# coding=utf-8 # Copyright 2023 The Suno AI Authors and 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. """ BARK model configuration""" import os from typing import Dict, Optional, Union from ...configuration_utils import PretrainedConfig from ...utils import add_start_docstrings, logging from ..auto import CONFIG_MAPPING logger = logging.get_logger(__name__) BARK_SUBMODELCONFIG_START_DOCSTRING = """ This is the configuration class to store the configuration of a [`{model}`]. It is used to instantiate the 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 Bark [suno/bark](https://huggingface.co/suno/bark) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: 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. num_layers (`int`, *optional*, defaults to 12): Number of hidden layers in the given sub-model. num_heads (`int`, *optional*, defaults to 12): Number of attention heads for each attention layer in the Transformer architecture. hidden_size (`int`, *optional*, defaults to 768): Dimensionality of the "intermediate" (often named feed-forward) layer in the architecture. dropout (`float`, *optional*, defaults to 0.0): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. bias (`bool`, *optional*, defaults to `True`): Whether or not to use bias in the linear layers and layer norm layers. 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). """ class BarkSubModelConfig(PretrainedConfig): model_type = "bark_module" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = { "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers", "vocab_size": "input_vocab_size", "window_size": "block_size", } def __init__( self, block_size=1024, input_vocab_size=10_048, output_vocab_size=10_048, num_layers=12, num_heads=12, hidden_size=768, dropout=0.0, bias=True, # True: bias in Linears and LayerNorms, like GPT-2. False: a bit better and faster initializer_range=0.02, use_cache=True, **kwargs, ): self.block_size = block_size self.input_vocab_size = input_vocab_size self.output_vocab_size = output_vocab_size self.num_layers = num_layers self.num_heads = num_heads self.hidden_size = hidden_size self.dropout = dropout self.bias = bias self.use_cache = use_cache self.initializer_range = initializer_range super().__init__(**kwargs) @classmethod def from_pretrained( cls, pretrained_model_name_or_path: Union[str, os.PathLike], cache_dir: Optional[Union[str, os.PathLike]] = None, force_download: bool = False, local_files_only: bool = False, token: Optional[Union[str, bool]] = None, revision: str = "main", **kwargs, ) -> "PretrainedConfig": kwargs["cache_dir"] = cache_dir kwargs["force_download"] = force_download kwargs["local_files_only"] = local_files_only kwargs["revision"] = revision cls._set_token_in_kwargs(kwargs, token) config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs) # get the config dict if we are loading from Bark if config_dict.get("model_type") == "bark": config_dict = config_dict[f"{cls.model_type}_config"] if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type: logger.warning( f"You are using a model of type {config_dict['model_type']} to instantiate a model of type " f"{cls.model_type}. This is not supported for all configurations of models and can yield errors." ) return cls.from_dict(config_dict, **kwargs) @add_start_docstrings( BARK_SUBMODELCONFIG_START_DOCSTRING.format(config="BarkSemanticConfig", model="BarkSemanticModel"), """ 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 ```""", ) class BarkSemanticConfig(BarkSubModelConfig): model_type = "semantic" @add_start_docstrings( BARK_SUBMODELCONFIG_START_DOCSTRING.format(config="BarkCoarseConfig", model="BarkCoarseModel"), """ 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 ```""", ) class BarkCoarseConfig(BarkSubModelConfig): model_type = "coarse_acoustics" @add_start_docstrings( BARK_SUBMODELCONFIG_START_DOCSTRING.format(config="BarkFineConfig", model="BarkFineModel"), """ 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 ```""", ) class BarkFineConfig(BarkSubModelConfig): model_type = "fine_acoustics" def __init__(self, tie_word_embeddings=True, n_codes_total=8, n_codes_given=1, **kwargs): self.n_codes_total = n_codes_total self.n_codes_given = n_codes_given super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs) class BarkConfig(PretrainedConfig): """ This is the configuration class to store the configuration of a [`BarkModel`]. It is used to instantiate a Bark model according to the specified sub-models configurations, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the Bark [suno/bark](https://huggingface.co/suno/bark) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: 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.from_sub_model_configs( ... 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 ``` """ model_type = "bark" def __init__( self, semantic_config: Dict = None, coarse_acoustics_config: Dict = None, fine_acoustics_config: Dict = None, codec_config: Dict = None, initializer_range=0.02, **kwargs, ): if semantic_config is None: semantic_config = {} logger.info("semantic_config is None. initializing the semantic model with default values.") if coarse_acoustics_config is None: coarse_acoustics_config = {} logger.info("coarse_acoustics_config is None. initializing the coarse model with default values.") if fine_acoustics_config is None: fine_acoustics_config = {} logger.info("fine_acoustics_config is None. initializing the fine model with default values.") if codec_config is None: codec_config = {} logger.info("codec_config is None. initializing the codec model with default values.") self.semantic_config = BarkSemanticConfig(**semantic_config) self.coarse_acoustics_config = BarkCoarseConfig(**coarse_acoustics_config) self.fine_acoustics_config = BarkFineConfig(**fine_acoustics_config) codec_model_type = codec_config["model_type"] if "model_type" in codec_config else "encodec" self.codec_config = CONFIG_MAPPING[codec_model_type](**codec_config) self.initializer_range = initializer_range super().__init__(**kwargs) @classmethod def from_sub_model_configs( cls, semantic_config: BarkSemanticConfig, coarse_acoustics_config: BarkCoarseConfig, fine_acoustics_config: BarkFineConfig, codec_config: PretrainedConfig, **kwargs, ): r""" Instantiate a [`BarkConfig`] (or a derived class) from bark sub-models configuration. Returns: [`BarkConfig`]: An instance of a configuration object """ return cls( semantic_config=semantic_config.to_dict(), coarse_acoustics_config=coarse_acoustics_config.to_dict(), fine_acoustics_config=fine_acoustics_config.to_dict(), codec_config=codec_config.to_dict(), **kwargs, )