Merge pull request #71 from pkuyym/fix-65

Decouple data provider and model configuration.

data_utils/data.py

@@ -60,9 +60,6 @@ class DataGenerator(object):
                                     be passed forward directly without
                                     converting to index sequence.
     :type keep_transcription_text: bool
-    :param num_conv_layers: The number of convolution layer, used to compute
-                            the sequence length.
-    :type num_conv_layers: int
     """
 
     def __init__(self,
@@ -78,8 +75,7 @@ class DataGenerator(object):
                  use_dB_normalization=True,
                  num_threads=multiprocessing.cpu_count() // 2,
                  random_seed=0,
-                 keep_transcription_text=False,
-                 num_conv_layers=2):
+                 keep_transcription_text=False):
         self._max_duration = max_duration
         self._min_duration = min_duration
         self._normalizer = FeatureNormalizer(mean_std_filepath)
@@ -100,7 +96,6 @@ class DataGenerator(object):
         self._local_data = local()
         self._local_data.tar2info = {}
         self._local_data.tar2object = {}
-        self._num_conv_layers = num_conv_layers
 
     def process_utterance(self, filename, transcript):
         """Load, augment, featurize and normalize for speech data.
@@ -219,14 +214,7 @@ class DataGenerator(object):
         :return: Data feeding dict.
         :rtype: dict
         """
-        feeding_dict = {
-            "audio_spectrogram": 0,
-            "transcript_text": 1,
-            "sequence_offset": 2,
-            "sequence_length": 3
-        }
-        for i in xrange(self._num_conv_layers):
-            feeding_dict["conv%d_index_range" % i] = len(feeding_dict)
+        feeding_dict = {"audio_spectrogram": 0, "transcript_text": 1}
         return feeding_dict
 
     @property
@@ -322,29 +310,7 @@ class DataGenerator(object):
             padded_audio[:, :audio.shape[1]] = audio
             if flatten:
                 padded_audio = padded_audio.flatten()
-
-            # Stride size for conv0 is (3, 2)
-            # Stride size for conv1 to convN is (1, 2)
-            # Same as the network, hard-coded here
-            padded_instance = [padded_audio, text]
-            padded_conv0_h = (padded_audio.shape[0] - 1) // 2 + 1
-            padded_conv0_w = (padded_audio.shape[1] - 1) // 3 + 1
-            valid_w = (audio.shape[1] - 1) // 3 + 1
-            padded_instance += [
-                [0],  # sequence offset, always 0
-                [valid_w],  # valid sequence length
-                # Index ranges for channel, height and width
-                # Please refer scale_sub_region layer to see details
-                [1, 32, 1, padded_conv0_h, valid_w + 1, padded_conv0_w]
-            ]
-            pre_padded_h = padded_conv0_h
-            for i in xrange(self._num_conv_layers - 1):
-                padded_h = (pre_padded_h - 1) // 2 + 1
-                pre_padded_h = padded_h
-                padded_instance += [
-                    [1, 32, 1, padded_h, valid_w + 1, padded_conv0_w]
-                ]
-
+            padded_instance = [padded_audio, text, audio.shape[1]]
             new_batch.append(padded_instance)
         return new_batch
 

deploy/demo_server.py

@@ -147,8 +147,7 @@ def start_server():
         augmentation_config='{}',
         specgram_type=args.specgram_type,
         num_threads=1,
-        keep_transcription_text=True,
-        num_conv_layers=args.num_conv_layers)
+        keep_transcription_text=True)
     # prepare ASR model
     ds2_model = DeepSpeech2Model(
         vocab_size=data_generator.vocab_size,
@@ -164,20 +163,9 @@ def start_server():
     # prepare ASR inference handler
     def file_to_transcript(filename):
         feature = data_generator.process_utterance(filename, "")
-        ins = []
-        conv0_h = (feature[0].shape[0] - 1) // 2 + 1
-        conv0_w = (feature[0].shape[1] - 1) // 3 + 1
-        ins += [feature[0], feature[1],
-                [0], [conv0_w],
-                [1, 32, 1, conv0_h, conv0_w + 1, conv0_w]]
-        pre_h = conv0_h
-        for i in xrange(args.num_conv_layers - 1):
-            h = (pre_h - 1) // 2 + 1
-            pre_h = h
-            ins += [[1, 32, 1, h, conv0_w + 1, conv0_w]]
 
         result_transcript = ds2_model.infer_batch(
-            infer_data=[ins],
+            infer_data=[feature],
             decoding_method=args.decoding_method,
             beam_alpha=args.alpha,
             beam_beta=args.beta,

