
loss_mel = avg_with_mask(
F.l1_loss(gt_mels, fake_mels, reduction="none"), spec_masks
)
self.log(
"train/generator/loss_mel",
loss_mel,
on_step=True,
on_epoch=False,
prog_bar=False,
logger=True,
sync_dist=True,
)
loss_kl = kl_loss(z_p, logs_q, m_p, logs_p, y_mask)
self.log(
"train/generator/loss_kl",
loss_kl,
on_step=True,
on_epoch=False,
prog_bar=False,
logger=True,
sync_dist=True,
)
loss = (
loss_mel * self.weight_mel + loss_kl * self.weight_kl + loss_adv + loss_fm
)
self.log(
"train/generator/loss",
loss,
on_step=True,
on_epoch=False,
prog_bar=True,
logger=True,
sync_dist=True,
)
# Backward
optim_g.zero_grad()
self.manual_backward(loss)
self.clip_gradients(
optim_g, gradient_clip_val=1000.0, gradient_clip_algorithm="norm"
)
optim_g.step()
# Manual LR Scheduler
scheduler_g, scheduler_d = self.lr_schedulers()
scheduler_g.step()
scheduler_d.step()
def validation_step(self, batch: Any, batch_idx: int):
audios, audio_lengths = batch["audios"], batch["audio_lengths"]
texts, text_lengths = batch["texts"], batch["text_lengths"]
audios = audios.float()
audios = audios[:, None, :]
gt_mels = self.mel_transform(audios)
gt_specs = self.spec_transform(audios)
spec_lengths = audio_lengths // self.hop_length
spec_masks = torch.unsqueeze(
sequence_mask(spec_lengths, gt_mels.shape[2]), 1
).to(gt_mels.dtype)
prior_audios = self.generator.infer(
audios, audio_lengths, gt_specs, spec_lengths, texts, text_lengths
)
posterior_audios = self.generator.infer_posterior(gt_specs, spec_lengths)
prior_mels = self.mel_transform(prior_audios.squeeze(1))
posterior_mels = self.mel_transform(posterior_audios.squeeze(1))
min_mel_length = min(
gt_mels.shape[-1], prior_mels.shape[-1], posterior_mels.shape[-1]
)
gt_mels = gt_mels[:, :, :min_mel_length]
prior_mels = prior_mels[:, :, :min_mel_length]
posterior_mels = posterior_mels[:, :, :min_mel_length]
prior_mel_loss = avg_with_mask(
F.l1_loss(gt_mels, prior_mels, reduction="none"), spec_masks
)
posterior_mel_loss = avg_with_mask(
F.l1_loss(gt_mels, posterior_mels, reduction="none"), spec_masks
)
self.log(
"val/prior_mel_loss",
prior_mel_loss,
on_step=False,
on_epoch=True,
prog_bar=False,
logger=True,
sync_dist=True,
)
self.log(
"val/posterior_mel_loss",
posterior_mel_loss,
on_step=False,
on_epoch=True,
prog_bar=False,
logger=True,
sync_dist=True,
)
# only log the first batch
if batch_idx != 0:
return
for idx, (
mel,
prior_mel,
posterior_mel,
audio,
prior_audio,
posterior_audio,
audio_len,
) in enumerate(
zip(
gt_mels,
prior_mels,
posterior_mels,
audios.detach().float(),
prior_audios.detach().float(),
posterior_audios.detach().float(),
audio_lengths,
)
):
mel_len = audio_len // self.hop_length
image_mels = plot_mel(
[
prior_mel[:, :mel_len],
posterior_mel[:, :mel_len],
mel[:, :mel_len],
],
[
"Prior (VQ)",
"Posterior (Reconstruction)",
"Ground-Truth",
],
)
if isinstance(self.logger, WandbLogger):
self.logger.experiment.log(
{
"reconstruction_mel": wandb.Image(image_mels, caption="mels"),
"wavs": [
wandb.Audio(
audio[0, :audio_len],
sample_rate=self.sampling_rate,
caption="gt",
),
wandb.Audio(
prior_audio[0, :audio_len],
sample_rate=self.sampling_rate,
caption="prior",
),
wandb.Audio(
posterior_audio[0, :audio_len],
sample_rate=self.sampling_rate,
caption="posterior",
),
],
},
)
if isinstance(self.logger, TensorBoardLogger):
self.logger.experiment.add_figure(
f"sample-{idx}/mels",
image_mels,
global_step=self.global_step,
)
self.logger.experiment.add_audio(
f"sample-{idx}/wavs/gt",
audio[0, :audio_len],
self.global_step,
sample_rate=self.sampling_rate,
)
self.logger.experiment.add_audio(
f"sample-{idx}/wavs/prior",
prior_audio[0, :audio_len],
self.global_step,
sample_rate=self.sampling_rate,
)
self.logger.experiment.add_audio(
f"sample-{idx}/wavs/posterior",
posterior_audio[0, :audio_len],
self.global_step,
sample_rate=self.sampling_rate,
)
plt.close(image_mels)
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