326 lines
12 KiB
Python
326 lines
12 KiB
Python
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# coding=utf-8
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# Copyright 2023 The Suno AI Authors and The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" BARK model configuration"""
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import os
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from typing import Dict, Optional, Union
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from ...configuration_utils import PretrainedConfig
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from ...utils import add_start_docstrings, logging
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from ..auto import CONFIG_MAPPING
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logger = logging.get_logger(__name__)
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BARK_SUBMODELCONFIG_START_DOCSTRING = """
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This is the configuration class to store the configuration of a [`{model}`]. It is used to instantiate the model
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according to the specified arguments, defining the model architecture. Instantiating a configuration with the
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defaults will yield a similar configuration to that of the Bark [suno/bark](https://huggingface.co/suno/bark)
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architecture.
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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block_size (`int`, *optional*, defaults to 1024):
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The maximum sequence length that this model might ever be used with. Typically set this to something large
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just in case (e.g., 512 or 1024 or 2048).
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input_vocab_size (`int`, *optional*, defaults to 10_048):
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Vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`{model}`]. Defaults to 10_048 but should be carefully thought with
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regards to the chosen sub-model.
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output_vocab_size (`int`, *optional*, defaults to 10_048):
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Output vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented
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by the: `output_ids` when passing forward a [`{model}`]. Defaults to 10_048 but should be carefully thought
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with regards to the chosen sub-model.
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num_layers (`int`, *optional*, defaults to 12):
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Number of hidden layers in the given sub-model.
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num_heads (`int`, *optional*, defaults to 12):
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Number of attention heads for each attention layer in the Transformer architecture.
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hidden_size (`int`, *optional*, defaults to 768):
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Dimensionality of the "intermediate" (often named feed-forward) layer in the architecture.
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dropout (`float`, *optional*, defaults to 0.0):
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The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
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bias (`bool`, *optional*, defaults to `True`):
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Whether or not to use bias in the linear layers and layer norm layers.
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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use_cache (`bool`, *optional*, defaults to `True`):
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Whether or not the model should return the last key/values attentions (not used by all models).
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"""
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class BarkSubModelConfig(PretrainedConfig):
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model_type = "bark_module"
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keys_to_ignore_at_inference = ["past_key_values"]
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attribute_map = {
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"num_attention_heads": "num_heads",
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"num_hidden_layers": "num_layers",
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"vocab_size": "input_vocab_size",
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"window_size": "block_size",
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}
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def __init__(
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self,
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block_size=1024,
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input_vocab_size=10_048,
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output_vocab_size=10_048,
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num_layers=12,
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num_heads=12,
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hidden_size=768,
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dropout=0.0,
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bias=True, # True: bias in Linears and LayerNorms, like GPT-2. False: a bit better and faster
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initializer_range=0.02,
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use_cache=True,
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**kwargs,
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):
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self.block_size = block_size
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self.input_vocab_size = input_vocab_size
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self.output_vocab_size = output_vocab_size
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self.num_layers = num_layers
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self.num_heads = num_heads
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self.hidden_size = hidden_size
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self.dropout = dropout
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self.bias = bias
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self.use_cache = use_cache
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self.initializer_range = initializer_range
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super().__init__(**kwargs)
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@classmethod
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def from_pretrained(
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cls,
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pretrained_model_name_or_path: Union[str, os.PathLike],
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cache_dir: Optional[Union[str, os.PathLike]] = None,
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force_download: bool = False,
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local_files_only: bool = False,
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token: Optional[Union[str, bool]] = None,
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revision: str = "main",
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**kwargs,
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) -> "PretrainedConfig":
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kwargs["cache_dir"] = cache_dir
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kwargs["force_download"] = force_download
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kwargs["local_files_only"] = local_files_only
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kwargs["revision"] = revision
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cls._set_token_in_kwargs(kwargs, token)
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config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
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# get the config dict if we are loading from Bark
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if config_dict.get("model_type") == "bark":
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config_dict = config_dict[f"{cls.model_type}_config"]
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if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
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logger.warning(
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f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
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f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
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)
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return cls.from_dict(config_dict, **kwargs)
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@add_start_docstrings(
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BARK_SUBMODELCONFIG_START_DOCSTRING.format(config="BarkSemanticConfig", model="BarkSemanticModel"),
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"""
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Example:
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```python
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>>> from transformers import BarkSemanticConfig, BarkSemanticModel
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>>> # Initializing a Bark sub-module style configuration
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>>> configuration = BarkSemanticConfig()
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>>> # Initializing a model (with random weights) from the suno/bark style configuration
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>>> model = BarkSemanticModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```""",
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)
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class BarkSemanticConfig(BarkSubModelConfig):
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model_type = "semantic"
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@add_start_docstrings(
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BARK_SUBMODELCONFIG_START_DOCSTRING.format(config="BarkCoarseConfig", model="BarkCoarseModel"),
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"""
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Example:
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```python
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>>> from transformers import BarkCoarseConfig, BarkCoarseModel
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>>> # Initializing a Bark sub-module style configuration
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>>> configuration = BarkCoarseConfig()
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>>> # Initializing a model (with random weights) from the suno/bark style configuration
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>>> model = BarkCoarseModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```""",
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)
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class BarkCoarseConfig(BarkSubModelConfig):
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model_type = "coarse_acoustics"
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@add_start_docstrings(
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BARK_SUBMODELCONFIG_START_DOCSTRING.format(config="BarkFineConfig", model="BarkFineModel"),
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"""
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n_codes_total (`int`, *optional*, defaults to 8):
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The total number of audio codebooks predicted. Used in the fine acoustics sub-model.
