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Code Issues Packages Projects Releases Wiki Activity

Remove cur_batch_idx (#1102)

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Fangjun Kuang 2023-05-30 14:49:54 +08:00 committed by GitHub
parent 1aeffa73bc
commit 7b0afbdc16
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GPG Key ID: 4AEE18F83AFDEB23
31 changed files with 0 additions and 334 deletions
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11
egs/aishell/ASR/pruned_transducer_stateless7/train.py
View File

@ -577,9 +577,6 @@ def load_checkpoint_if_available(
if "cur_epoch" in saved_params: if "cur_epoch" in saved_params:
params["start_epoch"] = saved_params["cur_epoch"] params["start_epoch"] = saved_params["cur_epoch"]
if "cur_batch_idx" in saved_params:
params["cur_batch_idx"] = saved_params["cur_batch_idx"]
return saved_params return saved_params
@ -806,13 +803,7 @@ def train_one_epoch(
tot_loss = MetricsTracker() tot_loss = MetricsTracker()
cur_batch_idx = params.get("cur_batch_idx", 0)
for batch_idx, batch in enumerate(train_dl): for batch_idx, batch in enumerate(train_dl):
if batch_idx < cur_batch_idx:
continue
cur_batch_idx = batch_idx
params.batch_idx_train += 1 params.batch_idx_train += 1
batch_size = len(batch["supervisions"]["text"]) batch_size = len(batch["supervisions"]["text"])
@ -859,7 +850,6 @@ def train_one_epoch(
params.batch_idx_train > 0 params.batch_idx_train > 0
and params.batch_idx_train % params.save_every_n == 0 and params.batch_idx_train % params.save_every_n == 0
): ):
params.cur_batch_idx = batch_idx
save_checkpoint_with_global_batch_idx( save_checkpoint_with_global_batch_idx(
out_dir=params.exp_dir, out_dir=params.exp_dir,
global_batch_idx=params.batch_idx_train, global_batch_idx=params.batch_idx_train,
@ -872,7 +862,6 @@ def train_one_epoch(
scaler=scaler, scaler=scaler,
rank=rank, rank=rank,
) )
del params.cur_batch_idx
remove_checkpoints( remove_checkpoints(
out_dir=params.exp_dir, out_dir=params.exp_dir,
topk=params.keep_last_k, topk=params.keep_last_k,

11
egs/aishell/ASR/pruned_transducer_stateless7/train2.py
View File

@ -580,9 +580,6 @@ def load_checkpoint_if_available(
if "cur_epoch" in saved_params: if "cur_epoch" in saved_params:
params["start_epoch"] = saved_params["cur_epoch"] params["start_epoch"] = saved_params["cur_epoch"]
if "cur_batch_idx" in saved_params:
params["cur_batch_idx"] = saved_params["cur_batch_idx"]
return saved_params return saved_params
@ -809,13 +806,7 @@ def train_one_epoch(
tot_loss = MetricsTracker() tot_loss = MetricsTracker()
cur_batch_idx = params.get("cur_batch_idx", 0)
for batch_idx, batch in enumerate(train_dl): for batch_idx, batch in enumerate(train_dl):
if batch_idx < cur_batch_idx:
continue
cur_batch_idx = batch_idx
params.batch_idx_train += 1 params.batch_idx_train += 1
batch_size = len(batch["supervisions"]["text"]) batch_size = len(batch["supervisions"]["text"])
@ -862,7 +853,6 @@ def train_one_epoch(
params.batch_idx_train > 0 params.batch_idx_train > 0
and params.batch_idx_train % params.save_every_n == 0 and params.batch_idx_train % params.save_every_n == 0
): ):
params.cur_batch_idx = batch_idx
save_checkpoint_with_global_batch_idx( save_checkpoint_with_global_batch_idx(
out_dir=params.exp_dir, out_dir=params.exp_dir,
global_batch_idx=params.batch_idx_train, global_batch_idx=params.batch_idx_train,
@ -875,7 +865,6 @@ def train_one_epoch(
scaler=scaler, scaler=scaler,
rank=rank, rank=rank,
) )
del params.cur_batch_idx
remove_checkpoints( remove_checkpoints(
out_dir=params.exp_dir, out_dir=params.exp_dir,
topk=params.keep_last_k, topk=params.keep_last_k,

11
egs/aishell/ASR/pruned_transducer_stateless7_bbpe/train.py
View File

@ -567,9 +567,6 @@ def load_checkpoint_if_available(
if "cur_epoch" in saved_params: if "cur_epoch" in saved_params:
params["start_epoch"] = saved_params["cur_epoch"] params["start_epoch"] = saved_params["cur_epoch"]
if "cur_batch_idx" in saved_params:
params["cur_batch_idx"] = saved_params["cur_batch_idx"]
return saved_params return saved_params
@ -799,13 +796,7 @@ def train_one_epoch(
tot_loss = MetricsTracker() tot_loss = MetricsTracker()
cur_batch_idx = params.get("cur_batch_idx", 0)
for batch_idx, batch in enumerate(train_dl): for batch_idx, batch in enumerate(train_dl):
if batch_idx < cur_batch_idx:
continue
cur_batch_idx = batch_idx
params.batch_idx_train += 1 params.batch_idx_train += 1
batch_size = len(batch["supervisions"]["text"]) batch_size = len(batch["supervisions"]["text"])
@ -852,7 +843,6 @@ def train_one_epoch(
params.batch_idx_train > 0 params.batch_idx_train > 0
and params.batch_idx_train % params.save_every_n == 0 and params.batch_idx_train % params.save_every_n == 0
): ):
params.cur_batch_idx = batch_idx
save_checkpoint_with_global_batch_idx( save_checkpoint_with_global_batch_idx(
out_dir=params.exp_dir, out_dir=params.exp_dir,
global_batch_idx=params.batch_idx_train, global_batch_idx=params.batch_idx_train,
@ -865,7 +855,6 @@ def train_one_epoch(
scaler=scaler, scaler=scaler,
rank=rank, rank=rank,
) )
del params.cur_batch_idx
remove_checkpoints( remove_checkpoints(
out_dir=params.exp_dir, out_dir=params.exp_dir,
topk=params.keep_last_k, topk=params.keep_last_k,

11
egs/aishell4/ASR/pruned_transducer_stateless5/train.py
View File

@ -512,9 +512,6 @@ def load_checkpoint_if_available(
if "cur_epoch" in saved_params: if "cur_epoch" in saved_params:
params["start_epoch"] = saved_params["cur_epoch"] params["start_epoch"] = saved_params["cur_epoch"]
if "cur_batch_idx" in saved_params:
params["cur_batch_idx"] = saved_params["cur_batch_idx"]
return saved_params return saved_params
@ -725,13 +722,7 @@ def train_one_epoch(
tot_loss = MetricsTracker() tot_loss = MetricsTracker()
cur_batch_idx = params.get("cur_batch_idx", 0)
for batch_idx, batch in enumerate(train_dl): for batch_idx, batch in enumerate(train_dl):
if batch_idx < cur_batch_idx:
continue
cur_batch_idx = batch_idx
params.batch_idx_train += 1 params.batch_idx_train += 1
batch_size = len(batch["supervisions"]["text"]) batch_size = len(batch["supervisions"]["text"])
# print(batch["supervisions"]) # print(batch["supervisions"])
@ -774,7 +765,6 @@ def train_one_epoch(
params.batch_idx_train > 0 params.batch_idx_train > 0
and params.batch_idx_train % params.save_every_n == 0 and params.batch_idx_train % params.save_every_n == 0
): ):
params.cur_batch_idx = batch_idx
save_checkpoint_with_global_batch_idx( save_checkpoint_with_global_batch_idx(
out_dir=params.exp_dir, out_dir=params.exp_dir,
global_batch_idx=params.batch_idx_train, global_batch_idx=params.batch_idx_train,
@ -787,7 +777,6 @@ def train_one_epoch(
scaler=scaler, scaler=scaler,
rank=rank, rank=rank,
) )
del params.cur_batch_idx
remove_checkpoints( remove_checkpoints(
out_dir=params.exp_dir, out_dir=params.exp_dir,
topk=params.keep_last_k, topk=params.keep_last_k,

