🎭vCon Faker

Generate synthetic vCons with LLM-written dialog, TTS audio, and S3 upload — the standard way to create test data.

Repo: vcon-dev/vcon_faker · Streamlit app: vcon-faker.streamlit.app

vcon_faker generates realistic synthetic vCons. It pairs an LLM (OpenAI by default) to write the dialog with a TTS service to render audio, then assembles a vCon with the audio referenced as external media and uploaded to S3.

When to use it

Filling a fresh database with believable test vCons during development.
Building a benchmark corpus for ASR providers.
Demoing vCon-based workflows without exposing real customer data.
Stress-testing pipelines.

Synthetic vCons should be auditably synthetic. The pattern:

Mark every party as synthetic. Set validation: "synthetic" on each party.
Attach a purpose: "synthetic_data_consent" attachment so downstream consumers can identify the vCon as synthetic in one tag-or-purpose lookup. The attachment body can be minimal — the existence of the purpose is the signal.

Example:

{
  "parties": [
    { "name": "Alice", "role": "customer", "validation": "synthetic" },
    { "name": "Bob",   "role": "agent",    "validation": "synthetic" }
  ],
  "attachments": [
    {
      "purpose": "synthetic_data_consent",
      "party": 0,
      "dialog": 0,
      "encoding": "json",
      "body": "{\"origin\":\"vcon_faker\",\"prompt_version\":\"2026-05-18\"}"
    }
  ]
}

For full GDPR-style audit, also attach a Lawful Basis entry using legitimate_interests and expiration: null, documenting the synthetic origin in a proof_mechanism of type external_system. Both attachments together give you the strongest possible "this is not real personal data" signal.

Running locally

The Streamlit app is the easiest entry point. Clone the repo, set OPENAI_API_KEY and S3 credentials in .env, and streamlit run app.py.

For programmatic generation, the underlying module is callable from Python — useful for batch generation in a notebook or pipeline.

When to use it

Use the synthetic-data consent pattern

Running locally

See also