Load WellSaid Labs data in Python using dltHub

Build a WellSaid Labs-to-database or-dataframe pipeline in Python using dlt with automatic Cursor support.

In this guide, we'll set up a complete WellSaid Labs data pipeline from API credentials to your first data load in just 10 minutes. You'll end up with a fully declarative Python pipeline based on dlt's REST API connector, like in the partial example code below:

Example code
@dlt.source
def well_said_labs_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.wellsaidlabs.com/v1/tts/replacement_libraries",
            "auth": {
                "type": "bearer",
                "token": access_token,
            }
        },
        "resources": [
            "library_id", "c1b7c009-904d-48c2-a2d6-4249863cb995"
        ],
    }
    [...]
    yield from rest_api_resources(config)


def get_data() -> None:
    # Connect to destination
    pipeline = dlt.pipeline(
        pipeline_name='well_said_labs_pipeline',
        destination='duckdb',
        dataset_name='well_said_labs_data', 
    )
    # Load the data
    load_info = pipeline.run(well_said_labs_source())
    print(load_info)

Why use dltHub Workspace with LLM Context to generate Python pipelines?

  • Accelerate pipeline development with AI-native context
  • Debug pipelines, validate schemas and data with the integrated Pipeline Dashboard
  • Build Python notebooks for end users of your data
  • Low maintenance thanks to Schema evolution with type inference, resilience and self documenting REST API connectors. A shallow learning curve makes the pipeline easy to extend by any team member
  • dlt is the tool of choice for Pythonic Iceberg Lakehouses, bringing mature data loading to pythonic Iceberg with or without catalogs

What you’ll do

We’ll show you how to generate a readable and easily maintainable Python script that fetches data from well_said_labs’s API and loads it into Iceberg, DataFrames, files, or a database of your choice. Here are some of the endpoints you can load:

  • TTS Replacement Libraries: Manage and retrieve different libraries for text-to-speech replacements, allowing for customized speech outputs.
  • TTS Clips: Create, manipulate, and combine audio clips generated through text-to-speech.
  • TTS Respellings: Get suggestions for respelling words to improve pronunciation or clarity in text-to-speech output.
  • TTS Avatars: Access and manage different avatars used in text-to-speech applications.
  • TTS Word Timing: Analyze timing for words in speech synthesis, useful for adjusting pacing in audio output.

You will then debug the WellSaid Labs pipeline using our Pipeline Dashboard tool to ensure it is copying the data correctly, before building a Notebook to explore your data and build reports.

Setup & steps to follow

💡
Before getting started, let's make sure Cursor is set up correctly:

Now you're ready to get started!

  1. ⚙️ Set up dlt Workspace

    Install dlt with duckdb support:

    pip install "dlt[workspace]"

    Initialize a dlt pipeline with WellSaid Labs support.

    dlt init dlthub:well_said_labs duckdb

    The init command will setup the necessary files and folders for the next step.

  2. 🤠 Start LLM-assisted coding

    Here’s a prompt to get you started:

    Prompt
    Please generate a REST API Source for WellSaid Labs API, as specified in @well_said_labs-docs.yaml 
Start with endpoints library_id and c1b7c009-904d-48c2-a2d6-4249863cb995 and skip incremental loading for now. 
Place the code in well_said_labs_pipeline.py and name the pipeline well_said_labs_pipeline. 
If the file exists, use it as a starting point. 
Do not add or modify any other files. 
Use @dlt rest api as a tutorial. 
After adding the endpoints, allow the user to run the pipeline with python well_said_labs_pipeline.py and await further instructions.

  3. 🔒 Set up credentials

    To create a Replacement, you need to include your API key in the header as 'X-API-KEY: <YOUR_API_KEY>' when making a POST request to https://api.wellsaidlabs.com/v1/tts/replacement_libraries/{library_id}/replacements or a GET request to https://api.wellsaidlabs.com/v1/tts/replacement_libraries/{library_id}/replacement.

    To get the appropriate API keys, please visit the original source at https://docs.wellsaidlabs.com/reference/ttsstream. If you want to protect your environment secrets in a production environment, look into setting up credentials with dlt.

  4. 🏃‍♀️ Run the pipeline in the Python terminal in Cursor

    python well_said_labs_pipeline.py

    If your pipeline runs correctly, you’ll see something like the following:

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

  5. 📈 Debug your pipeline and data with the Pipeline Dashboard

    Now that you have a running pipeline, you need to make sure it’s correct, so you do not introduce silent failures like misconfigured pagination or incremental loading errors. By launching the dlt Workspace Pipeline Dashboard, you can see various information about the pipeline to enable you to test it. Here you can see:

    • Pipeline overview: State, load metrics
    • Data’s schema: tables, columns, types, hints
    • You can query the data itself
    dlt pipeline well_said_labs_pipeline show

  6. 🐍 Build a Notebook with data explorations and reports

    With the pipeline and data partially validated, you can continue with custom data explorations and reports. To get started, paste the snippet below into a new marimo Notebook and ask your LLM to go from there. Jupyter Notebooks and regular Python scripts are supported as well.

    import dlt

data = dlt.pipeline("well_said_labs_pipeline").dataset()
# get "library_id" table as Pandas frame
data."library_id".df().head()

Extra resources:

Next steps