import os
import librosa
import pytorch_lightning as pl
import soundfile as sf
import torch
from torch import nn
from torch.utils.data import DataLoader
from torchmetrics.audio.pesq import PerceptualEvaluationSpeechQuality as PESQ
from torchmetrics.audio.stoi import ShortTimeObjectiveIntelligibility as STOI
from PLCMOS.plc_mos import PLCMOSEstimator
from config import CONFIG
from loss import Loss
from models.blocks import Encoder, Predictor
from utils.utils import visualize, LSD
plcmos = PLCMOSEstimator()
class PLCModel(pl.LightningModule):
def __init__(self, train_dataset=None, val_dataset=None, window_size=960, enc_layers=4, enc_in_dim=384, enc_dim=768,
pred_dim=512, pred_layers=1, pred_ckpt_path='lightning_logs/predictor/checkpoints/predictor.ckpt'):
super(PLCModel, self).__init__()
self.window_size = window_size
self.hop_size = window_size // 2
self.learning_rate = CONFIG.TRAIN.lr
self.hparams.batch_size = CONFIG.TRAIN.batch_size
self.enc_layers = enc_layers
self.enc_in_dim = enc_in_dim
self.enc_dim = enc_dim
self.pred_dim = pred_dim
self.pred_layers = pred_layers
self.train_dataset = train_dataset
self.val_dataset = val_dataset
self.stoi = STOI(48000)
self.pesq = PESQ(16000, 'wb')
if pred_ckpt_path is not None:
self.predictor = Predictor.load_from_checkpoint(pred_ckpt_path)
else:
self.predictor = Predictor(window_size=self.window_size, lstm_dim=self.pred_dim,
lstm_layers=self.pred_layers)
self.joiner = nn.Sequential(
nn.Conv2d(3, 48, kernel_size=(9, 1), stride=1, padding=(4, 0), padding_mode='reflect',
groups=3),
nn.LeakyReLU(0.2),
nn.Conv2d(48, 2, kernel_size=1, stride=1, padding=0, groups=2),
)
self.encoder = Encoder(in_dim=self.window_size, dim=self.enc_in_dim, depth=self.enc_layers,
mlp_dim=self.enc_dim)
self.loss = Loss()
self.window = torch.sqrt(torch.hann_window(self.window_size))
self.save_hyperparameters('window_size', 'enc_layers', 'enc_in_dim', 'enc_dim', 'pred_dim', 'pred_layers')
def forward(self, x):
"""
Input: real-imaginary; shape (B, F, T, 2); F = hop_size + 1
Output: real-imaginary
"""
B, C, F, T = x.shape
x = x.permute(3, 0, 1, 2).unsqueeze(-1)
prev_mag = torch.zeros((B, 1, F, 1), device=x.device)
predictor_state = torch.zeros((2, self.predictor.lstm_layers, B, self.predictor.lstm_dim), device=x.device)
mlp_state = torch.zeros((self.encoder.depth, 2, 1, B, self.encoder.dim), device=x.device)
result = []
for step in x:
feat, mlp_state = self.encoder(step, mlp_state)
prev_mag, predictor_state = self.predictor(prev_mag, predictor_state)
feat = torch.cat((feat, prev_mag), 1)
feat = self.joiner(feat)
feat = feat + step
result.append(feat)
prev_mag = torch.linalg.norm(feat, dim=1, ord=1, keepdims=True) # compute magnitude
output = torch.cat(result, -1)
return output
def forward_onnx(self, x, prev_mag, predictor_state=None, mlp_state=None):
prev_mag, predictor_state = self.predictor(prev_mag, predictor_state)
feat, mlp_state = self.encoder(x, mlp_state)
feat = torch.cat((feat, prev_mag), 1)
feat = self.joiner(feat)
prev_mag = torch.linalg.norm(feat, dim=1, ord=1, keepdims=True)
feat = feat + x
return feat, prev_mag, predictor_state, mlp_state
def train_dataloader(self):
return DataLoader(self.train_dataset, shuffle=False, batch_size=self.hparams.batch_size,
num_workers=CONFIG.TRAIN.workers, persistent_workers=True)
def val_dataloader(self):
return DataLoader(self.val_dataset, shuffle=False, batch_size=self.hparams.batch_size,
num_workers=CONFIG.TRAIN.workers, persistent_workers=True)
def training_step(self, batch, batch_idx):
x_in, y = batch
f_0 = x_in[:, :, 0:1, :]
x = x_in[:, :, 1:, :]
x = self(x)
x = torch.cat([f_0, x], dim=2)
loss = self.loss(x, y)
self.log('train_loss', loss, logger=True)
return loss
def validation_step(self, val_batch, batch_idx):
x, y = val_batch
f_0 = x[:, :, 0:1, :]
x_in = x[:, :, 1:, :]
pred = self(x_in)
pred = torch.cat([f_0, pred], dim=2)
loss = self.loss(pred, y)
