final refining on old data provider: enable pruning & add evaluation & code cleanup

deep_speech_2/decoder.py

@@ -5,7 +5,6 @@
 import os
 from itertools import groupby
 import numpy as np
-import copy
 import kenlm
 import multiprocessing
 
@@ -73,11 +72,25 @@ class Scorer(object):
         return len(words)
 
     # execute evaluation
-    def __call__(self, sentence):
+    def __call__(self, sentence, log=False):
+        """
+        Evaluation function
+
+        :param sentence: The input sentence for evalutation
+        :type sentence: basestring
+        :param log: Whether return the score in log representation.
+        :type log: bool
+        :return: Evaluation score, in the decimal or log.
+        :rtype: float
+        """
         lm = self.language_model_score(sentence)
         word_cnt = self.word_count(sentence)
+        if log == False:
             score = np.power(lm, self._alpha) \
                     * np.power(word_cnt, self._beta)
+        else:
+            score = self._alpha * np.log(lm) \
+                    + self._beta * np.log(word_cnt)
         return score
 
 
@@ -85,13 +98,14 @@ def ctc_beam_search_decoder(probs_seq,
                             beam_size,
                             vocabulary,
                             blank_id=0,
+                            cutoff_prob=1.0,
                             ext_scoring_func=None,
                             nproc=False):
     '''
     Beam search decoder for CTC-trained network, using beam search with width
     beam_size to find many paths to one label, return  beam_size labels in
-    the order of probabilities. The implementation is based on Prefix Beam
-    Search(https://arxiv.org/abs/1408.2873), and the unclear part is
+    the descending order of probabilities. The implementation is based on Prefix
+    Beam Search(https://arxiv.org/abs/1408.2873), and the unclear part is
     redesigned, need to be verified.
 
     :param probs_seq: 2-D list with length num_time_steps, each element
@@ -102,22 +116,25 @@ def ctc_beam_search_decoder(probs_seq,
     :type beam_size: int
     :param vocabulary: Vocabulary list.
     :type vocabulary: list
+    :param blank_id: ID of blank, default 0.
+    :type blank_id: int
+    :param cutoff_prob: Cutoff probability in pruning,
+                        default 1.0, no pruning.
+    :type cutoff_prob: float
     :param ext_scoring_func: External defined scoring function for
                             partially decoded sentence, e.g. word count
                             and language model.
     :type external_scoring_function: function
-    :param blank_id: id of blank, default 0.
-    :type blank_id: int
     :param nproc: Whether the decoder used in multiprocesses.
     :type nproc: bool
-    :return: Decoding log probability and result string.
+    :return: Decoding log probabilities and result sentences in descending order.
     :rtype: list
 
     '''
     # dimension check
     for prob_list in probs_seq:
         if not len(prob_list) == len(vocabulary) + 1:
-            raise ValueError("probs dimension mismatchedd with vocabulary")
+            raise ValueError("probs dimension mismatched with vocabulary")
     num_time_steps = len(probs_seq)
 
     # blank_id check
@@ -137,19 +154,35 @@ def ctc_beam_search_decoder(probs_seq,
     probs_b_prev, probs_nb_prev = {'\t': 1.0}, {'\t': 0.0}
 
     ## extend prefix in loop
-    for time_step in range(num_time_steps):
+    for time_step in xrange(num_time_steps):
         # the set containing candidate prefixes
         prefix_set_next = {}
         probs_b_cur, probs_nb_cur = {}, {}
-        for l in prefix_set_prev:
         prob = probs_seq[time_step]
+        prob_idx = [[i, prob[i]] for i in xrange(len(prob))]
+        cutoff_len = len(prob_idx)
+        #If pruning is enabled
+        if (cutoff_prob < 1.0):
+            prob_idx = sorted(prob_idx, key=lambda asd: asd[1], reverse=True)
+            cutoff_len = 0
+            cum_prob = 0.0
+            for i in xrange(len(prob_idx)):
+                cum_prob += prob_idx[i][1]
+                cutoff_len += 1
+                if cum_prob >= cutoff_prob:
+                    break
+            prob_idx = prob_idx[0:cutoff_len]
+
+        for l in prefix_set_prev:
             if not prefix_set_next.has_key(l):
                 probs_b_cur[l], probs_nb_cur[l] = 0.0, 0.0
 
