cCi'SrSSKJr SSKJr SSKJr \R"\5r "SS\5r "SS \5r S S/r g ) zParakeet model configuration.)Union)PretrainedConfig)loggingcb^\rSrSrSrSrS/rSU4SjjrSrU=r $)ParakeetEncoderConfiga  This is the configuration class to store the configuration of a [`ParakeetEncoder`]. It is used to instantiate a `ParakeetEncoder` model according to the specified arguments, defining the model architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: hidden_size (`int`, *optional*, defaults to 1024): Dimension of the layers and the hidden states. num_hidden_layers (`int`, *optional*, defaults to 24): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 8): Number of attention heads for each attention layer in the Transformer encoder. intermediate_size (`int`, *optional*, defaults to 4096): Dimension of the "intermediate" (often named feed-forward) layer in the Transformer encoder. hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): The non-linear activation function (function or string) in the encoder and pooler. attention_bias (`bool`, *optional*, defaults to `True`): Whether to use bias in the attention layers. conv_kernel_size (`int`, *optional*, defaults to 9): The kernel size of the convolution layers in the Conformer block. subsampling_factor (`int`, *optional*, defaults to 8): The factor by which the input sequence is subsampled. subsampling_conv_channels (`int`, *optional*, defaults to 256): The number of channels in the subsampling convolution layers. num_mel_bins (`int`, *optional*, defaults to 80): Number of mel features. subsampling_conv_kernel_size (`int`, *optional*, defaults to 3): The kernel size of the subsampling convolution layers. subsampling_conv_stride (`int`, *optional*, defaults to 2): The stride of the subsampling convolution layers. dropout (`float`, *optional*, defaults to 0.1): The dropout ratio for all fully connected layers in the embeddings, encoder, and pooler. dropout_positions (`float`, *optional*, defaults to 0.0): The dropout ratio for the positions in the input sequence. layerdrop (`float`, *optional*, defaults to 0.1): The dropout ratio for the layers in the encoder. activation_dropout (`float`, *optional*, defaults to 0.1): The dropout ratio for activations inside the fully connected layer. attention_dropout (`float`, *optional*, defaults to 0.1): The dropout ratio for the attention layers. max_position_embeddings (`int`, *optional*, defaults to 5000): The maximum sequence length that this model might ever be used with. scale_input (`bool`, *optional*, defaults to `True`): Whether to scale the input embeddings. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. Example: ```python >>> from transformers import ParakeetEncoderModel, ParakeetEncoderConfig >>> # Initializing a `ParakeetEncoder` configuration >>> configuration = ParakeetEncoderConfig() >>> # Initializing a model from the configuration >>> model = ParakeetEncoderModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ``` This configuration class is based on the ParakeetEncoder architecture from NVIDIA NeMo. You can find more details and pre-trained models at [nvidia/parakeet-ctc-1.1b](https://huggingface.co/nvidia/parakeet-ctc-1.1b). parakeet_encoderpast_key_valuesc `>[TU]"S0UD6 XlX lX0lX0lX@lXPlX`lUS- S-S:wa[SU35eXpl Xl Xl Xl XlXlXlXlXlUUlUUlUUlUUlUUlg)Nrz"conv_kernel_size must be odd, got )super__init__ hidden_sizenum_hidden_layersnum_attention_headsnum_key_value_headsintermediate_size hidden_actattention_bias ValueErrorconv_kernel_sizesubsampling_conv_kernel_sizesubsampling_conv_stridesubsampling_factorsubsampling_conv_channels num_mel_binsdropoutdropout_positions layerdropactivation_dropoutattention_dropoutmax_position_embeddings scale_inputinitializer_range)selfrrrrrrrrrrrrr r!r"r#r$r%r&r'kwargs __class__s m/home/james-whalen/.local/lib/python3.13/site-packages/transformers/models/parakeet/configuration_parakeet.pyrParakeetEncoderConfig.__init__as0   '!2#6 #6 !2$, q A % *ABRASTU U 0,H)'>$"4)B&( !2""4!2'>$&!2)r#rr$rr r!rrr'rr"r%rrrrr&rrrr)isiluT r0Prr皙?gr5r5r5iTg{Gz?) __name__ __module__ __qualname____firstlineno____doc__ model_typekeys_to_ignore_at_inferencer__static_attributes__ __classcell__r*s@r+rrsbAF$J#4"5"%%& ! $+5353r-rcp^\rSrSrSrSrS\0rSS\\ \44U4Sjjjr \ S\4Sj5r Sr U=r$) ParakeetCTCConfigag This is the configuration class to store the configuration of a [`ParakeetForCTC`]. It is used to instantiate a Parakeet CTC model according to the specified arguments, defining the model architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 1025): Vocabulary size of the model. ctc_loss_reduction (`str`, *optional*, defaults to `"mean"`): Specifies the reduction to apply to the output of `torch.nn.CTCLoss`. Only relevant when training an instance of [`ParakeetForCTC`]. ctc_zero_infinity (`bool`, *optional*, defaults to `True`): Whether to zero infinite losses and the associated gradients of `torch.nn.CTCLoss`. Infinite losses mainly occur when the inputs are too short to be aligned to the targets. Only relevant when training an instance of [`ParakeetForCTC`]. encoder_config (`Union[dict, ParakeetEncoderConfig]`, *optional*): The config object or dictionary of the encoder. pad_token_id (`int`, *optional*, defaults to 1024): Padding token id. Also used as blank token id. Example: ```python >>> from transformers import ParakeetForCTC, ParakeetCTCConfig >>> # Initializing a Parakeet configuration >>> configuration = ParakeetCTCConfig() >>> # Initializing a model from the configuration >>> model = ParakeetForCTC(configuration) >>> # Accessing the model configuration >>> configuration = model.config ``` This configuration class is based on the Parakeet CTC architecture from NVIDIA NeMo. You can find more details and pre-trained models at [nvidia/parakeet-ctc-1.1b](https://huggingface.co/nvidia/parakeet-ctc-1.1b). parakeet_ctcencoder_configc >XlX lX0l[U[5(a[ S0UD6UlOUc[ 5UlUR UlUR RUl[TU]$"SSU0UD6 g)N pad_token_idr) vocab_sizectc_loss_reductionctc_zero_infinity isinstancedictrrDr'rr)r(rGrHrIrDrFr)r*s r+rParakeetCTCConfig.__init__s%"4!2 nd + +"7"I."ID   #"7"9D "11!%!4!4!F!F  %  r-c 2U"SSUR50UD6$)z Instantiate a [`ParakeetCTCConfig`] (or a derived class) from parakeet encoder model configuration. Returns: [`ParakeetCTCConfig`]: An instance of a configuration object rDr)to_dict)clsrDr)s r+from_encoder_config%ParakeetCTCConfig.from_encoder_configs E."8"8":EfEEr-)rHrIrDr'rG)imeanTNr.)r6r7r8r9r:r;r sub_configsrrKr classmethodrPr=r>r?s@r+rArAsj&P J#%:;K!=A  d$99:   4F1FFFr-rAN) r:typingrconfiguration_utilsrutilsr get_loggerr6loggerrrA__all__rr-r+r[sT$3   H %|3,|3~OF(OFd  7 8r-