Amazon Transcribe examples using SDK for Python (Boto3) - AWS SDK Code Examples
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Amazon Transcribe examples using SDK for Python (Boto3)

The following code examples show you how to perform actions and implement common scenarios by using the AWS SDK for Python (Boto3) with Amazon Transcribe.

Actions are code excerpts from larger programs and must be run in context. While actions show you how to call individual service functions, you can see actions in context in their related scenarios.

Scenarios are code examples that show you how to accomplish specific tasks by calling multiple functions within a service or combined with other AWS services.

Each example includes a link to the complete source code, where you can find instructions on how to set up and run the code in context.

Actions

The following code example shows how to use CreateVocabulary.

SDK for Python (Boto3)
Note

There's more on GitHub. Find the complete example and learn how to set up and run in the AWS Code Examples Repository. 

def create_vocabulary(
    vocabulary_name, language_code, transcribe_client, phrases=None, table_uri=None
):
    """
    Creates a custom vocabulary that can be used to improve the accuracy of
    transcription jobs. This function returns as soon as the vocabulary processing
    is started. Call get_vocabulary to get the current status of the vocabulary.
    The vocabulary is ready to use when its status is 'READY'.

    :param vocabulary_name: The name of the custom vocabulary.
    :param language_code: The language code of the vocabulary.
                          For example, en-US or nl-NL.
    :param transcribe_client: The Boto3 Transcribe client.
    :param phrases: A list of comma-separated phrases to include in the vocabulary.
    :param table_uri: A table of phrases and pronunciation hints to include in the
                      vocabulary.
    :return: Information about the newly created vocabulary.
    """
    try:
        vocab_args = {"VocabularyName": vocabulary_name, "LanguageCode": language_code}
        if phrases is not None:
            vocab_args["Phrases"] = phrases
        elif table_uri is not None:
            vocab_args["VocabularyFileUri"] = table_uri
        response = transcribe_client.create_vocabulary(**vocab_args)
        logger.info("Created custom vocabulary %s.", response["VocabularyName"])
    except ClientError:
        logger.exception("Couldn't create custom vocabulary %s.", vocabulary_name)
        raise
    else:
        return response

  • For API details, see CreateVocabulary in AWS SDK for Python (Boto3) API Reference.

The following code example shows how to use DeleteTranscriptionJob.

SDK for Python (Boto3)
Note

There's more on GitHub. Find the complete example and learn how to set up and run in the AWS Code Examples Repository. 

def delete_job(job_name, transcribe_client):
    """
    Deletes a transcription job. This also deletes the transcript associated with
    the job.

    :param job_name: The name of the job to delete.
    :param transcribe_client: The Boto3 Transcribe client.
    """
    try:
        transcribe_client.delete_transcription_job(TranscriptionJobName=job_name)
        logger.info("Deleted job %s.", job_name)
    except ClientError:
        logger.exception("Couldn't delete job %s.", job_name)
        raise

The following code example shows how to use DeleteVocabulary.

SDK for Python (Boto3)
Note

There's more on GitHub. Find the complete example and learn how to set up and run in the AWS Code Examples Repository. 

def delete_vocabulary(vocabulary_name, transcribe_client):
    """
    Deletes a custom vocabulary.

    :param vocabulary_name: The name of the vocabulary to delete.
    :param transcribe_client: The Boto3 Transcribe client.
    """
    try:
        transcribe_client.delete_vocabulary(VocabularyName=vocabulary_name)
        logger.info("Deleted vocabulary %s.", vocabulary_name)
    except ClientError:
        logger.exception("Couldn't delete vocabulary %s.", vocabulary_name)
        raise

  • For API details, see DeleteVocabulary in AWS SDK for Python (Boto3) API Reference.

The following code example shows how to use GetTranscriptionJob.

SDK for Python (Boto3)
Note

There's more on GitHub. Find the complete example and learn how to set up and run in the AWS Code Examples Repository. 

def get_job(job_name, transcribe_client):
    """
    Gets details about a transcription job.

    :param job_name: The name of the job to retrieve.
    :param transcribe_client: The Boto3 Transcribe client.
    :return: The retrieved transcription job.
    """
    try:
        response = transcribe_client.get_transcription_job(
            TranscriptionJobName=job_name
        )
        job = response["TranscriptionJob"]
        logger.info("Got job %s.", job["TranscriptionJobName"])
    except ClientError:
        logger.exception("Couldn't get job %s.", job_name)
        raise
    else:
        return job

The following code example shows how to use GetVocabulary.

