diff --git a/fluid/DeepASR/data_utils/async_data_reader.py b/fluid/DeepASR/data_utils/async_data_reader.py index d6949257b6d4f142d9e5040ffab39f0236814de3..731c55de71e8d4b7db156f1ae72172c36eb1be7a 100644 --- a/fluid/DeepASR/data_utils/async_data_reader.py +++ b/fluid/DeepASR/data_utils/async_data_reader.py @@ -30,11 +30,12 @@ class SampleInfo(object): label_bin_path (str): File containing the label data. label_size (int): Byte count of the sample's label data. label_frame_num (int): Label number of the sample. + sample_name (str): Key of the sample """ def __init__(self, feature_bin_path, feature_start, feature_size, feature_frame_num, feature_dim, label_bin_path, label_start, - label_size, label_frame_num): + label_size, label_frame_num, sample_name): self.feature_bin_path = feature_bin_path self.feature_start = feature_start self.feature_size = feature_size @@ -45,6 +46,7 @@ class SampleInfo(object): self.label_start = label_start self.label_size = label_size self.label_frame_num = label_frame_num + self.sample_name = sample_name class SampleInfoBucket(object): @@ -102,24 +104,33 @@ class SampleInfoBucket(object): feature_bin_path = self._feature_bin_paths[block_idx] feature_desc_path = self._feature_desc_paths[block_idx] - label_desc_lines = open(label_desc_path).readlines() feature_desc_lines = open(feature_desc_path).readlines() - sample_num = int(label_desc_lines[0].split()[1]) - assert sample_num == int(feature_desc_lines[0].split()[1]) + label_desc_lines = [] + if label_desc_path != "": + label_desc_lines = open(label_desc_path).readlines() + sample_num = int(feature_desc_lines[0].split()[1]) + + if label_desc_path != "": + assert sample_num == int(label_desc_lines[0].split()[1]) for i in xrange(sample_num): feature_desc_split = feature_desc_lines[i + 1].split() + sample_name = feature_desc_split[0] feature_start = int(feature_desc_split[2]) feature_size = int(feature_desc_split[3]) feature_frame_num = int(feature_desc_split[4]) feature_dim = int(feature_desc_split[5]) - label_desc_split = label_desc_lines[i + 1].split() - label_start = int(label_desc_split[2]) - label_size = int(label_desc_split[3]) - label_frame_num = int(label_desc_split[4]) - assert feature_frame_num == label_frame_num + label_start = -1 + label_size = -1 + label_frame_num = feature_frame_num + if label_desc_path != "": + label_desc_split = label_desc_lines[i + 1].split() + label_start = int(label_desc_split[2]) + label_size = int(label_desc_split[3]) + label_frame_num = int(label_desc_split[4]) + assert feature_frame_num == label_frame_num if self._split_sentence_threshold == -1 or \ self._split_perturb == -1 or \ @@ -129,7 +140,7 @@ class SampleInfoBucket(object): SampleInfo(feature_bin_path, feature_start, feature_size, feature_frame_num, feature_dim, label_bin_path, label_start, label_size, - label_frame_num)) + label_frame_num, sample_name)) #split sentence else: cur_frame_pos = 0 @@ -150,13 +161,12 @@ class SampleInfoBucket(object): * feature_dim * 4, cur_frame_len * feature_dim * 4, cur_frame_len, feature_dim, label_bin_path, label_start + cur_frame_pos * 4, cur_frame_len * - 4, cur_frame_len)) + 4, cur_frame_len, sample_name)) remain_frame_num -= cur_frame_len cur_frame_pos += cur_frame_len if remain_frame_num <= 0: break - return sample_info_list @@ -192,7 +202,7 @@ class AsyncDataReader(object): def __init__(self, feature_file_list, - label_file_list, + label_file_list="", drop_frame_len=512, proc_num=10, sample_buffer_size=1024, @@ -221,16 +231,24 @@ class AsyncDataReader(object): def generate_bucket_list(self, is_shuffle): if self._block_info_list is None: block_feature_info_lines = open(self._feature_file_list).readlines() - block_label_info_lines = open(self._label_file_list).readlines() - assert len(block_feature_info_lines) == len(block_label_info_lines) self._block_info_list = [] - for i in xrange(0, len(block_feature_info_lines), 2): - block_info = (block_feature_info_lines[i], - block_feature_info_lines[i + 1], - block_label_info_lines[i], - block_label_info_lines[i + 1]) - self._block_info_list.append( - map(lambda line: line.strip(), block_info)) + if self._label_file_list != "": + block_label_info_lines = open(self._label_file_list).readlines() + assert len(block_feature_info_lines) == len( + block_label_info_lines) + for i in xrange(0, len(block_feature_info_lines), 