def add_task_arguments(cls, parser: argparse.ArgumentParser): group = parser.add_argument_group(description="Task related") # NOTE(kamo): add_arguments(..., required=True) can't be used # to provide --print_config mode. Instead of it, do as # required = parser.get_default("required") group.add_argument( "--init", type=lambda x: str_or_none(x.lower()), default=None, help="The initialization method", choices=[ "chainer", "xavier_uniform", "xavier_normal", "kaiming_uniform", "kaiming_normal", None, ], ) group.add_argument( "--model_conf", action=NestedDictAction, default=get_default_kwargs(ESPnetEnhancementModel), help="The keyword arguments for model class.", ) group.add_argument( "--criterions", action=NestedDictAction, default=[ { "name": "si_snr", "conf": {}, "wrapper": "fixed_order", "wrapper_conf": {}, }, ], help="The criterions binded with the loss wrappers.", ) group = parser.add_argument_group(description="Preprocess related") group.add_argument( "--use_preprocessor", type=str2bool, default=False, help="Apply preprocessing to data or not", ) for class_choices in cls.class_choices_list: # Append --<name> and --<name>_conf. # e.g. --encoder and --encoder_conf class_choices.add_arguments(group)
def add_task_arguments(cls, parser: argparse.ArgumentParser): group = parser.add_argument_group(description="Task related") group.add_argument( "--num_spk", type=int_or_none, default=None, help= "The number fo speakers (for each recording) used in system training", ) group.add_argument( "--init", type=lambda x: str_or_none(x.lower()), default=None, help="The initialization method", choices=[ "chainer", "xavier_uniform", "xavier_normal", "kaiming_uniform", "kaiming_normal", None, ], ) group.add_argument( "--input_size", type=int_or_none, default=None, help="The number of input dimension of the feature", ) group.add_argument( "--model_conf", action=NestedDictAction, default=get_default_kwargs(ESPnetDiarizationModel), help="The keyword arguments for model class.", ) group = parser.add_argument_group(description="Preprocess related") group.add_argument( "--use_preprocessor", type=str2bool, default=True, help="Apply preprocessing to data or not", ) for class_choices in cls.class_choices_list: # Append --<name> and --<name>_conf. # e.g. --encoder and --encoder_conf class_choices.add_arguments(group)
def add_arguments(self, parser): parser.add_argument( f"--{self.name}", type=lambda x: str_or_none(x.lower()), default=self.default, choices=self.choices(), help=f"The {self.name} type", ) parser.add_argument( f"--{self.name}_conf", action=NestedDictAction, default=dict(), help=f"The keyword arguments for {self.name}", )
def add_task_arguments(cls, parser: argparse.ArgumentParser): group = parser.add_argument_group(description="Task related") # NOTE(kamo): add_arguments(..., required=True) can't be used # to provide --print_config mode. Instead of it, do as required = parser.get_default("required") group.add_argument( "--init", type=lambda x: str_or_none(x.lower()), default=None, help="The initialization method", choices=[ "chainer", "xavier_uniform", "xavier_normal", "kaiming_uniform", "kaiming_normal", None, ], ) group.add_argument( "--input_size", type=int_or_none, default=None, help="The number of input dimension of the feature", ) group.add_argument( "--model_conf", action=NestedDictAction, default=get_default_kwargs(HynetImgrModel), help="The keyword arguments for model class.", ) for class_choices in cls.class_choices_list: # Append --<name> and --<name>_conf. # e.g. --encoder and --encoder_conf class_choices.add_arguments(group)
def add_task_arguments(cls, parser: argparse.ArgumentParser): # NOTE(kamo): Use '_' instead of '-' to avoid confusion assert check_argument_types() group = parser.add_argument_group(description="Task related") # NOTE(kamo): add_arguments(..., required=True) can't be used # to provide --print_config mode. Instead of it, do as required = parser.get_default("required") required += ["token_list"] group.add_argument( "--token_list", type=str_or_none, default=None, help="A text mapping int-id to token", ) group.add_argument( "--init", type=lambda x: str_or_none(x.lower()), default=None, help="The initialization method", choices=[ "chainer", "xavier_uniform", "xavier_normal", "kaiming_uniform", "kaiming_normal", None, ], ) group.add_argument( "--model_conf", action=NestedDictAction, default=get_default_kwargs(ESPnetLanguageModel), help="The