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import torch
from torch import Tensor
import pytorch_lightning as pl
from einops import rearrange
import wandb
from audio_diffusion_pytorch import AudioDiffusionModel
import sys
sys.path.append("/Users/matthewrice/Developer/remfx/umx/")
from umx.openunmix.model import OpenUnmix, Separator
SAMPLE_RATE = 22050 # From audio-diffusion-pytorch
class OpenUnmixModel(pl.LightningModule):
def __init__(
self,
n_fft: int = 2048,
hop_length: int = 512,
alpha: float = 0.3,
):
super().__init__()
self.model = OpenUnmix(
nb_channels=1,
nb_bins=n_fft // 2 + 1,
)
self.n_fft = n_fft
self.hop_length = hop_length
self.alpha = alpha
window = torch.hann_window(n_fft)
self.register_buffer("window", window)
def forward(self, x: torch.Tensor):
return self.model(x)
def training_step(self, batch, batch_idx):
loss, _ = self.common_step(batch, batch_idx, mode="train")
return loss
def validation_step(self, batch, batch_idx):
loss, Y = self.common_step(batch, batch_idx, mode="val")
return loss, Y
def common_step(self, batch, batch_idx, mode: str = "train"):
x, target, label = batch
X = spectrogram(x, self.window, self.n_fft, self.hop_length, self.alpha)
Y = self(X)
Y_hat = spectrogram(
target, self.window, self.n_fft, self.hop_length, self.alpha
)
loss = torch.nn.functional.mse_loss(Y, Y_hat)
self.log(f"{mode}_loss", loss, on_step=True, on_epoch=True)
return loss, Y
def configure_optimizers(self):
return torch.optim.Adam(
self.parameters(), lr=1e-4, betas=(0.95, 0.999), eps=1e-6, weight_decay=1e-3
)
def on_validation_epoch_start(self):
self.log_next = True
def on_validation_batch_start(self, batch, batch_idx, dataloader_idx):
if self.log_next:
x, target, label = batch
s = Separator(
target_models={"other": self.model},
nb_channels=1,
sample_rate=SAMPLE_RATE,
n_fft=self.n_fft,
n_hop=self.hop_length,
)
outputs = s(x).squeeze(1)
log_wandb_audio_batch(
id="sample",
samples=x,
sampling_rate=SAMPLE_RATE,
caption=f"Epoch {self.current_epoch}",
)
log_wandb_audio_batch(
id="prediction",
samples=outputs,
sampling_rate=SAMPLE_RATE,
caption=f"Epoch {self.current_epoch}",
)
log_wandb_audio_batch(
id="target",
samples=target,
sampling_rate=SAMPLE_RATE,
caption=f"Epoch {self.current_epoch}",
)
self.log_next = False
class DiffusionGenerationModel(pl.LightningModule):
def __init__(self, model: torch.nn.Module):
super().__init__()
self.model = model
def forward(self, x: torch.Tensor):
return self.model(x)
def sample(self, *args, **kwargs) -> Tensor:
return self.model.sample(*args, **kwargs)
def training_step(self, batch, batch_idx):
loss = self.common_step(batch, batch_idx, mode="train")
return loss
def validation_step(self, batch, batch_idx):
loss = self.common_step(batch, batch_idx, mode="val")
def common_step(self, batch, batch_idx, mode: str = "train"):
x, target, label = batch
loss = self(x)
self.log(f"{mode}_loss", loss, on_step=True, on_epoch=True)
return loss
def configure_optimizers(self):
return torch.optim.Adam(
self.parameters(), lr=1e-4, betas=(0.95, 0.999), eps=1e-6, weight_decay=1e-3
)
def on_validation_epoch_start(self):
self.log_next = True
def on_validation_batch_start(self, batch, batch_idx, dataloader_idx):
x, target, label = batch
if self.log_next:
self.log_sample(x)
self.log_next = False
@torch.no_grad()
def log_sample(self, batch, num_steps=10):
# Get start diffusion noise
noise = torch.randn(batch.shape, device=self.device)
sampled = self.sample(noise=noise, num_steps=num_steps) # Suggested range: 2-50
log_wandb_audio_batch(
id="sample",
samples=sampled,
sampling_rate=SAMPLE_RATE,
caption=f"Sampled in {num_steps} steps",
)
def log_wandb_audio_batch(
id: str, samples: Tensor, sampling_rate: int, caption: str = ""
):
num_items = samples.shape[0]
samples = rearrange(samples, "b c t -> b t c")
for idx in range(num_items):
wandb.log(
{
f"{id}_{idx}": wandb.Audio(
samples[idx].cpu().numpy(),
caption=caption,
sample_rate=sampling_rate,
)
}
)
def spectrogram(
x: torch.Tensor,
window: torch.Tensor,
n_fft: int,
hop_length: int,
alpha: float,
) -> torch.Tensor:
bs, chs, samp = x.size()
x = x.view(bs * chs, -1) # move channels onto batch dim
X = torch.stft(
x,
n_fft=n_fft,
hop_length=hop_length,
window=window,
return_complex=True,
)
# move channels back
X = X.view(bs, chs, X.shape[-2], X.shape[-1])
return torch.pow(X.abs() + 1e-8, alpha)
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