speechtotext.benchmark.customBenchmarks.AzureAPIBenchmark

class AzureAPIBenchmark(with_cleaning=True)

Bases: Benchmark

Benchmark for Azure API.

Create benchmark object.

Parameters:

with_cleaning (bool, optional) – Clean. Defaults to True.

Methods

convert_to_pandas	Convert metrics to dataframe.
create_models	Creates an list of ModelWrappers.
save_to_csv	Save outputs of benchmark to csv.
set_dataset	Set dataset for Benchmark class.
update_samples	Update the sample dataset.

Attributes

BENCHMARK_SAMPLES	Dataset samples.
DATASET	Original dataset.
ERROR_LIST	List of errors.
MODEL_BASE	Name of base model.

BENCHMARK_SAMPLES: Dataset = None

Dataset samples.

Type:

Dataset

DATASET: Dataset = None

Original dataset.

Type:

Dataset

ERROR_LIST: list[DataFrame] = []

List of errors.

Type:

list[pd.core.frame.DataFrame]

MODEL_BASE: str = 'AzureAPI'

Name of base model.

Type:

str

__call__(number_of_samples, with_cleaning=True)

Benchmark n samples.benchmark_results_to_csv

Parameters:

• number_of_samples (int) – Number of samples to benchmark.

• with_cleaning (bool, optional) – Set True to clean transcripts. Defaults to True.

convert_to_pandas()

Convert metrics to dataframe.

Returns:

Pandas dataframe.

Return type:

pd.core.frame.DataFrame

create_models()

Creates an list of ModelWrappers.

Returns:

List of model wrappers.

Return type:

list[ModelWrapper]

save_to_csv(save_name)

Save outputs of benchmark to csv.

Parameters:

save_name (str) – Filename of output.

classmethod set_dataset(dataset)

Set dataset for Benchmark class.

Parameters:

dataset (Dataset) – Dataset to use with benchmark.

classmethod update_samples(cls, number_of_samples)

Update the sample dataset.

Parameters:

number_of_samples (int) – Number of samples.