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Display losses for gigaspeech and librispeech separately.

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This commit is contained in:
Fangjun Kuang 2022-02-16 18:38:56 +08:00
parent 018d03cd08
commit 1930d72b17
1 changed files with 31 additions and 5 deletions
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36
egs/librispeech/ASR/transducer_stateless_multi_datasets/train.py
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@ -516,6 +516,8 @@ def train_one_epoch(
""" """
model.train() model.train()
libri_tot_loss = MetricsTracker()
giga_tot_loss = MetricsTracker()
tot_loss = MetricsTracker() tot_loss = MetricsTracker()
# index 0: for LibriSpeech # index 0: for LibriSpeech
@ -542,6 +544,8 @@ def train_one_epoch(
params.batch_idx_train += 1 params.batch_idx_train += 1
batch_size = len(batch["supervisions"]["text"]) batch_size = len(batch["supervisions"]["text"])
libri = is_libri(batch["supervisions"]["cut"][0])
loss, loss_info = compute_loss( loss, loss_info = compute_loss(
params=params, params=params,
model=model, model=model,
@ -551,6 +555,16 @@ def train_one_epoch(
) )
# summary stats # summary stats
tot_loss = (tot_loss * (1 - 1 / params.reset_interval)) + loss_info tot_loss = (tot_loss * (1 - 1 / params.reset_interval)) + loss_info
if libri:
libri_tot_loss = (
libri_tot_loss * (1 - 1 / params.reset_interval)
) + loss_info
prefix = "libri" # for logging only
else:
giga_tot_loss = (
giga_tot_loss * (1 - 1 / params.reset_interval)
) + loss_info
prefix = "giga"
# NOTE: We use reduction==sum and loss is computed over utterances # NOTE: We use reduction==sum and loss is computed over utterances
# in the batch and there is no normalization to it so far. # in the batch and there is no normalization to it so far.
@ -563,19 +577,29 @@ def train_one_epoch(
if batch_idx % params.log_interval == 0: if batch_idx % params.log_interval == 0:
logging.info( logging.info(
f"Epoch {params.cur_epoch}, " f"Epoch {params.cur_epoch}, "
f"batch {batch_idx}, loss[{loss_info}], " f"batch {batch_idx}, {prefix}_loss[{loss_info}], "
f"tot_loss[{tot_loss}], batch size: {batch_size}" f"tot_loss[{tot_loss}], "
f"libri_tot_loss[{libri_tot_loss}], "
f"giga_tot_loss[{giga_tot_loss}], "
f"batch size: {batch_size}"
) )
if batch_idx % params.log_interval == 0: if batch_idx % params.log_interval == 0:
if tb_writer is not None: if tb_writer is not None:
loss_info.write_summary( loss_info.write_summary(
tb_writer, "train/current_", params.batch_idx_train tb_writer,
f"train/current_{prefix}_",
params.batch_idx_train,
) )
tot_loss.write_summary( tot_loss.write_summary(
tb_writer, "train/tot_", params.batch_idx_train tb_writer, "train/tot_", params.batch_idx_train
) )
libri_tot_loss.write_summary(
tb_writer, "train/libri_tot_", params.batch_idx_train
)
giga_tot_loss.write_summary(
tb_writer, "train/giga_tot_", params.batch_idx_train
)
if batch_idx > 0 and batch_idx % params.valid_interval == 0: if batch_idx > 0 and batch_idx % params.valid_interval == 0:
logging.info("Computing validation loss") logging.info("Computing validation loss")
@ -738,7 +762,9 @@ def run(rank, world_size, args):
valid_cuts += librispeech.dev_other_cuts() valid_cuts += librispeech.dev_other_cuts()
valid_dl = asr_datamodule.valid_dataloaders(valid_cuts) valid_dl = asr_datamodule.valid_dataloaders(valid_cuts)
for dl in [train_dl, giga_train_dl]: # It's time consuming to include `giga_train_dl` here
# for dl in [train_dl, giga_train_dl]:
for dl in [train_dl]:
scan_pessimistic_batches_for_oom( scan_pessimistic_batches_for_oom(
model=model, model=model,
train_dl=dl, train_dl=dl,