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import copy |
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from transformers.configuration_utils import PretrainedConfig |
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from transformers.utils import logging |
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from models.configuration_wav2vec2 import Wav2Vec2Config |
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from models.configuration_bart import BartConfig |
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from transformers import AutoConfig |
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logger = logging.get_logger(__name__) |
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class SpeechEncoderDecoderConfig(PretrainedConfig): |
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r""" |
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[`SpeechEncoderDecoderConfig`] is the configuration class to store the configuration of a |
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[`SpeechEncoderDecoderModel`]. It is used to instantiate an Encoder Decoder model according to the specified |
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arguments, defining the encoder and decoder configs. |
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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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kwargs (*optional*): |
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Dictionary of keyword arguments. Notably: |
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- **encoder** ([`PretrainedConfig`], *optional*) -- An instance of a configuration object that defines |
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the encoder config. |
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- **decoder** ([`PretrainedConfig`], *optional*) -- An instance of a configuration object that defines |
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the decoder config. |
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Examples: |
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```python |
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>>> from transformers import BertConfig, Wav2Vec2Config, SpeechEncoderDecoderConfig, SpeechEncoderDecoderModel |
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>>> # Initializing a Wav2Vec2 & BERT style configuration |
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>>> config_encoder = Wav2Vec2Config() |
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>>> config_decoder = BertConfig() |
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>>> config = SpeechEncoderDecoderConfig.from_encoder_decoder_configs(config_encoder, config_decoder) |
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>>> # Initializing a Wav2Vec2Bert model from a Wav2Vec2 & bert-base-uncased style configurations |
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>>> model = SpeechEncoderDecoderModel(config=config) |
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>>> # Accessing the model configuration |
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>>> config_encoder = model.config.encoder |
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>>> config_decoder = model.config.decoder |
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>>> # set decoder config to causal lm |
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>>> config_decoder.is_decoder = True |
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>>> config_decoder.add_cross_attention = True |
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>>> # Saving the model, including its configuration |
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>>> model.save_pretrained("my-model") |
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>>> # loading model and config from pretrained folder |
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>>> encoder_decoder_config = SpeechEncoderDecoderConfig.from_pretrained("my-model") |
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>>> model = SpeechEncoderDecoderModel.from_pretrained("my-model", config=encoder_decoder_config) |
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```""" |
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model_type = "speech-encoder-decoder" |
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is_composition = True |
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def __init__(self, **kwargs): |
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super().__init__(**kwargs) |
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if "encoder" not in kwargs or "decoder" not in kwargs: |
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raise ValueError( |
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f"A configuraton of type {self.model_type} cannot be instantiated because not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}" |
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) |
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encoder_config = kwargs.pop("encoder") |
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decoder_config = kwargs.pop("decoder") |
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self.encoder = Wav2Vec2Config(**encoder_config) |
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self.decoder = BartConfig(**decoder_config) |
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self.is_encoder_decoder = True |
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@classmethod |
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def from_encoder_decoder_configs( |
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cls, encoder_config: PretrainedConfig, decoder_config: PretrainedConfig, **kwargs |
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) -> PretrainedConfig: |
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r""" |
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Instantiate a [`SpeechEncoderDecoderConfig`] (or a derived class) from a pre-trained encoder model |
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configuration and decoder model configuration. |
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Returns: |
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[`SpeechEncoderDecoderConfig`]: An instance of a configuration object |
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""" |
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logger.info("Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config") |
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decoder_config.is_decoder = True |
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decoder_config.add_cross_attention = True |
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return cls(encoder=encoder_config.to_dict(), decoder=decoder_config.to_dict(), **kwargs) |
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def to_dict(self): |
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""" |
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Serializes this instance to a Python dictionary. Override the default *to_dict()* from *PretrainedConfig*. |
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Returns: |
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`Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance, |
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""" |
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output = copy.deepcopy(self.__dict__) |
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output["encoder"] = self.encoder.to_dict() |
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output["decoder"] = self.decoder.to_dict() |
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output["model_type"] = self.__class__.model_type |
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return output |
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