SDK for Python (Boto3)
Note

There's more on GitHub. Find the complete example and learn how to set up and run in the AWS Code Examples Repository. 

def get_vocabulary(vocabulary_name, transcribe_client):
    """
    Gets information about a custom vocabulary.

    :param vocabulary_name: The name of the vocabulary to retrieve.
    :param transcribe_client: The Boto3 Transcribe client.
    :return: Information about the vocabulary.
    """
    try:
        response = transcribe_client.get_vocabulary(VocabularyName=vocabulary_name)
        logger.info("Got vocabulary %s.", response["VocabularyName"])
    except ClientError:
        logger.exception("Couldn't get vocabulary %s.", vocabulary_name)
        raise
    else:
        return response

  • For API details, see GetVocabulary in AWS SDK for Python (Boto3) API Reference.

The following code example shows how to use ListTranscriptionJobs.

SDK for Python (Boto3)
Note

There's more on GitHub. Find the complete example and learn how to set up and run in the AWS Code Examples Repository. 

def list_jobs(job_filter, transcribe_client):
    """
    Lists summaries of the transcription jobs for the current AWS account.

    :param job_filter: The list of returned jobs must contain this string in their
                       names.
    :param transcribe_client: The Boto3 Transcribe client.
    :return: The list of retrieved transcription job summaries.
    """
    try:
        response = transcribe_client.list_transcription_jobs(JobNameContains=job_filter)
        jobs = response["TranscriptionJobSummaries"]
        next_token = response.get("NextToken")
        while next_token is not None:
            response = transcribe_client.list_transcription_jobs(
                JobNameContains=job_filter, NextToken=next_token
            )
            jobs += response["TranscriptionJobSummaries"]
            next_token = response.get("NextToken")
        logger.info("Got %s jobs with filter %s.", len(jobs), job_filter)
    except ClientError:
        logger.exception("Couldn't get jobs with filter %s.", job_filter)
        raise
    else:
        return jobs

The following code example shows how to use ListVocabularies.

SDK for Python (Boto3)
Note

There's more on GitHub. Find the complete example and learn how to set up and run in the AWS Code Examples Repository. 

def list_vocabularies(vocabulary_filter, transcribe_client):
    """
    Lists the custom vocabularies created for this AWS account.

    :param vocabulary_filter: The returned vocabularies must contain this string in
                              their names.
    :param transcribe_client: The Boto3 Transcribe client.
    :return: The list of retrieved vocabularies.
    """
    try:
        response = transcribe_client.list_vocabularies(NameContains=vocabulary_filter)
        vocabs = response["Vocabularies"]
        next_token = response.get("NextToken")
        while next_token is not None:
            response = transcribe_client.list_vocabularies(
                NameContains=vocabulary_filter, NextToken=next_token
            )
            vocabs += response["Vocabularies"]
            next_token = response.get("NextToken")
        logger.info(
            "Got %s vocabularies with filter %s.", len(vocabs), vocabulary_filter
        )
    except ClientError:
        logger.exception(
            "Couldn't list vocabularies with filter %s.", vocabulary_filter
        )
        raise
    else:
        return vocabs

  • For API details, see ListVocabularies in AWS SDK for Python (Boto3) API Reference.

The following code example shows how to use StartTranscriptionJob.

SDK for Python (Boto3)
Note

There's more on GitHub. Find the complete example and learn how to set up and run in the AWS Code Examples Repository. 

def start_job(
    job_name,
    media_uri,
    media_format,
    language_code,
    transcribe_client,
    vocabulary_name=None,
):
    """
    Starts a transcription job. This function returns as soon as the job is started.
    To get the current status of the job, call get_transcription_job. The job is
    successfully completed when the job status is 'COMPLETED'.