11
egs/alimeeting/ASR_v2/pruned_transducer_stateless7/train.py
View File

@ -554,9 +554,6 @@ def load_checkpoint_if_available(
if "cur_epoch" in saved_params: if "cur_epoch" in saved_params:
params["start_epoch"] = saved_params["cur_epoch"] params["start_epoch"] = saved_params["cur_epoch"]
if "cur_batch_idx" in saved_params:
params["cur_batch_idx"] = saved_params["cur_batch_idx"]
return saved_params return saved_params
@ -779,13 +776,7 @@ def train_one_epoch(
tot_loss = MetricsTracker() tot_loss = MetricsTracker()
cur_batch_idx = params.get("cur_batch_idx", 0)
for batch_idx, batch in enumerate(train_dl): for batch_idx, batch in enumerate(train_dl):
if batch_idx < cur_batch_idx:
continue
cur_batch_idx = batch_idx
params.batch_idx_train += 1 params.batch_idx_train += 1
batch_size = len(batch["supervisions"]["text"]) batch_size = len(batch["supervisions"]["text"])
@ -832,7 +823,6 @@ def train_one_epoch(
params.batch_idx_train > 0 params.batch_idx_train > 0
and params.batch_idx_train % params.save_every_n == 0 and params.batch_idx_train % params.save_every_n == 0
): ):
params.cur_batch_idx = batch_idx
save_checkpoint_with_global_batch_idx( save_checkpoint_with_global_batch_idx(
out_dir=params.exp_dir, out_dir=params.exp_dir,
global_batch_idx=params.batch_idx_train, global_batch_idx=params.batch_idx_train,
@ -845,7 +835,6 @@ def train_one_epoch(
scaler=scaler, scaler=scaler,
rank=rank, rank=rank,
) )
del params.cur_batch_idx
remove_checkpoints( remove_checkpoints(
out_dir=params.exp_dir, out_dir=params.exp_dir,
topk=params.keep_last_k, topk=params.keep_last_k,

11
egs/ami/ASR/pruned_transducer_stateless7/train.py
View File

@ -549,9 +549,6 @@ def load_checkpoint_if_available(
if "cur_epoch" in saved_params: if "cur_epoch" in saved_params:
params["start_epoch"] = saved_params["cur_epoch"] params["start_epoch"] = saved_params["cur_epoch"]
if "cur_batch_idx" in saved_params:
params["cur_batch_idx"] = saved_params["cur_batch_idx"]
return saved_params return saved_params
@ -770,13 +767,7 @@ def train_one_epoch(
tot_loss = MetricsTracker() tot_loss = MetricsTracker()
cur_batch_idx = params.get("cur_batch_idx", 0)
for batch_idx, batch in enumerate(train_dl): for batch_idx, batch in enumerate(train_dl):
if batch_idx < cur_batch_idx:
continue
cur_batch_idx = batch_idx
params.batch_idx_train += 1 params.batch_idx_train += 1
batch_size = len(batch["supervisions"]["text"]) batch_size = len(batch["supervisions"]["text"])
@ -823,7 +814,6 @@ def train_one_epoch(
params.batch_idx_train > 0 params.batch_idx_train > 0
and params.batch_idx_train % params.save_every_n == 0 and params.batch_idx_train % params.save_every_n == 0
): ):
params.cur_batch_idx = batch_idx
save_checkpoint_with_global_batch_idx( save_checkpoint_with_global_batch_idx(
out_dir=params.exp_dir, out_dir=params.exp_dir,
global_batch_idx=params.batch_idx_train, global_batch_idx=params.batch_idx_train,
@ -836,7 +826,6 @@ def train_one_epoch(
scaler=scaler, scaler=scaler,
rank=rank, rank=rank,
) )
del params.cur_batch_idx
remove_checkpoints( remove_checkpoints(
out_dir=params.exp_dir, out_dir=params.exp_dir,
topk=params.keep_last_k, topk=params.keep_last_k,

11
egs/commonvoice/ASR/pruned_transducer_stateless7/train.py
View File

@ -567,9 +567,6 @@ def load_checkpoint_if_available(
if "cur_epoch" in saved_params: if "cur_epoch" in saved_params:
params["start_epoch"] = saved_params["cur_epoch"] params["start_epoch"] = saved_params["cur_epoch"]
if "cur_batch_idx" in saved_params:
params["cur_batch_idx"] = saved_params["cur_batch_idx"]
return saved_params return saved_params
@ -799,13 +796,7 @@ def train_one_epoch(
tot_loss = MetricsTracker() tot_loss = MetricsTracker()
cur_batch_idx = params.get("cur_batch_idx", 0)
for batch_idx, batch in enumerate(train_dl): for batch_idx, batch in enumerate(train_dl):
if batch_idx < cur_batch_idx:
continue
cur_batch_idx = batch_idx
params.batch_idx_train += 1 params.batch_idx_train += 1
batch_size = len(batch["supervisions"]["text"]) batch_size = len(batch["supervisions"]["text"])
@ -852,7 +843,6 @@ def train_one_epoch(
params.batch_idx_train > 0 params.batch_idx_train > 0
and params.batch_idx_train % params.save_every_n == 0 and params.batch_idx_train % params.save_every_n == 0
): ):
params.cur_batch_idx = batch_idx
save_checkpoint_with_global_batch_idx( save_checkpoint_with_global_batch_idx(
out_dir=params.exp_dir, out_dir=params.exp_dir,
global_batch_idx=params.batch_idx_train, global_batch_idx=params.batch_idx_train,
@ -865,7 +855,6 @@ def train_one_epoch(
scaler=scaler, scaler=scaler,
rank=rank, rank=rank,
) )
del params.cur_batch_idx
remove_checkpoints( remove_checkpoints(
out_dir=params.exp_dir, out_dir=params.exp_dir,
topk=params.keep_last_k, topk=params.keep_last_k,

11
egs/csj/ASR/pruned_transducer_stateless7_streaming/train.py
View File

@ -606,9 +606,6 @@ def load_checkpoint_if_available(
if "cur_epoch" in saved_params: if "cur_epoch" in saved_params:
params["start_epoch"] = saved_params["cur_epoch"] params["start_epoch"] = saved_params["cur_epoch"]
if "cur_batch_idx" in saved_params:
params["cur_batch_idx"] = saved_params["cur_batch_idx"]
return saved_params return saved_params
@ -835,13 +832,7 @@ def train_one_epoch(
tot_loss = MetricsTracker() tot_loss = MetricsTracker()
cur_batch_idx = params.get("cur_batch_idx", 0)
for batch_idx, batch in enumerate(train_dl): for batch_idx, batch in enumerate(train_dl):
if batch_idx < cur_batch_idx:
continue
cur_batch_idx = batch_idx
params.batch_idx_train += 1 params.batch_idx_train += 1
batch_size = len(batch["supervisions"]["text"]) batch_size = len(batch["supervisions"]["text"])
@ -889,7 +880,6 @@ def train_one_epoch(
params.batch_idx_train > 0 params.batch_idx_train > 0
and params.batch_idx_train % params.save_every_n == 0 and params.batch_idx_train % params.save_every_n == 0
): ):
params.cur_batch_idx = batch_idx
save_checkpoint_with_global_batch_idx( save_checkpoint_with_global_batch_idx(
out_dir=params.exp_dir, out_dir=params.exp_dir,
global_batch_idx=params.batch_idx_train, global_batch_idx=params.batch_idx_train,
@ -902,7 +892,6 @@ def train_one_epoch(
scaler=scaler, scaler=scaler,
rank=rank, rank=rank,
) )
del params.cur_batch_idx
remove_checkpoints( remove_checkpoints(
out_dir=params.exp_dir, out_dir=params.exp_dir,
topk=params.keep_last_k, topk=params.keep_last_k,