keyword arguments for model class.", ) group = parser.add_argument_group(description="Preprocess related") group.add_argument( "--use_preprocessor", type=str2bool, default=False, help="Apply preprocessing to data or not", ) group.add_argument( "--token_type", type=str, default="bpe", choices=["bpe", "char", "word"], help="", ) group.add_argument( "--bpemodel", type=str_or_none, default=None, help="The model file fo sentencepiece", ) parser.add_argument( "--non_linguistic_symbols", type=str_or_none, help="non_linguistic_symbols file path", ) for class_choices in cls.class_choices_list: # Append --<name> and --<name>_conf. # e.g. --encoder and --encoder_conf class_choices.add_arguments(group) assert check_return_type(parser) return parser
def add_task_arguments(cls, parser: argparse.ArgumentParser): group = parser.add_argument_group(description="Task related") # NOTE(kamo): add_arguments(..., required=True) can't be used # to provide --print_config mode. Instead of it, do as required = parser.get_default("required") required += ["token_list"] group.add_argument( "--token_list", type=str_or_none, default=None, help="A text mapping int-id to token", ) group.add_argument( "--init", type=lambda x: str_or_none(x.lower()), default=None, help="The initialization method", choices=[ "chainer", "xavier_uniform", "xavier_normal", "kaiming_uniform", "kaiming_normal", None, ], ) group.add_argument( "--input_size", type=int_or_none, default=None, help="The number of input dimension of the feature", ) group.add_argument( "--ctc_conf", action=NestedDictAction, default=get_default_kwargs(CTC), help="The keyword arguments for CTC class.", ) group.add_argument( "--asr_model_conf", action=NestedDictAction, default=get_default_kwargs(ESPnetASRModel), help="The keyword arguments for model class.", ) group.add_argument( "--enh_model_conf", action=NestedDictAction, default=get_default_kwargs(ESPnetEnhancementModel), help="The keyword arguments for model class.", ) group = parser.add_argument_group(description="Preprocess related") group.add_argument( "--use_preprocessor", type=str2bool, default=False, help="Apply preprocessing to data or not", ) group.add_argument( "--token_type", type=str, default="bpe", choices=["bpe", "char", "word", "phn"], help="The text will be tokenized " "in the specified level token", ) group.add_argument( "--bpemodel", type=str_or_none, default=None, help="The model file of sentencepiece", ) parser.add_argument( "--non_linguistic_symbols", type=str_or_none, help="non_linguistic_symbols file path", ) parser.add_argument( "--cleaner", type=str_or_none, choices=[None, "tacotron", "jaconv", "vietnamese"], default=None, help="Apply text cleaning", ) parser.add_argument( "--g2p", type=str_or_none, choices=[None, "g2p_en", "pyopenjtalk", "pyopenjtalk_kana"], default=None, help="Specify g2p method if --token_type=phn", ) for class_choices in cls.class_choices_list: # Append --<name> and --<name>_conf. # e.g. --encoder and --encoder_conf class_choices.add_arguments(group)
def add_task_arguments(cls, parser: argparse.ArgumentParser): group = parser.add_argument_group(description="Task related") # NOTE(kamo): add_arguments(..., required=True) can't be used # to provide --print_config mode. Instead of it, do as required = parser.get_default("required") required += ["src_token_list", "token_list"] group.add_argument( "--token_list", type=str_or_none, default=None, help="A text mapping int-id to token (for target language)", ) group.add_argument( "--src_token_list", type=str_or_none, default=None, help="A text mapping int-id to token (for source language)", ) group.add_argument( "--init", type=lambda x: str_or_none(x.lower()), default=None, help="The initialization method", choices=[ "chainer", "xavier_uniform", "xavier_normal", "kaiming_uniform", "kaiming_normal", None, ], ) group.add_argument( "--input_size", type=int_or_none, default=None, help="The number of input dimension of the feature", ) group.add_argument( "--ctc_conf", action=NestedDictAction, default=get_default_kwargs(CTC), help="The keyword arguments for CTC class.", ) group.add_argument( "--model_conf", action=NestedDictAction, default=get_default_kwargs(ESPnetSTModel), help="The keyword arguments for model class.", ) group = parser.add_argument_group(description="Preprocess related") group.add_argument( "--use_preprocessor", type=str2bool, default=True, help="Apply