    :param job_name: The name of the transcription job. This must be unique for
                     your AWS account.
    :param media_uri: The URI where the audio file is stored. This is typically
                      in an Amazon S3 bucket.
    :param media_format: The format of the audio file. For example, mp3 or wav.
    :param language_code: The language code of the audio file.
                          For example, en-US or ja-JP
    :param transcribe_client: The Boto3 Transcribe client.
    :param vocabulary_name: The name of a custom vocabulary to use when transcribing
                            the audio file.
    :return: Data about the job.
    """
    try:
        job_args = {
            "TranscriptionJobName": job_name,
            "Media": {"MediaFileUri": media_uri},
            "MediaFormat": media_format,
            "LanguageCode": language_code,
        }
        if vocabulary_name is not None:
            job_args["Settings"] = {"VocabularyName": vocabulary_name}
        response = transcribe_client.start_transcription_job(**job_args)
        job = response["TranscriptionJob"]
        logger.info("Started transcription job %s.", job_name)
    except ClientError:
        logger.exception("Couldn't start transcription job %s.", job_name)
        raise
    else:
        return job

The following code example shows how to use UpdateVocabulary.

SDK for Python (Boto3)
Note

There's more on GitHub. Find the complete example and learn how to set up and run in the AWS Code Examples Repository. 

def update_vocabulary(
    vocabulary_name, language_code, transcribe_client, phrases=None, table_uri=None
):
    """
    Updates an existing custom vocabulary. The entire vocabulary is replaced with
    the contents of the update.

    :param vocabulary_name: The name of the vocabulary to update.
    :param language_code: The language code of the vocabulary.
    :param transcribe_client: The Boto3 Transcribe client.
    :param phrases: A list of comma-separated phrases to include in the vocabulary.
    :param table_uri: A table of phrases and pronunciation hints to include in the
                      vocabulary.
    """
    try:
        vocab_args = {"VocabularyName": vocabulary_name, "LanguageCode": language_code}
        if phrases is not None:
            vocab_args["Phrases"] = phrases
        elif table_uri is not None:
            vocab_args["VocabularyFileUri"] = table_uri
        response = transcribe_client.update_vocabulary(**vocab_args)
        logger.info("Updated custom vocabulary %s.", response["VocabularyName"])
    except ClientError:
        logger.exception("Couldn't update custom vocabulary %s.", vocabulary_name)
        raise

  • For API details, see UpdateVocabulary in AWS SDK for Python (Boto3) API Reference.

Scenarios

The following code example shows how to:

  • Upload an audio file to Amazon S3.

  • Run an Amazon Transcribe job to transcribe the file and get the results.

  • Create and refine a custom vocabulary to improve transcription accuracy.

  • Run jobs with custom vocabularies and get the results.

SDK for Python (Boto3)
Note

There's more on GitHub. Find the complete example and learn how to set up and run in the AWS Code Examples Repository.

Transcribe an audio file that contains a reading of Jabberwocky by Lewis Carroll. Start by creating functions that wrap Amazon Transcribe actions.

def start_job(
    job_name,
    media_uri,
    media_format,
    language_code,
    transcribe_client,
    vocabulary_name=None,
):
    """
    Starts a transcription job. This function returns as soon as the job is started.
    To get the current status of the job, call get_transcription_job. The job is
    successfully completed when the job status is 'COMPLETED'.

    :param job_name: The name of the transcription job. This must be unique for
                     your AWS account.
    :param media_uri: The URI where the audio file is stored. This is typically
                      in an Amazon S3 bucket.
    :param media_format: The format of the audio file. For example, mp3 or wav.
    :param language_code: The language code of the audio file.
                          For example, en-US or ja-JP
    :param transcribe_client: The Boto3 Transcribe client.
    :param vocabulary_name: The name of a custom vocabulary to use when transcribing
                            the audio file.
    :return: Data about the job.
    """
    try:
        job_args = {
            "TranscriptionJobName": job_name,
            "Media": {"MediaFileUri": media_uri},
            "MediaFormat": media_format,
            "LanguageCode": language_code,
        }
        if vocabulary_name is not None:
            job_args["Settings"] = {"VocabularyName": vocabulary_name}
        response = transcribe_client.start_transcription_job(**job_args)
        job = response["TranscriptionJob"]
        logger.info("Started transcription job %s.", job_name)
    except ClientError:
        logger.exception("Couldn't start transcription job %s.", job_name)
        raise
    else:
        return job



def get_job(job_name, transcribe_client):
    """
    Gets details about a transcription job.

