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lm redis bool fix

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nckcard
2025-07-03 14:05:04 -07:00
parent 6fee75015b
commit 2b9707cf42
2 changed files with 32 additions and 30 deletions
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58
language_model/language-model-standalone.py
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@@ -450,20 +450,20 @@ def main(args):
# create a nice dict of params to put into redis
lm_args = {
'lm_path': lm_path,
'max_active': max_active,
'min_active': min_active,
'beam': beam,
'lattice_beam': lattice_beam,
'acoustic_scale': acoustic_scale,
'ctc_blank_skip_threshold': ctc_blank_skip_threshold,
'length_penalty': length_penalty,
'nbest': nbest,
'blank_penalty': blank_penalty,
'alpha': alpha,
'do_opt': do_opt,
'rescore': rescore,
'top_candidates_to_augment': top_candidates_to_augment,
'score_penalty_percent': score_penalty_percent,
'max_active': int(max_active),
'min_active': int(min_active),
'beam': float(beam),
'lattice_beam': float(lattice_beam),
'acoustic_scale': float(acoustic_scale),
'ctc_blank_skip_threshold': float(ctc_blank_skip_threshold),
'length_penalty': float(length_penalty),
'nbest': int(nbest),
'blank_penalty': float(blank_penalty),
'alpha': float(alpha),
'do_opt': int(do_opt),
'rescore': int(rescore),
'top_candidates_to_augment': int(top_candidates_to_augment),
'score_penalty_percent': float(score_penalty_percent),
}
# pick GPU
@@ -671,7 +671,6 @@ def main(args):
blank_penalty = float(entry_data.get(b'blank_penalty', blank_penalty))
alpha = float(entry_data.get(b'alpha', alpha))
do_opt = int(entry_data.get(b'do_opt', do_opt))
# opt_cache_dir = entry_data.get(b'opt_cache_dir', opt_cache_dir).decode()
rescore = int(entry_data.get(b'rescore', rescore))
top_candidates_to_augment = int(entry_data.get(b'top_candidates_to_augment', top_candidates_to_augment))
score_penalty_percent = float(entry_data.get(b'score_penalty_percent', score_penalty_percent))
@@ -679,21 +678,20 @@ def main(args):
# make sure that the update remote lm args are put into redis nicely
lm_args = {
'lm_path': lm_path,
'max_active': max_active,
'min_active': min_active,
'beam': beam,
'lattice_beam': lattice_beam,
'acoustic_scale': acoustic_scale,
'ctc_blank_skip_threshold': ctc_blank_skip_threshold,
'length_penalty': length_penalty,
'nbest': nbest,
'blank_penalty': blank_penalty,
'alpha': alpha,
'do_opt': do_opt,
# 'opt_cache_dir': opt_cache_dir,
'rescore': rescore,
'top_candidates_to_augment': top_candidates_to_augment,
'score_penalty_percent': score_penalty_percent,
'max_active': int(max_active),
'min_active': int(min_active),
'beam': float(beam),
'lattice_beam': float(lattice_beam),
'acoustic_scale': float(acoustic_scale),
'ctc_blank_skip_threshold': float(ctc_blank_skip_threshold),
'length_penalty': float(length_penalty),
'nbest': int(nbest),
'blank_penalty': float(blank_penalty),
'alpha': float(alpha),
'do_opt': int(do_opt),
'rescore': int(rescore),
'top_candidates_to_augment': int(top_candidates_to_augment),
'score_penalty_percent': float(score_penalty_percent),
}
r.xadd('remote_lm_args', lm_args)