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train_non_tonal.py
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train_non_tonal.py
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from mdd.datasets import SupervisedDataset, collate_fn
from mdd.models import (
EffConformer,
Conformer,
ConvLSTMNet,
ConformerPitch,
ConformerPitchTonal,
)
from mdd.utils import greedy_decode, decode_phone
import lightning.pytorch as pl
from torch.utils.data import DataLoader
import torch
from torch import nn, optim
import os
import jiwer
class CTCModel(pl.LightningModule):
def __init__(
self,
input_dim=80,
model_dim=144,
num_heads=4,
num_layers=[2, 2, 4],
model_kernel_size=15,
dropout=0.2,
vocab_size=123,
num_tonals=6,
lr=3e-4,
cfg_lr_scheduler=None,
):
super().__init__()
# self.model = EffConformer(
# input_dim=input_dim,
# model_dim=model_dim,
# num_heads=num_heads,
# num_layers=num_layers,
# model_kernel_size=model_kernel_size,
# vocab_size=vocab_size
# )
# self.model = Conformer(
# input_dim=input_dim,
# model_dim=model_dim,
# num_heads=num_heads,
# num_layers=num_layers,
# model_kernel_size=model_kernel_size,
# dropout=dropout,
# vocab_size=vocab_size
# )
self.model = ConformerPitch(
input_dim=input_dim,
model_dim=model_dim,
num_heads=num_heads,
num_layers=num_layers,
model_kernel_size=model_kernel_size,
dropout=dropout,
vocab_size=vocab_size,
)
# self.model = ConvLSTMNet(input_dim, model_dim,
# num_layers=num_layers, vocab_size=vocab_size)
self.lr = lr
self.cfg_lr_scheduler = cfg_lr_scheduler
self.cfg_lr_scheduler["max_lr"] = lr
self.criterion = nn.CTCLoss()
self.actual_phonemes = []
self.predict_phonemes = []
def configure_optimizers(self):
optimizer = optim.AdamW(self.parameters(), lr=self.lr)
lr_scheduler = {
"scheduler": optim.lr_scheduler.OneCycleLR(
optimizer, **self.cfg_lr_scheduler
),
"name": "lr_scheduler_logger",
"interval": "step", # or 'epoch'
"frequency": 1,
}
return {"optimizer": optimizer, "lr_scheduler": lr_scheduler}
def training_step(self, batch, batch_idx):
x, x_len, y, y_len, pitches, tonal, tonal_len = batch
log_probs, out_len = self.model(
x.permute(0, 2, 1), x_len, pitches.permute(0, 2, 1)
)
# after: log_probs (bs, seq len, vocab size)
loss = self.criterion(log_probs.permute(1, 0, 2), y, out_len, y_len)
self.log("train_loss", loss, sync_dist=True)
return loss
def validation_step(self, batch, batch_idx):
x, x_len, y, y_len, pitches, tonal, tonal_len = batch
log_probs, out_len = self.model(
x.permute(0, 2, 1), x_len, pitches.permute(0, 2, 1)
)
# after: log_probs (bs, seq len, vocab size)
loss = self.criterion(log_probs.permute(1, 0, 2), y, out_len, y_len)
self.log("val_loss", loss, sync_dist=True)
actuals = [decode_phone(i.detach().cpu().tolist()) for i in y]
predicts = greedy_decode(log_probs.argmax(dim=-1).detach().cpu())
self.actual_phonemes.extend(actuals)
self.predict_phonemes.extend(predicts)
def on_validation_epoch_end(self):
all_actuals = self.actual_phonemes
all_predicts = self.predict_phonemes
all_actuals = [" ".join(i) for i in all_actuals]
all_predicts = [" ".join(i) for i in all_predicts]
