speechtotext.model.deepgramWrapper.DeepgramAPIWrapper

class DeepgramAPIWrapper(model_version)[source]

Bases: ModelWrapper

Wrapper for deepgram API. DEEPGRAM_API_KEY needs to be in the ‘.env’ file in current directory.

Wrapper for deepgram model.

Parameters:

model_version (deepgramAPIVersion) – Model version of deepgram STT API to use.

Methods

benchmark_n_samples

Benchmark n samples with model.

benchmark_sample

Benchmark sample with model.

benchmark_samples

Benchmark samples with model.

convert_sample

Convert sample to correct format.

get_model

Get model.

get_transcript_of_file

Attributes

LANGUAGE_CODE

Code for the language to transcribe.

PATH_OF_TEMP_CONVERTED_AUDIO_FILE

path to temp file that will be created to convert the audio files to an accepted audio format.
LANGUAGE_CODE: str = 'nl'

Code for the language to transcribe.

See supported languages for deepgram

Type:

str

PATH_OF_TEMP_CONVERTED_AUDIO_FILE: str = 'converted_audio_file.wav'

path to temp file that will be created to convert the audio files to an accepted audio format.

Type:

PATH_OF_TEMP_CONVERTED_AUDIO_FILE

_append_error(samples, audio_id, error)

Append error to model_errors.

Parameters:

  • samples (SampleDataset) – Dataset of audio.

  • id (str) – Id of failed sample.

  • error (str) – Error message.

_benchmark_sample_with_time(dataset, audio_id, with_cleaning=True)

Benchmark sample for model with timer.

Parameters:

  • dataset (Dataset) – Dataset of audio.

  • id (str) – Id of audio file.

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

Returns:

Metrics of the transcript.

Return type:

Metrics

benchmark_n_samples(dataset, number_of_samples, with_cleaning=True)

Benchmark n samples with model.

Parameters:

  • dataset (Dataset) – Dataset of audio.

  • number_of_samples (int) – Number of random samples to benchmerk.

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

Returns:

List of metrics for each sample.

Return type:

list

benchmark_sample(dataset, audio_id, with_cleaning=True)

Benchmark sample with model.

Parameters:

  • dataset (Dataset) – Dataset of audio.

  • id (str) – Id of audio file.

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

Returns:

Metrics of the transcript.

Return type:

Metrics

benchmark_samples(samples, with_cleaning=True)

Benchmark samples with model.

Parameters:

  • dataset (Dataset) – Dataset of audio.

  • number_of_samples (int) – Number of random samples to benchmerk.

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

Returns:

List of metrics for each sample.

Return type:

list

convert_sample(path_to_sample)

Convert sample to correct format.

Parameters:

  • path_to_sample (str) – Path to sample.

  • override (bool, optional) – Override original file. Defaults to False.

Returns:

Path to converted sample.

Return type:

str

get_model()[source]

Get model.