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Filter the training data of T < S for Wenet train recipe (#753)

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* filter the case of T <  S  for training data

* fix style issues

* fix style issues

* fix style issues

Co-authored-by: 张云斌 <zhangyunbin@MacBook-Air.local>
This commit is contained in:
wzy 2022-12-11 20:16:10 +08:00 committed by GitHub
parent 02c18ba4b2
commit e83409cbe5
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2 changed files with 58 additions and 6 deletions
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32
egs/wenetspeech/ASR/pruned_transducer_stateless2/train.py
View File

@ -861,15 +861,41 @@ def run(rank, world_size, args):
valid_cuts = wenetspeech.valid_cuts()
def remove_short_and_long_utt(c: Cut):
# Keep only utterances with duration between 1 second and 15.0 seconds
# Keep only utterances with duration between 1 second and 10 seconds
#
# Caution: There is a reason to select 15.0 here. Please see
# Caution: There is a reason to select 10.0 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 <= 15.0
if c.duration < 1.0 or c.duration > 10.0:
logging.warning(
f"Exclude cut with ID {c.id} from training. Duration: {c.duration}"
)
return False
# In pruned RNN-T, we require that T >= S
# where T is the number of feature frames after subsampling
# and S is the number of tokens in the utterance
# In ./conformer.py, the conv module uses the following expression
# for subsampling
T = ((c.num_frames - 1) // 2 - 1) // 2
tokens = c.supervisions[0].text.replace(" ", "")
if T < len(tokens):
logging.warning(
f"Exclude cut with ID {c.id} from training. "
f"Number of frames (before subsampling): {c.num_frames}. "
f"Number of frames (after subsampling): {T}. "
f"Text: {c.supervisions[0].text}. "
f"Tokens: {tokens}. "
f"Number of tokens: {len(tokens)}"
)
return False
return True
train_cuts = train_cuts.filter(remove_short_and_long_utt)

32
egs/wenetspeech/ASR/pruned_transducer_stateless5/train.py
View File

@ -1006,15 +1006,41 @@ def run(rank, world_size, args):
valid_cuts = wenetspeech.valid_cuts()
def remove_short_and_long_utt(c: Cut):
# Keep only utterances with duration between 1 second and 15.0 seconds
# Keep only utterances with duration between 1 second and 10 seconds
#
# Caution: There is a reason to select 15.0 here. Please see
# Caution: There is a reason to select 10.0 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 <= 15.0
if c.duration < 1.0 or c.duration > 10.0:
logging.warning(
f"Exclude cut with ID {c.id} from training. Duration: {c.duration}"
)
return False
# In pruned RNN-T, we require that T >= S
# where T is the number of feature frames after subsampling
# and S is the number of tokens in the utterance
# In ./conformer.py, the conv module uses the following expression
# for subsampling
T = ((c.num_frames - 1) // 2 - 1) // 2
tokens = c.supervisions[0].text.replace(" ", "")
if T < len(tokens):
logging.warning(
f"Exclude cut with ID {c.id} from training. "
f"Number of frames (before subsampling): {c.num_frames}. "
f"Number of frames (after subsampling): {T}. "
f"Text: {c.supervisions[0].text}. "
f"Tokens: {tokens}. "
f"Number of tokens: {len(tokens)}"
)
return False
return True
train_cuts = train_cuts.filter(remove_short_and_long_utt)