---
license: cc-by-4.0
pretty_name: Fullduplex catalogue
language:
- en
tags:
- full-duplex
- speech-to-speech
- spoken-dialogue
- voice-ai
size_categories:
- n<1K
configs:
- config_name: models
  data_files: data/models.jsonl
- config_name: datasets
  data_files: data/datasets.jsonl
- config_name: benchmarks
  data_files: data/benchmarks.jsonl
---

# Fullduplex catalogue

The models, datasets and benchmarks for full-duplex and speech-to-speech voice AI that [fullduplex.ai](https://www.fullduplex.ai) tracks, one row per entry. Every row links to the entry's own pages (official page, paper, repository) and to its page on fullduplex.ai.

| Config | Rows | One row per |
|---|---|---|
| `models` | 70 | model, hosted API, voice-agent platform or framework |
| `datasets` | 107 | speech dataset |
| `benchmarks` | 64 | benchmark |

Snapshot of 2026-11-09. The site is updated every week, so it can be newer than this file.

## Fields worth knowing

- `full_duplex`: for models, one model listens and speaks at the same time (`duplex_type` is `full-duplex`; the others are `cascade`, `half-duplex` and `other`). For datasets, each speaker is recorded on a separate channel. For benchmarks, the benchmark measures full-duplex behaviour such as turn-taking and interruptions.
- `first_response_ms`: the vendor's own claim, not a measurement by fullduplex.ai.
- `speaker_consent`: `consented`, `public` (collected from public media), `mixed` or `unknown`.
- `license_class`: `permissive`, `share-alike`, `non-commercial`, `gated` or `custom`. The exact terms are in `license`.
- `page_url`: the entry's page on fullduplex.ai, with the sources and the weekly reports that mention it.

## Load it

```python
from datasets import load_dataset

models = load_dataset("Fullduplex-ai/catalog", "models", split="train")
```

## Licence and citation

CC BY 4.0. Please credit Fullduplex and link to https://www.fullduplex.ai.

```bibtex
@misc{fullduplex_catalog,
  title        = {Fullduplex catalogue: models, datasets and benchmarks for full-duplex voice AI},
  author       = {{Fullduplex}},
  year         = {2026},
  howpublished = {\url{https://huggingface.co/datasets/Fullduplex-ai/catalog}}
}
```