11
egs/csj/ASR/pruned_transducer_stateless7_streaming/train2.py
View File

@ -607,9 +607,6 @@ def load_checkpoint_if_available(
if "cur_epoch" in saved_params: if "cur_epoch" in saved_params:
params["start_epoch"] = saved_params["cur_epoch"] params["start_epoch"] = saved_params["cur_epoch"]
if "cur_batch_idx" in saved_params:
params["cur_batch_idx"] = saved_params["cur_batch_idx"]
return saved_params return saved_params
@ -836,13 +833,7 @@ def train_one_epoch(
tot_loss = MetricsTracker() tot_loss = MetricsTracker()
cur_batch_idx = params.get("cur_batch_idx", 0)
for batch_idx, batch in enumerate(train_dl): for batch_idx, batch in enumerate(train_dl):
if batch_idx < cur_batch_idx:
continue
cur_batch_idx = batch_idx
params.batch_idx_train += 1 params.batch_idx_train += 1
batch_size = len(batch["supervisions"]["text"]) batch_size = len(batch["supervisions"]["text"])
@ -890,7 +881,6 @@ def train_one_epoch(
params.batch_idx_train > 0 params.batch_idx_train > 0
and params.batch_idx_train % params.save_every_n == 0 and params.batch_idx_train % params.save_every_n == 0
): ):
params.cur_batch_idx = batch_idx
save_checkpoint_with_global_batch_idx( save_checkpoint_with_global_batch_idx(
out_dir=params.exp_dir, out_dir=params.exp_dir,
global_batch_idx=params.batch_idx_train, global_batch_idx=params.batch_idx_train,
@ -903,7 +893,6 @@ def train_one_epoch(
scaler=scaler, scaler=scaler,
rank=rank, rank=rank,
) )
del params.cur_batch_idx
remove_checkpoints( remove_checkpoints(
out_dir=params.exp_dir, out_dir=params.exp_dir,
topk=params.keep_last_k, topk=params.keep_last_k,

11
egs/gigaspeech/ASR/pruned_transducer_stateless2/train.py
View File

@ -462,9 +462,6 @@ def load_checkpoint_if_available(
if "cur_epoch" in saved_params: if "cur_epoch" in saved_params:
params["start_epoch"] = saved_params["cur_epoch"] params["start_epoch"] = saved_params["cur_epoch"]
if "cur_batch_idx" in saved_params:
params["cur_batch_idx"] = saved_params["cur_batch_idx"]
return saved_params return saved_params
@ -674,13 +671,7 @@ def train_one_epoch(
tot_loss = MetricsTracker() tot_loss = MetricsTracker()
cur_batch_idx = params.get("cur_batch_idx", 0)
for batch_idx, batch in enumerate(train_dl): for batch_idx, batch in enumerate(train_dl):
if batch_idx < cur_batch_idx:
continue
cur_batch_idx = batch_idx
params.batch_idx_train += 1 params.batch_idx_train += 1
batch_size = len(batch["supervisions"]["text"]) batch_size = len(batch["supervisions"]["text"])
@ -712,7 +703,6 @@ def train_one_epoch(
params.batch_idx_train > 0 params.batch_idx_train > 0
and params.batch_idx_train % params.save_every_n == 0 and params.batch_idx_train % params.save_every_n == 0
): ):
params.cur_batch_idx = batch_idx
save_checkpoint_with_global_batch_idx( save_checkpoint_with_global_batch_idx(
out_dir=params.exp_dir, out_dir=params.exp_dir,
global_batch_idx=params.batch_idx_train, global_batch_idx=params.batch_idx_train,
@ -725,7 +715,6 @@ def train_one_epoch(
scaler=scaler, scaler=scaler,
rank=rank, rank=rank,
) )
del params.cur_batch_idx
remove_checkpoints( remove_checkpoints(
out_dir=params.exp_dir, out_dir=params.exp_dir,
topk=params.keep_last_k, topk=params.keep_last_k,

11
egs/librispeech/ASR/conformer_ctc2/train.py
View File

@ -410,9 +410,6 @@ def load_checkpoint_if_available(
if "cur_epoch" in saved_params: if "cur_epoch" in saved_params:
params["start_epoch"] = saved_params["cur_epoch"] params["start_epoch"] = saved_params["cur_epoch"]
if "cur_batch_idx" in saved_params:
params["cur_batch_idx"] = saved_params["cur_batch_idx"]
return saved_params return saved_params
@ -675,13 +672,7 @@ def train_one_epoch(
tot_loss = MetricsTracker() tot_loss = MetricsTracker()
cur_batch_idx = params.get("cur_batch_idx", 0)
for batch_idx, batch in enumerate(train_dl): for batch_idx, batch in enumerate(train_dl):
if batch_idx < cur_batch_idx:
continue
cur_batch_idx = batch_idx
params.batch_idx_train += 1 params.batch_idx_train += 1
batch_size = len(batch["supervisions"]["text"]) batch_size = len(batch["supervisions"]["text"])
batch_name = batch["supervisions"]["uttid"] batch_name = batch["supervisions"]["uttid"]
@ -736,7 +727,6 @@ def train_one_epoch(
params.batch_idx_train > 0 params.batch_idx_train > 0
and params.batch_idx_train % params.save_every_n == 0 and params.batch_idx_train % params.save_every_n == 0
): ):
params.cur_batch_idx = batch_idx
save_checkpoint_with_global_batch_idx( save_checkpoint_with_global_batch_idx(
out_dir=params.exp_dir, out_dir=params.exp_dir,
global_batch_idx=params.batch_idx_train, global_batch_idx=params.batch_idx_train,
@ -749,7 +739,6 @@ def train_one_epoch(
scaler=scaler, scaler=scaler,
rank=rank, rank=rank,
) )
del params.cur_batch_idx
remove_checkpoints( remove_checkpoints(
out_dir=params.exp_dir, out_dir=params.exp_dir,
topk=params.keep_last_k, topk=params.keep_last_k,

