mirror of
https://github.com/modelscope/FunASR
synced 2025-09-15 14:48:36 +08:00
69 lines
2.2 KiB
Python
69 lines
2.2 KiB
Python
import logging
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from funasr.models.target_delay_transformer import TargetDelayTransformer
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from funasr.models.vad_realtime_transformer import VadRealtimeTransformer
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from funasr.torch_utils.initialize import initialize
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from funasr.train.abs_model import PunctuationModel
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from funasr.train.class_choices import ClassChoices
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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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vad_realtime=VadRealtimeTransformer
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),
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default="target_delay",
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)
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model_choices = ClassChoices(
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"model",
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classes=dict(
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punc=PunctuationModel,
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),
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default="punc",
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)
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class_choices_list = [
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# --punc and --punc_conf
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punc_choices,
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# --model and --model_conf
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model_choices
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]
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def build_punc_model(args):
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# token_list and punc list
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if isinstance(args.token_list, str):
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with open(args.token_list, encoding="utf-8") as f:
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token_list = [line.rstrip() for line in f]
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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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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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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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raise RuntimeError("token_list must be str or dict")
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vocab_size = len(token_list)
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punc_size = len(punc_list)
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logging.info(f"Vocabulary size: {vocab_size}")
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# punc
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punc_class = punc_choices.get_class(args.punctuation)
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punc = punc_class(vocab_size=vocab_size, punc_size=punc_size, **args.punctuation_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 = PunctuationModel(punc_model=punc, vocab_size=vocab_size, punc_weight=punc_weight_list, **args.model_conf)
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# initialize
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if args.init is not None:
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initialize(model, args.init)
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return model
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