preprocessing to data or not", ) group.add_argument( "--token_type", type=str, default="bpe", choices=["bpe", "char", "word", "phn"], help="The target text will be tokenized " "in the specified level token", ) group.add_argument( "--src_token_type", type=str, default="bpe", choices=["bpe", "char", "word", "phn"], help="The source text will be tokenized " "in the specified level token", ) group.add_argument( "--bpemodel", type=str_or_none, default=None, help="The model file of sentencepiece (for target language)", ) group.add_argument( "--src_bpemodel", type=str_or_none, default=None, help="The model file of sentencepiece (for source language)", ) parser.add_argument( "--non_linguistic_symbols", type=str_or_none, help="non_linguistic_symbols file path", ) parser.add_argument( "--cleaner", type=str_or_none, choices=[None, "tacotron", "jaconv", "vietnamese"], default=None, help="Apply text cleaning", ) parser.add_argument( "--g2p", type=str_or_none, choices=g2p_choices, default=None, help="Specify g2p method if --token_type=phn", ) parser.add_argument( "--speech_volume_normalize", type=float_or_none, default=None, help="Scale the maximum amplitude to the given value.", ) parser.add_argument( "--rir_scp", type=str_or_none, default=None, help="The file path of rir scp file.", ) parser.add_argument( "--rir_apply_prob", type=float, default=1.0, help="THe probability for applying RIR convolution.", ) parser.add_argument( "--noise_scp", type=str_or_none, default=None, help="The file path of noise scp file.", ) parser.add_argument( "--noise_apply_prob", type=float, default=1.0, help="The probability applying Noise adding.", ) parser.add_argument( "--noise_db_range", type=str, default="13_15", help="The range of noise decibel level.", ) for class_choices in cls.class_choices_list: # Append --<name> and --<name>_conf. # e.g. --encoder and --encoder_conf class_choices.add_arguments(group)
def test_str_or_none(value: str, desired: Any): with pytest_raise_or_nothing(desired): assert str_or_none(value) == desired
def add_task_arguments(cls, parser: argparse.ArgumentParser): """Add Transducer task arguments. Args: cls: ASRTransducerTask object. parser: Transducer arguments parser. """ group = parser.add_argument_group(description="Task related") required = parser.get_default("required") required += ["token_list"] group.add_argument( "--token_list", type=str_or_none, default=None, help="A text mapping int-id to token", ) group.add_argument( "--input_size", type=int_or_none, default=None, help="The number of input dimension of the feature", ) group.add_argument( "--init", type=lambda x: str_or_none(x.lower()), default=None, help="The initialization method", choices=[ "chainer", "chainer_espnet1", "xavier_uniform", "xavier_normal", "kaiming_uniform", "kaiming_normal", None, ], ) group.add_argument( "--model_conf", action=NestedDictAction, default=get_default_kwargs(ESPnetASRTransducerModel), help="The keyword arguments for model class.", ) group.add_argument( "--encoder_conf", action=NestedDictAction, default={}, help="The keyword arguments for encoder class.", ) group.add_argument( "--joint_network_conf", action=NestedDictAction, default={}, help="The keyword arguments for joint network class.", ) group = parser.add_argument_group(description="Preprocess related") group.add_argument( "--use_preprocessor", type=str2bool, default=True, help="Apply preprocessing to data or not", ) group.add_argument( "--token_type", type=str, default="bpe", choices=["bpe", "char", "word", "phn"], help="The text will be tokenized " "in the specified level token", ) group.add_argument( "--bpemodel", type=str_or_none, default=None, help="The model file of sentencepiece", ) parser.add_argument( "--non_linguistic_symbols", type=str_or_none, help="non_linguistic_symbols file path", ) parser.add_argument( "--cleaner", type=str_or_none, choices=[None, "tacotron", "jaconv", "vietnamese"], default=None, help="Apply text cleaning", ) parser.add_argument( "--g2p", type=str_or_none, choices=g2p_choices, default=None, help="Specify g2p method if --token_type=phn", ) parser.add_argument( "--speech_volume_normalize", type=float_or_none, default=None, help="Scale the maximum amplitude to the given value.", ) parser.add_argument( "--rir_scp", type=str_or_none, default=None, help="The file path of rir scp file.", ) parser.add_argument( "--rir_apply_prob", type=float, default=1.0, help="The probability for applying RIR convolution.", ) parser.add_argument( "--noise_scp", type=str_or_none, default=None, help="The file path of noise scp file.", ) parser.add_argument( "--noise_apply_prob", type=float, default=1.0, help="The probability applying Noise adding.", ) parser.add_argument( "--noise_db_range", type=str, default="13_15", help="The range of noise decibel level.", ) for class_choices in cls.class_choices_list: # Append --<name> and --<name>_conf. # e.g. --decoder and --decoder_conf class_choices.add_arguments(group)