-            # extend prefix by travering vocabulary
-            for c in range(0, probs_dim):
+            # extend prefix by travering prob_idx
+            for index in xrange(cutoff_len):
+                c, prob_c = prob_idx[index][0], prob_idx[index][1]
+
                 if c == blank_id:
-                    probs_b_cur[l] += prob[c] * (
+                    probs_b_cur[l] += prob_c * (
                         probs_b_prev[l] + probs_nb_prev[l])
                 else:
                     last_char = l[-1]
@@ -159,18 +192,18 @@ def ctc_beam_search_decoder(probs_seq,
                         probs_b_cur[l_plus], probs_nb_cur[l_plus] = 0.0, 0.0
 
                     if new_char == last_char:
-                        probs_nb_cur[l_plus] += prob[c] * probs_b_prev[l]
-                        probs_nb_cur[l] += prob[c] * probs_nb_prev[l]
+                        probs_nb_cur[l_plus] += prob_c * probs_b_prev[l]
+                        probs_nb_cur[l] += prob_c * probs_nb_prev[l]
                     elif new_char == ' ':
                         if (ext_scoring_func is None) or (len(l) == 1):
                             score = 1.0
                         else:
                             prefix = l[1:]
                             score = ext_scoring_func(prefix)
-                        probs_nb_cur[l_plus] += score * prob[c] * (
+                        probs_nb_cur[l_plus] += score * prob_c * (
                             probs_b_prev[l] + probs_nb_prev[l])
                     else:
-                        probs_nb_cur[l_plus] += prob[c] * (
+                        probs_nb_cur[l_plus] += prob_c * (
                             probs_b_prev[l] + probs_nb_prev[l])
                     # add l_plus into prefix_set_next
                     prefix_set_next[l_plus] = probs_nb_cur[
@@ -203,6 +236,7 @@ def ctc_beam_search_decoder_nproc(probs_split,
                                   beam_size,
                                   vocabulary,
                                   blank_id=0,
+                                  cutoff_prob=1.0,
                                   ext_scoring_func=None,
                                   num_processes=None):
     '''
@@ -216,16 +250,19 @@ def ctc_beam_search_decoder_nproc(probs_split,
     :type beam_size: int
     :param vocabulary: Vocabulary list.
     :type vocabulary: list
+    :param blank_id: ID of blank, default 0.
+    :type blank_id: int
+    :param cutoff_prob: Cutoff probability in pruning,
+                        default 0, no pruning.
+    :type cutoff_prob: float
     :param ext_scoring_func: External defined scoring function for
                             partially decoded sentence, e.g. word count
                             and language model.
     :type external_scoring_function: function
-    :param blank_id: id of blank, default 0.
-    :type blank_id: int
     :param num_processes: Number of processes, default None, equal to the
                  number of CPUs.
     :type num_processes: int
-    :return: Decoding log probability and result string.
+    :return: Decoding log probabilities and result sentences in descending order.
     :rtype: list
 
     '''
@@ -243,7 +280,8 @@ def ctc_beam_search_decoder_nproc(probs_split,
     pool = multiprocessing.Pool(processes=num_processes)
     results = []
     for i, probs_list in enumerate(probs_split):
-        args = (probs_list, beam_size, vocabulary, blank_id, None, nproc)
+        args = (probs_list, beam_size, vocabulary, blank_id, cutoff_prob, None,
+                nproc)
         results.append(pool.apply_async(ctc_beam_search_decoder, args))
 
     pool.close()

