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import torch
from torch.nn.functional import ctc_loss, log_softmax
from torch.optim import RAdam
from pytorch_lightning import LightningModule, Trainer
from pytorch_lightning.callbacks import LearningRateMonitor, ModelCheckpoint
from torchmetrics.functional import word_error_rate, char_error_rate
import sentencepiece as spm
from nemo.collections.asr.modules import (
AudioToMelSpectrogramPreprocessor,
SpectrogramAugmentation,
SqueezeformerEncoder,
ConvASRDecoder,
)
from nemo.core import typecheck
from torchaudio.models.decoder import ctc_decoder
typecheck.set_typecheck_enabled(False)
# -------------------------
# Tokenizer
# -------------------------
sp = spm.SentencePieceProcessor()
sp.Load("ressources/tokenizer/128_v7.model")
# -------------------------
# CTC Decoder
# -------------------------
tokens_file = "ressources/tokenizer/128_v7.txt"
decoder = ctc_decoder(
lexicon=None,
tokens=tokens_file,
beam_size=1,
beam_threshold=1,
beam_size_token=1,
nbest=1,
log_add=True,
blank_token="_",
sil_token="|",
unk_word="<unk>",
)
# -------------------------
# Hyperparameters
# -------------------------
LR = 2e-4
NONE_COUNT = 0
# -------------------------
# LightningModule
# -------------------------
class MySqueezeformer(LightningModule):
def __init__(self, LR=LR):
super().__init__()
self.LR = LR
self.processor = AudioToMelSpectrogramPreprocessor(
sample_rate=16000,
features=80,
n_fft=512,
window_size=0.025,
window_stride=0.01,
log=True,
frame_splicing=True,
)
self.augmentation = SpectrogramAugmentation(2, 5, 27, 0.05)
self.encoder = SqueezeformerEncoder(
feat_in=80,
feat_out=-1,
n_layers=16,
d_model=144,
adaptive_scale=True,
time_reduce_idx=7,
dropout_emb=0,
dropout_att=0.1,
subsampling_factor=4,
)
self.decoder = ConvASRDecoder(feat_in=144, num_classes=128)
# -------------------------
# Forward
# -------------------------
def forward(self, x, lengths):
spec, lengths = self.processor(x, lengths)
if self.training:
spec = self.augmentation(spec, lengths)
encoded = self.encoder(spec, lengths)
decoded = self.decoder(encoded[0])
logits_lengths = torch.tensor([len(d) for d in decoded], device=x.device)
return decoded, logits_lengths
# -------------------------
# Training Step
# -------------------------
def training_step(self, batch, batch_idx):
spectrograms, transcriptions, specs_lengths, transcriptions_lengths = batch
outputs, logits_lengths = self(spectrograms, specs_lengths)
outputs = torch.stack(outputs).transpose(0, 1)
outputs = log_softmax(outputs, dim=2)
loss = ctc_loss(
outputs,
transcriptions,
logits_lengths,
transcriptions_lengths,
blank=1,
zero_infinity=True,
)
global NONE_COUNT
if torch.isnan(loss) or torch.isinf(loss):
NONE_COUNT += 1
self.log("N_c", float(NONE_COUNT), prog_bar=True, sync_dist=True)
return None
self.log("loss", loss, sync_dist=True, on_epoch=True, on_step=False)
return loss
# -------------------------
# Validation Step
# -------------------------
@torch.no_grad()
def validation_step(self, batch, batch_idx):
spectrograms, transcriptions, specs_lengths, transcriptions_lengths = batch
outputs, logits_lengths = self(spectrograms, specs_lengths)
all_transcriptions = []
all_targets = []
# Decode targets
for i, tgt in enumerate(transcriptions):
tgt_sentence = sp.Decode(tgt[: transcriptions_lengths[i]].tolist())
all_targets.append(tgt_sentence)
# Decode predictions
for i, out in enumerate(outputs):
result = decoder(out.cpu().unsqueeze(0))[0][0]
tokens = decoder.idxs_to_tokens(result.tokens)
transcription = "".join(tokens).replace("_", "").replace("|", "")
transcription = " ".join(transcription.split("▁"))
transcription = re.sub(r"-{2,}", "-", transcription)
transcription = transcription.strip()
all_transcriptions.append(transcription)
wer = word_error_rate(all_transcriptions, all_targets)
cer = char_error_rate(all_transcriptions, all_targets)
# Compute CTC loss for logging
stacked_outputs = torch.stack(outputs).transpose(0, 1)
stacked_outputs = log_softmax(stacked_outputs, dim=2)
val_loss = ctc_loss(
stacked_outputs,
transcriptions,
logits_lengths,
transcriptions_lengths,
blank=1,
zero_infinity=True,
)
self.log("val_loss", val_loss, sync_dist=True, on_epoch=True)
self.log("wer", wer, prog_bar=True, sync_dist=True, on_epoch=True)
self.log("cer", cer, sync_dist=True, on_epoch=True)
# -------------------------
# Optimizer
# -------------------------
def configure_optimizers(self):
optimizer = RAdam(
self.parameters(),
lr=self.LR,
betas=[0.9, 0.98],
weight_decay=1e-6,
eps=1e-9,
)
return optimizer
# -------------------------
# Training
# -------------------------
if __name__ == "__main__":
callbacks = [
LearningRateMonitor(logging_interval="epoch"),
ModelCheckpoint(
dirpath="./checkpoints_vZ2/val_loss",
verbose=False,
save_on_train_epoch_end=True,
save_top_k=1,
save_last=True,
monitor="val_loss",
),
ModelCheckpoint(
dirpath="./checkpoints_vZ2/wer",
verbose=False,
save_on_train_epoch_end=True,
save_top_k=1,
save_last=False,
monitor="wer",
),
ModelCheckpoint(
dirpath="./checkpoints_vZ2/cer",
verbose=False,
save_on_train_epoch_end=True,
save_top_k=1,
save_last=False,
monitor="cer",
),
]
model = MySqueezeformer()
trainer = Trainer(
accelerator="auto",
precision="bf16",
callbacks=callbacks,
default_root_dir="./checkpoints_vZ2/logs",
reload_dataloaders_every_n_epochs=1,
max_epochs=300,
)
# trainer.fit(
# model, train_dataloaders=train_dataloader, val_dataloaders=validation_dataloader
# )
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