# coding=utf-8 # Copyright 2025 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Parakeet model configuration.""" from typing import Union from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) class ParakeetEncoderConfig(PretrainedConfig): r""" 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). """ model_type = "parakeet_encoder" keys_to_ignore_at_inference = ["past_key_values"] def __init__( self, hidden_size=1024, num_hidden_layers=24, num_attention_heads=8, intermediate_size=4096, hidden_act="silu", attention_bias=True, conv_kernel_size=9, subsampling_factor=8, subsampling_conv_channels=256, num_mel_bins=80, subsampling_conv_kernel_size=3, subsampling_conv_stride=2, dropout=0.1, dropout_positions=0.0, layerdrop=0.1, activation_dropout=0.1, attention_dropout=0.1, max_position_embeddings=5000, scale_input=True, initializer_range=0.02, **kwargs, ): super().__init__( **kwargs, ) self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.num_key_value_heads = num_attention_heads # LlamaAttention compatibility self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.attention_bias = attention_bias if (conv_kernel_size - 1) % 2 != 0: raise ValueError(f"conv_kernel_size must be odd, got {conv_kernel_size}") self.conv_kernel_size = conv_kernel_size self.subsampling_conv_kernel_size = subsampling_conv_kernel_size self.subsampling_conv_stride = subsampling_conv_stride self.subsampling_factor = subsampling_factor self.subsampling_conv_channels = subsampling_conv_channels self.num_mel_bins = num_mel_bins self.dropout = dropout self.dropout_positions = dropout_positions self.layerdrop = layerdrop self.activation_dropout = activation_dropout self.attention_dropout = attention_dropout self.max_position_embeddings = max_position_embeddings self.scale_input = scale_input self.initializer_range = initializer_range class ParakeetCTCConfig(PretrainedConfig): r""" 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). """ model_type = "parakeet_ctc" sub_configs = {"encoder_config": ParakeetEncoderConfig} def __init__( self, vocab_size=1025, ctc_loss_reduction="mean", ctc_zero_infinity=True, encoder_config: Union[dict, ParakeetEncoderConfig] = None, pad_token_id=1024, **kwargs, ): self.vocab_size = vocab_size self.ctc_loss_reduction = ctc_loss_reduction self.ctc_zero_infinity = ctc_zero_infinity if isinstance(encoder_config, dict): self.encoder_config = ParakeetEncoderConfig(**encoder_config) elif encoder_config is None: self.encoder_config = ParakeetEncoderConfig() self.encoder_config = self.encoder_config self.initializer_range = self.encoder_config.initializer_range super().__init__( pad_token_id=pad_token_id, **kwargs, ) @classmethod def from_encoder_config(cls, encoder_config: ParakeetEncoderConfig, **kwargs): r""" Instantiate a [`ParakeetCTCConfig`] (or a derived class) from parakeet encoder model configuration. Returns: [`ParakeetCTCConfig`]: An instance of a configuration object """ return cls(encoder_config=encoder_config.to_dict(), **kwargs) __all__ = ["ParakeetCTCConfig", "ParakeetEncoderConfig"]