deep_speech_2/evaluate.py

+"""
+   Evaluation for a simplifed version of Baidu DeepSpeech2 model.
+"""
+
+import paddle.v2 as paddle
+import distutils.util
+import argparse
+import gzip
+from audio_data_utils import DataGenerator
+from model import deep_speech2
+from decoder import *
+from error_rate import wer
+
+parser = argparse.ArgumentParser(
+    description='Simplified version of DeepSpeech2 evaluation.')
+parser.add_argument(
+    "--num_samples",
+    default=100,
+    type=int,
+    help="Number of samples for evaluation. (default: %(default)s)")
+parser.add_argument(
+    "--num_conv_layers",
+    default=2,
+    type=int,
+    help="Convolution layer number. (default: %(default)s)")
+parser.add_argument(
+    "--num_rnn_layers",
+    default=3,
+    type=int,
+    help="RNN layer number. (default: %(default)s)")
+parser.add_argument(
+    "--rnn_layer_size",
+    default=512,
+    type=int,
+    help="RNN layer cell number. (default: %(default)s)")
+parser.add_argument(
+    "--use_gpu",
+    default=True,
+    type=distutils.util.strtobool,
+    help="Use gpu or not. (default: %(default)s)")
+parser.add_argument(
+    "--decode_method",
+    default='beam_search_nproc',
+    type=str,
+    help="Method for ctc decoding, best_path, "
+    "beam_search or beam_search_nproc. (default: %(default)s)")
+parser.add_argument(
+    "--language_model_path",
+    default="./data/1Billion.klm",
+    type=str,
+    help="Path for language model. (default: %(default)s)")
+parser.add_argument(
+    "--alpha",
+    default=0.26,
+    type=float,
+    help="Parameter associated with language model. (default: %(default)f)")
+parser.add_argument(
+    "--beta",
+    default=0.1,
+    type=float,
+    help="Parameter associated with word count. (default: %(default)f)")
+parser.add_argument(
+    "--cutoff_prob",
+    default=0.99,
+    type=float,
+    help="The cutoff probability of pruning"
+    "in beam search. (default: %(default)f)")
+parser.add_argument(
+    "--beam_size",
+    default=500,
+    type=int,
+    help="Width for beam search decoding. (default: %(default)d)")
+parser.add_argument(
+    "--normalizer_manifest_path",
+    default='data/manifest.libri.train-clean-100',
+    type=str,
+    help="Manifest path for normalizer. (default: %(default)s)")
+parser.add_argument(
+    "--decode_manifest_path",
+    default='data/manifest.libri.test-clean',
+    type=str,
+    help="Manifest path for decoding. (default: %(default)s)")
+parser.add_argument(
+    "--model_filepath",
+    default='./params.tar.gz',
+    type=str,
+    help="Model filepath. (default: %(default)s)")
+parser.add_argument(
+    "--vocab_filepath",
+    default='data/eng_vocab.txt',
+    type=str,
+    help="Vocabulary filepath. (default: %(default)s)")
+args = parser.parse_args()
+
+
+def evaluate():
+    """
+    Evaluate on whole test data for DeepSpeech2.
+    """
+    # initialize data generator
+    data_generator = DataGenerator(
+        vocab_filepath=args.vocab_filepath,
+        normalizer_manifest_path=args.normalizer_manifest_path,
+        normalizer_num_samples=200,
+        max_duration=20.0,
+        min_duration=0.0,
+        stride_ms=10,
+        window_ms=20)
+
+    # create network config
+    dict_size = data_generator.vocabulary_size()
+    vocab_list = data_generator.vocabulary_list()
+    audio_data = paddle.layer.data(
+        name="audio_spectrogram",
+        height=161,
+        width=2000,
+        type=paddle.data_type.dense_vector(322000))
+    text_data = paddle.layer.data(
+        name="transcript_text",
+        type=paddle.data_type.integer_value_sequence(dict_size))
+    output_probs = deep_speech2(
+        audio_data=audio_data,
+        text_data=text_data,
+        dict_size=dict_size,
+        num_conv_layers=args.num_conv_layers,
+        num_rnn_layers=args.num_rnn_layers,
+        rnn_size=args.rnn_layer_size,
+        is_inference=True)
+
+    # load parameters
+    parameters = paddle.parameters.Parameters.from_tar(
+        gzip.open(args.model_filepath))
+
+    # prepare infer data
+    feeding = data_generator.data_name_feeding()
+    test_batch_reader = data_generator.batch_reader_creator(
+        manifest_path=args.decode_manifest_path,
+        batch_size=args.num_samples,
+        padding_to=2000,
+        flatten=True,
+        sort_by_duration=False,
+        shuffle=False)
+
+    # define inferer
+    inferer = paddle.inference.Inference(
+        output_layer=output_probs, parameters=parameters)
+
+    # initialize external scorer for beam search decoding
+    if args.decode_method == 'beam_search' or \
+       args.decode_method == 'beam_search_nproc':
+        ext_scorer = Scorer(args.alpha, args.beta, args.language_model_path)
+
+    wer_counter, wer_sum = 0, 0.0
+    for infer_data in test_batch_reader():
+        # run inference
+        infer_results = inferer.infer(input=infer_data)
+        num_steps = len(infer_results) / len(infer_data)
+        probs_split = [
+            infer_results[i * num_steps:(i + 1) * num_steps]
+            for i in xrange(0, len(infer_data))
+        ]
+
+        # decode and print
+        # best path decode
+        if args.decode_method == "best_path":
+            for i, probs in enumerate(probs_split):
+                output_transcription = ctc_decode(
+                    probs_seq=probs, vocabulary=vocab_list, method="best_path")
+                target_transcription = ''.join(
+                    [vocab_list[index] for index in infer_data[i][1]])
+                wer_sum += wer(target_transcription, output_transcription)
+                wer_counter += 1
+        # beam search decode in single process
+        elif args.decode_method == "beam_search":
+            for i, probs in enumerate(probs_split):
+                target_transcription = ''.join(
+                    [vocab_list[index] for index in infer_data[i][1]])
+                beam_search_result = ctc_beam_search_decoder(
+                    probs_seq=probs,
+                    vocabulary=vocab_list,
+                    beam_size=args.beam_size,
+                    blank_id=len(vocab_list),
+                    ext_scoring_func=ext_scorer,
+                    cutoff_prob=args.cutoff_prob, )
+                wer_sum += wer(target_transcription, beam_search_result[0][1])
+                wer_counter += 1
+        # beam search using multiple processes
+        elif args.decode_method == "beam_search_nproc":
+            beam_search_nproc_results = ctc_beam_search_decoder_nproc(
+                probs_split=probs_split,
+                vocabulary=vocab_list,
+                beam_size=args.beam_size,
+                blank_id=len(vocab_list),
+                ext_scoring_func=ext_scorer,
+                cutoff_prob=args.cutoff_prob, )
+            for i, beam_search_result in enumerate(beam_search_nproc_results):
+                target_transcription = ''.join(
+                    [vocab_list[index] for index in infer_data[i][1]])
+                wer_sum += wer(target_transcription, beam_search_result[0][1])
+                wer_counter += 1
+        else:
+            raise ValueError("Decoding method [%s] is not supported." % method)
+
+        print("Cur WER = %f" % (wer_sum / wer_counter))
+    print("Final WER = %f" % (wer_sum / wer_counter))
+
+
+def main():
+    paddle.init(use_gpu=args.use_gpu, trainer_count=1)
+    evaluate()
+
+
+if __name__ == '__main__':
+    main()

