Systems and methods for configuring and using an audio transcript correction machine learning model
A system, method, and computer-program product includes constructing a transcript correction training data corpus that includes a plurality of labeled audio transcription training data samples, wherein each of the plurality of labeled audio transcription training data samples includes: an incorrect audio transcription of a target piece of audio data; a correct audio transcription of the target piece of audio data; and a transcript correction identifier that, when applied to a model input that includes a likely incorrect audio transcript, defines a text-to-text transformation objective causing an audio transcript correction machine learning model to predict a corrected audio transcript based on the likely incorrect audio transcript; configuring the audio transcript correction machine learning model based on a training of a machine learning text-to-text transformer model using the transcript correction training data corpus; and executing the audio transcript correction machine learning model within a speech-to-text post-processing sequence of a speech-to-text service.
1. A computer-program product embodied in a non-transitory machine-readable storage medium storing computer instructions that, when executed by one or more processors, perform operations comprising:
constructing a transcript correction training data corpus comprising a plurality of labeled audio transcription training data samples, wherein each of the plurality of labeled audio transcription training data samples includes:
an incorrect audio transcription of a target piece of audio data, wherein the incorrect audio transcription is assigned an incorrect audio transcription label;
a correct audio transcription of the target piece of audio data, wherein the correct audio transcription is assigned a correct audio transcription label; and
a transcript correction identifier that, when applied to a model input comprising a likely incorrect audio transcript, defines a text-to-text transformation objective causing an audio transcript correction machine learning model to predict a corrected audio transcript based on the likely incorrect audio transcript;
configuring the audio transcript correction machine learning model based on a training of a machine learning text-to-text transformer model using the transcript correction training data corpus;
constructing an anomalous transcript correction training data corpus comprising a plurality of anomalous audio transcription training data samples, wherein each of the plurality of anomalous audio transcription training data samples includes:
an anomalous training sample pairing between (a) an anomalous audio transcript of a target audio data segment and (b) an annotated audio transcript of the target audio data segment, wherein:
each anomalous audio transcript of the plurality of anomalous audio transcription training data samples relates to an inaccurate audio transcription representation of the target audio data segment; and
each anomalous audio transcript of the plurality of anomalous audio transcription training data samples was computed by the audio transcript correction machine learning model;
adapting the audio transcript correction machine learning model to an adapted audio transcript correction machine learning model based on a training of the audio transcript correction machine learning model using the anomalous transcript correction training data corpus;
replacing the audio transcript correction machine learning model with the adapted audio transcript correction machine learning model based on one or more model replacement efficacy metrics computed for the adapted audio transcript correction machine learning model satisfying a minimum model replacement efficacy value; and
executing the adapted audio transcript correction machine learning model within a speech-to-text post-processing sequence of a speech-to-text service based on the adapted audio transcript correction machine learning model satisfying a minimum audio transcript correction efficacy value.
2. The computer-program product according to claim 1 , further comprising:
receiving a target audio data file comprising one or more utterances associated with a target user;
generating, via a speech-to-text machine learning model, a predicted audio transcript of the target audio data file based on an input of the target audio data file to the speech-to-text machine learning model, wherein the predicted audio transcript comprises a target incorrect audio transcription that includes one or more transcription errors; and
generating, via the adapted audio transcript correction machine learning model, a corrected audio transcript that corrects the one or more transcription errors of the predicted audio transcript based on an input of the predicted audio transcript to the adapted audio transcript correction machine learning model.
3. The computer-program product according to claim 1 , wherein:
each of the plurality of labeled audio transcription training data samples includes:
a first component comprising the transcript correction identifier and the incorrect audio transcription of the target piece of audio data, wherein the transcript correction identifier is a prefix to the incorrect audio transcription; and
a second component comprising the correct audio transcription of the target piece of audio data.
4. The computer-program product according to claim 1 , wherein:
the machine learning text-to-text transformer model is configured to perform a plurality of trained text-to-text transformation tasks;
the transcript correction identifier defines an audio transcription correction task that is distinct from the plurality of trained text-to-text transformation tasks; and
the adapted audio transcript correction machine learning model is configured to perform the plurality of trained text-to-text transformation tasks and the audio transcription correction task based on the training of the machine learning text-to-text transformer model with the transcript correction training data corpus.
