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Wav2Vec2ForCTC

Wav2Vec2는 음성 처리를 위한 자기지도 학습 모델입니다. Connectionist Temporal Classification (CTC)을 사용하여 가변 길이 오디오 입력을 텍스트 전사로 매핑합니다. 이 모델은 자동 음성 인식(ASR) 작업에 특히 효과적입니다. RBLN NPU는 Optimum RBLN을 사용하여 Wav2Vec2 모델 추론을 가속화할 수 있습니다.

API 참조

Classes

RBLNWav2Vec2ForCTC

Bases: RBLNModelForMaskedLM

Wav2Vec2 Model with a language modeling head on top for Connectionist Temporal Classification (CTC).

This model inherits from [RBLNModelForMaskedLM]. Check the superclass documentation for the generic methods the library implements for all its model.

It implements the methods to convert a pre-trained Wav2Vec2 model into a RBLN Wav2Vec2 model by:

  • transferring the checkpoint weights of the original into an optimized RBLN graph,
  • compiling the resulting graph using the RBLN compiler.

Functions

from_pretrained(model_id, export=None, rbln_config=None, **kwargs) classmethod

The from_pretrained() function is utilized in its standard form as in the HuggingFace transformers library. User can use this function to load a pre-trained model from the HuggingFace library and convert it to a RBLN model to be run on RBLN NPUs.

Parameters:

Name Type Description Default
model_id Union[str, Path]

The model id of the pre-trained model to be loaded. It can be downloaded from the HuggingFace model hub or a local path, or a model id of a compiled model using the RBLN Compiler.

required
export Optional[bool]

A boolean flag to indicate whether the model should be compiled. If None, it will be determined based on the existence of the compiled model files in the model_id.

None
rbln_config Optional[Union[Dict, RBLNModelConfig]]

Configuration for RBLN model compilation and runtime. This can be provided as a dictionary or an instance of the model's configuration class (e.g., RBLNLlamaForCausalLMConfig for Llama models). For detailed configuration options, see the specific model's configuration class documentation.

None
kwargs Any

Additional keyword arguments. Arguments with the prefix rbln_ are passed to rbln_config, while the remaining arguments are passed to the HuggingFace library.

{}

Returns:

Type Description
RBLNModel

A RBLN model instance ready for inference on RBLN NPU devices.

save_pretrained(save_directory, push_to_hub=False, **kwargs)

Saves a model and its configuration file to a directory, so that it can be re-loaded using the [~optimum.rbln.modeling_base.RBLNBaseModel.from_pretrained] class method.

Parameters:

Name Type Description Default
save_directory Union[str, Path]

Directory where to save the model file.

required
push_to_hub bool

Whether or not to push your model to the HuggingFace model hub after saving it.

False
from_model(model, config=None, rbln_config=None, model_save_dir=None, subfolder='', **kwargs) classmethod

Converts and compiles a pre-trained HuggingFace library model into a RBLN model. This method performs the actual model conversion and compilation process.

Parameters:

Name Type Description Default
model PreTrainedModel

The PyTorch model to be compiled. The object must be an instance of the HuggingFace transformers PreTrainedModel class.

required
config Optional[PretrainedConfig]

The configuration object associated with the model.

None
rbln_config Optional[Union[RBLNModelConfig, Dict]]

Configuration for RBLN model compilation and runtime. This can be provided as a dictionary or an instance of the model's configuration class (e.g., RBLNLlamaForCausalLMConfig for Llama models). For detailed configuration options, see the specific model's configuration class documentation.

None
kwargs Any

Additional keyword arguments. Arguments with the prefix rbln_ are passed to rbln_config, while the remaining arguments are passed to the HuggingFace library.

{}

The method performs the following steps:

  1. Compiles the PyTorch model into an optimized RBLN graph
  2. Configures the model for the specified NPU device
  3. Creates the necessary runtime objects if requested
  4. Saves the compiled model and configurations

Returns:

Type Description
RBLNModel

A RBLN model instance ready for inference on RBLN NPU devices.

Classes

RBLNWav2Vec2ForCTCConfig

Bases: RBLNModelForMaskedLMConfig

Configuration class for RBLNWav2Vec2ForCTC.

This configuration class stores the configuration parameters specific to RBLN-optimized Wav2Vec2 models for Connectionist Temporal Classification (CTC) tasks.

Functions

__init__(max_seq_len=None, batch_size=None, model_input_names=None, model_input_shapes=None, **kwargs)

Parameters:

Name Type Description Default
max_seq_len Optional[int]

Maximum sequence length supported by the model.

None
batch_size Optional[int]

The batch size for inference. Defaults to 1.

None
model_input_names Optional[List[str]]

Names of the input tensors for the model. Defaults to class-specific rbln_model_input_names if not provided.

None
kwargs Any

Additional arguments passed to the parent RBLNModelConfig.

{}

Raises:

Type Description
ValueError

If batch_size is not a positive integer.

load(path, **kwargs) classmethod

Load a RBLNModelConfig from a path.

Parameters:

Name Type Description Default
path str

Path to the RBLNModelConfig file or directory containing the config file.

required
kwargs Any

Additional keyword arguments to override configuration values. Keys starting with 'rbln_' will have the prefix removed and be used to update the configuration.

{}

Returns:

Name Type Description
RBLNModelConfig RBLNModelConfig

The loaded configuration instance.

Note

This method loads the configuration from the specified path and applies any provided overrides. If the loaded configuration class doesn't match the expected class, a warning will be logged.