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.
주요 클래스¶
RBLNWav2Vec2ForCTCConfig
: RBLN Wav2Vec2 CTC 모델의 설정 클래스입니다.RBLNWav2Vec2ForCTC
: Connectionist Temporal Classification (CTC) 작업을 위한 RBLN Wav2Vec2 모델입니다.
API 참조¶
Classes¶
RBLNWav2Vec2ForCTCConfig
¶
Bases: RBLNModelForMaskedLMConfig
Functions¶
__init__(max_seq_len=None, batch_size=None, model_input_names=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
|
Dict[str, Any]
|
Additional arguments passed to the parent RBLNModelConfig. |
{}
|
Raises:
Type | Description |
---|---|
ValueError
|
If batch_size is not a positive integer. |
Classes¶
RBLNWav2Vec2ForCTC
¶
Bases: RBLNModelForMaskedLM
Functions¶
from_pretrained(model_id, export=False, 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
|
bool
|
A boolean flag to indicate whether the model should be compiled. |
False
|
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., |
None
|
kwargs
|
Dict[str, 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 |
---|---|
Self
|
A RBLN model instance ready for inference on RBLN NPU devices. |
from_model(model, *, rbln_config=None, **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 |
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., |
None
|
kwargs
|
Dict[str, 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:
- Compiles the PyTorch model into an optimized RBLN graph
- Configures the model for the specified NPU device
- Creates the necessary runtime objects if requested
- Saves the compiled model and configurations
Returns:
Type | Description |
---|---|
Self
|
A RBLN model instance ready for inference on RBLN NPU devices. |
save_pretrained(save_directory)
¶
Saves a model and its configuration file to a directory, so that it can be re-loaded using the
[from_pretrained
] class method.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
save_directory
|
Union[str, PathLike]
|
The directory to save the model and its configuration files. Will be created if it doesn't exist. |
required |