AWS Transcribe Python API Docs | dltHub

Amazon Transcribe is a service that converts speech to text for batch and real-time (streaming) audio. The REST API base URL is Regionalized: https://transcribe.{region}.amazonaws.com (example: https://transcribe.us-east-1.amazonaws.com). Streaming endpoints use https://transcribestreaming.{region}.amazonaws.com for real-time. and All API requests require AWS Signature Version 4 signing using AWS credentials..

dlt is an open-source Python library that handles authentication, pagination, and schema evolution automatically. dlthub provides AI context files that enable code assistants to generate production-ready pipelines. Install with uv pip install "dlt[workspace]" and start loading AWS Transcribe data in under 10 minutes.

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What data can I load from AWS Transcribe?

Here are some of the endpoints you can load from AWS Transcribe:

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How do I authenticate with the AWS Transcribe API?

Amazon Transcribe REST API uses AWS Signature Version 4 for authentication. Requests must be signed with AWS access key ID and secret access key (and session token if using temporary credentials) and include the appropriate Host, X-Amz-Date, and Authorization headers (and X-Amz-Security-Token when using session tokens).

1. Get your credentials

1. Sign in to AWS Management Console and open IAM. 2) Create or select an IAM user or role with AmazonTranscribeFullAccess (or specific minimal permissions) and optionally AmazonS3 access for media. 3) For a user, create access keys in the Security credentials tab and save the Access Key ID and Secret Access Key. For roles, assume the role and obtain temporary credentials via STS (AccessKeyId, SecretAccessKey, SessionToken).

2. Add them to .dlt/secrets.toml

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[sources.aws_transcribe_source]
aws_access_key_id = "YOUR_AWS_ACCESS_KEY_ID"
aws_secret_access_key = "YOUR_AWS_SECRET_ACCESS_KEY"
aws_session_token = "OPTIONAL_AWS_SESSION_TOKEN"
region = "us-east-1"

dlt reads this automatically at runtime — never hardcode tokens in your pipeline script. For production environments, see setting up credentials with dlt for environment variable and vault-based options.

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How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

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dlt ai init --agent <your-agent> # <agent>: claude | cursor | codex

This installs project rules, a secrets management skill, appropriate ignore files, and configures the dlt MCP server for your agent. Learn more →

2. Install the rest-api-pipeline toolkit:

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dlt ai toolkit rest-api-pipeline install

This loads the skills and context about dlt the agent uses to build the pipeline iteratively, efficiently, and safely. The agent uses MCP tools to inspect credentials — it never needs to read your secrets.toml directly. Learn more →

3. Start LLM-assisted coding:

Use /find-source to load data from the AWS Transcribe API into DuckDB.

The rest-api-pipeline toolkit takes over from here — it reads relevant API documentation, presents you with options for which endpoints to load, and follows a structured workflow to scaffold, debug, and validate the pipeline step by step.

4. Run the pipeline:

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python aws_transcribe_pipeline.py

If everything is configured correctly, you'll see output like this:

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Pipeline aws_transcribe_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset aws_transcribe_data
The duckdb destination used duckdb:/aws_transcribe.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

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dlt pipeline aws_transcribe_pipeline show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.

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Python pipeline example

This example loads transcription_jobs and vocabularies from the AWS Transcribe API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def aws_transcribe_source(aws_credentials=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "Regionalized: https://transcribe.{region}.amazonaws.com (example: https://transcribe.us-east-1.amazonaws.com). Streaming endpoints use https://transcribestreaming.{region}.amazonaws.com for real-time.",
            "auth": {
                "type": "aws_sigv4",
                "aws_secret_access_key": aws_credentials,
            },
        },
        "resources": [
            {"name": "transcription_jobs", "endpoint": {"path": "v1/transcription-jobs (ListTranscriptionJobs API operation)", "data_selector": "TranscriptionJobSummaries"}},
            {"name": "vocabularies", "endpoint": {"path": "vocabularies (ListVocabularies)", "data_selector": "Vocabularies"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="aws_transcribe_pipeline",
        destination="duckdb",
        dataset_name="aws_transcribe_data",
    )
    load_info = pipeline.run(aws_transcribe_source())
    print(load_info)

To add more endpoints, append entries from the resource table to the "resources" list using the same name, path, and data_selector pattern.

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How do I query the loaded data?

Once the pipeline runs, dlt creates one table per resource. You can query with Python or SQL.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("aws_transcribe_pipeline").dataset()
sessions_df = data.transcription_jobs.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM aws_transcribe_data.transcription_jobs LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("aws_transcribe_pipeline").dataset()
data.transcription_jobs.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

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What destinations can I load AWS Transcribe data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.

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Troubleshooting

Authentication failures

If requests return 403 Forbidden or SignatureDoesNotMatch, verify that requests are signed with AWS Signature Version 4, that the system clock is accurate (within a few minutes), the correct region and service ("transcribe") are used when signing, and that Access Key ID / Secret are valid. If using temporary credentials include X-Amz-Security-Token.

Rate limits and Throttling

Amazon Transcribe enforces service quotas per account and region; throttling returns 429 TooManyRequestsException. Implement exponential backoff and check service quotas in the AWS Console to request increases.

Pagination

List operations return paginated results and include NextToken (or NextToken-like) in responses; use that token in subsequent List requests (NextToken parameter) until no token returned.

S3 permissions and roles for batch jobs

Batch transcription jobs require media files in S3 and a role that grants transcribe.amazonaws.com read access to the S3 bucket (IAM role with proper trust policy). Missing permissions produce AccessDenied errors.

Streaming specifics

Streaming uses separate streaming endpoints and requires WebSocket or HTTP/2 connections; ensure you use the transcribestreaming.{region}.amazonaws.com endpoint and SigV4 signing for the websocket/HTTP/2 handshake.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

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Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI Workbench:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-runtime — Deploy, schedule, and monitor your pipeline in production.

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dlt ai toolkit data-exploration install
dlt ai toolkit dlthub-runtime install

• How to deploy a pipeline
• How to explore your data in marimo Notebooks
• How to query your data in Python with dataset
• Join the dlt community on Slack