2): + block_info = (block_feature_info_lines[i], + block_feature_info_lines[i + 1], + block_label_info_lines[i], + block_label_info_lines[i + 1]) + self._block_info_list.append( + map(lambda line: line.strip(), block_info)) + else: + for i in xrange(0, len(block_feature_info_lines), 2): + block_info = (block_feature_info_lines[i], + block_feature_info_lines[i + 1], "", "") + self._block_info_list.append( + map(lambda line: line.strip(), block_info)) if is_shuffle: self._rng.shuffle(self._block_info_list) @@ -310,19 +328,25 @@ class AsyncDataReader(object): sample_info.feature_dim, len(feature_bytes)) - label_bytes = read_bytes(sample_info.label_bin_path, - sample_info.label_start, - sample_info.label_size) - - assert sample_info.label_frame_num * 4 == len(label_bytes), ( - sample_info.label_bin_path, sample_info.label_array, - len(label_bytes)) - - label_array = struct.unpack('I' * sample_info.label_frame_num, - label_bytes) - label_data = np.array( - label_array, dtype='int64').reshape( - (sample_info.label_frame_num, 1)) + label_data = None + if sample_info.label_bin_path != "": + label_bytes = read_bytes(sample_info.label_bin_path, + sample_info.label_start, + sample_info.label_size) + + assert sample_info.label_frame_num * 4 == len( + label_bytes), (sample_info.label_bin_path, + sample_info.label_array, + len(label_bytes)) + + label_array = struct.unpack( + 'I' * sample_info.label_frame_num, label_bytes) + label_data = np.array( + label_array, dtype='int64').reshape( + (sample_info.label_frame_num, 1)) + else: + label_data = np.zeros( + (sample_info.label_frame_num, 1), dtype='int64') feature_frame_num = sample_info.feature_frame_num feature_dim = sample_info.feature_dim @@ -332,12 +356,11 @@ class AsyncDataReader(object): feature_data = np.array( feature_array, dtype='float32').reshape(( sample_info.feature_frame_num, sample_info.feature_dim)) - - sample_data = (feature_data, label_data) + sample_data = (feature_data, label_data, + sample_info.sample_name) for transformer in self._transformers: # @TODO(pkuyym) to make transfomer only accept feature_data sample_data = transformer.perform_trans(sample_data) - while order_id != out_order[0]: time.sleep(0.001) @@ -387,12 +410,14 @@ class AsyncDataReader(object): batch_feature = np.zeros((lod[-1], frame_dim), dtype="float32") batch_label = np.zeros((lod[-1], 1), dtype="int64") start = 0 + name_lst = [] for sample in batch_samples: frame_num = sample[0].shape[0] batch_feature[start:start + frame_num, :] = sample[0] batch_label[start:start + frame_num, :] = sample[1] start += frame_num - return (batch_feature, batch_label) + name_lst.append(sample[2]) + return (batch_feature, batch_label, name_lst) @suppress_complaints(verbose=self._verbose, notify=self._force_exit) def batch_assembling_task(sample_generator, batch_queue): @@ -402,16 +427,16 @@ class AsyncDataReader(object): batch_samples.append(sample) lod.append(lod[-1] + sample[0].shape[0]) if len(batch_samples) == batch_size: - (batch_feature, batch_label) = batch_to_ndarray( + (batch_feature, batch_label, name_lst) = batch_to_ndarray( batch_samples, lod) - batch_queue.put((batch_feature, batch_label, lod)) + batch_queue.put((batch_feature, batch_label, lod, name_lst)) batch_samples = [] lod = [0] if len(batch_samples) >= minimum_batch_size: - (batch_feature, batch_label) = batch_to_ndarray(batch_samples, - lod) - batch_queue.put((batch_feature, batch_label, lod)) + (batch_feature, batch_label, name_lst) = batch_to_ndarray( + batch_samples, lod) + batch_queue.put((batch_feature, batch_label, lod, name_lst)) batch_queue.put(EpochEndSignal()) diff --git a/fluid/DeepASR/data_utils/augmentor/tests/test_data_trans.py b/fluid/DeepASR/data_utils/augmentor/tests/test_data_trans.py index 157ab02eee0093fe5d683e642b3d18d842cb4e19..9f76a9f8590d5f148398c4ffaff77dc95421df83 100644 --- a/fluid/DeepASR/data_utils/augmentor/tests/test_data_trans.py +++ b/fluid/DeepASR/data_utils/augmentor/tests/test_data_trans.py @@ -22,7 +22,7 @@ class TestTransMeanVarianceNorm(unittest.TestCase): feature = np.zeros((2, 120), dtype="float32") feature.fill(1) trans = trans_mean_variance_norm.TransMeanVarianceNorm(self._file_path) - (feature1, label1) = trans.perform_trans((feature, None)) + (feature1, label1, name) = trans.perform_trans((feature, None, None)) (mean, var) = trans.get_mean_var() feature_flat1 = feature1.flatten() feature_flat = feature.flatten() @@ -70,7 +70,7 @@ class TestTransAddDelta(unittest.TestCase): feature[2, 0:40].fill(3) feature[3, 0:40].fill(4) trans = trans_add_delta.TransAddDelta() - (feature, label) = trans.perform_trans((feature, None)) + (feature, label, name) = trans.perform_trans((feature, None, None)) self.assertAlmostEqual(feature.shape[0], 4) self.assertAlmostEqual(feature.shape[1], 120) self.assertAlmostEqual(1.0, feature[0][0]) @@ -93,7 +93,7 @@ class TestTransSplict(unittest.TestCase): feature[i, :].fill(i) trans = trans_splice.TransSplice() - (feature, label) = trans.perform_trans((feature, None)) + (feature, label, name) = trans.perform_trans((feature, None, None)) self.assertEqual(feature.shape[1], 110) for i in xrange(8): diff --git a/fluid/DeepASR/data_utils/augmentor/trans_add_delta.py b/fluid/DeepASR/data_utils/augmentor/trans_add_delta.py index dc1a4fa45be38152eba773c35e67d0ad3e4a13cb..aa8062f87c932b76dd8a79db825d07e8be273857 100644 --- a/fluid/DeepASR/data_utils/augmentor/trans_add_delta.py +++ b/fluid/DeepASR/data_utils/augmentor/trans_add_delta.py @@ -32,9 +32,9 @@ class TransAddDelta(object): Args: sample(object,tuple): contain feature numpy and label numpy Returns: - (feature, label) + (feature, label, name) """ - (feature, label) = sample + (feature, label, name) = sample frame_dim = feature.shape[1] d_frame_dim = frame_dim * 3 head_filled = 5 @@ -64,7 +64,7 @@ class TransAddDelta(object): start * d_frame_dim + 2 * frame_dim, frame_dim, nframe, d_frame_dim) mat.shape = tmp_shape - return (mat[head_filled:mat.shape[0] - tail_filled, :], label) + return (mat[head_filled:mat.shape[0] - tail_filled, :], label, name) def _regress(self, data_in, start_in, data_out, start_out, size, n, step): """ regress diff --git a/fluid/DeepASR/data_utils/augmentor/trans_mean_variance_norm.py b/fluid/DeepASR/data_utils/augmentor/trans_mean_variance_norm.py index 5b541d426c61364639f7a9d9f50bd51a2c06efa5..9f91b726ea2bcd432340cd06a3cb9006cd5f83f4 100644 --- a/fluid/DeepASR/data_utils/augmentor/trans_mean_variance_norm.py +++ b/fluid/DeepASR/data_utils/augmentor/trans_mean_variance_norm.py @@ -53,9 +53,9 @@ class TransMeanVarianceNorm(object): Args: sample(object):input sample, contain feature numpy and label numpy Returns: - (feature, label) + (feature, label, name) """ - (feature, label) = sample + (feature, label, name) = sample shape = feature.shape assert len(shape) == 2 nfeature_len = shape[0] * shape[1] @@ -68,4 +68,4 @@ class TransMeanVarianceNorm(object): feature[ncur_idx:ncur_idx + self._nLen] = block ncur_idx += self._nLen feature = feature.reshape(shape) - return (feature, label) + return (feature, label, name) diff --git a/fluid/DeepASR/data_utils/augmentor/trans_splice.py b/fluid/DeepASR/data_utils/augmentor/trans_splice.py index 94f5258de316045d41999b26c6963f8487e9c55a..1fab3d6b442c1613f18d16fd0b0ee89464dbeb2c 100644 --- a/fluid/DeepASR/data_utils/augmentor/trans_splice.py +++ b/fluid/DeepASR/data_utils/augmentor/trans_splice.py @@ -30,9 +30,9 @@ class TransSplice(object): Args: sample(object): input sample(feature, label) Return: - (feature, label) + (feature, label, name) """ - (feature, label) = sample + (feature, label, name) = sample nframe_num = feature.shape[0] nframe_dim = feature.shape[1] nnew_frame_dim = nframe_dim * ( @@ -61,4 +61,4 @@ class TransSplice(object): np.copyto(ret[i * nnew_frame_dim:(i + 1) * nnew_frame_dim], mat[i * nframe_dim:i * nframe_dim + nnew_frame_dim]) ret = ret.reshape((nframe_num, nnew_frame_dim)) - return (ret, label) + return (ret, label, name) diff --git a/fluid/DeepASR/train.py b/fluid/DeepASR/train.py index 917807987f3a5fa79254f84c99309ef7bc1b4f1a..3908a550cdcf095057ea6ab0b89e07dcecda51f9 100644 --- a/fluid/DeepASR/train.py +++ b/fluid/DeepASR/train.py @@ -210,6 +210,7 @@ def train(args): # train data reader train_data_reader = reader.AsyncDataReader(args.train_feature_lst, args.train_label_lst, -1) + train_data_reader.set_transformers(ltrans) # train for pass_id in xrange(args.pass_num): @@ -218,7 +219,7 @@ def train(args): train_data_reader.batch_iterator(args.batch_size, args.minimum_batch_size)): # load_data - (features, labels, lod) = batch_data + (features, labels, lod, name_lst) = batch_data feature_t.set(features, place) feature_t.set_lod([lod]) label_t.set(labels, place)