Naver CLOVA Speech Recognition Python API Docs | dltHub

Naver CLOVA Speech Recognition is a REST API service that converts speech to audio files into text. The REST API base URL is https://naveropenapi.apigw.ntruss.com/recog/v1 and all requests require specific Client ID and Client Secret headers for authentication.

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 add "dlt[hub]" and start loading Naver CLOVA Speech Recognition data in under 10 minutes.

---

What data can I load from Naver CLOVA Speech Recognition?

Here are some of the endpoints you can load from Naver CLOVA Speech Recognition:

---

How do I authenticate with the Naver CLOVA Speech Recognition API?

Authentication is performed by passing a Client ID and Client Secret in the HTTP headers 'x-ncp-apigw-api-key-id' and 'x-ncp-apigw-api-key', respectively. These credentials must be obtained from the NAVER Cloud Platform console after registering an application.

1. Get your credentials

1. Log in to the NAVER Cloud Platform console.\n2. Navigate to Services > AI Services > CLOVA Speech Recognition (CSR) or AI Services > CLOVA Speech depending on your specific product.\n3. Create an Application in the console under the AI/NAVER API section (this is a mandatory step for CSR to obtain unique credentials).\n4. After creating the application, you will be issued a unique Client ID and Client Secret.\n5. You can view and manage these credentials in the application details page. Use the copy icon to retrieve them for your API headers.

2. Add them to .dlt/secrets.toml

Copy
[sources.naver_clova_speech_recognition_source]
# For CLOVA Speech Recognition (CSR) API:\nx_ncp_apigw_api_key_id = \"your_client_id_here\"\nx_ncp_apigw_api_key = \"your_client_secret_here\"\n\n# For CLOVA Speech (Long-sentence/Modern API):\nx_clovaspeech_api_key = \"your_secret_key_here\"

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.

---

How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

Copy
uv init
uv add "dlt[hub]"

1. Install the dlt AI harness:

Copy
uv run dlthub 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:

Copy
uv run dlthub ai toolkit install rest-api-pipeline

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 Naver CLOVA Speech Recognition 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:

Copy
uv run python naver_clova_speech_recognition_pipeline.py

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

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

Inspect your pipeline and data:

Copy
uv run dlthub show

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

---

Python pipeline example

This example loads For short-sentence recognition, use /recog/v1/stt. For long-sentence or external file recognition, use /recognizer/url. from the Naver CLOVA Speech Recognition API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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

@dlt.source
def naver_clova_speech_recognition_source(api_key_id=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://naveropenapi.apigw.ntruss.com/recog/v1",
            "auth": {"type": "api_key", "api_key": api_key_id},
        },
        "resources": [
            {"name": "job_status", "endpoint": {"path": "recognizer/results/{token}"}},
            {"name": "long_sentence_url", "endpoint": {"path": "recognizer/url"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="naver_clova_speech_recognition_pipeline",
        destination="duckdb",
        dataset_name="naver_clova_speech_recognition_data",
    )
    load_info = pipeline.run(naver_clova_speech_recognition_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.

---

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):

Copy
import dlt

data = dlt.pipeline("naver_clova_speech_recognition_pipeline").dataset()
sessions_df = data.job_status.df()
print(sessions_df.head())

SQL (DuckDB example):

Copy
SELECT * FROM naver_clova_speech_recognition_data.job_status LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("naver_clova_speech_recognition_pipeline").dataset()
data.job_status.df().head()

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

---

What destinations can I load Naver CLOVA Speech Recognition 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.

---

Next steps

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

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

Copy
uv run dlthub ai toolkit install data-exploration
uv run dlthub ai toolkit install dlthub-platform

• 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