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Fix aishell. (#416)

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Fangjun Kuang 2022-06-10 11:47:43 +08:00 committed by GitHub
parent dbda1644b5
commit bfeab319c9
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3 changed files with 17 additions and 23 deletions
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21
egs/aishell/ASR/transducer_stateless/train.py
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@ -604,21 +604,18 @@ def run(rank, world_size, args):
train_cuts = aishell.train_cuts()
def remove_short_and_long_utt(c: Cut):
# Keep only utterances with duration between 1 second and 20 seconds
return 1.0 <= c.duration <= 20.0
num_in_total = len(train_cuts)
# Keep only utterances with duration between 1 second and 12 seconds
#
# Caution: There is a reason to select 12.0 here. Please see
# ../local/display_manifest_statistics.py
#
# You should use ../local/display_manifest_statistics.py to get
# an utterance duration distribution for your dataset to select
# the threshold
return 1.0 <= c.duration <= 12.0
train_cuts = train_cuts.filter(remove_short_and_long_utt)
num_left = len(train_cuts)
num_removed = num_in_total - num_left
removed_percent = num_removed / num_in_total * 100
logging.info(f"Before removing short and long utterances: {num_in_total}")
logging.info(f"After removing short and long utterances: {num_left}")
logging.info(f"Removed {num_removed} utterances ({removed_percent:.5f}%)")
train_dl = aishell.train_dataloaders(train_cuts)
valid_dl = aishell.valid_dataloaders(aishell.valid_cuts())

2
egs/aishell/ASR/transducer_stateless_modified-2/train.py
View File

@ -640,7 +640,7 @@ def train_one_epoch(
def filter_short_and_long_utterances(cuts: CutSet) -> CutSet:
def remove_short_and_long_utt(c: Cut):
# Keep only utterances with duration between 1 second and 20 seconds
# Keep only utterances with duration between 1 second and 12 seconds
#
# Caution: There is a reason to select 12.0 here. Please see
# ../local/display_manifest_statistics.py

17
egs/aishell/ASR/transducer_stateless_modified/train.py
View File

@ -630,20 +630,17 @@ def run(rank, world_size, args):
def remove_short_and_long_utt(c: Cut):
# Keep only utterances with duration between 1 second and 12 seconds
#
# Caution: There is a reason to select 12.0 here. Please see
# ../local/display_manifest_statistics.py
#
# You should use ../local/display_manifest_statistics.py to get
# an utterance duration distribution for your dataset to select
# the threshold
return 1.0 <= c.duration <= 12.0
num_in_total = len(train_cuts)
train_cuts = train_cuts.filter(remove_short_and_long_utt)
num_left = len(train_cuts)
num_removed = num_in_total - num_left
removed_percent = num_removed / num_in_total * 100
logging.info(f"Before removing short and long utterances: {num_in_total}")
logging.info(f"After removing short and long utterances: {num_left}")
logging.info(f"Removed {num_removed} utterances ({removed_percent:.5f}%)")
train_dl = aishell.train_dataloaders(train_cuts)
valid_dl = aishell.valid_dataloaders(aishell.valid_cuts())