problem when training

[LionnelBall]

when training the demo, I encountered this problem:

== Train voice (proc no. 1 (word_splitter)) == <Tokenisers.RegexTokeniser object at 0x7f6bc78bc400> ['call', 'class', 'delattr', 'dict', 'dir', 'doc', 'eq', 'format', 'ge', 'getattribute', 'gt', 'hash', 'init', 'init_subclass', 'le', 'lt', 'module', 'ne', 'new', 'reduce', 'reduce_ex', 'repr', 'setattr', 'sizeof', 'str', 'subclasshook', 'weakref', 'add_safetext', 'add_terminal_tokens', 'add_token_classes', 'apply_to_utt', 'apply_to_utts_which_have', 'child_node_type', 'class_attribute', 'class_patterns', 'classify_token', 'component_path', 'default_class', 'do_training', 'get_location', 'get_training_dir', 'language', 'lowercase_safetext', 'parallelisable', 'process_utterance', 'processor_name', 'regex', 'reuse_component', 'safetext_attribute', 'safetext_token', 'split_attribute', 'split_pattern', 'splitting_function', 'target_nodes', 'train', 'train_on_utts_which_have', 'trained', 'verify', 'voice_resources'] <class 'Tokenisers.RegexTokeniser'> False Train processor word_splitter RegexTokeniser requires no training Applying processor word_splitter ppppppppppppppppppppppppppppp

== Train voice (proc no. 2 (segment_adder)) == <Phonetisers.NaivePhonetiser object at 0x7f6bc78bc8d0> ['call', 'class', 'delattr', 'dict', 'dir', 'doc', 'eq', 'format', 'ge', 'getattribute', 'gt', 'hash', 'init', 'init_subclass', 'le', 'lt', 'module', 'ne', 'new', 'reduce', 'reduce_ex', 'repr', 'setattr', 'sizeof', 'str', 'subclasshook', 'weakref', 'apply_to_utt', 'apply_to_utts_which_have', 'child_node_type', 'class_attribute', 'component_path', 'do_training', 'get_location', 'get_phonetic_segments', 'get_training_dir', 'language', 'output_attribute', 'parallelisable', 'possible_pause_classes', 'probable_pause_classes', 'process_utterance', 'processor_name', 'reuse_component', 'target_attribute', 'target_nodes', 'train', 'train_on_utts_which_have', 'trained', 'verify', 'voice_resources', 'word_classes'] <class 'Phonetisers.NaivePhonetiser'> False Train processor segment_adder NaivePhonetiser requires no training Applying processor segment_adder uuuuuuuuuuuuuuuuuuuuuuuuuuuuu

== Train voice (proc no. 3 (word_vector_tagger)) == <VSMTagger.VSMTagger object at 0x7f6bc78bc3c8> ['call', 'class', 'delattr', 'dict', 'dir', 'doc', 'eq', 'format', 'ge', 'getattribute', 'gt', 'hash', 'init', 'init_subclass', 'le', 'lt', 'module', 'ne', 'new', 'reduce', 'reduce_ex', 'repr', 'setattr', 'sizeof', 'str', 'subclasshook', 'weakref', '_process_text_line', 'apply_to_utt', 'apply_to_utts_which_have', 'component_path', 'context_size', 'discretisation_method', 'do_training', 'get_location', 'get_training_dir', 'input_attribute', 'language', 'n_discretisation_bins', 'norm_counts', 'output_attribute_stem', 'parallelisable', 'process_utterance', 'processor_name', 'rank', 'replace_whitespace', 'reuse_component', 'svd_algorithm', 'table_file', 'target_nodes', 'tokenisation_pattern', 'train', 'train_on_utts_which_have', 'trained', 'unseen_method', 'verify', 'voice_resources', 'vsm'] <class 'VSMTagger.VSMTagger'> True Applying processor word_vector_tagger p<Utterance.Utterance object at 0x7f6d0904b588> //token[@token_class='word'] [] Traceback (most recent call last): File "./scripts/train.py", line 148, in main_work() File "./scripts/train.py", line 83, in main_work train(opts, dirs) File "./scripts/train.py", line 122, in train voice.train(corpus) File "/home/chenliang/1_code/2_TTS/Ossian/scripts/main/Voice.py", line 378, in train processor.apply_to_utt(utterance, voice_mode=self.run_mode) File "/home/chenliang/1_code/2_TTS/Ossian/scripts/processors/UtteranceProcessor.py", line 222, in apply_to_utt self.process_utterance(utterance) File "/home/chenliang/1_code/2_TTS/Ossian/scripts/processors/VSMTagger.py", line 76, in processutterance kwargs={"field": "dim%s"%(i)}) File "/home/chenliang/1_code/2_TTS/Ossian/scripts/util/NodeProcessors.py", line 35, in enrich_nodes assert len(nodes) > 0 AssertionError

Does anyone know why this happend? How to solve it? Thank you for helping.

[A-d-DASARE]

Did you get help? I am also facing same error. For one data set it works. For other it throws up error.