deep_speech_2/infer.py

@@ -9,14 +9,14 @@ import gzip
 from audio_data_utils import DataGenerator
 from model import deep_speech2
 from decoder import *
-import kenlm
 from error_rate import wer
+import time
 
 parser = argparse.ArgumentParser(
     description='Simplified version of DeepSpeech2 inference.')
 parser.add_argument(
     "--num_samples",
-    default=10,
+    default=100,
     type=int,
     help="Number of samples for inference. (default: %(default)s)")
 parser.add_argument(
@@ -46,7 +46,7 @@ parser.add_argument(
     help="Manifest path for normalizer. (default: %(default)s)")
 parser.add_argument(
     "--decode_manifest_path",
-    default='data/manifest.libri.test-clean',
+    default='data/manifest.libri.test-100sample',
     type=str,
     help="Manifest path for decoding. (default: %(default)s)")
 parser.add_argument(
@@ -63,11 +63,13 @@ parser.add_argument(
     "--decode_method",
     default='beam_search_nproc',
     type=str,
-    help="Method for ctc decoding, best_path, beam_search or beam_search_nproc. (default: %(default)s)"
-)
+    help="Method for ctc decoding:"
+    "  best_path,"
+    "  beam_search, "
+    "   or beam_search_nproc. (default: %(default)s)")
 parser.add_argument(
     "--beam_size",
-    default=50,
+    default=500,
     type=int,
     help="Width for beam search decoding. (default: %(default)d)")
 parser.add_argument(
@@ -82,14 +84,20 @@ parser.add_argument(
     help="Path for language model. (default: %(default)s)")
 parser.add_argument(
     "--alpha",
-    default=0.0,
+    default=0.26,
     type=float,
     help="Parameter associated with language model. (default: %(default)f)")
 parser.add_argument(
     "--beta",
-    default=0.0,
+    default=0.1,
     type=float,
     help="Parameter associated with word count. (default: %(default)f)")
+parser.add_argument(
+    "--cutoff_prob",
+    default=0.99,
+    type=float,
+    help="The cutoff probability of pruning"
+    "in beam search. (default: %(default)f)")
 args = parser.parse_args()
 