11
egs/librispeech/ASR/conv_emformer_transducer_stateless/train.py
View File

@ -550,9 +550,6 @@ def load_checkpoint_if_available(
if "cur_epoch" in saved_params: if "cur_epoch" in saved_params:
params["start_epoch"] = saved_params["cur_epoch"] params["start_epoch"] = saved_params["cur_epoch"]
if "cur_batch_idx" in saved_params:
params["cur_batch_idx"] = saved_params["cur_batch_idx"]
return saved_params return saved_params
@ -771,13 +768,7 @@ def train_one_epoch(
tot_loss = MetricsTracker() tot_loss = MetricsTracker()
cur_batch_idx = params.get("cur_batch_idx", 0)
for batch_idx, batch in enumerate(train_dl): for batch_idx, batch in enumerate(train_dl):
if batch_idx < cur_batch_idx:
continue
cur_batch_idx = batch_idx
params.batch_idx_train += 1 params.batch_idx_train += 1
batch_size = len(batch["supervisions"]["text"]) batch_size = len(batch["supervisions"]["text"])
@ -819,7 +810,6 @@ def train_one_epoch(
params.batch_idx_train > 0 params.batch_idx_train > 0
and params.batch_idx_train % params.save_every_n == 0 and params.batch_idx_train % params.save_every_n == 0
): ):
params.cur_batch_idx = batch_idx
save_checkpoint_with_global_batch_idx( save_checkpoint_with_global_batch_idx(
out_dir=params.exp_dir, out_dir=params.exp_dir,
global_batch_idx=params.batch_idx_train, global_batch_idx=params.batch_idx_train,
@ -832,7 +822,6 @@ def train_one_epoch(
scaler=scaler, scaler=scaler,
rank=rank, rank=rank,
) )
del params.cur_batch_idx
remove_checkpoints( remove_checkpoints(
out_dir=params.exp_dir, out_dir=params.exp_dir,
topk=params.keep_last_k, topk=params.keep_last_k,

11
egs/librispeech/ASR/conv_emformer_transducer_stateless2/train.py
View File

@ -550,9 +550,6 @@ def load_checkpoint_if_available(
if "cur_epoch" in saved_params: if "cur_epoch" in saved_params:
params["start_epoch"] = saved_params["cur_epoch"] params["start_epoch"] = saved_params["cur_epoch"]
if "cur_batch_idx" in saved_params:
params["cur_batch_idx"] = saved_params["cur_batch_idx"]
return saved_params return saved_params
@ -771,13 +768,7 @@ def train_one_epoch(
tot_loss = MetricsTracker() tot_loss = MetricsTracker()
cur_batch_idx = params.get("cur_batch_idx", 0)
for batch_idx, batch in enumerate(train_dl): for batch_idx, batch in enumerate(train_dl):
if batch_idx < cur_batch_idx:
continue
cur_batch_idx = batch_idx
params.batch_idx_train += 1 params.batch_idx_train += 1
batch_size = len(batch["supervisions"]["text"]) batch_size = len(batch["supervisions"]["text"])
@ -819,7 +810,6 @@ def train_one_epoch(
params.batch_idx_train > 0 params.batch_idx_train > 0
and params.batch_idx_train % params.save_every_n == 0 and params.batch_idx_train % params.save_every_n == 0
): ):
params.cur_batch_idx = batch_idx
save_checkpoint_with_global_batch_idx( save_checkpoint_with_global_batch_idx(
out_dir=params.exp_dir, out_dir=params.exp_dir,
global_batch_idx=params.batch_idx_train, global_batch_idx=params.batch_idx_train,
@ -832,7 +822,6 @@ def train_one_epoch(
scaler=scaler, scaler=scaler,
rank=rank, rank=rank,
) )
del params.cur_batch_idx
remove_checkpoints( remove_checkpoints(
out_dir=params.exp_dir, out_dir=params.exp_dir,
topk=params.keep_last_k, topk=params.keep_last_k,

11
egs/librispeech/ASR/conv_emformer_transducer_stateless2/train2.py
View File

@ -552,9 +552,6 @@ def load_checkpoint_if_available(
if "cur_epoch" in saved_params: if "cur_epoch" in saved_params:
params["start_epoch"] = saved_params["cur_epoch"] params["start_epoch"] = saved_params["cur_epoch"]
if "cur_batch_idx" in saved_params:
params["cur_batch_idx"] = saved_params["cur_batch_idx"]
return saved_params return saved_params
@ -773,13 +770,7 @@ def train_one_epoch(
tot_loss = MetricsTracker() tot_loss = MetricsTracker()
cur_batch_idx = params.get("cur_batch_idx", 0)
for batch_idx, batch in enumerate(train_dl): for batch_idx, batch in enumerate(train_dl):
if batch_idx < cur_batch_idx:
continue
cur_batch_idx = batch_idx
params.batch_idx_train += 1 params.batch_idx_train += 1
batch_size = len(batch["supervisions"]["text"]) batch_size = len(batch["supervisions"]["text"])
@ -821,7 +812,6 @@ def train_one_epoch(
params.batch_idx_train > 0 params.batch_idx_train > 0
and params.batch_idx_train % params.save_every_n == 0 and params.batch_idx_train % params.save_every_n == 0
): ):
params.cur_batch_idx = batch_idx
save_checkpoint_with_global_batch_idx( save_checkpoint_with_global_batch_idx(
out_dir=params.exp_dir, out_dir=params.exp_dir,
global_batch_idx=params.batch_idx_train, global_batch_idx=params.batch_idx_train,
@ -834,7 +824,6 @@ def train_one_epoch(
scaler=scaler, scaler=scaler,
rank=rank, rank=rank,
) )
del params.cur_batch_idx
remove_checkpoints( remove_checkpoints(
out_dir=params.exp_dir, out_dir=params.exp_dir,
topk=params.keep_last_k, topk=params.keep_last_k,

11
egs/librispeech/ASR/pruned2_knowledge/train.py
View File

@ -444,9 +444,6 @@ def load_checkpoint_if_available(
if "cur_epoch" in saved_params: if "cur_epoch" in saved_params:
params["start_epoch"] = saved_params["cur_epoch"] params["start_epoch"] = saved_params["cur_epoch"]
if "cur_batch_idx" in saved_params:
params["cur_batch_idx"] = saved_params["cur_batch_idx"]
return saved_params return saved_params
@ -649,13 +646,7 @@ def train_one_epoch(
tot_loss = MetricsTracker() tot_loss = MetricsTracker()
cur_batch_idx = params.get("cur_batch_idx", 0)
for batch_idx, batch in enumerate(train_dl): for batch_idx, batch in enumerate(train_dl):
if batch_idx < cur_batch_idx:
continue
cur_batch_idx = batch_idx
params.batch_idx_train += 1 params.batch_idx_train += 1
batch_size = len(batch["supervisions"]["text"]) batch_size = len(batch["supervisions"]["text"])
@ -686,7 +677,6 @@ def train_one_epoch(
params.batch_idx_train > 0 params.batch_idx_train > 0
and params.batch_idx_train % params.save_every_n == 0 and params.batch_idx_train % params.save_every_n == 0
): ):
params.cur_batch_idx = batch_idx
save_checkpoint_with_global_batch_idx( save_checkpoint_with_global_batch_idx(
out_dir=params.exp_dir, out_dir=params.exp_dir,
global_batch_idx=params.batch_idx_train, global_batch_idx=params.batch_idx_train,
@ -698,7 +688,6 @@ def train_one_epoch(
scaler=scaler, scaler=scaler,
rank=rank, rank=rank,
) )
del params.cur_batch_idx
remove_checkpoints( remove_checkpoints(
out_dir=params.exp_dir, out_dir=params.exp_dir,
topk=params.keep_last_k, topk=params.keep_last_k,