5. The computer-program product according to claim 1 , further comprising:
obtaining audio data comprising one or more utterances;
generating, via a speech-to-text machine learning model, a predicted audio transcript based on an input of the audio data;
generating, via the adapted audio transcript correction machine learning model, an adjusted audio transcript of the predicted audio transcript based on an input of a task-specific instruction to the adapted audio transcript correction machine learning model, wherein the task-specific instruction includes:
an instructional prefix component comprising the transcript correction identifier; and
an input text string comprising the predicted audio transcript.
6. The computer-program product according to claim 5 , wherein:
the adapted audio transcript correction machine learning model identifies a task type of the instructional prefix component, wherein the task type of the instructional prefix component corresponds to the transcript correction identifier;
the computer-program product further includes computer instructions for performing operations including:
obtaining, from a memory, a set of weights and biases generated from the training of the machine learning text-to-text transformer model that corresponds to the transcript correction identifier; and
the executing the adapted audio transcript correction machine learning model includes using the set of weights and biases to generate the adjusted audio transcript.
7. The computer-program product according to claim 1 , wherein:
each of the plurality of labeled audio transcription training data samples defines a distinct training sample pairing between the incorrect audio transcription of the target piece of audio data and the correct audio transcription of the target piece of audio data.
8. The computer-program product according to claim 1 , wherein:
the likely incorrect audio transcript is generated by a speech-to-text machine learning model;
the likely incorrect audio transcript includes a grammatical error;
the computer-program product further includes computer instructions for performing operations including:
providing the model input comprising the likely incorrect audio transcript to the audio transcript correction machine learning model;
based on providing the model input to the audio transcript correction machine learning model, the adapted audio transcript correction machine learning model:
identifies a likely incorrect token in the likely incorrect audio transcript;
generates the corrected audio transcript in which a likely correct token replaces the likely incorrect token; and
outputs the corrected audio transcript that corrects the grammatical error.
9. The computer-program product according to claim 1 , wherein:
the likely incorrect audio transcript is generated by a speech-to-text machine learning model;
the likely incorrect audio transcript includes a semantically incorrect text string; and
the computer-program product further includes computer instructions for performing operations including:
providing the model input comprising the likely incorrect audio transcript to the audio transcript correction machine learning model;
based on providing the model input to the adapted audio transcript correction machine learning model, the adapted audio transcript correction machine learning model:
identifies a likely incorrect text string in the likely incorrect audio transcript;
generates the corrected audio transcript in which a likely correct text string replaces the likely incorrect text string; and
outputs the corrected audio transcript that corrects the semantically incorrect text string.
10. The computer-program product according to claim 1 , further comprising:
generating, via a speech-to-text machine learning model, a raw audio transcript based on an input of audio data to the speech-to-text machine learning model; and
commencing the speech-to-text post-processing sequence in response to the generating of the raw audio transcript, wherein the speech-to-text post-processing sequence includes:
obtaining the raw audio transcript;
configuring a task-specific instruction that includes the transcript correction identifier and the raw audio transcript in response to obtaining the raw audio transcript;
providing the task-specific instruction as model input to the audio transcript correction machine learning model; and
generating a corrected audio transcript based on the model input of the task-specific instruction to the adapted audio transcript correction machine learning model.
11. The computer-program product according to claim 1 , wherein:
the adapted audio transcript correction machine learning model satisfies the minimum audio transcript correction efficacy value when a predictive accuracy value of the adapted audio transcript correction machine learning model satisfies a predetermined, minimum audio transcript correction accuracy value.
