avoid repeated infer for same batch in the tuning of DS2

deep_speech_2/tools/tune.py

@@ -7,10 +7,14 @@ import sys
 import numpy as np
 import argparse
 import functools
+import gzip
+import logging
 import paddle.v2 as paddle
 import _init_paths
 from data_utils.data import DataGenerator
-from model_utils.model import DeepSpeech2Model
+from decoders.swig_wrapper import Scorer
+from decoders.swig_wrapper import ctc_beam_search_decoder_batch
+from model_utils.model import deep_speech_v2_network
 from utils.error_rate import wer, cer
 from utils.utility import add_arguments, print_arguments
 
@@ -66,6 +70,9 @@ add_arg('specgram_type',    str,
 args = parser.parse_args()
 
 
+logging.basicConfig(
+    format='[%(levelname)s %(asctime)s %(filename)s:%(lineno)d] %(message)s')
+
 def tune():
     """Tune parameters alpha and beta incrementally."""
     if not args.num_alphas >= 0:
@@ -79,29 +86,55 @@ def tune():
         augmentation_config='{}',
         specgram_type=args.specgram_type,
         num_threads=1)
+
+    audio_data = paddle.layer.data(
+ name="audio_spectrogram",
+ type=paddle.data_type.dense_array(161 * 161))
+    text_data = paddle.layer.data(
+ name="transcript_text",
+ type=paddle.data_type.integer_value_sequence(data_generator.vocab_size))
+
+    output_probs, _ = deep_speech_v2_network(
+ audio_data=audio_data,
+ text_data=text_data,
+ dict_size=data_generator.vocab_size,
+ num_conv_layers=args.num_conv_layers,
+ num_rnn_layers=args.num_rnn_layers,
+ rnn_size=args.rnn_layer_size,
+ use_gru=args.use_gru,
+ share_rnn_weights=args.share_rnn_weights)
+
     batch_reader = data_generator.batch_reader_creator(
         manifest_path=args.tune_manifest,
         batch_size=args.batch_size,
         sortagrad=False,
         shuffle_method=None)
-    tune_data = batch_reader().next()
-    target_transcripts = [
-        ''.join([data_generator.vocab_list[token] for token in transcript])
-        for _, transcript in tune_data
-    ]
-
-    ds2_model = DeepSpeech2Model(
-        vocab_size=data_generator.vocab_size,
-        num_conv_layers=args.num_conv_layers,
-        num_rnn_layers=args.num_rnn_layers,
-        rnn_layer_size=args.rnn_layer_size,
-        use_gru=args.use_gru,
-        pretrained_model_path=args.model_path,
-        share_rnn_weights=args.share_rnn_weights)
 
+    # load parameters
+    parameters = paddle.parameters.Parameters.from_tar(
+        gzip.open(args.model_path))
+
+    inferer = paddle.inference.Inference(
+        output_layer=output_probs, parameters=parameters)
     # decoders only accept string encoded in utf-8
     vocab_list = [chars.encode("utf-8") for chars in data_generator.vocab_list]
 
+    # init logger
+    logger = logging.getLogger("")
+    logger.setLevel(level=logging.INFO)
+    # init external scorer
+    logger.info("begin to initialize the external scorer for tuning")
+    ext_scorer = Scorer(
+        alpha=args.alpha_from,
+        beta=args.beta_from,
+        model_path=args.lang_model_path,
+        vocabulary=vocab_list)
+    logger.info("language model: "
+                "is_character_based = %d," % ext_scorer.is_character_based() +
+                " max_order = %d," % ext_scorer.get_max_order() +
+                " dict_size = %d" % ext_scorer.get_dict_size())
+    logger.info("end initializing scorer. Start tuning ...")
+
     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)
@@ -116,6 +149,13 @@ def tune():
     for infer_data in batch_reader():
         if (args.num_batches >= 0) and (cur_batch >= args.num_batches):
             break
+        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(len(infer_data))
+        ]
 
         target_transcripts = [
             ''.join([data_generator.vocab_list[token] for token in transcript])
@@ -125,18 +165,18 @@ def tune():
         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)
-
+            # reset alpha & beta
+            ext_scorer.reset_params(alpha, beta)
+            beam_search_results = ctc_beam_search_decoder_batch(
+         probs_split=probs_split,
+         vocabulary=vocab_list,
+         beam_size=args.beam_size,
+         num_processes=args.num_proc_bsearch,
+         cutoff_prob=args.cutoff_prob,
+         cutoff_top_n=args.cutoff_top_n,
+         ext_scoring_func=ext_scorer, )
+
+            result_transcripts = [res[0][1] for res in beam_search_results]
             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
@@ -167,7 +207,7 @@ def tune():
             % (args.num_batches, "%.3f" % params_grid[min_index][0],
               "%.3f" % params_grid[min_index][1]))
 
-    ds2_model.logger.info("finish inference")
+    logger.info("finish tuning")
 
 
 def main():