11
egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/train.py
View File

@ -487,9 +487,6 @@ def load_checkpoint_if_available(
if "cur_epoch" in saved_params: if "cur_epoch" in saved_params:
params["start_epoch"] = saved_params["cur_epoch"] params["start_epoch"] = saved_params["cur_epoch"]
if "cur_batch_idx" in saved_params:
params["cur_batch_idx"] = saved_params["cur_batch_idx"]
return saved_params return saved_params
@ -692,13 +689,7 @@ def train_one_epoch(
tot_loss = MetricsTracker() tot_loss = MetricsTracker()
cur_batch_idx = params.get("cur_batch_idx", 0)
for batch_idx, batch in enumerate(train_dl): for batch_idx, batch in enumerate(train_dl):
if batch_idx < cur_batch_idx:
continue
cur_batch_idx = batch_idx
params.batch_idx_train += 1 params.batch_idx_train += 1
batch_size = len(batch["supervisions"]["text"]) batch_size = len(batch["supervisions"]["text"])
@ -738,7 +729,6 @@ def train_one_epoch(
params.batch_idx_train > 0 params.batch_idx_train > 0
and params.batch_idx_train % params.save_every_n == 0 and params.batch_idx_train % params.save_every_n == 0
): ):
params.cur_batch_idx = batch_idx
save_checkpoint_with_global_batch_idx( save_checkpoint_with_global_batch_idx(
out_dir=params.exp_dir, out_dir=params.exp_dir,
global_batch_idx=params.batch_idx_train, global_batch_idx=params.batch_idx_train,
@ -750,7 +740,6 @@ def train_one_epoch(
scaler=scaler, scaler=scaler,
rank=rank, rank=rank,
) )
del params.cur_batch_idx
remove_checkpoints( remove_checkpoints(
out_dir=params.exp_dir, out_dir=params.exp_dir,
topk=params.keep_last_k, topk=params.keep_last_k,

11
egs/librispeech/ASR/pruned_transducer_stateless7/finetune.py
View File

@ -620,9 +620,6 @@ def load_checkpoint_if_available(
if "cur_epoch" in saved_params: if "cur_epoch" in saved_params:
params["start_epoch"] = saved_params["cur_epoch"] params["start_epoch"] = saved_params["cur_epoch"]
if "cur_batch_idx" in saved_params:
params["cur_batch_idx"] = saved_params["cur_batch_idx"]
return saved_params return saved_params
@ -896,13 +893,7 @@ def train_one_epoch(
tot_loss = MetricsTracker() tot_loss = MetricsTracker()
cur_batch_idx = params.get("cur_batch_idx", 0)
for batch_idx, batch in enumerate(train_dl): for batch_idx, batch in enumerate(train_dl):
if batch_idx < cur_batch_idx:
continue
cur_batch_idx = batch_idx
params.batch_idx_train += 1 params.batch_idx_train += 1
batch_size = len(batch["supervisions"]["text"]) batch_size = len(batch["supervisions"]["text"])
@ -953,7 +944,6 @@ def train_one_epoch(
params.batch_idx_train > 0 params.batch_idx_train > 0
and params.batch_idx_train % params.save_every_n == 0 and params.batch_idx_train % params.save_every_n == 0
): ):
params.cur_batch_idx = batch_idx
save_checkpoint_with_global_batch_idx( save_checkpoint_with_global_batch_idx(
out_dir=params.exp_dir, out_dir=params.exp_dir,
global_batch_idx=params.batch_idx_train, global_batch_idx=params.batch_idx_train,
@ -966,7 +956,6 @@ def train_one_epoch(
scaler=scaler, scaler=scaler,
rank=rank, rank=rank,
) )
del params.cur_batch_idx
remove_checkpoints( remove_checkpoints(
out_dir=params.exp_dir, out_dir=params.exp_dir,
topk=params.keep_last_k, topk=params.keep_last_k,

11
egs/librispeech/ASR/pruned_transducer_stateless7/train.py
View File

@ -578,9 +578,6 @@ def load_checkpoint_if_available(
if "cur_epoch" in saved_params: if "cur_epoch" in saved_params:
params["start_epoch"] = saved_params["cur_epoch"] params["start_epoch"] = saved_params["cur_epoch"]
if "cur_batch_idx" in saved_params:
params["cur_batch_idx"] = saved_params["cur_batch_idx"]
return saved_params return saved_params
@ -811,13 +808,7 @@ def train_one_epoch(
tot_loss = MetricsTracker() tot_loss = MetricsTracker()
cur_batch_idx = params.get("cur_batch_idx", 0)
for batch_idx, batch in enumerate(train_dl): for batch_idx, batch in enumerate(train_dl):
if batch_idx < cur_batch_idx:
continue
cur_batch_idx = batch_idx
params.batch_idx_train += 1 params.batch_idx_train += 1
batch_size = len(batch["supervisions"]["text"]) batch_size = len(batch["supervisions"]["text"])
@ -864,7 +855,6 @@ def train_one_epoch(
params.batch_idx_train > 0 params.batch_idx_train > 0
and params.batch_idx_train % params.save_every_n == 0 and params.batch_idx_train % params.save_every_n == 0
): ):
params.cur_batch_idx = batch_idx
save_checkpoint_with_global_batch_idx( save_checkpoint_with_global_batch_idx(
out_dir=params.exp_dir, out_dir=params.exp_dir,
global_batch_idx=params.batch_idx_train, global_batch_idx=params.batch_idx_train,
@ -877,7 +867,6 @@ def train_one_epoch(
scaler=scaler, scaler=scaler,
rank=rank, rank=rank,
) )
del params.cur_batch_idx
remove_checkpoints( remove_checkpoints(
out_dir=params.exp_dir, out_dir=params.exp_dir,
topk=params.keep_last_k, topk=params.keep_last_k,

11
egs/librispeech/ASR/pruned_transducer_stateless7_ctc/train.py
View File

@ -577,9 +577,6 @@ def load_checkpoint_if_available(
if "cur_epoch" in saved_params: if "cur_epoch" in saved_params:
params["start_epoch"] = saved_params["cur_epoch"] params["start_epoch"] = saved_params["cur_epoch"]
if "cur_batch_idx" in saved_params:
params["cur_batch_idx"] = saved_params["cur_batch_idx"]
return saved_params return saved_params
@ -830,13 +827,7 @@ def train_one_epoch(
tot_loss = MetricsTracker() tot_loss = MetricsTracker()
cur_batch_idx = params.get("cur_batch_idx", 0)
for batch_idx, batch in enumerate(train_dl): for batch_idx, batch in enumerate(train_dl):
if batch_idx < cur_batch_idx:
continue
cur_batch_idx = batch_idx
params.batch_idx_train += 1 params.batch_idx_train += 1
batch_size = len(batch["supervisions"]["text"]) batch_size = len(batch["supervisions"]["text"])
@ -883,7 +874,6 @@ def train_one_epoch(
params.batch_idx_train > 0 params.batch_idx_train > 0
and params.batch_idx_train % params.save_every_n == 0 and params.batch_idx_train % params.save_every_n == 0
): ):
params.cur_batch_idx = batch_idx
save_checkpoint_with_global_batch_idx( save_checkpoint_with_global_batch_idx(
out_dir=params.exp_dir, out_dir=params.exp_dir,
global_batch_idx=params.batch_idx_train, global_batch_idx=params.batch_idx_train,
@ -896,7 +886,6 @@ def train_one_epoch(
scaler=scaler, scaler=scaler,
rank=rank, rank=rank,
) )
del params.cur_batch_idx
remove_checkpoints( remove_checkpoints(
out_dir=params.exp_dir, out_dir=params.exp_dir,
topk=params.keep_last_k, topk=params.keep_last_k,

