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"""Beam search parameters tuning for DeepSpeech2 model."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import sys
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import numpy as np
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import argparse
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import functools
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import paddle.v2 as paddle
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import _init_paths
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from data_utils.data import DataGenerator
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from model_utils.model import DeepSpeech2Model
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from utils.error_rate import wer
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from utils.utility import add_arguments, print_arguments
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parser = argparse.ArgumentParser(description=__doc__)
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add_arg = functools.partial(add_arguments, argparser=parser)
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# yapf: disable
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add_arg('num_batches', int, -1, "# of batches tuning on. "
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"Default -1, on whole dev set.")
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add_arg('batch_size', int, 256, "# of samples per batch.")
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add_arg('trainer_count', int, 8, "# of Trainers (CPUs or GPUs).")
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add_arg('beam_size', int, 500, "Beam search width.")
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add_arg('num_proc_bsearch', int, 12, "# of CPUs for beam search.")
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add_arg('num_conv_layers', int, 2, "# of convolution layers.")
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add_arg('num_rnn_layers', int, 3, "# of recurrent layers.")
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add_arg('rnn_layer_size', int, 2048, "# of recurrent cells per layer.")
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add_arg('num_alphas', int, 45, "# of alpha candidates for tuning.")
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add_arg('num_betas', int, 8, "# of beta candidates for tuning.")
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add_arg('alpha_from', float, 1.0, "Where alpha starts tuning from.")
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add_arg('alpha_to', float, 3.2, "Where alpha ends tuning with.")
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add_arg('beta_from', float, 0.1, "Where beta starts tuning from.")
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add_arg('beta_to', float, 0.45, "Where beta ends tuning with.")
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add_arg('cutoff_prob', float, 1.0, "Cutoff probability for pruning.")
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add_arg('cutoff_top_n', int, 40, "Cutoff number for pruning.")
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add_arg('output_fig', bool, True, "Output error rate figure or not.")
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add_arg('use_gru', bool, False, "Use GRUs instead of simple RNNs.")
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add_arg('use_gpu', bool, True, "Use GPU or not.")
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add_arg('share_rnn_weights',bool, True, "Share input-hidden weights across "
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"bi-directional RNNs. Not for GRU.")
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add_arg('tune_manifest', str,
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'data/librispeech/manifest.dev-clean',
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"Filepath of manifest to tune.")
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add_arg('mean_std_path', str,
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'data/librispeech/mean_std.npz',
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"Filepath of normalizer's mean & std.")
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add_arg('vocab_path', str,
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'data/librispeech/vocab.txt',
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"Filepath of vocabulary.")
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add_arg('lang_model_path', str,
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'models/lm/common_crawl_00.prune01111.trie.klm',
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"Filepath for language model.")
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add_arg('model_path', str,
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'./checkpoints/libri/params.latest.tar.gz',
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"If None, the training starts from scratch, "
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"otherwise, it resumes from the pre-trained model.")
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add_arg('error_rate_type', str,
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'wer',
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"Error rate type for evaluation.",
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choices=['wer', 'cer'])
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add_arg('specgram_type', str,
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'linear',
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"Audio feature type. Options: linear, mfcc.",
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choices=['linear', 'mfcc'])
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# yapf: disable
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args = parser.parse_args()
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def plot_error_surface(params_grid, err_ave, fig_name):
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import matplotlib.pyplot as plt
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import mpl_toolkits.mplot3d as Axes3D
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fig = plt.figure()
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ax = Axes3D(fig)
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alphas = [ param[0] for param in params_grid ]
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betas = [ param[1] for param in params_grid]
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ALPHAS = np.reshape(alphas, (args.num_alphas, args.num_betas))
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BETAS = np.reshape(betas, (args.num_alphas, args.num_betas))
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ERR_AVE = np.reshape(err_ave, (args.num_alphas, args.num_betas))
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ax.plot_surface(ALPHAS, BETAS, WERS,
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rstride=1, cstride=1, alpha=0.8, cmap='rainbow')
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ax.set_xlabel('alpha')
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ax.set_ylabel('beta')
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z_label = 'WER' if args.error_rate_type == 'wer' else 'CER'
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ax.set_zlabel(z_label)
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plt.savefig(fig_name)
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def tune():
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"""Tune parameters alpha and beta on one minibatch."""
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if not args.num_alphas >= 0:
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raise ValueError("num_alphas must be non-negative!")
