81 lines
2.4 KiB
Python
81 lines
2.4 KiB
Python
#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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from __future__ import absolute_import, print_function
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import sys
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import absl.app
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import optuna
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import tensorflow.compat.v1 as tfv1
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from coqui_stt_ctcdecoder import Scorer
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from coqui_stt_training.evaluate import evaluate
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from coqui_stt_training.train import create_model
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from coqui_stt_training.util.config import Config, initialize_globals
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from coqui_stt_training.util.evaluate_tools import wer_cer_batch
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from coqui_stt_training.util.flags import FLAGS, create_flags
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from coqui_stt_training.util.logging import log_error
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def character_based():
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is_character_based = False
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if FLAGS.scorer_path:
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scorer = Scorer(
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FLAGS.lm_alpha, FLAGS.lm_beta, FLAGS.scorer_path, Config.alphabet
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)
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is_character_based = scorer.is_utf8_mode()
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return is_character_based
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def objective(trial):
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FLAGS.lm_alpha = trial.suggest_uniform("lm_alpha", 0, FLAGS.lm_alpha_max)
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FLAGS.lm_beta = trial.suggest_uniform("lm_beta", 0, FLAGS.lm_beta_max)
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is_character_based = trial.study.user_attrs["is_character_based"]
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samples = []
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for step, test_file in enumerate(FLAGS.test_files.split(",")):
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tfv1.reset_default_graph()
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current_samples = evaluate([test_file], create_model)
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samples += current_samples
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# Report intermediate objective value.
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wer, cer = wer_cer_batch(current_samples)
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trial.report(cer if is_character_based else wer, step)
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# Handle pruning based on the intermediate value.
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if trial.should_prune():
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raise optuna.exceptions.TrialPruned()
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wer, cer = wer_cer_batch(samples)
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return cer if is_character_based else wer
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def main(_):
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initialize_globals()
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if not FLAGS.test_files:
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log_error(
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"You need to specify what files to use for evaluation via "
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"the --test_files flag."
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)
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sys.exit(1)
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is_character_based = character_based()
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study = optuna.create_study()
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study.set_user_attr("is_character_based", is_character_based)
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study.optimize(objective, n_jobs=1, n_trials=FLAGS.n_trials)
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print(
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"Best params: lm_alpha={} and lm_beta={} with WER={}".format(
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study.best_params["lm_alpha"],
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study.best_params["lm_beta"],
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study.best_value,
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)
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)
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if __name__ == "__main__":
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create_flags()
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absl.app.run(main)
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