self.window = self.window.to(pred.device)
pred = torch.view_as_complex(pred.permute(0, 2, 3, 1).contiguous())
pred = torch.istft(pred, self.window_size, self.hop_size, window=self.window)
y = torch.view_as_complex(y.permute(0, 2, 3, 1).contiguous())
y = torch.istft(y, self.window_size, self.hop_size, window=self.window)
self.log('val_loss', loss, on_step=False, on_epoch=True, logger=True, prog_bar=True, sync_dist=True)
if batch_idx == 0:
i = torch.randint(0, x.shape[0], (1,)).item()
x = torch.view_as_complex(x.permute(0, 2, 3, 1).contiguous())
x = torch.istft(x[i], self.window_size, self.hop_size, window=self.window)
self.trainer.logger.log_spectrogram(y[i], x, pred[i], self.current_epoch)
self.trainer.logger.log_audio(y[i], x, pred[i], self.current_epoch)
def test_step(self, test_batch, batch_idx):
inp, tar, inp_wav, tar_wav = test_batch
inp_wav = inp_wav.squeeze()
tar_wav = tar_wav.squeeze()
f_0 = inp[:, :, 0:1, :]
x = inp[:, :, 1:, :]
pred = self(x)
pred = torch.cat([f_0, pred], dim=2)
pred = torch.istft(pred.squeeze(0).permute(1, 2, 0), self.window_size, self.hop_size,
window=self.window.to(pred.device))
stoi = self.stoi(pred, tar_wav)
tar_wav = tar_wav.cpu().numpy()
inp_wav = inp_wav.cpu().numpy()
pred = pred.detach().cpu().numpy()
lsd, _ = LSD(tar_wav, pred)
if batch_idx in [5, 7, 9]:
sample_path = os.path.join(CONFIG.LOG.sample_path)
path = os.path.join(sample_path, 'sample_' + str(batch_idx))
visualize(tar_wav, inp_wav, pred, path)
sf.write(os.path.join(path, 'enhanced_output.wav'), pred, samplerate=CONFIG.DATA.sr, subtype='PCM_16')
sf.write(os.path.join(path, 'lossy_input.wav'), inp_wav, samplerate=CONFIG.DATA.sr, subtype='PCM_16')
sf.write(os.path.join(path, 'target.wav'), tar_wav, samplerate=CONFIG.DATA.sr, subtype='PCM_16')
if CONFIG.DATA.sr != 16000:
pred = librosa.resample(pred, orig_sr=48000, target_sr=16000)
tar_wav = librosa.resample(tar_wav, orig_sr=48000, target_sr=16000, res_type='kaiser_fast')
ret = plcmos.run(pred, tar_wav)
pesq = self.pesq(torch.tensor(pred), torch.tensor(tar_wav))
metrics = {
"Intrusive": ret[0],
"Non-intrusive": ret[1],
'LSD': lsd,
'STOI': stoi,
'PESQ': pesq,
}
self.log_dict(metrics)
return metrics
def predict_step(self, batch, batch_idx: int, dataloader_idx: int = 0):
f_0 = batch[:, :, 0:1, :]
x = batch[:, :, 1:, :]
pred = self(x)
pred = torch.cat([f_0, pred], dim=2)
pred = torch.istft(pred.squeeze(0).permute(1, 2, 0), self.window_size, self.hop_size,
window=self.window.to(pred.device))
return pred
def configure_optimizers(self):
optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)
lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, patience=CONFIG.TRAIN.patience,
factor=CONFIG.TRAIN.factor, verbose=True)
scheduler = {
'scheduler': lr_scheduler,
'reduce_on_plateau': True,
'monitor': 'val_loss'
}
return [optimizer], [scheduler]
class OnnxWrapper(pl.LightningModule):
def __init__(self, model, *args, **kwargs):
super().__init__(*args, **kwargs)
self.model = model
batch_size = 1
pred_states = torch.zeros((2, 1, batch_size, model.predictor.lstm_dim))
mlp_states = torch.zeros((model.encoder.depth, 2, 1, batch_size, model.encoder.dim))
mag = torch.zeros((batch_size, 1, model.hop_size, 1))
x = torch.randn(batch_size, model.hop_size + 1, 2)
self.sample = (x, mag, pred_states, mlp_states)
self.input_names = ['input', 'mag_in_cached_', 'pred_state_in_cached_', 'mlp_state_in_cached_']
self.output_names = ['output', 'mag_out_cached_', 'pred_state_out_cached_', 'mlp_state_out_cached_']
def forward(self, x, prev_mag, predictor_state=None, mlp_state=None):
x = x.permute(0, 2, 1).unsqueeze(-1)
f_0 = x[:, :, 0:1, :]
x = x[:, :, 1:, :]
output, prev_mag, predictor_state, mlp_state = self.model.forward_onnx(x, prev_mag, predictor_state, mlp_state)
output = torch.cat([f_0, output], dim=2)
output = output.squeeze(-1).permute(0, 2, 1)
return output, prev_mag, predictor_state, mlp_state