^jH-dZddlmZddlmZddlmZmZddlm Z m Z eje Z ed eGd d eZed eGd d eZed eGddeZed eGddeZed eGddeZgdZy)zBARK model configuration)strict)PreTrainedConfig)auto_docstringlogging)CONFIG_MAPPING AutoConfigz suno/bark) checkpointceZdZUdZdgZdddddZdZeed<d Z eed<d Z eed <d Z eed<d Z eed<d Z eed <dZeezed<dZeed<dZeed<dZeed<y)BarkSubModelConfiga block_size (`int`, *optional*, defaults to 1024): 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). input_vocab_size (`int`, *optional*, defaults to 10_048): Vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`{model}`]. Defaults to 10_048 but should be carefully thought with regards to the chosen sub-model. output_vocab_size (`int`, *optional*, defaults to 10_048): Output vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented by the: `output_ids` when passing forward a [`{model}`]. Defaults to 10_048 but should be carefully thought with regards to the chosen sub-model. bias (`bool`, *optional*, defaults to `True`): Whether or not to use bias in the linear layers and layer norm layers. past_key_values num_heads num_layersinput_vocab_size block_size)num_attention_headsnum_hidden_layers vocab_size window_sizeii@'output_vocab_size i hidden_sizegdropoutTbias{Gz?initializer_range use_cacheN)__name__ __module__ __qualname____doc__keys_to_ignore_at_inference attribute_maprint__annotations__rrrrrrfloatrboolrrv/var/www/ramen.bs-engineer-server.com/venv/lib/python3.12/site-packages/transformers/models/bark/configuration_bark.pyr r s $5"5 +)(# MJ"c"#s#JIsKGUS[D$#u#Itr*r ceZdZdZdZdZy)BarkSemanticConfiga block_size (`int`, *optional*, defaults to 1024): 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). input_vocab_size (`int`, *optional*, defaults to 10_048): Vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`{model}`]. Defaults to 10_048 but should be carefully thought with regards to the chosen sub-model. output_vocab_size (`int`, *optional*, defaults to 10_048): Output vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented by the: `output_ids` when passing forward a [`{model}`]. Defaults to 10_048 but should be carefully thought with regards to the chosen sub-model. bias (`bool`, *optional*, defaults to `True`): Whether or not to use bias in the linear layers and layer norm layers Example: ```python >>> from transformers import BarkSemanticConfig, BarkSemanticModel >>> # Initializing a Bark sub-module style configuration >>> configuration = BarkSemanticConfig() >>> # Initializing a model (with random weights) from the suno/bark style configuration >>> model = BarkSemanticModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```semanticsemantic_configNrr r!r" model_typebase_config_keyr)r*r+r-r-Bs <J'Or*r-ceZdZdZdZdZy)BarkCoarseConfiga block_size (`int`, *optional*, defaults to 1024): 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). input_vocab_size (`int`, *optional*, defaults to 10_048): Vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`{model}`]. Defaults to 10_048 but should be carefully thought with regards to the chosen sub-model. output_vocab_size (`int`, *optional*, defaults to 10_048): Output vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented by the: `output_ids` when passing forward a [`{model}`]. Defaults to 10_048 but should be carefully thought with regards to the chosen sub-model. bias (`bool`, *optional*, defaults to `True`): Whether or not to use bias in the linear layers and layer norm layers Example: ```python >>> from transformers import BarkCoarseConfig, BarkCoarseModel >>> # Initializing a Bark sub-module style configuration >>> configuration = BarkCoarseConfig() >>> # Initializing a model (with random weights) from the suno/bark style configuration >>> model = BarkCoarseModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```coarse_acousticscoarse_acoustics_configNr0r)r*r+r4r4gs <$J/Or*r4cDeZdZUdZdZdZdZeed<dZ e ed<dZ e ed <y ) BarkFineConfiga block_size (`int`, *optional*, defaults to 1024): 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). input_vocab_size (`int`, *optional*, defaults to 10_048): Vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`{model}`]. Defaults to 10_048 but should be carefully thought with regards to the chosen sub-model. output_vocab_size (`int`, *optional*, defaults to 10_048): Output vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented by the: `output_ids` when passing forward a [`{model}`]. Defaults to 10_048 but should be carefully thought with regards to the chosen sub-model. bias (`bool`, *optional*, defaults to `True`): Whether or not to use bias in the linear layers and layer norm layers n_codes_total (`int`, *optional*, defaults to 8): The total number of audio codebooks predicted. Used in the fine acoustics sub-model. n_codes_given (`int`, *optional*, defaults to 1): The number of audio codebooks predicted in the coarse acoustics sub-model. Used in the acoustics sub-models. Example: ```python >>> from transformers import BarkFineConfig, BarkFineModel >>> # Initializing a Bark sub-module style configuration >>> configuration = BarkFineConfig() >>> # Initializing a model (with random weights) from the suno/bark style configuration >>> model = BarkFineModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```fine_acousticsfine_acoustics_configTtie_word_embeddings n_codes_total n_codes_givenN) rr r!r"r1r2r;r(r&r=r%r?r)r*r+r8r8s3! F"J-O $$M3M3r*r8ceZdZUdZdZeeeedZ dZ e e zdze d<dZe e zdze d<dZe e zdze d<dZe e zdze d<d Zee d <fd ZxZS) BarkConfigaf semantic_config ([`BarkSemanticConfig`], *optional*): Configuration of the underlying semantic sub-model. coarse_acoustics_config ([`BarkCoarseConfig`], *optional*): Configuration of the underlying coarse acoustics sub-model. fine_acoustics_config ([`BarkFineConfig`], *optional*): Configuration of the underlying fine acoustics sub-model. codec_config ([`AutoConfig`], *optional*): Configuration of the underlying codec sub-model. Example: ```python >>> from transformers import ( ... BarkSemanticConfig, ... BarkCoarseConfig, ... BarkFineConfig, ... BarkModel, ... BarkConfig, ... AutoConfig, ... ) >>> # Initializing Bark sub-modules configurations. >>> semantic_config = BarkSemanticConfig() >>> coarse_acoustics_config = BarkCoarseConfig() >>> fine_acoustics_config = BarkFineConfig() >>> codec_config = AutoConfig.from_pretrained("facebook/encodec_24khz") >>> # Initializing a Bark module style configuration >>> configuration = BarkConfig( ... semantic_config, coarse_acoustics_config, fine_acoustics_config, codec_config ... ) >>> # Initializing a model (with random weights) >>> model = BarkModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ``` bark)r/r6r: codec_configNr/r6r:rCrrc |j%t|_tjdn4t |jt rtdi|j|_|j %t|_tjdn4t |j t rtdi|j |_|j%t|_tjdn4t |jt rtdi|j|_|j(td|_ tjdnSt |jt r9|jjdd}t|di|j|_ t|8di|y)NzW`semantic_config` is `None`. Initializing the `BarkSemanticConfig` with default values.z]`coarse_acoustics_config` is `None`. Initializing the `BarkCoarseConfig` with default values.zY`fine_acoustics_config` is `None`. Initializing the `BarkFineConfig` with default values.encodeczN`codec_config` is `None`. Initializing the `codec_config` with default values.r1r))r/r-loggerinfo isinstancedictr6r4r:r8rCr getsuper __post_init__)selfkwargscodec_model_type __class__s r+rLzBarkConfig.__post_init__sc    '#5#7D KKq r ,,d 3#5#M8L8L#MD  ' ' /+;+=D ( KKo 44d ;+;+[d>Z>Z+[D (  % % -)7)9D & KKs t 22D 9)7)U$:T:T)UD &    $ .y 9 ;D  KKh i ))4 0#0044\9M  ./? @ U4CTCT UD  ''r*)rr r!r"r1r-r4r8r sub_configsr/rIrr&r6r:rCrr'rL __classcell__)rPs@r+rArAs(TJ-#3!/" K 7;OT,,t3:>BT$44t;B<@4"22T9@37L$))D07#u#((r*rA)r4rAr8r-N)r"huggingface_hub.dataclassesrconfiguration_utilsrutilsrrautor r get_loggerrrFr r-r4r8rA__all__r)r*r+rYs.3,-   H %;'#)#(#L;' (+ (( (F;' 0) 0( 0F;')')()X;'T(!T((T(n Ur*