How to tune alpha lm weight and beta word count weight for our custom training set

[JRMeyer]

>>> dbanka
[December 29, 2017, 6:39am]

Hi, I have trained using custom dataset of 1000 hours. I wanted to know
how are the values of lm_weight, word_count_weight and
valid_word_count_weight decided/optimized?

[This is an archived TTS discussion thread from discourse.mozilla.org/t/how-to-tune-alpha-lm-weight-and-beta-word-count-weight-for-our-custom-training-set]

[JRMeyer]

>>> lissyx
[January 5, 2018, 5:40am]

did
that, he might be able to weight some knowledges
![:slight_smile:](

[Archived Post]

[JRMeyer]

>>> reuben
[January 5, 2018, 9:52am]

They are decided/optimized by doing a search over the possible values
and choosing the value that yields the best WER on the validation set.

[Archived Post]

[JRMeyer]

>>> tanmayjain
[April 15, 2020, 12:24pm]

Hello, slash
Can you please share the file name, path and function name where we can
tune this function? slash
Thanks

[Archived Post]

[JRMeyer]

>>> reuben
[April 16, 2020, 2:54pm]

github.com

#### mozilla/DeepSpeech/blob/master/lm_optimizer.py

#!/usr/bin/env python
# -- coding: utf-8 --
from future import absolute_import, print_function

import absl.app
import optuna
import sys
import tensorflow.compat.v1 as tfv1

from deepspeech_training.evaluate import evaluate
from deepspeech_training.train import create_model
from deepspeech_training.util.config import Config, initialize_globals
from deepspeech_training.util.flags import create_flags, FLAGS
from deepspeech_training.util.logging import log_error
from deepspeech_training.util.evaluate_tools import wer_cer_batch
from ds_ctcdecoder import Scorer

def character_based():
is_character_based = False

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[Archived Post]