Load Coqui STT data to DuckDB

Coqui STT (and related Coqui services) provides an API for speech-to-text, voice cloning, and text-to-speech services. Everything needed to build a working Coqui STT → DuckDB pipeline is on this page: the API's base URL, authentication, endpoints, pagination and incremental field — plus a prompt that hands the whole job to your coding agent.

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Build your Coqui STT to DuckDB pipeline

Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.

PromptCopy prompt
Run uvx dlthub-init@latest to build a pipeline from Coqui STT to DuckDB and run it on dltHub

That scaffolds a dltHub workspace and installs the dltHub AI harness — the project rules, the secrets-management skill, and the dlt MCP server your agent needs to work safely. From there it reads the Coqui STT API, proposes the endpoints to load, then writes, runs and validates the pipeline while you review rather than type. Credentials are inspected through MCP tools, so your agent never reads secrets.toml itself. How the LLM-native workflow works →

Prefer to write it yourself? Every fact the agent uses is below.

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Coqui STT API at a glance

These values come from the Coqui STT API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Coqui STT API?

Authentication is handled by passing an API key as a Bearer token in the Authorization header of requests.

1. Get your credentials

Coqui STT does not use a traditional cloud-hosted dashboard for API keys. Instead, it is an open-source, self-hosted toolkit. To obtain an API key for your local deployment, you generate one using the command-line interface by running coqui setup in your terminal. Alternatively, you can define your own key within your workspace environment by setting the COQUI_API_KEY environment variable in your .env file.

2. Add them to .dlt/secrets.toml

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[sources.coqui_stt_source]
api_key = "your_generated_api_key_here"

dlt reads this file automatically at runtime. With the harness, the setup-secrets skill prompts you for the values and never handles the raw credential in chat. For production, see setting up credentials with dlt.

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What Coqui STT data can I load into DuckDB?

These are the Coqui STT endpoints dlt can load into DuckDB:

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How do I load only new Coqui STT records?

The Coqui STT API reference does not document a timestamp or sequence field for these endpoints, so there is nothing to advertise here as verified. Pick a field from the endpoints table above that increases with every write, then set it as the cursor_path.

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{"name": "transcript", "endpoint": {
    "path": "transcript",
    # Replace with a field that increases on every write.
    "incremental": {"cursor_path": "REPLACE_ME", "initial_value": "2024-01-01T00:00:00Z"},
}}

On the first run dlt loads everything from initial_value; on every run after that it requests only what changed and appends with write_disposition="merge" if you set a primary key. See incremental loading.

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What does the generated Coqui STT pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading GET /api/v1/health and POST /transcript or POST /api/v1/sessions/{id}/files from the Coqui STT API into DuckDB:

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

@dlt.source
def coqui_stt_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://app.coqui.ai",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "transcript", "endpoint": {"path": "transcript"}}
        ],
    }
    yield from rest_api_resources(config)


def load_coqui_stt_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="coqui_stt_pipeline",
        destination="duckdb",
        dataset_name="coqui_stt_data",
    )
    load_info = pipeline.run(coqui_stt_source())
    print(load_info)


if __name__ == "__main__":
    load_coqui_stt_to_duckdb()

Run it with python coqui_stt_pipeline.py. The agent iterates on this until it loads cleanly — you review and approve, rather than write it from scratch.

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How do I query Coqui STT data in DuckDB?

dlt creates one table per resource. Query the loaded data with Python or SQL — or ask your agent to, through the MCP server's execute_sql_query tool.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("coqui_stt_pipeline").dataset()
df = data.transcript.df()
print(df.head())

SQL:

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SELECT * FROM coqui_stt_data.transcript LIMIT 10;

See querying your data with dataset and exploring it in marimo notebooks.

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How do I deploy the Coqui STT to DuckDB pipeline in production?

The pipeline runs locally, which is ideal for prototyping and one-off analysis. When you need it on a schedule, monitored on every load, and shared with your team, deploy the same dlt code on the dltHub platform — no infrastructure to maintain. The prompt above already ends with "run it on dltHub", so your agent can take it there directly.

• Deploy & schedule — run the pipeline as a managed job with automatic retries.
• Monitor — observable job queues, alerting, and load metrics for every run.
• Transform — promote raw Coqui STT loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

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What other destinations can I load Coqui STT data to?

dlt loads into any of these — only the destination argument changes:

Set dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. On the dltHub platform the same pipeline runs against a managed Iceberg lakehouse. See the full destinations list.

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Next steps

• Ready for production? Book a demo of the dltHub platform →
• dlt LLM-native workflow docs
• REST API source reference
• Join the dlt community on Slack