11
egs/librispeech/ASR/pruned_transducer_stateless7_ctc_bs/train.py
View File

@ -570,9 +570,6 @@ def load_checkpoint_if_available(
if "cur_epoch" in saved_params: if "cur_epoch" in saved_params:
params["start_epoch"] = saved_params["cur_epoch"] params["start_epoch"] = saved_params["cur_epoch"]
if "cur_batch_idx" in saved_params:
params["cur_batch_idx"] = saved_params["cur_batch_idx"]
return saved_params return saved_params
@ -819,13 +816,7 @@ def train_one_epoch(
tot_loss = MetricsTracker() tot_loss = MetricsTracker()
cur_batch_idx = params.get("cur_batch_idx", 0)
for batch_idx, batch in enumerate(train_dl): for batch_idx, batch in enumerate(train_dl):
if batch_idx < cur_batch_idx:
continue
cur_batch_idx = batch_idx
params.batch_idx_train += 1 params.batch_idx_train += 1
batch_size = len(batch["supervisions"]["text"]) batch_size = len(batch["supervisions"]["text"])
@ -872,7 +863,6 @@ def train_one_epoch(
params.batch_idx_train > 0 params.batch_idx_train > 0
and params.batch_idx_train % params.save_every_n == 0 and params.batch_idx_train % params.save_every_n == 0
): ):
params.cur_batch_idx = batch_idx
save_checkpoint_with_global_batch_idx( save_checkpoint_with_global_batch_idx(
out_dir=params.exp_dir, out_dir=params.exp_dir,
global_batch_idx=params.batch_idx_train, global_batch_idx=params.batch_idx_train,
@ -885,7 +875,6 @@ def train_one_epoch(
scaler=scaler, scaler=scaler,
rank=rank, rank=rank,
) )
del params.cur_batch_idx
remove_checkpoints( remove_checkpoints(
out_dir=params.exp_dir, out_dir=params.exp_dir,
topk=params.keep_last_k, topk=params.keep_last_k,

11
egs/librispeech/ASR/pruned_transducer_stateless7_streaming/train.py
View File

@ -586,9 +586,6 @@ def load_checkpoint_if_available(
if "cur_epoch" in saved_params: if "cur_epoch" in saved_params:
params["start_epoch"] = saved_params["cur_epoch"] params["start_epoch"] = saved_params["cur_epoch"]
if "cur_batch_idx" in saved_params:
params["cur_batch_idx"] = saved_params["cur_batch_idx"]
return saved_params return saved_params
@ -807,13 +804,7 @@ def train_one_epoch(
tot_loss = MetricsTracker() tot_loss = MetricsTracker()
cur_batch_idx = params.get("cur_batch_idx", 0)
for batch_idx, batch in enumerate(train_dl): for batch_idx, batch in enumerate(train_dl):
if batch_idx < cur_batch_idx:
continue
cur_batch_idx = batch_idx
params.batch_idx_train += 1 params.batch_idx_train += 1
batch_size = len(batch["supervisions"]["text"]) batch_size = len(batch["supervisions"]["text"])
@ -860,7 +851,6 @@ def train_one_epoch(
params.batch_idx_train > 0 params.batch_idx_train > 0
and params.batch_idx_train % params.save_every_n == 0 and params.batch_idx_train % params.save_every_n == 0
): ):
params.cur_batch_idx = batch_idx
save_checkpoint_with_global_batch_idx( save_checkpoint_with_global_batch_idx(
out_dir=params.exp_dir, out_dir=params.exp_dir,
global_batch_idx=params.batch_idx_train, global_batch_idx=params.batch_idx_train,
@ -873,7 +863,6 @@ def train_one_epoch(
scaler=scaler, scaler=scaler,
rank=rank, rank=rank,
) )
del params.cur_batch_idx
remove_checkpoints( remove_checkpoints(
out_dir=params.exp_dir, out_dir=params.exp_dir,
topk=params.keep_last_k, topk=params.keep_last_k,

11
egs/librispeech/ASR/pruned_transducer_stateless7_streaming/train2.py
View File

@ -587,9 +587,6 @@ def load_checkpoint_if_available(
if "cur_epoch" in saved_params: if "cur_epoch" in saved_params:
params["start_epoch"] = saved_params["cur_epoch"] params["start_epoch"] = saved_params["cur_epoch"]
if "cur_batch_idx" in saved_params:
params["cur_batch_idx"] = saved_params["cur_batch_idx"]
return saved_params return saved_params
@ -808,13 +805,7 @@ def train_one_epoch(
tot_loss = MetricsTracker() tot_loss = MetricsTracker()
cur_batch_idx = params.get("cur_batch_idx", 0)
for batch_idx, batch in enumerate(train_dl): for batch_idx, batch in enumerate(train_dl):
if batch_idx < cur_batch_idx:
continue
cur_batch_idx = batch_idx
params.batch_idx_train += 1 params.batch_idx_train += 1
batch_size = len(batch["supervisions"]["text"]) batch_size = len(batch["supervisions"]["text"])
@ -861,7 +852,6 @@ def train_one_epoch(
params.batch_idx_train > 0 params.batch_idx_train > 0
and params.batch_idx_train % params.save_every_n == 0 and params.batch_idx_train % params.save_every_n == 0
): ):
params.cur_batch_idx = batch_idx
save_checkpoint_with_global_batch_idx( save_checkpoint_with_global_batch_idx(
out_dir=params.exp_dir, out_dir=params.exp_dir,
global_batch_idx=params.batch_idx_train, global_batch_idx=params.batch_idx_train,
@ -874,7 +864,6 @@ def train_one_epoch(
scaler=scaler, scaler=scaler,
rank=rank, rank=rank,
) )
del params.cur_batch_idx
remove_checkpoints( remove_checkpoints(
out_dir=params.exp_dir, out_dir=params.exp_dir,
topk=params.keep_last_k, topk=params.keep_last_k,

5
egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/train.py
View File

@ -604,9 +604,6 @@ def load_checkpoint_if_available(
if "cur_epoch" in saved_params: if "cur_epoch" in saved_params:
params["start_epoch"] = saved_params["cur_epoch"] params["start_epoch"] = saved_params["cur_epoch"]
if "cur_batch_idx" in saved_params:
params["cur_batch_idx"] = saved_params["cur_batch_idx"]
return saved_params return saved_params
@ -921,7 +918,6 @@ def train_one_epoch(
params.batch_idx_train > 0 params.batch_idx_train > 0
and params.batch_idx_train % params.save_every_n == 0 and params.batch_idx_train % params.save_every_n == 0
): ):
params.cur_batch_idx = batch_idx
save_checkpoint_with_global_batch_idx( save_checkpoint_with_global_batch_idx(
out_dir=params.exp_dir, out_dir=params.exp_dir,
global_batch_idx=params.batch_idx_train, global_batch_idx=params.batch_idx_train,
@ -934,7 +930,6 @@ def train_one_epoch(
scaler=scaler, scaler=scaler,
rank=rank, rank=rank,
) )
del params.cur_batch_idx
remove_checkpoints( remove_checkpoints(
out_dir=params.exp_dir, out_dir=params.exp_dir,
topk=params.keep_last_k, topk=params.keep_last_k,