12. A computer-implemented method comprising:
constructing a transcript correction training data corpus comprising a plurality of labeled audio transcription training data samples, wherein each of the plurality of labeled audio transcription training data samples includes:
an incorrect audio transcription of a target piece of audio data, wherein the incorrect audio transcription is assigned an incorrect audio transcription label;
a correct audio transcription of the target piece of audio data, wherein the correct audio transcription is assigned a correct audio transcription label; and
a transcript correction identifier that, when applied to a model input comprising a likely incorrect audio transcript, defines a text-to-text transformation objective causing an audio transcript correction machine learning model to predict a corrected audio transcript based on the likely incorrect audio transcript;
configuring the audio transcript correction machine learning model based on a training of a machine learning text-to-text transformer model using the transcript correction training data corpus;
constructing an anomalous transcript correction training data corpus comprising a plurality of anomalous audio transcription training data samples, wherein each of the plurality of anomalous audio transcription training data samples includes:
an anomalous training sample pairing between (a) an anomalous audio transcript of a target audio data segment and (b) an annotated audio transcript of the target audio data segment, wherein:
each anomalous audio transcript of the plurality of anomalous audio transcription training data samples relates to an inaccurate audio transcription representation of the target audio data segment; and
each anomalous audio transcript of the plurality of anomalous audio transcription training data samples was computed by the audio transcript correction machine learning model;
adapting the audio transcript correction machine learning model to an adapted audio transcript correction machine learning model based on a training of the audio transcript correction machine learning model using the anomalous transcript correction training data corpus;
replacing the audio transcript correction machine learning model with the adapted audio transcript correction machine learning model based on one or more model replacement efficacy metrics computed for the adapted audio transcript correction machine learning model satisfying a minimum model replacement efficacy value; and
executing the adapted audio transcript correction machine learning model within a speech-to-text post-processing sequence of a speech-to-text service based on the adapted audio transcript correction machine learning model satisfying a minimum audio transcript correction efficacy value.
13. The computer-implemented method according to claim 12 , further comprising:
receiving a target audio data file comprising one or more utterances associated with a target user;
generating, via a speech-to-text machine learning model, a predicted audio transcript of the target audio data file based on an input of the target audio data file to the speech-to-text machine learning model, wherein the predicted audio transcript comprises a target incorrect audio transcription that includes one or more transcription errors; and
generating, via the audio transcript correction machine learning model, a corrected audio transcript that corrects the one or more transcription errors of the predicted audio transcript based on an input of the predicted audio transcript to the adapted audio transcript correction machine learning model.
14. The computer-implemented method according to claim 12 , wherein:
each of the plurality of labeled audio transcription training data samples includes:
a first component comprising the transcript correction identifier and the incorrect audio transcription of the target piece of audio data, wherein the transcript correction identifier is a prefix to the incorrect audio transcription; and
a second component comprising the correct audio transcription of the target piece of audio data.
15. The computer-implemented method according to claim 12 , wherein:
the machine learning text-to-text transformer model is configured to perform a plurality of trained text-to-text transformation tasks;
the transcript correction identifier defines an audio transcription correction task that is distinct from the plurality of trained text-to-text transformation tasks; and
the adapted audio transcript correction machine learning model is configured to perform the plurality of trained text-to-text transformation tasks and the audio transcription correction task based on the training of the machine learning text-to-text transformer model with the transcript correction training data corpus.
16. The computer-implemented method according to claim 12 , further comprising:
obtaining audio data comprising one or more utterances;
generating, via a speech-to-text machine learning model, a predicted audio transcript based on an input of the audio data;
generating, via the adapted audio transcript correction machine learning model, an adjusted audio transcript of the predicted audio transcript based on an input of a task-specific instruction to the adapted audio transcript correction machine learning model, wherein the task-specific instruction includes:
an instructional prefix component comprising the transcript correction identifier; and
an input text string comprising the predicted audio transcript.
17. The computer-implemented method according to claim 16 , wherein:
the adapted audio transcript correction machine learning model identifies a task type of the instructional prefix component, wherein the task type of the instructional prefix component corresponds to the transcript correction identifier;
the computer-program product further includes computer instructions for performing operations including:
obtaining, from a memory, a set of weights and biases generated from the training of the machine learning text-to-text transformer model that corresponds to the transcript correction identifier; and
the executing the adapted audio transcript correction machine learning model includes using the set of weights and biases to generate the adjusted audio transcript.
18. The computer-implemented method according to claim 12 , wherein:
each of the plurality of labeled audio transcription training data samples defines a distinct training sample pairing between the incorrect audio transcription of the target piece of audio data and the correct audio transcription of the target piece of audio data.
19. The computer-implemented method according to claim 12 , wherein:
the likely incorrect audio transcript is generated by a speech-to-text machine learning model;
the likely incorrect audio transcript includes a grammatical error;
the computer-program product further includes computer instructions for performing operations including:
providing the model input comprising the likely incorrect audio transcript to the adapted audio transcript correction machine learning model;
based on providing the model input to the adapted audio transcript correction machine learning model, the adapted audio transcript correction machine learning model:
identifies a likely incorrect token in the likely incorrect audio transcript;
generates the corrected audio transcript in which a likely correct token replaces the likely incorrect token; and
outputs the corrected audio transcript that corrects the grammatical error.