    :param job_name: The name of the job to retrieve.
    :param transcribe_client: The Boto3 Transcribe client.
    :return: The retrieved transcription job.
    """
    try:
        response = transcribe_client.get_transcription_job(
            TranscriptionJobName=job_name
        )
        job = response["TranscriptionJob"]
        logger.info("Got job %s.", job["TranscriptionJobName"])
    except ClientError:
        logger.exception("Couldn't get job %s.", job_name)
        raise
    else:
        return job



def delete_job(job_name, transcribe_client):
    """
    Deletes a transcription job. This also deletes the transcript associated with
    the job.

    :param job_name: The name of the job to delete.
    :param transcribe_client: The Boto3 Transcribe client.
    """
    try:
        transcribe_client.delete_transcription_job(TranscriptionJobName=job_name)
        logger.info("Deleted job %s.", job_name)
    except ClientError:
        logger.exception("Couldn't delete job %s.", job_name)
        raise



def create_vocabulary(
    vocabulary_name, language_code, transcribe_client, phrases=None, table_uri=None
):
    """
    Creates a custom vocabulary that can be used to improve the accuracy of
    transcription jobs. This function returns as soon as the vocabulary processing
    is started. Call get_vocabulary to get the current status of the vocabulary.
    The vocabulary is ready to use when its status is 'READY'.

    :param vocabulary_name: The name of the custom vocabulary.
    :param language_code: The language code of the vocabulary.
                          For example, en-US or nl-NL.
    :param transcribe_client: The Boto3 Transcribe client.
    :param phrases: A list of comma-separated phrases to include in the vocabulary.
    :param table_uri: A table of phrases and pronunciation hints to include in the
                      vocabulary.
    :return: Information about the newly created vocabulary.
    """
    try:
        vocab_args = {"VocabularyName": vocabulary_name, "LanguageCode": language_code}
        if phrases is not None:
            vocab_args["Phrases"] = phrases
        elif table_uri is not None:
            vocab_args["VocabularyFileUri"] = table_uri
        response = transcribe_client.create_vocabulary(**vocab_args)
        logger.info("Created custom vocabulary %s.", response["VocabularyName"])
    except ClientError:
        logger.exception("Couldn't create custom vocabulary %s.", vocabulary_name)
        raise
    else:
        return response



def get_vocabulary(vocabulary_name, transcribe_client):
    """
    Gets information about a custom vocabulary.

    :param vocabulary_name: The name of the vocabulary to retrieve.
    :param transcribe_client: The Boto3 Transcribe client.
    :return: Information about the vocabulary.
    """
    try:
        response = transcribe_client.get_vocabulary(VocabularyName=vocabulary_name)
        logger.info("Got vocabulary %s.", response["VocabularyName"])
    except ClientError:
        logger.exception("Couldn't get vocabulary %s.", vocabulary_name)
        raise
    else:
        return response



def update_vocabulary(
    vocabulary_name, language_code, transcribe_client, phrases=None, table_uri=None
):
    """
    Updates an existing custom vocabulary. The entire vocabulary is replaced with
    the contents of the update.

    :param vocabulary_name: The name of the vocabulary to update.
    :param language_code: The language code of the vocabulary.
    :param transcribe_client: The Boto3 Transcribe client.
    :param phrases: A list of comma-separated phrases to include in the vocabulary.
    :param table_uri: A table of phrases and pronunciation hints to include in the
                      vocabulary.
    """
    try:
        vocab_args = {"VocabularyName": vocabulary_name, "LanguageCode": language_code}
        if phrases is not None:
            vocab_args["Phrases"] = phrases
        elif table_uri is not None:
            vocab_args["VocabularyFileUri"] = table_uri
        response = transcribe_client.update_vocabulary(**vocab_args)
        logger.info("Updated custom vocabulary %s.", response["VocabularyName"])
    except ClientError:
        logger.exception("Couldn't update custom vocabulary %s.", vocabulary_name)
        raise



def list_vocabularies(vocabulary_filter, transcribe_client):
    """
    Lists the custom vocabularies created for this AWS account.