infer.py

@@ -69,8 +69,7 @@ def infer():
         augmentation_config='{}',
         specgram_type=args.specgram_type,
         num_threads=1,
-        keep_transcription_text=True,
-        num_conv_layers=args.num_conv_layers)
+        keep_transcription_text=True)
     batch_reader = data_generator.batch_reader_creator(
         manifest_path=args.infer_manifest,
         batch_size=args.num_samples,

model_utils/model.py

@@ -8,6 +8,8 @@ import os
 import time
 import logging
 import gzip
+import copy
+import inspect
 from distutils.dir_util import mkpath
 import paddle.v2 as paddle
 from decoders.swig_wrapper import Scorer
@@ -48,6 +50,7 @@ class DeepSpeech2Model(object):
         self._inferer = None
         self._loss_inferer = None
         self._ext_scorer = None
+        self._num_conv_layers = num_conv_layers
         self.logger = logging.getLogger("")
         self.logger.setLevel(level=logging.INFO)
 
@@ -91,6 +94,11 @@ class DeepSpeech2Model(object):
         if not os.path.exists(output_model_dir):
             mkpath(output_model_dir)
 
+        # adapt the feeding dict and reader according to the network
+        adapted_feeding_dict = self._adapt_feeding_dict(feeding_dict)
+        adapted_train_batch_reader = self._adapt_data(train_batch_reader)
+        adapted_dev_batch_reader = self._adapt_data(dev_batch_reader)
+
         # prepare optimizer and trainer
         optimizer = paddle.optimizer.Adam(
             learning_rate=learning_rate,
@@ -128,7 +136,8 @@ class DeepSpeech2Model(object):
                           (time.time() - start_time, event.pass_id))
                 else:
                     result = trainer.test(
-                        reader=dev_batch_reader, feeding=feeding_dict)
+                        reader=adapted_dev_batch_reader,
+                        feeding=adapted_feeding_dict)
                     print(
                         "\n------- Time: %d sec,  Pass: %d, "
                         "ValidationCost: %s" %
@@ -140,11 +149,12 @@ class DeepSpeech2Model(object):
 
         # run train
         trainer.train(
-            reader=train_batch_reader,
+            reader=adapted_train_batch_reader,
             event_handler=event_handler,
             num_passes=num_passes,
-            feeding=feeding_dict)
+            feeding=adapted_feeding_dict)
 
+    # TODO(@pkuyym) merge this function into infer_batch
     def infer_loss_batch(self, infer_data):
         """Model inference. Infer the ctc loss for a batch of speech
         utterances.
@@ -205,15 +215,17 @@ class DeepSpeech2Model(object):
         if self._inferer == None:
             self._inferer = paddle.inference.Inference(
                 output_layer=self._log_probs, parameters=self._parameters)
+        adapted_feeding_dict = self._adapt_feeding_dict(feeding_dict)
+        adapted_infer_data = self._adapt_data(infer_data)
         # run inference
         infer_results = self._inferer.infer(
-            input=infer_data, feeding=feeding_dict)
-        start_pos = [0] * (len(infer_data) + 1)
-        for i in xrange(len(infer_data)):
-            start_pos[i + 1] = start_pos[i] + infer_data[i][3][0]
+            input=adapted_infer_data, feeding=adapted_feeding_dict)
+        start_pos = [0] * (len(adapted_infer_data) + 1)
+        for i in xrange(len(adapted_infer_data)):
+            start_pos[i + 1] = start_pos[i] + adapted_infer_data[i][3][0]
         probs_split = [
             infer_results[start_pos[i]:start_pos[i + 1]]
-            for i in xrange(0, len(infer_data))
+            for i in xrange(0, len(adapted_infer_data))
         ]
         # run decoder
         results = []
@@ -260,6 +272,100 @@ class DeepSpeech2Model(object):
                              decoding_method)
         return results
 