 
@@ -154,6 +162,7 @@ def infer():
     ## decode and print
     # best path decode
     wer_sum, wer_counter = 0, 0
+    total_time = 0.0
     if args.decode_method == "best_path":
         for i, probs in enumerate(probs_split):
             target_transcription = ''.join(
@@ -177,11 +186,12 @@ def infer():
                 probs_seq=probs,
                 vocabulary=vocab_list,
                 beam_size=args.beam_size,
-                ext_scoring_func=ext_scorer,
-                blank_id=len(vocab_list))
+                blank_id=len(vocab_list),
+                cutoff_prob=args.cutoff_prob,
+                ext_scoring_func=ext_scorer, )
             print("\nTarget Transcription:\t%s" % target_transcription)
 
-            for index in range(args.num_results_per_sample):
+            for index in xrange(args.num_results_per_sample):
                 result = beam_search_result[index]
                 #output: index, log prob, beam result
                 print("Beam %d: %f \t%s" % (index, result[0], result[1]))
@@ -190,21 +200,21 @@ def infer():
             wer_counter += 1
             print("cur wer = %f , average wer = %f" %
                   (wer_cur, wer_sum / wer_counter))
-    # beam search using multiple processes
     elif args.decode_method == "beam_search_nproc":
         ext_scorer = Scorer(args.alpha, args.beta, args.language_model_path)
         beam_search_nproc_results = ctc_beam_search_decoder_nproc(
             probs_split=probs_split,
             vocabulary=vocab_list,
             beam_size=args.beam_size,
-            ext_scoring_func=ext_scorer,
-            blank_id=len(vocab_list))
+            blank_id=len(vocab_list),
+            cutoff_prob=args.cutoff_prob,
+            ext_scoring_func=ext_scorer, )
         for i, beam_search_result in enumerate(beam_search_nproc_results):
             target_transcription = ''.join(
                 [vocab_list[index] for index in infer_data[i][1]])
             print("\nTarget Transcription:\t%s" % target_transcription)
 
-            for index in range(args.num_results_per_sample):
+            for index in xrange(args.num_results_per_sample):
                 result = beam_search_result[index]
                 #output: index, log prob, beam result
                 print("Beam %d: %f \t%s" % (index, result[0], result[1]))

deep_speech_2/tune.py

 """
-   Tune parameters for beam search decoder in Deep Speech 2.
+   Parameters tuning for beam search decoder in Deep Speech 2.
 """
 
 import paddle.v2 as paddle
@@ -12,7 +12,7 @@ from decoder import *
 from error_rate import wer
 