def add_task_arguments(cls, parser: argparse.ArgumentParser): # NOTE(kamo): Use '_' instead of '-' to avoid confusion assert check_argument_types() group = parser.add_argument_group(description="Task related") # NOTE(kamo): add_arguments(..., required=True) can't be used # to provide --print_config mode. Instead of it, do as required = parser.get_default("required") required += ["token_list"] group.add_argument( "--token_list", type=str_or_none, default=None, help="A text mapping int-id to token", ) group.add_argument( "--init", type=lambda x: str_or_none(x.lower()), default=None, help="The initialization method", choices=[ "chainer", "xavier_uniform", "xavier_normal", "kaiming_uniform", "kaiming_normal", None, ], ) group.add_argument( "--model_conf", action=NestedDictAction, default=get_default_kwargs(ESPnetLanguageModel), help="The keyword arguments for model class.", ) group = parser.add_argument_group(description="Preprocess related") group.add_argument( "--use_preprocessor", type=str2bool, default=True, help="Apply preprocessing to data or not", ) group.add_argument( "--token_type", type=str, default="bpe", choices=["bpe", "char", "word"], help="", ) group.add_argument( "--bpemodel", type=str_or_none, default=None, help="The model file fo sentencepiece", ) parser.add_argument( "--non_linguistic_symbols", type=str_or_none, help="non_linguistic_symbols file path", ) parser.add_argument( "--cleaner", type=str_or_none, choices=[None, "tacotron", "jaconv", "vietnamese"], default=None, help="Apply text cleaning", ) parser.add_argument( "--g2p", type=str_or_none, choices=[ None, "g2p_en", "g2p_en_no_space", "pyopenjtalk", "pyopenjtalk_kana", "pyopenjtalk_accent", "pyopenjtalk_accent_with_pause", "pypinyin_g2p", "pypinyin_g2p_phone", "espeak_ng_arabic", "espeak_ng_german", "espeak_ng_french", "espeak_ng_spanish", "espeak_ng_russian", "g2pk", "g2pk_no_space", ], default=None, help="Specify g2p method if --token_type=phn", ) for class_choices in cls.class_choices_list: # Append --<name> and --<name>_conf. # e.g. --encoder and --encoder_conf class_choices.add_arguments(group) assert check_return_type(parser) return parser
def add_task_arguments(cls, parser: argparse.ArgumentParser): group = parser.add_argument_group(description="Task related") # NOTE(kamo): add_arguments(..., required=True) can't be used # to provide --print_config mode. Instead of it, do as required = parser.get_default("required") group.add_argument( "--model_conf", action=NestedDictAction, default=get_default_kwargs(CepNet), help="The keyword arguments for model class.", ) group.add_argument( "--init", type=lambda x: str_or_none(x.lower()), default=None, help="The initialization method", choices=[ "chainer", "xavier_uniform", "xavier_normal", "kaiming_uniform", "kaiming_normal", None, ], ) group.add_argument( "--input_size", type=int_or_none, default=None, help="The number of input dimension of the feature", ) group = parser.add_argument_group(description="Preprocess related") group.add_argument( "--use_preprocessor", type=str2bool, default=True, help="Apply preprocessing to data or not", ) parser.add_argument( "--speech_volume_normalize", type=float_or_none, default=None, help="Scale the maximum amplitude to the given value.", ) parser.add_argument( "--rir_scp", type=str_or_none, default=None, help="The file path of rir scp file.", ) parser.add_argument( "--rir_apply_prob", type=float, default=1.0, help="THe probability for applying RIR convolution.", ) parser.add_argument( "--noise_scp", type=str_or_none, default=None, help="The file path of noise scp file.", ) parser.add_argument( "--noise_apply_prob", type=float, default=1.0, help="The probability applying Noise adding.", ) parser.add_argument( "--noise_db_range", type=str, default="13_15", help="The range of noise decibel level.", ) for class_choices in cls.class_choices_list: # Append --<name> and --<name>_conf. # e.g. --encoder and --encoder_conf class_choices.add_arguments(group)
