From a54b748a02550fe523b05c6ccc5845170ba99114 Mon Sep 17 00:00:00 2001 From: behnamasefi Date: Fri, 30 Dec 2022 03:06:09 +0000 Subject: [PATCH] check for utterance len (#795) Co-authored-by: behnam --- .../pruned_transducer_stateless7_ctc/train.py | 28 ++++++++++++++++++- .../train.py | 28 ++++++++++++++++++- 2 files changed, 54 insertions(+), 2 deletions(-) diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7_ctc/train.py b/egs/librispeech/ASR/pruned_transducer_stateless7_ctc/train.py index 162ad8412..5a05e1836 100755 --- a/egs/librispeech/ASR/pruned_transducer_stateless7_ctc/train.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless7_ctc/train.py @@ -1086,7 +1086,33 @@ def run(rank, world_size, args): # 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 <= 20.0 + if c.duration < 1.0 or c.duration > 20.0: + 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 ./zipformer.py, the conv module uses the following expression + # for subsampling + T = ((c.num_frames - 7) // 2 + 1) // 2 + tokens = sp.encode(c.supervisions[0].text, out_type=str) + + 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) diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7_ctc_bs/train.py b/egs/librispeech/ASR/pruned_transducer_stateless7_ctc_bs/train.py index 63e9d6e90..522ecc974 100755 --- a/egs/librispeech/ASR/pruned_transducer_stateless7_ctc_bs/train.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless7_ctc_bs/train.py @@ -1077,7 +1077,33 @@ def run(rank, world_size, args): # 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 <= 20.0 + if c.duration < 1.0 or c.duration > 20.0: + 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 ./zipformer.py, the conv module uses the following expression + # for subsampling + T = ((c.num_frames - 7) // 2 + 1) // 2 + tokens = sp.encode(c.supervisions[0].text, out_type=str) + + 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)