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PaddleSpeech
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add tuning script & enable ploting error surface

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pull/2/head
Yibing Liu 7 years ago
parent f7032c8256
commit 4c64841c16
2 changed files with 121 additions and 48 deletions
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22
examples/librispeech/run_tune.sh
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@ -5,27 +5,29 @@ pushd ../.. > /dev/null
# grid-search for hyper-parameters in language model
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
python -u tools/tune.py \
--num_samples=100 \
--num_batches=2 \
--batch_size=24 \
--trainer_count=8 \
--beam_size=500 \
--num_proc_bsearch=12 \
--num_conv_layers=2 \
--num_rnn_layers=3 \
--rnn_layer_size=2048 \
--num_alphas=14 \
--num_betas=20 \
--alpha_from=0.1 \
--alpha_to=0.36 \
--beta_from=0.05 \
--beta_to=1.0 \
--cutoff_prob=0.99 \
--num_alphas=2 \
--num_betas=2 \
--alpha_from=1.0 \
--alpha_to=3.2 \
--beta_from=0.1 \
--beta_to=0.45 \
--cutoff_prob=1.0 \
--cutoff_top_n=40 \
--use_gru=False \
--use_gpu=True \
--share_rnn_weights=True \
--tune_manifest='data/librispeech/manifest.dev-clean' \
--mean_std_path='data/librispeech/mean_std.npz' \
--vocab_path='data/librispeech/vocab.txt' \
--model_path='checkpoints/libri/params.latest.tar.gz' \
--vocab_path='models/librispeech/vocab.txt' \
--model_path='models/librispeech/params.tar.gz' \
--lang_model_path='models/lm/common_crawl_00.prune01111.trie.klm' \
--error_rate_type='wer' \
--specgram_type='linear'