def add_task_arguments(cls, parser: argparse.ArgumentParser): group = parser.add_argument_group(description="Task related") required = parser.get_default("required") required += ["token_list"] group.add_argument("--train_pseudo_shape_file", type=str, action="append", default=[]) group.add_argument( "--train_pseudo_data_path_and_name_and_type", type=str2triple_str, action="append", default=[], help= "Give three words splitted by comma. It's used for the training data.", ) group.add_argument( "--token_list", type=str_or_none, default=None, help="A text mapping int-id to token", ) group.add_argument( "--pis_ratio", type=float, default=1, help="The multi-iteration ratio for SSL", ) group.add_argument( "--init", type=lambda x: str_or_none(x.lower()), default=None, help="The initialization method", choices=[ "chainer", "xavier_uniform", "xavier_normal", "kaiming_uniform", "kaiming_normal", None, ], ) group.add_argument( "--input_size", type=int_or_none, default=None, help="The number of input dimension of the feature", ) group.add_argument( "--ctc_conf", action=NestedDictAction, default=get_default_kwargs(CTC), help="The keyword arguments for CTC class.", ) group.add_argument( "--model_conf", action=NestedDictAction, default=get_default_kwargs(ESPnetASRModel), help="The keyword arguments for model class.", ) group = parser.add_argument_group(description="Preprocess related") group.add_argument( "--use_preprocessor", type=str2bool, default=True, help="Apply preprocessing to data or not", ) group.add_argument( "--token_type", type=str, default="bpe", choices=["bpe", "char", "word", "phn"], help="The text will be tokenized " "in the specified level token", ) group.add_argument( "--bpemodel", type=str_or_none, default=None, help="The model file of sentencepiece", ) parser.add_argument( "--non_linguistic_symbols", type=str_or_none, help="non_linguistic_symbols file path", ) parser.add_argument( "--cleaner", type=str_or_none, choices=[None, "tacotron", "jaconv", "vietnamese"], default=None, help="Apply text cleaning", ) parser.add_argument( "--g2p", type=str_or_none, choices=[None, "g2p_en", "pyopenjtalk", "pyopenjtalk_kana"], default=None, help="Specify g2p method if --token_type=phn", ) parser.add_argument( "--speech_volume_normalize", type=float_or_none, default=None, help="Scale the maximum amplitude to the given value.", ) parser.add_argument( "--rir_scp", type=str_or_none, default=None, help="The file path of rir scp file.", ) parser.add_argument( "--rir_apply_prob", type=float, default=1.0, help="THe probability for applying RIR convolution.", ) parser.add_argument( "--noise_scp", type=str_or_none, default=None, help="The file path of noise scp file.", ) parser.add_argument( "--noise_apply_prob", type=float, default=1.0, help="The probability applying Noise adding.", ) parser.add_argument( "--noise_db_range", type=str, default="13_15", help="The range of noise decibel level.", ) for class_choices in cls.class_choices_list: # Append --<name> and --<name>_conf. # e.g. --encoder and --encoder_conf class_choices.add_arguments(group)
def add_task_arguments(cls, parser: argparse.ArgumentParser): group = parser.add_argument_group(description="Task related") group.add_argument( "--token_list", type=str_or_none, default=None, help="A text mapping int-id to token", ) group.add_argument( "--src_token_list", type=str_or_none, default=None, help="A text mapping int-id to token (for source language)", ) group.add_argument( "--init", type=lambda x: str_or_none(x.lower()), default=None, help="The initialization method", choices=[ "chainer", "xavier_uniform", "xavier_normal", "kaiming_uniform", "kaiming_normal", None, ], ) group.add_argument( "--input_size", type=int_or_none, default=None, help="The number of input dimension of the feature", ) group.add_argument( "--ctc_conf", action=NestedDictAction, default=get_default_kwargs(CTC), help="The keyword arguments for CTC class.", ) group.add_argument( "--enh_criterions", action=NestedDictAction, default=[ { "name": "si_snr", "conf": {}, "wrapper": "fixed_order", "wrapper_conf": {}, }, ], help="The criterions binded with the loss wrappers.", ) group.add_argument( "--diar_num_spk", type=int_or_none, default=None, help="The number of speakers (for each recording) for diar