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289 lines
11 KiB
289 lines
11 KiB
# 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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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, AutoConfig
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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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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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@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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base_config_key = "semantic_config"
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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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base_config_key = "coarse_acoustics_config"
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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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base_config_key = "fine_acoustics_config"
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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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self.tie_word_embeddings = tie_word_embeddings
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super().__init__(**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(
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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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sub_configs = {
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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,
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}
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def __init__(
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self,
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semantic_config: dict | None = None,
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coarse_acoustics_config: dict | None = None,
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fine_acoustics_config: dict | None = None,
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codec_config: dict | None = 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 = BarkSemanticConfig()
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logger.info("`semantic_config` is `None`. Initializing the `BarkSemanticConfig` with default values.")
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elif isinstance(semantic_config, dict):
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semantic_config = BarkSemanticConfig(**semantic_config)
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if coarse_acoustics_config is None:
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coarse_acoustics_config = BarkCoarseConfig()
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logger.info(
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"`coarse_acoustics_config` is `None`. Initializing the `BarkCoarseConfig` with default values."
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)
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elif isinstance(coarse_acoustics_config, dict):
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coarse_acoustics_config = BarkCoarseConfig(**coarse_acoustics_config)
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if fine_acoustics_config is None:
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fine_acoustics_config = BarkFineConfig()
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logger.info("`fine_acoustics_config` is `None`. Initializing the `BarkFineConfig` with default values.")
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elif isinstance(fine_acoustics_config, dict):
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fine_acoustics_config = BarkFineConfig(**fine_acoustics_config)
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if codec_config is None:
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codec_config = CONFIG_MAPPING["encodec"]()
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logger.info("`codec_config` is `None`. Initializing the `codec_config` with default values.")
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elif isinstance(codec_config, dict):
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codec_model_type = codec_config.get("model_type", "encodec")
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codec_config = CONFIG_MAPPING[codec_model_type](**codec_config)
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self.semantic_config = semantic_config
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self.coarse_acoustics_config = coarse_acoustics_config
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self.fine_acoustics_config = fine_acoustics_config
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self.codec_config = codec_config
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self.initializer_range = initializer_range
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super().__init__(**kwargs)
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__all__ = ["BarkCoarseConfig", "BarkConfig", "BarkFineConfig", "BarkSemanticConfig"]
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