10
egs/librispeech/ASR/zipformer/train.py
View File

@ -667,9 +667,6 @@ def load_checkpoint_if_available(
if "cur_epoch" in saved_params: if "cur_epoch" in saved_params:
params["start_epoch"] = saved_params["cur_epoch"] params["start_epoch"] = saved_params["cur_epoch"]
if "cur_batch_idx" in saved_params:
params["cur_batch_idx"] = saved_params["cur_batch_idx"]
return saved_params return saved_params
@ -895,8 +892,6 @@ def train_one_epoch(
tot_loss = MetricsTracker() tot_loss = MetricsTracker()
cur_batch_idx = params.get("cur_batch_idx", 0)
saved_bad_model = False saved_bad_model = False
def save_bad_model(suffix: str = ""): def save_bad_model(suffix: str = ""):
@ -913,9 +908,6 @@ def train_one_epoch(
for batch_idx, batch in enumerate(train_dl): for batch_idx, batch in enumerate(train_dl):
if batch_idx % 10 == 0: if batch_idx % 10 == 0:
set_batch_count(model, get_adjusted_batch_count(params)) set_batch_count(model, get_adjusted_batch_count(params))
if batch_idx < cur_batch_idx:
continue
cur_batch_idx = batch_idx
params.batch_idx_train += 1 params.batch_idx_train += 1
batch_size = len(batch["supervisions"]["text"]) batch_size = len(batch["supervisions"]["text"])
@ -963,7 +955,6 @@ def train_one_epoch(
params.batch_idx_train > 0 params.batch_idx_train > 0
and params.batch_idx_train % params.save_every_n == 0 and params.batch_idx_train % params.save_every_n == 0
): ):
params.cur_batch_idx = batch_idx
save_checkpoint_with_global_batch_idx( save_checkpoint_with_global_batch_idx(
out_dir=params.exp_dir, out_dir=params.exp_dir,
global_batch_idx=params.batch_idx_train, global_batch_idx=params.batch_idx_train,
@ -976,7 +967,6 @@ def train_one_epoch(
scaler=scaler, scaler=scaler,
rank=rank, rank=rank,
) )
del params.cur_batch_idx
remove_checkpoints( remove_checkpoints(
out_dir=params.exp_dir, out_dir=params.exp_dir,
topk=params.keep_last_k, topk=params.keep_last_k,

11
egs/librispeech/ASR/zipformer_mmi/train.py
View File

@ -503,9 +503,6 @@ def load_checkpoint_if_available(
if "cur_epoch" in saved_params: if "cur_epoch" in saved_params:
params["start_epoch"] = saved_params["cur_epoch"] params["start_epoch"] = saved_params["cur_epoch"]
if "cur_batch_idx" in saved_params:
params["cur_batch_idx"] = saved_params["cur_batch_idx"]
return saved_params return saved_params
@ -741,13 +738,7 @@ def train_one_epoch(
tot_loss = MetricsTracker() tot_loss = MetricsTracker()
cur_batch_idx = params.get("cur_batch_idx", 0)
for batch_idx, batch in enumerate(train_dl): for batch_idx, batch in enumerate(train_dl):
if batch_idx < cur_batch_idx:
continue
cur_batch_idx = batch_idx
params.batch_idx_train += 1 params.batch_idx_train += 1
batch_size = len(batch["supervisions"]["text"]) batch_size = len(batch["supervisions"]["text"])
@ -797,7 +788,6 @@ def train_one_epoch(
params.batch_idx_train > 0 params.batch_idx_train > 0
and params.batch_idx_train % params.save_every_n == 0 and params.batch_idx_train % params.save_every_n == 0
): ):
params.cur_batch_idx = batch_idx
save_checkpoint_with_global_batch_idx( save_checkpoint_with_global_batch_idx(
out_dir=params.exp_dir, out_dir=params.exp_dir,
global_batch_idx=params.batch_idx_train, global_batch_idx=params.batch_idx_train,
@ -810,7 +800,6 @@ def train_one_epoch(
scaler=scaler, scaler=scaler,
rank=rank, rank=rank,
) )
del params.cur_batch_idx
remove_checkpoints( remove_checkpoints(
out_dir=params.exp_dir, out_dir=params.exp_dir,
topk=params.keep_last_k, topk=params.keep_last_k,

11
egs/mgb2/ASR/pruned_transducer_stateless5/train.py
View File

@ -506,9 +506,6 @@ def load_checkpoint_if_available(
if "cur_epoch" in saved_params: if "cur_epoch" in saved_params:
params["start_epoch"] = saved_params["cur_epoch"] params["start_epoch"] = saved_params["cur_epoch"]
if "cur_batch_idx" in saved_params:
params["cur_batch_idx"] = saved_params["cur_batch_idx"]
return saved_params return saved_params
@ -748,15 +745,9 @@ def train_one_epoch(
tot_loss = MetricsTracker() tot_loss = MetricsTracker()
cur_batch_idx = params.get("cur_batch_idx", 0)
for batch_idx, batch in enumerate(train_dl): for batch_idx, batch in enumerate(train_dl):
if batch["inputs"].shape[0] == len(batch["supervisions"]["text"]): if batch["inputs"].shape[0] == len(batch["supervisions"]["text"]):
if batch_idx < cur_batch_idx:
continue
cur_batch_idx = batch_idx
params.batch_idx_train += 1 params.batch_idx_train += 1
batch_size = len(batch["supervisions"]["text"]) batch_size = len(batch["supervisions"]["text"])
@ -805,7 +796,6 @@ def train_one_epoch(
params.batch_idx_train > 0 params.batch_idx_train > 0
and params.batch_idx_train % params.save_every_n == 0 and params.batch_idx_train % params.save_every_n == 0
): ):
params.cur_batch_idx = batch_idx
save_checkpoint_with_global_batch_idx( save_checkpoint_with_global_batch_idx(
out_dir=params.exp_dir, out_dir=params.exp_dir,
global_batch_idx=params.batch_idx_train, global_batch_idx=params.batch_idx_train,
@ -818,7 +808,6 @@ def train_one_epoch(
scaler=scaler, scaler=scaler,
rank=rank, rank=rank,
) )
del params.cur_batch_idx
remove_checkpoints( remove_checkpoints(
out_dir=params.exp_dir, out_dir=params.exp_dir,
topk=params.keep_last_k, topk=params.keep_last_k,

11
egs/spgispeech/ASR/pruned_transducer_stateless2/train.py
View File

@ -443,9 +443,6 @@ def load_checkpoint_if_available(
if "cur_epoch" in saved_params: if "cur_epoch" in saved_params:
params["start_epoch"] = saved_params["cur_epoch"] params["start_epoch"] = saved_params["cur_epoch"]
if "cur_batch_idx" in saved_params:
params["cur_batch_idx"] = saved_params["cur_batch_idx"]
return saved_params return saved_params
@ -648,13 +645,7 @@ def train_one_epoch(
tot_loss = MetricsTracker() tot_loss = MetricsTracker()
cur_batch_idx = params.get("cur_batch_idx", 0)
for batch_idx, batch in enumerate(train_dl): for batch_idx, batch in enumerate(train_dl):
if batch_idx < cur_batch_idx:
continue
cur_batch_idx = batch_idx
params.batch_idx_train += 1 params.batch_idx_train += 1
batch_size = len(batch["supervisions"]["text"]) batch_size = len(batch["supervisions"]["text"])
@ -685,7 +676,6 @@ def train_one_epoch(
params.batch_idx_train > 0 params.batch_idx_train > 0
and params.batch_idx_train % params.save_every_n == 0 and params.batch_idx_train % params.save_every_n == 0
): ):
params.cur_batch_idx = batch_idx
save_checkpoint_with_global_batch_idx( save_checkpoint_with_global_batch_idx(
out_dir=params.exp_dir, out_dir=params.exp_dir,
global_batch_idx=params.batch_idx_train, global_batch_idx=params.batch_idx_train,
@ -697,7 +687,6 @@ def train_one_epoch(
scaler=scaler, scaler=scaler,
rank=rank, rank=rank,
) )
del params.cur_batch_idx
remove_checkpoints( remove_checkpoints(
out_dir=params.exp_dir, out_dir=params.exp_dir,
topk=params.keep_last_k, topk=params.keep_last_k,