submodel class", ) group.add_argument( "--diar_input_size", type=int_or_none, default=None, help="The number of input dimension of the feature", ) group.add_argument( "--enh_model_conf", action=NestedDictAction, default=get_default_kwargs(ESPnetEnhancementModel), help="The keyword arguments for enh submodel class.", ) group.add_argument( "--asr_model_conf", action=NestedDictAction, default=get_default_kwargs(ESPnetASRModel), help="The keyword arguments for asr submodel class.", ) group.add_argument( "--st_model_conf", action=NestedDictAction, default=get_default_kwargs(ESPnetEnhancementModel), help="The keyword arguments for st submodel class.", ) group.add_argument( "--diar_model_conf", action=NestedDictAction, default=get_default_kwargs(ESPnetDiarizationModel), help="The keyword arguments for diar submodel class.", ) group.add_argument( "--subtask_series", type=str, nargs="+", default=("enh", "asr"), choices=["enh", "asr", "st", "diar"], help="The series of subtasks in the pipeline.", ) group.add_argument( "--model_conf", action=NestedDictAction, default=get_default_kwargs(ESPnetEnhS2TModel), help="The keyword arguments for model class.", ) group = parser.add_argument_group(description="Preprocess related") group.add_argument( "--use_preprocessor", type=str2bool, default=False, help="Apply preprocessing to data or not", ) group.add_argument( "--token_type", type=str, default="bpe", choices=["bpe", "char", "word", "phn"], help="The text will be tokenized " "in the specified level token", ) group.add_argument( "--bpemodel", type=str_or_none, default=None, help="The model file of sentencepiece", ) group.add_argument( "--src_token_type", type=str, default="bpe", choices=["bpe", "char", "word", "phn"], help="The source text will be tokenized " "in the specified level token", ) group.add_argument( "--src_bpemodel", type=str_or_none, default=None, help="The model file of sentencepiece (for source language)", ) group.add_argument( "--non_linguistic_symbols", type=str_or_none, help="non_linguistic_symbols file path", ) group.add_argument( "--cleaner", type=str_or_none, choices=[None, "tacotron", "jaconv", "vietnamese"], default=None, help="Apply text cleaning", ) group.add_argument( "--g2p", type=str_or_none, choices=g2p_choices, default=None, help="Specify g2p method if --token_type=phn", ) for class_choices in cls.class_choices_list: # Append --<name> and --<name>_conf. # e.g. --encoder and --encoder_conf class_choices.add_arguments(group)
def add_task_arguments(cls, parser: argparse.ArgumentParser): group = parser.add_argument_group(description="Task related") # NOTE(kamo): add_arguments(..., required=True) can't be used # to provide --print_config mode. Instead of it, do as # required = parser.get_default("required") group.add_argument( "--init", type=lambda x: str_or_none(x.lower()), default=None, help="The initialization method", choices=[ "chainer", "xavier_uniform", "xavier_normal", "kaiming_uniform", "kaiming_normal", None, ], ) group.add_argument( "--model_conf", action=NestedDictAction, default=get_default_kwargs(ESPnetEnhancementModel), help="The keyword arguments for model class.", ) group.add_argument( "--criterions", action=NestedDictAction, default=[ { "name": "si_snr", "conf": {}, "wrapper": "fixed_order", "wrapper_conf": {}, }, ], help="The criterions binded with the loss wrappers.", ) group = parser.add_argument_group(description="Preprocess related") group.add_argument( "--use_preprocessor", type=str2bool, default=False, help="Whether to apply preprocessing to data or not", ) group.add_argument( "--speech_volume_normalize", type=str_or_none, default=None, help="Scale the maximum amplitude to the given value or range. " "e.g. --speech_volume_normalize 1.0 scales it to 1.0.