| | |
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| | | |
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| | | def build_diar_model(args): |
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| | | # token_list |
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| | | if args.token_list is not None: |
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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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| | |
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| | | raise RuntimeError("token_list must be str or list") |
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| | | vocab_size = len(token_list) |
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| | | logging.info(f"Vocabulary size: {vocab_size}") |
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| | | else: |
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| | | token_list = None |
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| | | vocab_size = None |
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| | | |
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| | | # frontend |
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| | | if args.input_size is None: |
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| | |
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| | | frontend = frontend_class(cmvn_file=args.cmvn_file, **args.frontend_conf) |
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| | | else: |
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| | | frontend = frontend_class(**args.frontend_conf) |
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| | | input_size = frontend.output_size() |
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| | | else: |
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| | | args.frontend = None |
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| | | args.frontend_conf = {} |
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| | | frontend = None |
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| | | input_size = args.input_size |
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| | | |
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| | | # encoder |
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| | | encoder_class = encoder_choices.get_class(args.encoder) |
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| | | encoder = encoder_class(input_size=input_size, **args.encoder_conf) |
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| | | encoder = encoder_class(**args.encoder_conf) |
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| | | |
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| | | if args.model == "sond": |
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| | | # data augmentation for spectrogram |
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| | |
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| | | **args.model_conf, |
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| | | ) |
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| | | |
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| | | elif args.model_name == "eend_ola": |
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| | | elif args.model == "eend_ola": |
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| | | # encoder-decoder attractor |
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| | | encoder_decoder_attractor_class = encoder_decoder_attractor_choices.get_class(args.encoder_decoder_attractor) |
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| | | encoder_decoder_attractor = encoder_decoder_attractor_class(**args.encoder_decoder_attractor_conf) |
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