20. The computer-implemented method according to claim 12 , wherein:
the likely incorrect audio transcript is generated by a speech-to-text machine learning model;
the likely incorrect audio transcript includes a semantically incorrect text string; and
the computer-program product further includes computer instructions for performing operations including:
providing the model input comprising the likely incorrect audio transcript to the adapted audio transcript correction machine learning model;
based on providing the model input to the adapted audio transcript correction machine learning model, the adapted audio transcript correction machine learning model:
identifies a likely incorrect text string in the likely incorrect audio transcript;
generates the corrected audio transcript in which a likely correct text string replaces the likely incorrect text string; and
outputs the corrected audio transcript that corrects the semantically incorrect text string.
21. The computer-implemented method according to claim 12 , further comprising:
generating, via a speech-to-text machine learning model, a raw audio transcript based on an input of audio data to the speech-to-text machine learning model; and
commencing the speech-to-text post-processing sequence in response to the generating of the raw audio transcript, wherein the speech-to-text post-processing sequence includes:
obtaining the raw audio transcript;
configuring a task-specific instruction that includes the transcript correction identifier and the raw audio transcript in response to obtaining the raw audio transcript;
providing the task-specific instruction as model input to the adapted audio transcript correction machine learning model; and
generating a corrected audio transcript based on the model input of the task-specific instruction to the adapted audio transcript correction machine learning model.
22. The computer-implemented method according to claim 12 , wherein:
the adapted audio transcript correction machine learning model satisfies the minimum audio transcript correction efficacy value when a predictive accuracy value of the adapted audio transcript correction machine learning model satisfies a predetermined, minimum audio transcript correction accuracy value.
23. A computer-implemented system comprising:
one or more processors;
a memory;
a computer-readable medium operably coupled to the one or more processors, the computer-readable medium having computer-readable instructions stored thereon that, when executed by the one or more processors, cause a computing device to perform operations comprising:
constructing a transcript correction training data corpus comprising a plurality of labeled audio transcription training data samples, wherein each of the plurality of labeled audio transcription training data samples includes:
an incorrect audio transcription of a target piece of audio data, wherein the incorrect audio transcription is assigned an incorrect audio transcription label;
a correct audio transcription of the target piece of audio data, wherein the correct audio transcription is assigned a correct audio transcription label; and
a transcript correction identifier that, when applied to a model input comprising a likely incorrect audio transcript, defines a text-to-text transformation objective causing an audio transcript correction machine learning model to predict a corrected audio transcript based on the likely incorrect audio transcript;
configuring the audio transcript correction machine learning model based on a training of a machine learning text-to-text transformer model using the transcript correction training data corpus;
constructing an anomalous transcript correction training data corpus comprising a plurality of anomalous audio transcription training data samples, wherein each of the plurality of anomalous audio transcription training data samples includes:
an anomalous training sample pairing between (a) an anomalous audio transcript of a target audio data segment and (b) an annotated audio transcript of the target audio data segment, wherein:
each anomalous audio transcript of the plurality of anomalous audio transcription training data samples relates to an inaccurate audio transcription representation of the target audio data segment; and
each anomalous audio transcript of the plurality of anomalous audio transcription training data samples was computed by the audio transcript correction machine learning model;
adapting the audio transcript correction machine learning model to an adapted audio transcript correction machine learning model based on a training of the audio transcript correction machine learning model using the anomalous transcript correction training data corpus;
replacing the audio transcript correction machine learning model with the adapted audio transcript correction machine learning model based on one or more model replacement efficacy metrics computed for the adapted audio transcript correction machine learning model satisfying a minimum model replacement efficacy value; and
executing the adapted audio transcript correction machine learning model within a speech-to-text post-processing sequence of a speech-to-text service based on the adapted audio transcript correction machine learning model satisfying a minimum audio transcript correction efficacy value.