+    def _adapt_feeding_dict(self, feeding_dict):
+        """Adapt feeding dict according to network struct.
+
+        To remove impacts from padding part, we add scale_sub_region layer and
+        sub_seq layer. For sub_seq layer, 'sequence_offset' and
+        'sequence_length' fields are appended. For each scale_sub_region layer
+        'convN_index_range' field is appended.
+
+        :param feeding_dict: Feeding is a map of field name and tuple index
+                             of the data that reader returns.
+        :type feeding_dict: dict|list
+        :return: Adapted feeding dict.
+        :rtype: dict|list
+        """
+        adapted_feeding_dict = copy.deepcopy(feeding_dict)
+        if isinstance(feeding_dict, dict):
+            adapted_feeding_dict["sequence_offset"] = len(adapted_feeding_dict)
+            adapted_feeding_dict["sequence_length"] = len(adapted_feeding_dict)
+            for i in xrange(self._num_conv_layers):
+                adapted_feeding_dict["conv%d_index_range" %i] = \
+                        len(adapted_feeding_dict)
+        elif isinstance(feeding_dict, list):
+            adapted_feeding_dict.append("sequence_offset")
+            adapted_feeding_dict.append("sequence_length")
+            for i in xrange(self._num_conv_layers):
+                adapted_feeding_dict.append("conv%d_index_range" % i)
+        else:
+            raise ValueError("Type of feeding_dict is %s, not supported." %
+                             type(feeding_dict))
+
+        return adapted_feeding_dict
+
+    def _adapt_data(self, data):
+        """Adapt data according to network struct.
+
+        For each convolution layer in the conv_group, to remove impacts from
+        padding data, we can multiply zero to the padding part of the outputs
+        of each batch normalization layer. We add a scale_sub_region layer after
+        each batch normalization layer to reset the padding data.
+        For rnn layers, to remove impacts from padding data, we can truncate the
+        padding part before output data feeded into the first rnn layer. We use
+        sub_seq layer to achieve this.
+
+        :param data: Data from data_provider.
+        :type data: list|function
+        :return: Adapted data.
+        :rtype: list|function
+        """
+
+        def adapt_instance(instance):
+            if len(instance) < 2 or len(instance) > 3:
+                raise ValueError("Size of instance should be 2 or 3.")
+            padded_audio = instance[0]
+            text = instance[1]
+            # no padding part
+            if len(instance) == 2:
+                audio_len = padded_audio.shape[1]
+            else:
+                audio_len = instance[2]
+            adapted_instance = [padded_audio, text]
+            # Stride size for conv0 is (3, 2)
+            # Stride size for conv1 to convN is (1, 2)
+            # Same as the network, hard-coded here
+            padded_conv0_h = (padded_audio.shape[0] - 1) // 2 + 1
+            padded_conv0_w = (padded_audio.shape[1] - 1) // 3 + 1
+            valid_w = (audio_len - 1) // 3 + 1
+            adapted_instance += [
+                [0],  # sequence offset, always 0
+                [valid_w],  # valid sequence length
+                # Index ranges for channel, height and width
+                # Please refer scale_sub_region layer to see details
+                [1, 32, 1, padded_conv0_h, valid_w + 1, padded_conv0_w]
+            ]
+            pre_padded_h = padded_conv0_h
+            for i in xrange(self._num_conv_layers - 1):
+                padded_h = (pre_padded_h - 1) // 2 + 1
+                pre_padded_h = padded_h
+                adapted_instance += [
+                    [1, 32, 1, padded_h, valid_w + 1, padded_conv0_w]
+                ]
+            return adapted_instance
+
+        if isinstance(data, list):
+            return map(adapt_instance, data)
+        elif inspect.isgeneratorfunction(data):
+
+            def adapted_reader():
+                for instance in data():
+                    yield map(adapt_instance, instance)
+
+            return adapted_reader
+        else:
+            raise ValueError("Type of data is %s, not supported." % type(data))
+
     def _create_parameters(self, model_path=None):
         """Load or create model parameters."""
         if model_path is None:

test.py

@@ -70,8 +70,7 @@ def evaluate():
         augmentation_config='{}',
         specgram_type=args.specgram_type,
         num_threads=args.num_proc_data,
-        keep_transcription_text=True,
-        num_conv_layers=args.num_conv_layers)
+        keep_transcription_text=True)
     batch_reader = data_generator.batch_reader_creator(
         manifest_path=args.test_manifest,
         batch_size=args.batch_size,

train.py

@@ -75,15 +75,13 @@ def train():
         max_duration=args.max_duration,
         min_duration=args.min_duration,
         specgram_type=args.specgram_type,
-        num_threads=args.num_proc_data,
-        num_conv_layers=args.num_conv_layers)
+        num_threads=args.num_proc_data)
     dev_generator = DataGenerator(
         vocab_filepath=args.vocab_path,
         mean_std_filepath=args.mean_std_path,
         augmentation_config="{}",
         specgram_type=args.specgram_type,
-        num_threads=args.num_proc_data,
-        num_conv_layers=args.num_conv_layers)
+        num_threads=args.num_proc_data)
     train_batch_reader = train_generator.batch_reader_creator(
         manifest_path=args.train_manifest,
         batch_size=args.batch_size,