msb-public
/
PaddleSpeech
mirror of https://github.com/PaddlePaddle/PaddleSpeech
1
0
Code Issues Projects Releases Wiki Activity

clean code in tuning script

Browse Source
pull/2/head
Yibing Liu 7 years ago
parent e6e7b13222
commit 764ce62445
1 changed files with 23 additions and 25 deletions
Show Stats Download Patch File Download Diff File

48
tools/tune.py
Unescape View File

@ -17,27 +17,27 @@ from utils.utility import add_arguments, print_arguments
parser = argparse.ArgumentParser(description=__doc__) parser = argparse.ArgumentParser(description=__doc__)
add_arg = functools.partial(add_arguments, argparser=parser) add_arg = functools.partial(add_arguments, argparser=parser)
# yapf: disable # yapf: disable
add_arg('num_batches', int, -1, "# of batches tuning on. " add_arg('num_batches', int, -1, "# of batches tuning on. "
"Default -1, on whole dev set.") "Default -1, on whole dev set.")
add_arg('batch_size', int, 256, "# of samples per batch.") add_arg('batch_size', int, 256, "# of samples per batch.")
add_arg('trainer_count', int, 8, "# of Trainers (CPUs or GPUs).") add_arg('trainer_count', int, 8, "# of Trainers (CPUs or GPUs).")
add_arg('beam_size', int, 500, "Beam search width.") add_arg('beam_size', int, 500, "Beam search width.")
add_arg('num_proc_bsearch', int, 12, "# of CPUs for beam search.") add_arg('num_proc_bsearch', int, 12, "# of CPUs for beam search.")
add_arg('num_conv_layers', int, 2, "# of convolution layers.") add_arg('num_conv_layers', int, 2, "# of convolution layers.")
add_arg('num_rnn_layers', int, 3, "# of recurrent layers.") add_arg('num_rnn_layers', int, 3, "# of recurrent layers.")
add_arg('rnn_layer_size', int, 2048, "# of recurrent cells per layer.") add_arg('rnn_layer_size', int, 2048, "# of recurrent cells per layer.")
add_arg('num_alphas', int, 45, "# of alpha candidates for tuning.") add_arg('num_alphas', int, 45, "# of alpha candidates for tuning.")
add_arg('num_betas', int, 8, "# of beta candidates for tuning.") add_arg('num_betas', int, 8, "# of beta candidates for tuning.")
add_arg('alpha_from', float, 1.0, "Where alpha starts tuning from.") add_arg('alpha_from', float, 1.0, "Where alpha starts tuning from.")
add_arg('alpha_to', float, 3.2, "Where alpha ends tuning with.") add_arg('alpha_to', float, 3.2, "Where alpha ends tuning with.")
add_arg('beta_from', float, 0.1, "Where beta starts tuning from.") add_arg('beta_from', float, 0.1, "Where beta starts tuning from.")
add_arg('beta_to', float, 0.45, "Where beta ends tuning with.") add_arg('beta_to', float, 0.45, "Where beta ends tuning with.")
add_arg('cutoff_prob', float, 1.0, "Cutoff probability for pruning.") add_arg('cutoff_prob', float, 1.0, "Cutoff probability for pruning.")
add_arg('cutoff_top_n', int, 40, "Cutoff number for pruning.") add_arg('cutoff_top_n', int, 40, "Cutoff number for pruning.")
add_arg('use_gru', bool, False, "Use GRUs instead of simple RNNs.") add_arg('use_gru', bool, False, "Use GRUs instead of simple RNNs.")
add_arg('use_gpu', bool, True, "Use GPU or not.") add_arg('use_gpu', bool, True, "Use GPU or not.")
add_arg('share_rnn_weights',bool, True, "Share input-hidden weights across " add_arg('share_rnn_weights',bool, True, "Share input-hidden weights across "
"bi-directional RNNs. Not for GRU.") "bi-directional RNNs. Not for GRU.")
add_arg('tune_manifest', str, add_arg('tune_manifest', str,
'data/librispeech/manifest.dev-clean', 'data/librispeech/manifest.dev-clean',
"Filepath of manifest to tune.") "Filepath of manifest to tune.")
@ -140,13 +140,11 @@ def tune():
for target, result in zip(target_transcripts, result_transcripts): for target, result in zip(target_transcripts, result_transcripts):
err_sum[index] += error_rate_func(target, result) err_sum[index] += error_rate_func(target, result)
err_ave[index] = err_sum[index] / num_ins err_ave[index] = err_sum[index] / num_ins
# print("alpha = %f, beta = %f, WER = %f" %
# (alpha, beta, err_ave[index]))
if index % 2 == 0: if index % 2 == 0:
sys.stdout.write('.') sys.stdout.write('.')
sys.stdout.flush() sys.stdout.flush()
# output on-line tuning result at the the end of current batch # output on-line tuning result at the end of current batch
err_ave_min = min(err_ave) err_ave_min = min(err_ave)
min_index = err_ave.index(err_ave_min) min_index = err_ave.index(err_ave_min)
print("\nBatch %d [%d/?], current opt (alpha, beta) = (%s, %s), " print("\nBatch %d [%d/?], current opt (alpha, beta) = (%s, %s), "
@ -156,7 +154,7 @@ def tune():
args.error_rate_type, err_ave_min)) args.error_rate_type, err_ave_min))
cur_batch += 1 cur_batch += 1
# output WER/CER at every point # output WER/CER at every (alpha, beta)
print("\nFinal %s:\n" % args.error_rate_type) print("\nFinal %s:\n" % args.error_rate_type)
for index in xrange(len(params_grid)): for index in xrange(len(params_grid)):
print("(alpha, beta) = (%s, %s), [%s] = %f" print("(alpha, beta) = (%s, %s), [%s] = %f"

Write Preview
Loading…
Cancel
Save