    :param vocabulary_filter: The returned vocabularies must contain this string in
                              their names.
    :param transcribe_client: The Boto3 Transcribe client.
    :return: The list of retrieved vocabularies.
    """
    try:
        response = transcribe_client.list_vocabularies(NameContains=vocabulary_filter)
        vocabs = response["Vocabularies"]
        next_token = response.get("NextToken")
        while next_token is not None:
            response = transcribe_client.list_vocabularies(
                NameContains=vocabulary_filter, NextToken=next_token
            )
            vocabs += response["Vocabularies"]
            next_token = response.get("NextToken")
        logger.info(
            "Got %s vocabularies with filter %s.", len(vocabs), vocabulary_filter
        )
    except ClientError:
        logger.exception(
            "Couldn't list vocabularies with filter %s.", vocabulary_filter
        )
        raise
    else:
        return vocabs



def delete_vocabulary(vocabulary_name, transcribe_client):
    """
    Deletes a custom vocabulary.

    :param vocabulary_name: The name of the vocabulary to delete.
    :param transcribe_client: The Boto3 Transcribe client.
    """
    try:
        transcribe_client.delete_vocabulary(VocabularyName=vocabulary_name)
        logger.info("Deleted vocabulary %s.", vocabulary_name)
    except ClientError:
        logger.exception("Couldn't delete vocabulary %s.", vocabulary_name)
        raise

Call the wrapper functions to transcribe audio without a custom vocabulary and then with different versions of a custom vocabulary to see improved results.

def usage_demo():
    """Shows how to use the Amazon Transcribe service."""
    logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s")

    s3_resource = boto3.resource("s3")
    transcribe_client = boto3.client("transcribe")

    print("-" * 88)
    print("Welcome to the Amazon Transcribe demo!")
    print("-" * 88)

    bucket_name = f"jabber-bucket-{time.time_ns()}"
    print(f"Creating bucket {bucket_name}.")
    bucket = s3_resource.create_bucket(
        Bucket=bucket_name,
        CreateBucketConfiguration={
            "LocationConstraint": transcribe_client.meta.region_name
        },
    )
    media_file_name = ".media/Jabberwocky.mp3"
    media_object_key = "Jabberwocky.mp3"
    print(f"Uploading media file {media_file_name}.")
    bucket.upload_file(media_file_name, media_object_key)
    media_uri = f"s3://{bucket.name}/{media_object_key}"

    job_name_simple = f"Jabber-{time.time_ns()}"
    print(f"Starting transcription job {job_name_simple}.")
    start_job(
        job_name_simple,
        f"s3://{bucket_name}/{media_object_key}",
        "mp3",
        "en-US",
        transcribe_client,
    )
    transcribe_waiter = TranscribeCompleteWaiter(transcribe_client)
    transcribe_waiter.wait(job_name_simple)
    job_simple = get_job(job_name_simple, transcribe_client)
    transcript_simple = requests.get(
        job_simple["Transcript"]["TranscriptFileUri"]
    ).json()
    print(f"Transcript for job {transcript_simple['jobName']}:")
    print(transcript_simple["results"]["transcripts"][0]["transcript"])

    print("-" * 88)
    print(
        "Creating a custom vocabulary that lists the nonsense words to try to "
        "improve the transcription."
    )
    vocabulary_name = f"Jabber-vocabulary-{time.time_ns()}"
    create_vocabulary(
        vocabulary_name,
        "en-US",
        transcribe_client,
        phrases=[
            "brillig",
            "slithy",
            "borogoves",
            "mome",
            "raths",
            "Jub-Jub",
            "frumious",
            "manxome",
            "Tumtum",
            "uffish",
            "whiffling",
            "tulgey",
            "thou",
            "frabjous",
            "callooh",
            "callay",
            "chortled",
        ],
    )
    vocabulary_ready_waiter = VocabularyReadyWaiter(transcribe_client)
    vocabulary_ready_waiter.wait(vocabulary_name)

    job_name_vocabulary_list = f"Jabber-vocabulary-list-{time.time_ns()}"
    print(f"Starting transcription job {job_name_vocabulary_list}.")
    start_job(
        job_name_vocabulary_list,
        media_uri,
        "mp3",
        "en-US",
        transcribe_client,
        vocabulary_name,
    )
    transcribe_waiter.wait(job_name_vocabulary_list)
    job_vocabulary_list = get_job(job_name_vocabulary_list, transcribe_client)
    transcript_vocabulary_list = requests.get(
        job_vocabulary_list["Transcript"]["TranscriptFileUri"]
    ).json()
    print(f"Transcript for job {transcript_vocabulary_list['jobName']}:")
    print(transcript_vocabulary_list["results"]["transcripts"][0]["transcript"])