wer = jiwer.wer(all_actuals, all_predicts)
self.log("val_wer", wer, sync_dist=True)
self.actual_phonemes.clear()
self.predict_phonemes.clear()
def test_step(self, batch, batch_idx):
x, x_len, y, y_len, pitches, tonal, tonal_len = batch
log_probs, out_len = self.model(
x.permute(0, 2, 1), x_len, pitches.permute(0, 2, 1)
)
# after: log_probs (bs, seq len, vocab size)
loss = self.criterion(log_probs.permute(1, 0, 2), y, out_len, y_len)
self.log("test_loss", loss, sync_dist=True)
self.log("test_loss", loss, sync_dist=True)
actuals = [decode_phone(i.detach().cpu().tolist()) for i in y]
predicts = greedy_decode(log_probs.argmax(dim=-1).detach().cpu())
self.actual_phonemes.extend(actuals)
self.predict_phonemes.extend(predicts)
def on_test_epoch_end(self):
all_actuals = self.actual_phonemes
all_predicts = self.predict_phonemes
all_actuals = [" ".join(i) for i in all_actuals]
all_predicts = [" ".join(i) for i in all_predicts]
wer = jiwer.wer(all_actuals, all_predicts)
self.log("test_wer", wer, sync_dist=True)
self.actual_phonemes.clear()
self.predict_phonemes.clear()
data_path = "/data/tuanio/data/share_with_150/data_vlsp_md_d_2023/splitted_data_113"
train_dataset = SupervisedDataset(os.path.join(data_path, "train.json"), True)
test_dataset = SupervisedDataset(os.path.join(data_path, "test.json"))
print("train: {}, test: {}".format(len(train_dataset), len(test_dataset)))
batch_size = 16
num_workers = 4
lr = 3e-4
max_epochs = 700
total_steps = len(train_dataset) * max_epochs
train_loader = DataLoader(
train_dataset,
batch_size,
shuffle=True,
num_workers=num_workers,
pin_memory=True,
persistent_workers=True,
collate_fn=collate_fn,
)
test_loader = DataLoader(
test_dataset,
batch_size,
shuffle=False,
num_workers=num_workers,
pin_memory=True,
persistent_workers=True,
collate_fn=collate_fn,
)
input_dim = 81
model_dim = 128
num_heads = 4
num_layers = 4
dropout = 0.2
model_kernel_size = 31
vocab_size = 123
cfg_lr_scheduler = {"pct_start": 0.1, "total_steps": total_steps // batch_size}
# model = CTCModel(
# input_dim=80,
# model_dim=96,
# num_heads=4,
# num_layers=[2, 2, 4],
# model_kernel_size=15,
# vocab_size=123,
# lr=lr,
# cfg_lr_scheduler=cfg_lr_scheduler
# )
model = CTCModel(
input_dim=input_dim,
model_dim=model_dim,
num_heads=num_heads,
num_layers=num_layers,
dropout=dropout,
model_kernel_size=model_kernel_size,
vocab_size=vocab_size,
lr=lr,
cfg_lr_scheduler=cfg_lr_scheduler,
)
# model = CTCModel(
# input_dim=80,
# model_dim=64,
# num_layers=2,
# vocab_size=123,
# lr=lr,
# cfg_lr_scheduler=cfg_lr_scheduler
# )
print(model)
print("Number of params:", sum(p.numel() for p in model.parameters()))
# wandb_logger = None
#
name = f"conformer_dim{model_dim}_heads{num_heads}_layers{num_layers}_drop{dropout}_kernel{model_kernel_size}_nfft512_hop128_warmup0.2_{max_epochs}epochs_inputdim81_pitch"
wandb_logger = pl.loggers.WandbLogger(project="md_d_vlsp_2023", name=name)
trainer = pl.Trainer(
devices=-1,
accelerator="gpu",
precision=16,
max_epochs=max_epochs,
logger=wandb_logger,
log_every_n_steps=50,
)
trainer.fit(model=model, train_dataloaders=train_loader, val_dataloaders=test_loader)