qi'xdZddlmZddlmZej eZGddeZGddeZ ddgZ y) zParakeet model configuration.)PreTrainedConfig)loggingcXeZdZdZdZdgZ dfd ZxZS)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. convolution_bias (`bool`, *optional*, defaults to `True`): Whether to use bias in convolutions of the conformer's convolution module. 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 X||_||_||_||_||_||_||_||_||_| |_ | |_ | |_ | |_ | |_ ||_||_||_||_||_||_||_||_t-|\di|yN) hidden_sizenum_hidden_layersnum_attention_headsnum_key_value_headsintermediate_size hidden_actattention_biasconvolution_biasconv_kernel_sizesubsampling_conv_kernel_sizesubsampling_conv_stridesubsampling_factorsubsampling_conv_channels num_mel_binsdropoutdropout_positions layerdropactivation_dropoutattention_dropoutmax_position_embeddings scale_inputinitializer_rangesuper__init__)selfr r rrrrrrrrrrrrrrrrrr r!kwargs __class__s e/opt/pipecat/venv/lib/python3.12/site-packages/transformers/models/parakeet/configuration_parakeet.pyr#zParakeetEncoderConfig.__init__`s2'!2#6 #6 !2$, 0 0,H)'>$"4)B&( !2""4!2'>$&!2   )isiluTT r+Pr皙?gr1r1r1iTg{Gz?)__name__ __module__ __qualname____doc__ model_typekeys_to_ignore_at_inferencer# __classcell__r&s@r'rrseCJ$J#4"5"%%& ! $-5 5 r(rc\eZdZdZdZdeiZ ddeezffd Ze defdZ xZ S)ParakeetCTCConfiga This is the configuration class to store the configuration of a [`ParakeetForCTC`]. It is used to instantiate a Parakeet CTC model according to the specified arguments, defining the model architecture. Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PreTrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 1025): Vocabulary size of the model. ctc_loss_reduction (`str`, *optional*, defaults to `"mean"`): Specifies the reduction to apply to the output of `torch.nn.CTCLoss`. Only relevant when training an instance of [`ParakeetForCTC`]. ctc_zero_infinity (`bool`, *optional*, defaults to `True`): Whether to zero infinite losses and the associated gradients of `torch.nn.CTCLoss`. Infinite losses mainly occur when the inputs are too short to be aligned to the targets. Only relevant when training an instance of [`ParakeetForCTC`]. encoder_config (`Union[dict, ParakeetEncoderConfig]`, *optional*): The config object or dictionary of the encoder. pad_token_id (`int`, *optional*, defaults to 1024): Padding token id. Also used as blank token id. Example: ```python >>> from transformers import ParakeetForCTC, ParakeetCTCConfig >>> # Initializing a Parakeet configuration >>> configuration = ParakeetCTCConfig() >>> # Initializing a model from the configuration >>> model = ParakeetForCTC(configuration) >>> # Accessing the model configuration >>> configuration = model.config ``` This configuration class is based on the Parakeet CTC architecture from NVIDIA NeMo. You can find more details and pre-trained models at [nvidia/parakeet-ctc-1.1b](https://huggingface.co/nvidia/parakeet-ctc-1.1b). parakeet_ctcencoder_configc ||_||_||_t|trt di||_n|t |_|j |_|j j|_||_t|(di|yr ) vocab_sizectc_loss_reductionctc_zero_infinity isinstancedictrr=r! pad_token_idr"r#)r$r?r@rAr=rDr%r&s r'r#zParakeetCTCConfig.__init__s%"4!2 nd +"7"I."ID   #"7"9D "11!%!4!4!F!F( "6"r(c 2|dd|ji|S)z Instantiate a [`ParakeetCTCConfig`] (or a derived class) from parakeet encoder model configuration. Returns: [`ParakeetCTCConfig`]: An instance of a configuration object r=r )to_dict)clsr=r%s r'from_encoder_configz%ParakeetCTCConfig.from_encoder_configs E."8"8":EfEEr()imeanTNr)) r2r3r4r5r6r sub_configsrCr# classmethodrHr8r9s@r'r;r;s`&P J#%:;K!7; # 44 #0F1FFFr(r;N) r5configuration_utilsrutilsr get_loggerr2loggerrr;__all__r r(r'rQsS$3   H %~ ,~ BMF(MF`  7 8r(