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medkit.audio.transcription.sb_transcriber

medkit.audio.transcription.sb_transcriber#

This module needs extra-dependencies not installed as core dependencies of medkit. To install them, use pip install medkit-lib[sb-transcriber].

Classes:

SBTranscriber(model, needs_decoder[, ...])

Transcriber operation based on a SpeechBrain model.
class SBTranscriber(model, needs_decoder, output_label='transcribed_text', add_trailing_dot=True, capitalize=True, cache_dir=None, device=-1, batch_size=1, uid=None)[source]#

Transcriber operation based on a SpeechBrain model.

For each segment given as input, a transcription attribute will be created with the transcribed text as value. If needed, a text document can later be created from all the transcriptions of a audio document using ~medkit.audio.transcription.TranscribedTextDocument.from_audio_doc

Parameters:

  • model (str or Path) – Name of the model on the Hugging Face models hub, or local path.

  • needs_decoder (bool) – Whether the model should be used with the speechbrain EncoderDecoderASR class or the EncoderASR class. If unsure, check the code snippets on the model card on the hub.

  • output_label (str, default="transcribed_text") – Label of the attribute containing the transcribed text that will be attached to the input segments

  • add_trailing_dot (bool, default=True) – If True, a dot will be added at the end of each transcription text.

  • capitalize (bool, default=True) – It True, the first letter of each transcription text will be uppercased and the rest lowercased.

  • cache_dir (str or Path, optional) – Directory where to store the downloaded model. If None, speechbrain will use “pretrained_models/” and “model_checkpoints/” directories in the current working directory.

  • device (int, default=-1) – Device to use for pytorch models. Follows the Hugging Face convention (-1 for cpu and device number for gpu, for instance 0 for “cuda:0”)

  • batch_size (int, default=1) – Number of segments in batches processed by the model.

  • uid (str, optional) – Identifier of the transcriber.

Methods:

run(segments)

Add a transcription attribute to each segment with a text value containing the transcribed text.

set_prov_tracer(prov_tracer)

Enable provenance tracing.

Attributes:

description

Contains all the operation init parameters.
run(segments)[source]#

Add a transcription attribute to each segment with a text value containing the transcribed text.

Parameters:

segments (list of Segment) – List of segments to transcribe

property description: OperationDescription#

Contains all the operation init parameters.

Return type:

OperationDescription

set_prov_tracer(prov_tracer)#

Enable provenance tracing.

Parameters:

prov_tracer (ProvTracer) – The provenance tracer used to trace the provenance.

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