| | |
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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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| | | with open(args.token_list) as f: |
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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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| | | # Overwriting token_list to keep it as "portable". |
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| | | args.token_list = list(token_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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| | | elif isinstance(args.token_list, (tuple, list)): |
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| | | token_list = list(args.token_list) |
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| | | else: |
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| | | vocab_size = None |
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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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| | | |
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| | | # frontend |
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| | | if args.input_size is None: |
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| | |
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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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| | | |
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| | | if args.model_name == "sond": |
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| | | if args.model == "sond": |
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| | | # data augmentation for spectrogram |
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| | | if args.specaug is not None: |
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| | | specaug_class = specaug_choices.get_class(args.specaug) |
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| | |
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| | | |
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| | | # decoder |
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| | | decoder_class = decoder_choices.get_class(args.decoder) |
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| | | decoder = decoder_class( |
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| | | vocab_size=vocab_size, |
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| | | encoder_output_size=encoder.output_size(), |
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| | | **args.decoder_conf, |
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| | | ) |
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| | | decoder = decoder_class(**args.decoder_conf) |
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| | | |
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| | | # logger aggregator |
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| | | if getattr(args, "label_aggregator", None) is not None: |
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