11
egs/tal_csasr/ASR/pruned_transducer_stateless5/train.py
View File

@ -513,9 +513,6 @@ def load_checkpoint_if_available(
if "cur_epoch" in saved_params: if "cur_epoch" in saved_params:
params["start_epoch"] = saved_params["cur_epoch"] params["start_epoch"] = saved_params["cur_epoch"]
if "cur_batch_idx" in saved_params:
params["cur_batch_idx"] = saved_params["cur_batch_idx"]
return saved_params return saved_params
@ -726,13 +723,7 @@ def train_one_epoch(
tot_loss = MetricsTracker() tot_loss = MetricsTracker()
cur_batch_idx = params.get("cur_batch_idx", 0)
for batch_idx, batch in enumerate(train_dl): for batch_idx, batch in enumerate(train_dl):
if batch_idx < cur_batch_idx:
continue
cur_batch_idx = batch_idx
params.batch_idx_train += 1 params.batch_idx_train += 1
batch_size = len(batch["supervisions"]["text"]) batch_size = len(batch["supervisions"]["text"])
# print(batch["supervisions"]) # print(batch["supervisions"])
@ -775,7 +766,6 @@ def train_one_epoch(
params.batch_idx_train > 0 params.batch_idx_train > 0
and params.batch_idx_train % params.save_every_n == 0 and params.batch_idx_train % params.save_every_n == 0
): ):
params.cur_batch_idx = batch_idx
save_checkpoint_with_global_batch_idx( save_checkpoint_with_global_batch_idx(
out_dir=params.exp_dir, out_dir=params.exp_dir,
global_batch_idx=params.batch_idx_train, global_batch_idx=params.batch_idx_train,
@ -788,7 +778,6 @@ def train_one_epoch(
scaler=scaler, scaler=scaler,
rank=rank, rank=rank,
) )
del params.cur_batch_idx
remove_checkpoints( remove_checkpoints(
out_dir=params.exp_dir, out_dir=params.exp_dir,
topk=params.keep_last_k, topk=params.keep_last_k,

11
egs/tal_csasr/ASR/pruned_transducer_stateless7_bbpe/train.py
View File

@ -566,9 +566,6 @@ def load_checkpoint_if_available(
if "cur_epoch" in saved_params: if "cur_epoch" in saved_params:
params["start_epoch"] = saved_params["cur_epoch"] params["start_epoch"] = saved_params["cur_epoch"]
if "cur_batch_idx" in saved_params:
params["cur_batch_idx"] = saved_params["cur_batch_idx"]
return saved_params return saved_params
@ -798,13 +795,7 @@ def train_one_epoch(
tot_loss = MetricsTracker() tot_loss = MetricsTracker()
cur_batch_idx = params.get("cur_batch_idx", 0)
for batch_idx, batch in enumerate(train_dl): for batch_idx, batch in enumerate(train_dl):
if batch_idx < cur_batch_idx:
continue
cur_batch_idx = batch_idx
params.batch_idx_train += 1 params.batch_idx_train += 1
batch_size = len(batch["supervisions"]["text"]) batch_size = len(batch["supervisions"]["text"])
@ -851,7 +842,6 @@ def train_one_epoch(
params.batch_idx_train > 0 params.batch_idx_train > 0
and params.batch_idx_train % params.save_every_n == 0 and params.batch_idx_train % params.save_every_n == 0
): ):
params.cur_batch_idx = batch_idx
save_checkpoint_with_global_batch_idx( save_checkpoint_with_global_batch_idx(
out_dir=params.exp_dir, out_dir=params.exp_dir,
global_batch_idx=params.batch_idx_train, global_batch_idx=params.batch_idx_train,
@ -864,7 +854,6 @@ def train_one_epoch(
scaler=scaler, scaler=scaler,
rank=rank, rank=rank,
) )
del params.cur_batch_idx
remove_checkpoints( remove_checkpoints(
out_dir=params.exp_dir, out_dir=params.exp_dir,
topk=params.keep_last_k, topk=params.keep_last_k,

11
egs/wenetspeech/ASR/pruned_transducer_stateless5/train.py
View File

@ -578,9 +578,6 @@ def load_checkpoint_if_available(
if "cur_epoch" in saved_params: if "cur_epoch" in saved_params:
params["start_epoch"] = saved_params["cur_epoch"] params["start_epoch"] = saved_params["cur_epoch"]
if "cur_batch_idx" in saved_params:
params["cur_batch_idx"] = saved_params["cur_batch_idx"]
return saved_params return saved_params
@ -794,13 +791,7 @@ def train_one_epoch(
tot_loss = MetricsTracker() tot_loss = MetricsTracker()
cur_batch_idx = params.get("cur_batch_idx", 0)
for batch_idx, batch in enumerate(train_dl): for batch_idx, batch in enumerate(train_dl):
if batch_idx < cur_batch_idx:
continue
cur_batch_idx = batch_idx
params.batch_idx_train += 1 params.batch_idx_train += 1
batch_size = len(batch["supervisions"]["text"]) batch_size = len(batch["supervisions"]["text"])
@ -846,7 +837,6 @@ def train_one_epoch(
params.batch_idx_train > 0 params.batch_idx_train > 0
and params.batch_idx_train % params.save_every_n == 0 and params.batch_idx_train % params.save_every_n == 0
): ):
params.cur_batch_idx = batch_idx
save_checkpoint_with_global_batch_idx( save_checkpoint_with_global_batch_idx(
out_dir=params.exp_dir, out_dir=params.exp_dir,
global_batch_idx=params.batch_idx_train, global_batch_idx=params.batch_idx_train,
@ -859,7 +849,6 @@ def train_one_epoch(
scaler=scaler, scaler=scaler,
rank=rank, rank=rank,
) )
del params.cur_batch_idx
remove_checkpoints( remove_checkpoints(
out_dir=params.exp_dir, out_dir=params.exp_dir,
topk=params.keep_last_k, topk=params.keep_last_k,

11
egs/xbmu_amdo31/ASR/pruned_transducer_stateless7/train.py
View File

@ -561,9 +561,6 @@ def load_checkpoint_if_available(
if "cur_epoch" in saved_params: if "cur_epoch" in saved_params:
params["start_epoch"] = saved_params["cur_epoch"] params["start_epoch"] = saved_params["cur_epoch"]
if "cur_batch_idx" in saved_params:
params["cur_batch_idx"] = saved_params["cur_batch_idx"]
return saved_params return saved_params
@ -782,13 +779,7 @@ def train_one_epoch(
tot_loss = MetricsTracker() tot_loss = MetricsTracker()
cur_batch_idx = params.get("cur_batch_idx", 0)
for batch_idx, batch in enumerate(train_dl): for batch_idx, batch in enumerate(train_dl):
if batch_idx < cur_batch_idx:
continue
cur_batch_idx = batch_idx
params.batch_idx_train += 1 params.batch_idx_train += 1
batch_size = len(batch["supervisions"]["text"]) batch_size = len(batch["supervisions"]["text"])
@ -835,7 +826,6 @@ def train_one_epoch(
params.batch_idx_train > 0 params.batch_idx_train > 0
and params.batch_idx_train % params.save_every_n == 0 and params.batch_idx_train % params.save_every_n == 0
): ):
params.cur_batch_idx = batch_idx
save_checkpoint_with_global_batch_idx( save_checkpoint_with_global_batch_idx(
out_dir=params.exp_dir, out_dir=params.exp_dir,
global_batch_idx=params.batch_idx_train, global_batch_idx=params.batch_idx_train,
@ -848,7 +838,6 @@ def train_one_epoch(
scaler=scaler, scaler=scaler,
rank=rank, rank=rank,
) )
del params.cur_batch_idx
remove_checkpoints( remove_checkpoints(
out_dir=params.exp_dir, out_dir=params.exp_dir,
topk=params.keep_last_k, topk=params.keep_last_k,