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n_codes_given (`int`, *optional*, defaults to 1):
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The number of audio codebooks predicted in the coarse acoustics sub-model. Used in the acoustics
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sub-models.
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Example:
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```python
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>>> from transformers import BarkFineConfig, BarkFineModel
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>>> # Initializing a Bark sub-module style configuration
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>>> configuration = BarkFineConfig()
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>>> # Initializing a model (with random weights) from the suno/bark style configuration
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>>> model = BarkFineModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```""",
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)
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class BarkFineConfig(BarkSubModelConfig):
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model_type = "fine_acoustics"
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def __init__(self, tie_word_embeddings=True, n_codes_total=8, n_codes_given=1, **kwargs):
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self.n_codes_total = n_codes_total
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self.n_codes_given = n_codes_given
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super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
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class BarkConfig(PretrainedConfig):
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"""
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This is the configuration class to store the configuration of a [`BarkModel`]. It is used to instantiate a Bark
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model according to the specified sub-models configurations, defining the model architecture.
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Instantiating a configuration with the defaults will yield a similar configuration to that of the Bark
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[suno/bark](https://huggingface.co/suno/bark) architecture.
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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semantic_config ([`BarkSemanticConfig`], *optional*):
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Configuration of the underlying semantic sub-model.
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coarse_acoustics_config ([`BarkCoarseConfig`], *optional*):
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Configuration of the underlying coarse acoustics sub-model.
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fine_acoustics_config ([`BarkFineConfig`], *optional*):
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Configuration of the underlying fine acoustics sub-model.
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codec_config ([`AutoConfig`], *optional*):
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Configuration of the underlying codec sub-model.
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Example:
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```python
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>>> from transformers import (
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... BarkSemanticConfig,
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... BarkCoarseConfig,
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... BarkFineConfig,
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... BarkModel,
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... BarkConfig,
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... AutoConfig,
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... )
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>>> # Initializing Bark sub-modules configurations.
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>>> semantic_config = BarkSemanticConfig()
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>>> coarse_acoustics_config = BarkCoarseConfig()
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>>> fine_acoustics_config = BarkFineConfig()
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>>> codec_config = AutoConfig.from_pretrained("facebook/encodec_24khz")
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>>> # Initializing a Bark module style configuration
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>>> configuration = BarkConfig.from_sub_model_configs(
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... semantic_config, coarse_acoustics_config, fine_acoustics_config, codec_config
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... )
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>>> # Initializing a model (with random weights)
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>>> model = BarkModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```
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"""
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model_type = "bark"
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def __init__(
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self,
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semantic_config: Dict = None,
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coarse_acoustics_config: Dict = None,
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fine_acoustics_config: Dict = None,
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codec_config: Dict = None,
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initializer_range=0.02,
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**kwargs,
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):
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if semantic_config is None:
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semantic_config = {}
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logger.info("semantic_config is None. initializing the semantic model with default values.")
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if coarse_acoustics_config is None:
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coarse_acoustics_config = {}
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logger.info("coarse_acoustics_config is None. initializing the coarse model with default values.")
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if fine_acoustics_config is None:
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fine_acoustics_config = {}
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logger.info("fine_acoustics_config is None. initializing the fine model with default values.")
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if codec_config is None:
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codec_config = {}
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logger.info("codec_config is None. initializing the codec model with default values.")
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self.semantic_config = BarkSemanticConfig(**semantic_config)
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self.coarse_acoustics_config = BarkCoarseConfig(**coarse_acoustics_config)
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self.fine_acoustics_config = BarkFineConfig(**fine_acoustics_config)
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codec_model_type = codec_config["model_type"] if "model_type" in codec_config else "encodec"
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self.codec_config = CONFIG_MAPPING[codec_model_type](**codec_config)
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self.initializer_range = initializer_range
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super().__init__(**kwargs)
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@classmethod
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def from_sub_model_configs(
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cls,
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semantic_config: BarkSemanticConfig,
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coarse_acoustics_config: BarkCoarseConfig,
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fine_acoustics_config: BarkFineConfig,
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codec_config: PretrainedConfig,
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**kwargs,
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):
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r"""
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Instantiate a [`BarkConfig`] (or a derived class) from bark sub-models configuration.
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Returns:
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[`BarkConfig`]: An instance of a configuration object
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"""
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return cls(
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semantic_config=semantic_config.to_dict(),
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coarse_acoustics_config=coarse_acoustics_config.to_dict(),
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fine_acoustics_config=fine_acoustics_config.to_dict(),
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codec_config=codec_config.to_dict(),
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**kwargs,
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)
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