 parser = argparse.ArgumentParser(
-    description='Parameters tuning script for ctc beam search decoder in  Deep Speech 2.'
+    description='Parameters tuning for ctc beam search decoder in  Deep Speech 2.'
 )
 parser.add_argument(
     "--num_samples",
@@ -82,34 +82,40 @@ parser.add_argument(
     help="Path for language model. (default: %(default)s)")
 parser.add_argument(
     "--alpha_from",
-    default=0.0,
+    default=0.1,
     type=float,
-    help="Where alpha starts from, <= alpha_to. (default: %(default)f)")
+    help="Where alpha starts from. (default: %(default)f)")
 parser.add_argument(
-    "--alpha_stride",
-    default=0.001,
-    type=float,
-    help="Step length for varying alpha. (default: %(default)f)")
+    "--num_alphas",
+    default=14,
+    type=int,
+    help="Number of candidate alphas. (default: %(default)d)")
 parser.add_argument(
     "--alpha_to",
-    default=0.01,
+    default=0.36,
     type=float,
-    help="Where alpha ends with, >= alpha_from. (default: %(default)f)")
+    help="Where alpha ends with. (default: %(default)f)")
 parser.add_argument(
     "--beta_from",
-    default=0.0,
+    default=0.05,
     type=float,
-    help="Where beta starts from, <= beta_to. (default: %(default)f)")
+    help="Where beta starts from. (default: %(default)f)")
 parser.add_argument(
-    "--beta_stride",
-    default=0.01,
+    "--num_betas",
+    default=20,
     type=float,
-    help="Step length for varying beta. (default: %(default)f)")
+    help="Number of candidate betas. (default: %(default)d)")
 parser.add_argument(
     "--beta_to",
-    default=0.0,
+    default=1.0,
+    type=float,
+    help="Where beta ends with. (default: %(default)f)")
+parser.add_argument(
+    "--cutoff_prob",
+    default=0.99,
     type=float,
-    help="Where beta ends with, >= beta_from. (default: %(default)f)")
+    help="The cutoff probability of pruning"
+    "in beam search. (default: %(default)f)")
 args = parser.parse_args()
 
 
@@ -118,15 +124,11 @@ def tune():
     Tune parameters alpha and beta on one minibatch.
     """
 
-    if not args.alpha_from <= args.alpha_to:
-        raise ValueError("alpha_from <= alpha_to doesn't satisfy!")
-    if not args.alpha_stride > 0:
-        raise ValueError("alpha_stride shouldn't be negative!")
+    if not args.num_alphas >= 0:
+        raise ValueError("num_alphas must be non-negative!")
 
-    if not args.beta_from <= args.beta_to:
-        raise ValueError("beta_from <= beta_to doesn't satisfy!")
-    if not args.beta_stride > 0:
-        raise ValueError("beta_stride shouldn't be negative!")
+    if not args.num_betas >= 0:
+        raise ValueError("num_betas must be non-negative!")
 
     # initialize data generator
     data_generator = DataGenerator(
@@ -171,6 +173,7 @@ def tune():
         flatten=True,
         sort_by_duration=False,
         shuffle=False)
+    # get one batch data for tuning
     infer_data = test_batch_reader().next()
 
     # run inference
@@ -182,11 +185,12 @@ def tune():
         for i in xrange(0, len(infer_data))
     ]
 
-    cand_alpha = np.arange(args.alpha_from, args.alpha_to + args.alpha_stride,
-                           args.alpha_stride)
-    cand_beta = np.arange(args.beta_from, args.beta_to + args.beta_stride,
-                          args.beta_stride)
-    params_grid = [(alpha, beta) for alpha in cand_alpha for beta in cand_beta]
+    # 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:
         wer_sum, wer_counter = 0, 0
@@ -200,8 +204,9 @@ def tune():
                     probs_seq=probs,
                     vocabulary=vocab_list,
                     beam_size=args.beam_size,
-                    ext_scoring_func=ext_scorer,
-                    blank_id=len(vocab_list))
+                    blank_id=len(vocab_list),
+                    cutoff_prob=args.cutoff_prob,
+                    ext_scoring_func=ext_scorer, )
                 wer_sum += wer(target_transcription, beam_search_result[0][1])
                 wer_counter += 1
         # beam search using multiple processes
@@ -210,9 +215,9 @@ def tune():
                 probs_split=probs_split,
                 vocabulary=vocab_list,
                 beam_size=args.beam_size,
-                ext_scoring_func=ext_scorer,
+                cutoff_prob=args.cutoff_prob,
                 blank_id=len(vocab_list),
-                num_processes=1)
+                ext_scoring_func=ext_scorer, )
             for i, beam_search_result in enumerate(beam_search_nproc_results):
                 target_transcription = ''.join(
                     [vocab_list[index] for index in infer_data[i][1]])