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lightning.py
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lightning.py
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import torch
import torchaudio
from cosine import WarmupCosineScheduler
from datamodule.transforms import TextTransform
from pytorch_lightning import LightningModule
from espnet.nets.batch_beam_search import BatchBeamSearch
from espnet.nets.pytorch_backend.e2e_asr_conformer import E2E
from espnet.nets.scorers.length_bonus import LengthBonus
from espnet.nets.scorers.ctc import CTCPrefixScorer
def compute_word_level_distance(seq1, seq2):
return torchaudio.functional.edit_distance(seq1.lower().split(), seq2.lower().split())
class ModelModule(LightningModule):
def __init__(self, cfg):
super().__init__()
self.save_hyperparameters(cfg)
self.cfg = cfg
if self.cfg.data.modality == "audio":
self.backbone_args = self.cfg.model.audio_backbone
elif self.cfg.data.modality == "video":
self.backbone_args = self.cfg.model.visual_backbone
self.text_transform = TextTransform()
self.token_list = self.text_transform.token_list
self.model = E2E(len(self.token_list), self.backbone_args)
# -- initialise
if self.cfg.pretrained_model_path:
ckpt = torch.load(self.cfg.pretrained_model_path, map_location=lambda storage, loc: storage)
if self.cfg.transfer_frontend:
tmp_ckpt = {k: v for k, v in ckpt["model_state_dict"].items() if k.startswith("trunk.") or k.startswith("frontend3D.")}
self.model.encoder.frontend.load_state_dict(tmp_ckpt)
elif self.cfg.transfer_encoder:
tmp_ckpt = {k.replace("encoder.", ""): v for k, v in ckpt.items() if k.startswith("encoder.")}
self.model.encoder.load_state_dict(tmp_ckpt, strict=True)
else:
self.model.load_state_dict(ckpt)
def configure_optimizers(self):
optimizer = torch.optim.AdamW([{"name": "model", "params": self.model.parameters(), "lr": self.cfg.optimizer.lr}], weight_decay=self.cfg.optimizer.weight_decay, betas=(0.9, 0.98))
scheduler = WarmupCosineScheduler(optimizer, self.cfg.optimizer.warmup_epochs, self.cfg.trainer.max_epochs, len(self.trainer.datamodule.train_dataloader()))
scheduler = {"scheduler": scheduler, "interval": "step", "frequency": 1}
return [optimizer], [scheduler]
def forward(self, sample):
self.beam_search = get_beam_search_decoder(self.model, self.token_list)
enc_feat, _ = self.model.encoder(sample.unsqueeze(0).to(self.device), None)
enc_feat = enc_feat.squeeze(0)
nbest_hyps = self.beam_search(enc_feat)
nbest_hyps = [h.asdict() for h in nbest_hyps[: min(len(nbest_hyps), 1)]]
predicted_token_id = torch.tensor(list(map(int, nbest_hyps[0]["yseq"][1:])))
predicted = self.text_transform.post_process(predicted_token_id).replace("<eos>", "")
return predicted
def training_step(self, batch, batch_idx):
return self._step(batch, batch_idx, step_type="train")
def validation_step(self, batch, batch_idx):
return self._step(batch, batch_idx, step_type="val")
def test_step(self, sample, sample_idx):
enc_feat, _ = self.model.encoder(sample["input"].unsqueeze(0).to(self.device), None)
enc_feat = enc_feat.squeeze(0)
nbest_hyps = self.beam_search(enc_feat)
nbest_hyps = [h.asdict() for h in nbest_hyps[: min(len(nbest_hyps), 1)]]
predicted_token_id = torch.tensor(list(map(int, nbest_hyps[0]["yseq"][1:])))
predicted = self.text_transform.post_process(predicted_token_id).replace("<eos>", "")
token_id = sample["target"]
actual = self.text_transform.post_process(token_id)
self.total_edit_distance += compute_word_level_distance(actual, predicted)
self.total_length += len(actual.split())
return
def _step(self, batch, batch_idx, step_type):
loss, loss_ctc, loss_att, acc = self.model(batch["inputs"], batch["input_lengths"], batch["targets"])
batch_size = len(batch["inputs"])
if step_type == "train":
self.log("loss", loss, on_step=True, on_epoch=True, batch_size=batch_size)
self.log("loss_ctc", loss_ctc, on_step=False, on_epoch=True, batch_size=batch_size)
self.log("loss_att", loss_att, on_step=False, on_epoch=True, batch_size=batch_size)
self.log("decoder_acc", acc, on_step=True, on_epoch=True, batch_size=batch_size)
else:
self.log("loss_val", loss, batch_size=batch_size)
self.log("loss_ctc_val", loss_ctc, batch_size=batch_size)
self.log("loss_att_val", loss_att, batch_size=batch_size)
self.log("decoder_acc_val", acc, batch_size=batch_size)
if step_type == "train":
self.log("monitoring_step", torch.tensor(self.global_step, dtype=torch.float32))
return loss
def on_train_epoch_start(self):
sampler = self.trainer.train_dataloader.loaders.batch_sampler
if hasattr(sampler, "set_epoch"):
sampler.set_epoch(self.current_epoch)
return super().on_train_epoch_start()
def on_test_epoch_start(self):
self.total_length = 0
self.total_edit_distance = 0
self.text_transform = TextTransform()
self.beam_search = get_beam_search_decoder(self.model, self.token_list)
def on_test_epoch_end(self):
self.log("wer", self.total_edit_distance / self.total_length)
def get_beam_search_decoder(model, token_list, ctc_weight=0.1, beam_size=40):
scorers = {
"decoder": model.decoder,
"ctc": CTCPrefixScorer(model.ctc, model.eos),
"length_bonus": LengthBonus(len(token_list)),
"lm": None
}
weights = {
"decoder": 1.0 - ctc_weight,
"ctc": ctc_weight,
"lm": 0.0,
"length_bonus": 0.0,
}
return BatchBeamSearch(
beam_size=beam_size,
vocab_size=len(token_list),
weights=weights,
scorers=scorers,
sos=model.sos,
eos=model.eos,
token_list=token_list,
pre_beam_score_key=None if ctc_weight == 1.0 else "decoder",
)