    print("-" * 88)
    print(
        "Updating the custom vocabulary with table data that provides additional "
        "pronunciation hints."
    )
    table_vocab_file = "jabber-vocabulary-table.txt"
    bucket.upload_file(table_vocab_file, table_vocab_file)
    update_vocabulary(
        vocabulary_name,
        "en-US",
        transcribe_client,
        table_uri=f"s3://{bucket.name}/{table_vocab_file}",
    )
    vocabulary_ready_waiter.wait(vocabulary_name)

    job_name_vocab_table = f"Jabber-vocab-table-{time.time_ns()}"
    print(f"Starting transcription job {job_name_vocab_table}.")
    start_job(
        job_name_vocab_table,
        media_uri,
        "mp3",
        "en-US",
        transcribe_client,
        vocabulary_name=vocabulary_name,
    )
    transcribe_waiter.wait(job_name_vocab_table)
    job_vocab_table = get_job(job_name_vocab_table, transcribe_client)
    transcript_vocab_table = requests.get(
        job_vocab_table["Transcript"]["TranscriptFileUri"]
    ).json()
    print(f"Transcript for job {transcript_vocab_table['jobName']}:")
    print(transcript_vocab_table["results"]["transcripts"][0]["transcript"])

    print("-" * 88)
    print("Getting data for jobs and vocabularies.")
    jabber_jobs = list_jobs("Jabber", transcribe_client)
    print(f"Found {len(jabber_jobs)} jobs:")
    for job_sum in jabber_jobs:
        job = get_job(job_sum["TranscriptionJobName"], transcribe_client)
        print(
            f"\t{job['TranscriptionJobName']}, {job['Media']['MediaFileUri']}, "
            f"{job['Settings'].get('VocabularyName')}"
        )

    jabber_vocabs = list_vocabularies("Jabber", transcribe_client)
    print(f"Found {len(jabber_vocabs)} vocabularies:")
    for vocab_sum in jabber_vocabs:
        vocab = get_vocabulary(vocab_sum["VocabularyName"], transcribe_client)
        vocab_content = requests.get(vocab["DownloadUri"]).text
        print(f"\t{vocab['VocabularyName']} contents:")
        print(vocab_content)

    print("-" * 88)
    print("Deleting demo jobs.")
    for job_name in [job_name_simple, job_name_vocabulary_list, job_name_vocab_table]:
        delete_job(job_name, transcribe_client)
    print("Deleting demo vocabulary.")
    delete_vocabulary(vocabulary_name, transcribe_client)
    print("Deleting demo bucket.")
    bucket.objects.delete()
    bucket.delete()
    print("Thanks for watching!")

The following code example shows how to:

  • Start a transcription job with Amazon Transcribe.

  • Wait for the job to complete.

  • Get the URI where the transcript is stored.

For more information, see Getting started with Amazon Transcribe.

SDK for Python (Boto3)
Note

There's more on GitHub. Find the complete example and learn how to set up and run in the AWS Code Examples Repository. 

import time
import boto3


def transcribe_file(job_name, file_uri, transcribe_client):
    transcribe_client.start_transcription_job(
        TranscriptionJobName=job_name,
        Media={"MediaFileUri": file_uri},
        MediaFormat="wav",
        LanguageCode="en-US",
    )

    max_tries = 60
    while max_tries > 0:
        max_tries -= 1
        job = transcribe_client.get_transcription_job(TranscriptionJobName=job_name)
        job_status = job["TranscriptionJob"]["TranscriptionJobStatus"]
        if job_status in ["COMPLETED", "FAILED"]:
            print(f"Job {job_name} is {job_status}.")
            if job_status == "COMPLETED":
                print(
                    f"Download the transcript from\n"
                    f"\t{job['TranscriptionJob']['Transcript']['TranscriptFileUri']}."
                )
            break
        else:
            print(f"Waiting for {job_name}. Current status is {job_status}.")
        time.sleep(10)


def main():
    transcribe_client = boto3.client("transcribe")
    file_uri = "s3://test-transcribe/answer2.wav"
    transcribe_file("Example-job", file_uri, transcribe_client)


if __name__ == "__main__":
    main()