\n" "--speech_volume_normalize 0.5_1.0 scales it to a random number in " "the range [0.5, 1.0)", ) group.add_argument( "--rir_scp", type=str_or_none, default=None, help="The file path of rir scp file.", ) group.add_argument( "--rir_apply_prob", type=float, default=1.0, help="THe probability for applying RIR convolution.", ) group.add_argument( "--noise_scp", type=str_or_none, default=None, help="The file path of noise scp file.", ) group.add_argument( "--noise_apply_prob", type=float, default=1.0, help="The probability applying Noise adding.", ) group.add_argument( "--noise_db_range", type=str, default="13_15", help="The range of signal-to-noise ratio (SNR) level in decibel.", ) group.add_argument( "--short_noise_thres", type=float, default=0.5, help= "If len(noise) / len(speech) is smaller than this threshold during " "dynamic mixing, a warning will be displayed.", ) group.add_argument( "--use_reverberant_ref", type=str2bool, default=False, help="Whether to use reverberant speech references " "instead of anechoic ones", ) group.add_argument( "--num_spk", type=int, default=1, help="Number of speakers in the input signal.", ) group.add_argument( "--num_noise_type", type=int, default=1, help="Number of noise types.", ) group.add_argument( "--sample_rate", type=int, default=8000, help="Sampling rate of the data (in Hz).", ) group.add_argument( "--force_single_channel", type=str2bool, default=False, help="Whether to force all data to be single-channel.", ) for class_choices in cls.class_choices_list: # Append --<name> and --<name>_conf. # e.g. --encoder and --encoder_conf class_choices.add_arguments(group)
def add_task_arguments(cls, parser: argparse.ArgumentParser): group = parser.add_argument_group(description="Task related") # NOTE(kamo): add_arguments(..., required=True) can't be used # to provide --print_config mode. Instead of it, do as required = parser.get_default("required") required += ["token_list"] group.add_argument( "--token_list", type=str_or_none, default=None, help="A text mapping int-id to token", ) group.add_argument( "--init", type=lambda x: str_or_none(x.lower()), default=None, help="The initialization method", choices=[ "chainer", "xavier_uniform", "xavier_normal", "kaiming_uniform", "kaiming_normal", None, ], ) group.add_argument( "--input_size", type=int_or_none, default=None, help="The number of input dimension of the feature", ) group.add_argument( "--ctc_conf", action=NestedDictAction, default=get_default_kwargs(CTC), help="The keyword arguments for CTC class.", ) group.add_argument( "--model_conf", action=NestedDictAction, default=get_default_kwargs(ESPnetASRModel), help="The keyword arguments for model class.", ) group.add_argument( "--use_curriculum", type=str, default=False, help="Use curriculum learning algorithms.", ) group.add_argument( "--curriculum_algo", type=str, default="exp3s", help="MAB algorithm to use for curriculum learning", ) group.add_argument( "--gain_type", type=str, default="PG", help="Loss-based gain for Curriculum Learning. Prediction Gain (PG) or Self-Prediction Gain (SPG)", ) group.add_argument("--refill_task", type=bool, default=1, help="Refill task after the task is exhausted") group.add_argument("--gen_log_dir", type=str, default='curriculum_log', help="Directory to store curriculum generator logs") group = parser.add_argument_group(description="Preprocess related") group.add_argument( "--use_preprocessor", type=str2bool, default=True, help="Apply preprocessing to data or not", ) group.add_argument( "--token_type", type=str, default="bpe", choices=["bpe", "char", "word", "phn"], help="The text will be tokenized " "in the specified level token", ) group.add_argument( "--bpemodel", type=str_or_none, default=None, help="The model file of sentencepiece", ) parser.add_argument( "--non_linguistic_symbols", type=str_or_none, help="non_linguistic_symbols file path", ) parser.add_argument( "--cleaner", type=str_or_none, choices=[None, "tacotron", "jaconv", "vietnamese"], default=None, help="Apply text cleaning", ) parser.add_argument( "--g2p", type=str_or_none, choices=[None, "g2p_en", "pyopenjtalk", "pyopenjtalk_kana"], default=None, help="Specify g2p method if --token_type=phn", ) parser.add_argument( "--speech_volume_normalize", type=float_or_none, default=None, help="Scale the maximum amplitude to the given value.", ) parser.add_argument( "--rir_scp", type=str_or_none, default=None, help="The file path of rir scp file.", ) parser.add_argument( "--rir_apply_prob", type=float, default=1.0, help="THe probability for applying RIR convolution.", ) parser.add_argument( "--noise_scp", type=str_or_none, default=None, help="The file path of noise scp file.", ) parser.add_argument( "--noise_apply_prob", type=float, default=1.0, help="The probability applying Noise adding.", ) parser.add_argument( "--noise_db_range", type=str, default="13_15", help="The range of noise decibel level.", ) for class_choices in cls.class_choices_list: # Append --<name> and --<name>_conf. # e.g. --encoder and --encoder_conf class_choices.add_arguments(group)