147
tools/tune.py
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@ -3,6 +3,7 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import sys
import numpy as np
import argparse
import functools
@ -16,26 +17,30 @@ from utils.utility import add_arguments, print_arguments
parser = argparse.ArgumentParser(description=__doc__)
add_arg = functools.partial(add_arguments, argparser=parser)
# yapf: disable
add_arg('num_samples', int, 100, "# of samples to infer.")
add_arg('trainer_count', int, 8, "# of Trainers (CPUs or GPUs).")
add_arg('beam_size', int, 500, "Beam search width.")
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_rnn_layers', int, 3, "# of recurrent layers.")
add_arg('rnn_layer_size', int, 2048, "# of recurrent cells per layer.")
add_arg('num_alphas', int, 14, "# of alpha candidates for tuning.")
add_arg('num_betas', int, 20, "# of beta candidates for tuning.")
add_arg('alpha_from', float, 0.1, "Where alpha starts tuning from.")
add_arg('alpha_to', float, 0.36, "Where alpha ends tuning with.")
add_arg('beta_from', float, 0.05, "Where beta starts tuning from.")
add_arg('beta_to', float, 1.0, "Where beta ends tuning with.")
add_arg('cutoff_prob', float, 0.99, "Cutoff probability for pruning.")
add_arg('use_gru', bool, False, "Use GRUs instead of simple RNNs.")
add_arg('use_gpu', bool, True, "Use GPU or not.")
add_arg('share_rnn_weights',bool, True, "Share input-hidden weights across "
"bi-directional RNNs. Not for GRU.")
add_arg('num_batches', int, -1, "# of batches tuning on. "
"Default -1, on whole dev set.")
add_arg('batch_size', int, 256, "# of samples per batch.")
add_arg('trainer_count', int, 8, "# of Trainers (CPUs or GPUs).")
add_arg('beam_size', int, 500, "Beam search width.")
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_rnn_layers', int, 3, "# of recurrent layers.")
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_betas', int, 8, "# of beta candidates for tuning.")
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('beta_from', float, 0.1, "Where beta starts tuning from.")
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_top_n', int, 40, "Cutoff number for pruning.")
add_arg('output_fig', bool, True, "Output error rate figure or not.")
add_arg('use_gru', bool, False, "Use GRUs instead of simple RNNs.")
add_arg('use_gpu', bool, True, "Use GPU or not.")
add_arg('share_rnn_weights',bool, True, "Share input-hidden weights across "
"bi-directional RNNs. Not for GRU.")
add_arg('tune_manifest', str,
'data/librispeech/manifest.dev',
'data/librispeech/manifest.dev-clean',
"Filepath of manifest to tune.")
add_arg('mean_std_path', str,
'data/librispeech/mean_std.npz',
@ -61,6 +66,23 @@ add_arg('specgram_type', str,
# yapf: disable
args = parser.parse_args()
def plot_error_surface(params_grid, err_ave, fig_name):
import matplotlib.pyplot as plt
import mpl_toolkits.mplot3d as Axes3D
fig = plt.figure()
ax = Axes3D(fig)
alphas = [ param[0] for param in params_grid ]
betas = [ param[1] for param in params_grid]
ALPHAS = np.reshape(alphas, (args.num_alphas, args.num_betas))
BETAS = np.reshape(betas, (args.num_alphas, args.num_betas))
ERR_AVE = np.reshape(err_ave, (args.num_alphas, args.num_betas))
ax.plot_surface(ALPHAS, BETAS, WERS,
rstride=1, cstride=1, alpha=0.8, cmap='rainbow')
ax.set_xlabel('alpha')
ax.set_ylabel('beta')
z_label = 'WER' if args.error_rate_type == 'wer' else 'CER'
ax.set_zlabel(z_label)
plt.savefig(fig_name)
def tune():
"""Tune parameters alpha and beta on one minibatch."""
@ -77,7 +99,7 @@ def tune():
num_threads=1)
batch_reader = data_generator.batch_reader_creator(
manifest_path=args.tune_manifest,
batch_size=args.num_samples,
batch_size=args.batch_size,
sortagrad=False,
shuffle_method=None)
tune_data = batch_reader().next()
@ -95,31 +117,80 @@ def tune():
pretrained_model_path=args.model_path,
share_rnn_weights=args.share_rnn_weights)
# decoders only accept string encoded in utf-8
vocab_list = [chars.encode("utf-8") for chars in data_generator.vocab_list]
error_rate_func = cer if args.error_rate_type == 'cer' else wer
# create grid for search
cand_alphas = np.linspace(args.alpha_from, args.alpha_to, args.num_alphas)
cand_betas = np.linspace(args.beta_from, args.beta_to, args.num_betas)
params_grid = [(alpha, beta) for alpha in cand_alphas
for beta in cand_betas]
## tune parameters in loop
for alpha, beta in params_grid:
result_transcripts = ds2_model.infer_batch(
infer_data=tune_data,
decoding_method='ctc_beam_search',
beam_alpha=alpha,
beam_beta=beta,
beam_size=args.beam_size,
cutoff_prob=args.cutoff_prob,
vocab_list=data_generator.vocab_list,
language_model_path=args.lang_model_path,
num_processes=args.num_proc_bsearch)
wer_sum, num_ins = 0.0, 0
for target, result in zip(target_transcripts, result_transcripts):
wer_sum += wer(target, result)
num_ins += 1
print("alpha = %f\tbeta = %f\tWER = %f" %
(alpha, beta, wer_sum / num_ins))
err_sum = [0.0 for i in xrange(len(params_grid))]
err_ave = [0.0 for i in xrange(len(params_grid))]
num_ins, cur_batch = 0, 0
## incremental tuning parameters over multiple batches
for infer_data in batch_reader():
if (args.num_batches >= 0) and (cur_batch >= args.num_batches):
break
target_transcripts = [
''.join([data_generator.vocab_list[token] for token in transcript])
for _, transcript in infer_data
]
num_ins += len(target_transcripts)
# grid search
for index, (alpha, beta) in enumerate(params_grid):
result_transcripts = ds2_model.infer_batch(
infer_data=infer_data,
decoding_method='ctc_beam_search',
beam_alpha=alpha,
beam_beta=beta,
beam_size=args.beam_size,
cutoff_prob=args.cutoff_prob,
cutoff_top_n=args.cutoff_top_n,
vocab_list=vocab_list,
language_model_path=args.lang_model_path,
num_processes=args.num_proc_bsearch)
for target, result in zip(target_transcripts, result_transcripts):
err_sum[index] += error_rate_func(target, result)
err_ave[index] = err_sum[index] / num_ins
# print("alpha = %f, beta = %f, WER = %f" %
# (alpha, beta, err_ave[index]))
if index % 10 == 0:
sys.stdout.write('.')
sys.stdout.flush()
# output on-line tuning result at the the end of current batch
err_ave_min = min(err_ave)
min_index = err_ave.index(err_ave_min)
print("\nBatch %d, opt.(alpha, beta) = (%f, %f), min. error_rate = %f"
%(cur_batch, params_grid[min_index][0],
params_grid[min_index][1], err_ave_min))
cur_batch += 1
# output WER/CER at every point
print("\nerror rate at each point:\n")
for index in xrange(len(params_grid)):
print("(%f, %f), error_rate = %f"
% (params_grid[index][0], params_grid[index][1], err_ave[index]))
err_ave_min = min(err_ave)
min_index = err_ave.index(err_ave_min)
print("\nTuning on %d batches, opt. (alpha, beta) = (%f, %f)"
% (args.num_batches, params_grid[min_index][0],
params_grid[min_index][1]))
if args.output_fig == True:
fig_name = ("error_surface_alphas_%d_betas_%d" %
(args.num_alphas, args.num_betas))
plot_error_surface(params_grid, err_ave, fig_name)
ds2_model.logger.info("output figure %s" % fig_name)
ds2_model.logger.info("finish inference")
def main():
print_arguments(args)

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