Whisper¶
Whisper는 다양한 오디오 데이터셋으로 훈련된 다목적 음성 인식 모델입니다. 다국어 음성 인식, 음성 번역 및 언어 식별을 수행할 수 있습니다. 이 모델은 인코더-디코더 아키텍처를 사용하며 다양한 음향 환경에서 강력한 성능을 보여줍니다. RBLN NPU는 Optimum RBLN을 사용하여 Whisper 모델 추론을 가속화할 수 있습니다.
API 참조¶
Classes¶
RBLNWhisperForConditionalGeneration
¶
Bases: RBLNModel
, RBLNWhisperGenerationMixin
Whisper model for speech recognition and transcription optimized for RBLN NPU.
This model inherits from [RBLNModel
]. It implements the methods to convert and run
pre-trained transformers based Whisper model on RBLN devices by:
- transferring the checkpoint weights of the original into an optimized RBLN graph,
- compiling the resulting graph using the RBLN compiler.
Example (Short form):
Functions¶
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., |
None
|
kwargs
|
Any
|
Additional keyword arguments. Arguments with the prefix |
{}
|
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 |
---|---|
RBLNModel
|
A RBLN model instance ready for inference on RBLN NPU devices. |
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., |
None
|
kwargs
|
Any
|
Additional keyword arguments. Arguments with the prefix |
{}
|
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
|
Classes¶
RBLNWhisperForConditionalGenerationConfig
¶
Bases: RBLNModelConfig
Configuration class for RBLNWhisperForConditionalGeneration.
This configuration class stores the configuration parameters specific to RBLN-optimized Whisper models for speech recognition and transcription tasks.
Functions¶
__init__(batch_size=None, token_timestamps=None, use_attention_mask=None, enc_max_seq_len=None, dec_max_seq_len=None, kvcache_num_blocks=None, kvcache_block_size=None, **kwargs)
¶
Parameters:
Name | Type | Description | Default |
---|---|---|---|
batch_size
|
int
|
The batch size for inference. Defaults to 1. |
None
|
token_timestamps
|
bool
|
Whether to output token timestamps during generation. Defaults to False. |
None
|
use_attention_mask
|
bool
|
Whether to use attention masks during inference. This is automatically |
None
|
enc_max_seq_len
|
int
|
Maximum sequence length for the encoder. |
None
|
dec_max_seq_len
|
int
|
Maximum sequence length for the decoder. |
None
|
kvcache_num_blocks
|
int
|
The total number of blocks to allocate for the PagedAttention KV cache for the SelfAttention. Defaults to batch_size. |
None
|
kvcache_block_size
|
int
|
Sets the size (in number of tokens) of each block in the PagedAttention KV cache for the SelfAttention. Defaults to dec_max_seq_len. |
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.