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if not args.num_betas >= 0:
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raise ValueError("num_betas must be non-negative!")
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data_generator = DataGenerator(
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vocab_filepath=args.vocab_path,
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mean_std_filepath=args.mean_std_path,
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augmentation_config='{}',
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specgram_type=args.specgram_type,
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num_threads=1)
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batch_reader = data_generator.batch_reader_creator(
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manifest_path=args.tune_manifest,
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batch_size=args.batch_size,
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sortagrad=False,
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shuffle_method=None)
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tune_data = batch_reader().next()
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target_transcripts = [
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''.join([data_generator.vocab_list[token] for token in transcript])
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for _, transcript in tune_data
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]
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ds2_model = DeepSpeech2Model(
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vocab_size=data_generator.vocab_size,
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num_conv_layers=args.num_conv_layers,
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num_rnn_layers=args.num_rnn_layers,
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rnn_layer_size=args.rnn_layer_size,
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use_gru=args.use_gru,
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pretrained_model_path=args.model_path,
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share_rnn_weights=args.share_rnn_weights)
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# decoders only accept string encoded in utf-8
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vocab_list = [chars.encode("utf-8") for chars in data_generator.vocab_list]
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error_rate_func = cer if args.error_rate_type == 'cer' else wer
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# create grid for search
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cand_alphas = np.linspace(args.alpha_from, args.alpha_to, args.num_alphas)
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cand_betas = np.linspace(args.beta_from, args.beta_to, args.num_betas)
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params_grid = [(alpha, beta) for alpha in cand_alphas
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for beta in cand_betas]
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err_sum = [0.0 for i in xrange(len(params_grid))]
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err_ave = [0.0 for i in xrange(len(params_grid))]
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num_ins, cur_batch = 0, 0
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## incremental tuning parameters over multiple batches
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for infer_data in batch_reader():
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if (args.num_batches >= 0) and (cur_batch >= args.num_batches):
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break
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target_transcripts = [
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''.join([data_generator.vocab_list[token] for token in transcript])
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for _, transcript in infer_data
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]
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num_ins += len(target_transcripts)
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# grid search
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for index, (alpha, beta) in enumerate(params_grid):
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result_transcripts = ds2_model.infer_batch(
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infer_data=infer_data,
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decoding_method='ctc_beam_search',
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beam_alpha=alpha,
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beam_beta=beta,
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beam_size=args.beam_size,
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cutoff_prob=args.cutoff_prob,
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cutoff_top_n=args.cutoff_top_n,
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vocab_list=vocab_list,
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language_model_path=args.lang_model_path,
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num_processes=args.num_proc_bsearch)
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for target, result in zip(target_transcripts, result_transcripts):
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err_sum[index] += error_rate_func(target, result)
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err_ave[index] = err_sum[index] / num_ins
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# print("alpha = %f, beta = %f, WER = %f" %
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# (alpha, beta, err_ave[index]))
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if index % 10 == 0:
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sys.stdout.write('.')
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sys.stdout.flush()
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# output on-line tuning result at the the end of current batch
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err_ave_min = min(err_ave)
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min_index = err_ave.index(err_ave_min)
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print("\nBatch %d, opt.(alpha, beta) = (%f, %f), min. error_rate = %f"
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%(cur_batch, params_grid[min_index][0],
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params_grid[min_index][1], err_ave_min))
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cur_batch += 1
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# output WER/CER at every point
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print("\nerror rate at each point:\n")
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for index in xrange(len(params_grid)):
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print("(%f, %f), error_rate = %f"
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% (params_grid[index][0], params_grid[index][1], err_ave[index]))
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err_ave_min = min(err_ave)
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min_index = err_ave.index(err_ave_min)
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print("\nTuning on %d batches, opt. (alpha, beta) = (%f, %f)"
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% (args.num_batches, params_grid[min_index][0],
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params_grid[min_index][1]))
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if args.output_fig == True:
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fig_name = ("error_surface_alphas_%d_betas_%d" %
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(args.num_alphas, args.num_betas))
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plot_error_surface(params_grid, err_ave, fig_name)
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ds2_model.logger.info("output figure %s" % fig_name)
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ds2_model.logger.info("finish inference")
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def main():
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print_arguments(args)
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paddle.init(use_gpu=args.use_gpu, trainer_count=args.trainer_count)
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tune()
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if __name__ == '__main__':
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main()
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