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| | | from typeguard import check_return_type |
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| | | from funasr.datasets.collate_fn import CommonCollateFn |
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| | | from funasr.datasets.preprocessor import MutliTokenizerCommonPreprocessor |
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| | | from funasr.datasets.preprocessor import PuncTrainTokenizerCommonPreprocessor |
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| | | from funasr.punctuation.abs_model import AbsPunctuation |
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| | | from funasr.punctuation.espnet_model import ESPnetPunctuationModel |
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| | | from funasr.punctuation.target_delay_transformer import TargetDelayTransformer |
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| | | from funasr.punctuation.vad_realtime_transformer import VadRealtimeTransformer |
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| | | from funasr.tasks.abs_task import AbsTask |
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| | | from funasr.text.phoneme_tokenizer import g2p_choices |
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| | | from funasr.torch_utils.initialize import initialize |
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| | | |
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| | | punc_choices = ClassChoices( |
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| | | "punctuation", |
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| | | classes=dict( |
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| | | target_delay=TargetDelayTransformer, |
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| | | ), |
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| | | classes=dict(target_delay=TargetDelayTransformer, vad_realtime=VadRealtimeTransformer), |
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| | | type_check=AbsPunctuation, |
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| | | default="TargetDelayTransformer", |
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| | | default="target_delay", |
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| | | ) |
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| | | |
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| | | |
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| | |
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| | | # NOTE(kamo): add_arguments(..., required=True) can't be used |
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| | | # to provide --print_config mode. Instead of it, do as |
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| | | required = parser.get_default("required") |
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| | | #import pdb;pdb.set_trace() |
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| | | #required += ["token_list"] |
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| | | |
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| | | group.add_argument( |
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| | | "--token_list", |
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| | |
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| | | bpemodels = [args.bpemodel, args.bpemodel] |
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| | | text_names = ["text", "punc"] |
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| | | if args.use_preprocessor: |
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| | | retval = MutliTokenizerCommonPreprocessor( |
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| | | retval = PuncTrainTokenizerCommonPreprocessor( |
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| | | train=train, |
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| | | token_type=token_types, |
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| | | token_list=token_lists, |
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| | |
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| | | def optional_data_names( |
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| | | cls, train: bool = True, inference: bool = False |
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| | | ) -> Tuple[str, ...]: |
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| | | retval = () |
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| | | retval = ("vad",) |
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| | | return retval |
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| | | |
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| | | @classmethod |
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| | |
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| | | args.token_list = token_list.copy() |
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| | | if isinstance(args.punc_list, str): |
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| | | with open(args.punc_list, encoding="utf-8") as f2: |
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| | | punc_list = [line.rstrip() for line in f2] |
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| | | pairs = [line.rstrip().split(":") for line in f2] |
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| | | punc_list = [pair[0] for pair in pairs] |
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| | | punc_weight_list = [float(pair[1]) for pair in pairs] |
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| | | args.punc_list = punc_list.copy() |
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| | | elif isinstance(args.punc_list, list): |
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| | | # This is in the inference code path. |
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| | | punc_list = args.punc_list.copy() |
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| | | punc_weight_list = [1] * len(punc_list) |
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| | | if isinstance(args.token_list, (tuple, list)): |
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| | | token_list = args.token_list.copy() |
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| | | else: |
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| | | |
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| | | # 2. Build ESPnetModel |
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| | | # Assume the last-id is sos_and_eos |
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| | | model = ESPnetPunctuationModel(punc_model=punc, vocab_size=vocab_size, **args.model_conf) |
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| | | if "punc_weight" in args.model_conf: |
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| | | args.model_conf.pop("punc_weight") |
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| | | model = ESPnetPunctuationModel(punc_model=punc, vocab_size=vocab_size, punc_weight=punc_weight_list, **args.model_conf) |
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| | | |
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| | | # FIXME(kamo): Should be done in model? |
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| | | # 3. Initialize |
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