24. The computer-implemented system according to claim 23 , further comprising:
receiving a target audio data file comprising one or more utterances associated with a target user;
generating, via a speech-to-text machine learning model, a predicted audio transcript of the target audio data file based on an input of the target audio data file to the speech-to-text machine learning model, wherein the predicted audio transcript comprises a target incorrect audio transcription that includes one or more transcription errors; and
generating, via the adapted audio transcript correction machine learning model, a corrected audio transcript that corrects the one or more transcription errors of the predicted audio transcript based on an input of the predicted audio transcript to the adapted audio transcript correction machine learning model.
25. The computer-implemented system according to claim 23 , wherein:
each of the plurality of labeled audio transcription training data samples includes:
a first component comprising the transcript correction identifier and the incorrect audio transcription of the target piece of audio data, wherein the transcript correction identifier is a prefix to the incorrect audio transcription; and
a second component comprising the correct audio transcription of the target piece of audio data.
26. The computer-implemented system according to claim 23 , wherein:
the machine learning text-to-text transformer model is configured to perform a plurality of trained text-to-text transformation tasks;
the transcript correction identifier defines an audio transcription correction task that is distinct from the plurality of trained text-to-text transformation tasks; and
the adapted audio transcript correction machine learning model is configured to perform the plurality of trained text-to-text transformation tasks and the audio transcription correction task based on the training of the machine learning text-to-text transformer model with the transcript correction training data corpus.
27. The computer-implemented system according to claim 23 , further comprising:
obtaining audio data comprising one or more utterances;
generating, via a speech-to-text machine learning model, a predicted audio transcript based on an input of the audio data;
generating, via the adapted audio transcript correction machine learning model, an adjusted audio transcript of the predicted audio transcript based on an input of a task-specific instruction to the adapted audio transcript correction machine learning model, wherein the task-specific instruction includes:
an instructional prefix component comprising the transcript correction identifier; and
an input text string comprising the predicted audio transcript.
28. The computer-implemented system according to claim 27 , wherein:
the adapted audio transcript correction machine learning model identifies a task type of the instructional prefix component, wherein the task type of the instructional prefix component corresponds to the transcript correction identifier;
the computer-program product further includes computer instructions for performing operations including:
obtaining, from a memory, a set of weights and biases generated from the training of the machine learning text-to-text transformer model that corresponds to the transcript correction identifier; and
the executing the adapted audio transcript correction machine learning model includes using the set of weights and biases to generate the adjusted audio transcript.
29. A computer-program product embodied in a non-transitory machine-readable storage medium storing computer instructions that, when executed by one or more processors, perform operations comprising:
constructing a transcript correction training data corpus comprising a plurality of labeled audio transcription training data samples, wherein each of the plurality of labeled audio transcription training data samples includes:
an incorrect audio transcription of a target piece of audio data, wherein the incorrect audio transcription is assigned an incorrect audio transcription label;
a correct audio transcription of the target piece of audio data, wherein the correct audio transcription is assigned a correct audio transcription label; and
a transcript correction identifier that, when applied to a model input comprising a likely incorrect audio transcript, defines a text-to-text transformation objective causing an audio transcript correction machine learning model to predict a corrected audio transcript based on the likely incorrect audio transcript;
configuring the audio transcript correction machine learning model based on a training of a machine learning text-to-text transformer model using the transcript correction training data corpus;
executing the audio transcript correction machine learning model within a speech-to-text post-processing sequence of a speech-to-text service based on the audio transcript correction machine learning model satisfying a minimum audio transcript correction efficacy value;
obtaining audio data comprising one or more utterances;
generating, via a speech-to-text machine learning model, a predicted audio transcript based on an input of the audio data;
generating, via the audio transcript correction machine learning model, an adjusted audio transcript of the predicted audio transcript based on an input of a task-specific instruction to the audio transcript correction machine learning model, wherein the audio transcript correction machine learning model identifies a task type of the instructional prefix component, wherein the task type of the instructional prefix component corresponds to the transcript correction identifier, and wherein the task-specific instruction includes:
an instructional prefix component comprising the transcript correction identifier; and
an input text string comprising the predicted audio transcript;
obtaining, from a memory, a set of weights and biases generated from the training of the machine learning text-to-text transformer model that corresponds to the transcript correction identifier, wherein the executing the audio transcript correction machine learning model includes using the set of weights and biases to generate the adjusted audio transcript.