# 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.s from ...configuration_utils import PretrainedConfig from ...utils import logging from ..auto.configuration_auto import AutoConfig logger = logging.get_logger(__name__) class KyutaiSpeechToTextConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`KyutaiSpeechToTextForConditionalGeneration`]. It is used to instantiate a Kyutai Speech-to-Text model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the 2.6b-en model. e.g. [kyutai/stt-2.6b-en-trfs](https://huggingface.co/kyutai/stt-2.6b-en-trfs) Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: codebook_vocab_size (`int`, *optional*, defaults to 2049): Vocabulary size of the codebook. Defines the number of different audio tokens that can be represented by each codebook. vocab_size (`int`, *optional*, defaults to 4001): Vocabulary size of the model. Defines the number of different tokens that can be represented by the `input_ids` passed when calling the model. hidden_size (`int`, *optional*, defaults to 2048): Dimensionality of the layers and the pooler layer of the main decoder. num_hidden_layers (`int`, *optional*, defaults to 48): Number of decoder layers. num_attention_heads (`int`, *optional*, defaults to 32): Number of attention heads for each attention layer in the main decoder block. num_key_value_heads (`int`, *optional*): This is the number of key_value heads that should be used to implement Grouped Query Attention. If `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed by meanpooling all the original heads within that group. For more details checkout [this paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `num_attention_heads`. max_position_embeddings (`int`, *optional*, defaults to 750): The maximum sequence length that this model might ever be used with. Typically, set this to something large just in case (e.g., 512 or 1024 or 2048). rope_theta (`float`, *optional*, defaults to 100000.0): The base period of the RoPE embeddings. hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): The non-linear activation function (function or string) in the decoder. head_dim (`int`, *optional*, defaults to `hidden_size // num_attention_heads`): The attention head dimension. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). Only relevant if `config.is_decoder=True`. sliding_window (`int`, *optional*, defaults to 375): Sliding window attention window size. If not specified, will default to `3000`. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. ffn_dim (`int`, *optional*, defaults to 11264): Dimensionality of the "intermediate" (often named feed-forward) layer in the main decoder block. Must be even. rms_norm_eps (`float`, *optional*, defaults to 1e-08): The epsilon used by the rms normalization layers. num_codebooks (`int`, *optional*, defaults to 32): The number of audio codebooks for each audio channels. audio_bos_token_id (`int`, *optional*, defaults to 2048): Beginning of stream token id for codebook tokens. audio_pad_token_id (`int`, *optional*, defaults to 69569): Padding token id for codebook tokens. tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether to tie weight embeddings. pad_token_id (`int`, *optional*, defaults to 3): Padding token id. bos_token_id (`int`, *optional*, defaults to 48000): Beginning of stream token id for text tokens. codec_config (`PretrainedConfig`, *optional*): Configuration for the codec. kwargs (*optional*): Dictionary of keyword arguments. Notably: - **audio_encoder_config** ([`PretrainedConfig`], *optional*) -- An instance of a configuration object that defines the audio encoder config. - **depth__config** ([`PretrainedConfig`], *optional*) -- An instance of a configuration object that defines the depth decoder config. Example: ```python >>> from transformers import KyutaiSpeechToTextConfig, KyutaiSpeechToTextForConditionalGeneration >>> # Initializing a KyutaiSpeechToTextConfig >>> configuration = KyutaiSpeechToTextConfig() >>> # Initializing a model >>> model = KyutaiSpeechToTextForConditionalGeneration(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "kyutai_speech_to_text" keys_to_ignore_at_inference = ["past_key_values"] sub_configs = {"codec_config": AutoConfig} def __init__( self, codebook_vocab_size=2049, vocab_size=4001, hidden_size=2048, num_hidden_layers=48, num_attention_heads=32, num_key_value_heads=None, max_position_embeddings=750, rope_theta=100000.0, hidden_act="silu", head_dim=None, initializer_range=0.02, use_cache=True, sliding_window=375, attention_dropout=0.0, ffn_dim=11264, rms_norm_eps=1e-8, num_codebooks=32, audio_bos_token_id=2048, audio_pad_token_id=69569, tie_word_embeddings=False, pad_token_id=3, bos_token_id=48000, codec_config=None, **kwargs, ): super().__init__( pad_token_id=pad_token_id, bos_token_id=bos_token_id, tie_word_embeddings=tie_word_embeddings, **kwargs ) if codec_config is None: self.codec_config = AutoConfig.for_model("mimi") logger.info("codec_config is None, using default audio encoder config.") elif isinstance(codec_config, dict): self.codec_config = AutoConfig.for_model(**codec_config) elif isinstance(codec_config, PretrainedConfig): self.codec_config = codec_config self.num_codebooks = num_codebooks self.frame_size = self.codec_config.frame_size self.audio_bos_token_id = audio_bos_token_id self.audio_pad_token_id = audio_pad_token_id self.codebook_vocab_size = codebook_vocab_size self.vocab_size = vocab_size self.max_position_embeddings = max_position_embeddings self.hidden_size = hidden_size if ffn_dim % 2 == 1: raise ValueError(f"`ffn_dim={ffn_dim}` must be even.") self.ffn_dim = ffn_dim self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads # for backward compatibility if num_key_value_heads is None: num_key_value_heads = num_attention_heads self.num_key_value_heads = num_key_value_heads self.hidden_act = hidden_act self.initializer_range = initializer_range self.rms_norm_eps = rms_norm_eps self.use_cache = use_cache self.rope_theta = rope_theta self.attention_dropout = attention_dropout self.head_dim = head_dim if head_dim is not None else self.hidden_size // self.num_attention_heads self.sliding_window = sliding_window __all__ = ["KyutaiSpeechToTextConfig"]