Base Bulk Inference

Bases: Bolt

AudioBulk is a class designed for bulk processing of audio data using various audio models from Hugging Face. It focuses on audio generation and transformation tasks, supporting a range of models and configurations.

Attributes:

Name	Type	Description
model	AutoModelForAudioClassification	The audio model for generation or transformation tasks.
processor	AutoFeatureExtractor	The processor for preparing input data for the model.

Parameters:

Name	Type	Description	Default
input	BatchInput	Configuration and data inputs for the batch process.	required
output	BatchOutput	Configurations for output data handling.	required
state	State	State management for the Bolt.	required
**kwargs		Arbitrary keyword arguments for extended configurations.	{}

Methods

audio(**kwargs: Any) -> Dict[str, Any]: Provides an API endpoint for audio processing functionality. Accepts various parameters for customizing the audio processing tasks.

process(audio_input: Union[str, bytes], **processing_params: Any) -> dict: Processes the audio input based on the provided parameters. Supports multiple processing methods.

__init__(input, output, state, **kwargs)

Initializes the AudioBulk with configurations and sets up logging. Prepares the environment for audio processing tasks.

Parameters:

Name	Type	Description	Default
input	BatchInput	The input data configuration for the audio processing task.	required
output	BatchOutput	The output data configuration for the results of the audio processing.	required
state	State	The state configuration for the Bolt, managing its operational status.	required
**kwargs		Additional keyword arguments for extended functionality and model configurations.	{}

done()

Finalizes the AudioBulk processing. Sends notification email if configured.

This method should be called after all audio processing tasks are complete. It handles any final steps such as sending notifications or cleaning up resources.

load_models(model_name, processor_name, model_revision=None, processor_revision=None, model_class='', processor_class='AutoFeatureExtractor', use_cuda=False, precision='float16', quantization=0, device_map='auto', max_memory={0: '24GB'}, torchscript=False, compile=False, flash_attention=False, better_transformers=False, use_whisper_cpp=False, use_faster_whisper=False, **model_args)

Loads and configures the specified audio model and processor for audio processing.

Parameters:

Name	Type	Description	Default
model_name	str	Name or path of the audio model to load.	required
processor_name	str	Name or path of the processor to load.	required
model_revision	Optional[str]	Specific model revision to load (e.g., commit hash).	None
processor_revision	Optional[str]	Specific processor revision to load.	None
model_class	str	Class of the model to be loaded.	''
processor_class	str	Class of the processor to be loaded.	'AutoFeatureExtractor'
use_cuda	bool	Flag to use CUDA for GPU acceleration.	False
precision	str	Desired precision for computations ("float32", "float16", etc.).	'float16'
quantization	int	Bit level for model quantization (0 for none, 8 for 8-bit).	0
device_map	Union[str, Dict, None]	Specific device(s) for model operations.	'auto'
max_memory	Dict[int, str]	Maximum memory allocation for the model.	{0: '24GB'}
torchscript	bool	Enable TorchScript for model optimization.	False
compile	bool	Enable Torch JIT compilation.	False
flash_attention	bool	Flag to enable Flash Attention optimization for faster processing.	False
better_transformers	bool	Flag to enable Better Transformers optimization for faster processing.	False
use_whisper_cpp	bool	Whether to use whisper.cpp to load the model. Defaults to False. Note: only works for these models: https://github.com/aarnphm/whispercpp/blob/524dd6f34e9d18137085fb92a42f1c31c9c6bc29/src/whispercpp/utils.py#L32	False
use_faster_whisper	bool	Whether to use faster-whisper.	False
**model_args	Any	Additional arguments for model loading.	{}

Returns:

Type	Description
Tuple[AutoModelForAudioClassification, AutoFeatureExtractor]	Tuple[AutoModelForAudioClassification, AutoFeatureExtractor]: Loaded model and processor.