Human-annotated behavioral signal dataset for dyadic video interactions from the Seamless Interaction Dataset. Contains fine-grained multimodal annotations across 11 behavioral facets (98 total signals) including prosody, gaze, gesture, turn-taking, and more.

Each annotation captures:

Morph classification for each speaker (Morph A / Morph B)
Confidence scores (1-5 scale) per speaker
Behavioral signals across 11 facets per speaker
Annotator comments and labeling metadata

Dataset Description

What's in the Dataset

Property	Value
Modality	Video annotations (behavioral signals)
Total Annotations	1,233
Speakers per Annotation	2
Behavioral Facets	11
Total Signals	98
Format	CSV / JSON

Annotation Ontology

The dataset uses a closed coding system with 11 behavioral facets:

Facet	Signals	Description
Prosody	13	Acoustic characteristics of speech (pitch, rate, volume)
Lexical Choice	12	Word and phrase selection patterns
Turn Taking	10	Conversational floor access management
Gaze	8	Eye direction and movement
Facial Expression	10	Visible facial muscle movements
Gesture	9	Hand and arm movements
Posture	9	Whole-body orientation and stability
Affect Regulation	7	Behaviours that modulate expressive output
Interactional Role	7	Positioning within conversational structure
Timing & Latency	6	Temporal characteristics of responses
Repair Behavior	7	Corrections and restarts during interaction

See schema.json for complete signal definitions.

Intended Use

Permitted (Non-Commercial) Uses

Academic research and publication
Benchmarking, evaluation, and reproducible experiments
Derivative annotations or improvements shared under CC BY-NC 4.0
Educational purposes and coursework
Non-profit research initiatives

Not Permitted Without Separate Agreement

Commercial product development
Internal commercial R&D in for-profit organisations
Training or tuning models for commercial services
Revenue-generating applications or services
Commercial API integration

License

This dataset is licensed under Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0).

You may share and adapt the dataset for non-commercial purposes only, provided you give appropriate attribution.

Commercial Use

Commercial use requires a separate license from the Licensor.

To obtain a commercial license, contact: l.alessandrorizzo@gmail.com

Commercial use includes but is not limited to:

Model training for commercial products or services
Internal R&D at for-profit entities
Integration into paid products, SaaS, or APIs
Use in revenue-generating systems

Attribution

If you use this dataset, please cite:

SeamlessInteractions Annotation Dataset (v1.0.0), 2026. Alessandro Rizzo.
Licensed under CC BY-NC 4.0.

BibTeX:

@dataset{seamless_interactions_annotations_2026,
  author       = {Rizzo, Alessandro},
  title        = {SeamlessInteractions Annotation Dataset},
  year         = {2026},
  version      = {v1.0.0},
  license      = {CC BY-NC 4.0},
  url          = {https://huggingface.co/datasets/lalessandrorizzo/seamless-interactions-morph-annotations}
}

Data Fields

Core Fields

Field	Type	Description
`id`	string	Unique annotation identifier (CUID)
`videoId`	string	Video identifier (V{vendor}_S{session}_I{interaction})
`vendorId`	integer	Vendor ID from source dataset
`sessionId`	integer	Session ID from source dataset
`interactionId`	integer	Interaction ID from source dataset

Speaker Identification

Field	Type	Description
`speaker1Id`	string	Participant 1 identifier
`speaker2Id`	string	Participant 2 identifier
`speaker1Label`	string	Morph classification ("Morph A" or "Morph B")
`speaker2Label`	string	Morph classification ("Morph A" or "Morph B")
`speaker1Confidence`	integer	Confidence score (1-5 scale)
`speaker2Confidence`	integer	Confidence score (1-5 scale)
`speaker1Comments`	string	Annotator notes for speaker 1
`speaker2Comments`	string	Annotator notes for speaker 2

Behavioral Signals (Per Speaker)

Each speaker has 11 behavioral signal fields (multi-select, semicolon-separated in CSV):

speaker{N}Prosody - Prosodic signals observed
speaker{N}LexicalChoice - Lexical choice signals observed
speaker{N}TurnTaking - Turn-taking signals observed
speaker{N}Gaze - Gaze signals observed
speaker{N}FacialExpression - Facial expression signals observed
speaker{N}Gesture - Gesture signals observed
speaker{N}Posture - Posture signals observed
speaker{N}AffectRegulation - Affect regulation signals observed
speaker{N}InteractionalRole - Interactional role signals observed
speaker{N}TimingLatency - Timing/latency signals observed
speaker{N}RepairBehavior - Repair behavior signals observed

Metadata

Field	Type	Description
`labelingTimeMs`	integer	Time spent annotating (milliseconds)
`createdAt`	datetime	Annotation creation timestamp (ISO 8601)
`updatedAt`	datetime	Last update timestamp (ISO 8601)
`userEmail`	string	Annotator email
`username`	string	Annotator username

Accessing the Dataset

This is a private dataset that requires authorization. You must request access and be granted permission before you can download or use it.

Once authorized, follow these steps to access it.

1. Install the Hugging Face CLI

macOS / Linux:

curl -LsSf https://hf.co/cli/install.sh | bash

Windows:

powershell -ExecutionPolicy ByPass -c "irm https://hf.co/cli/install.ps1 | iex"

2. Authenticate with Hugging Face

Authentication is required for private datasets.

hf auth login

Paste your Hugging Face access token when prompted.

Alternatively, set the token as an environment variable:

export HF_TOKEN=hf_xxxxxxxxx

3. Install Python dependencies

pip install huggingface_hub pandas

Or with uv:

uv add huggingface_hub pandas

4. Data files

This repository contains two equivalent representations of the same data:

File	Format	Notes
`data.csv`	CSV	Has comment header lines (starting with `#`)
`data.json`	JSON	Nested structure with `watermark` metadata and `data` array

5. Download and load the dataset

Using huggingface_hub (recommended)

import json
import pandas as pd
from huggingface_hub import hf_hub_download

REPO_ID = "lalessandrorizzo/seamless-interactions-morph-annotations"
REVISION = "v1.0.0"  # or "main" for latest

# Download CSV
csv_path = hf_hub_download(
    repo_id=REPO_ID,
    filename="data.csv",
    revision=REVISION,
    repo_type="dataset",
)

# Load CSV (skip comment lines starting with #)
df = pd.read_csv(csv_path, comment="#")

Load JSON instead

# Download JSON
json_path = hf_hub_download(
    repo_id=REPO_ID,
    filename="data.json",
    revision=REVISION,
    repo_type="dataset",
)

# Load JSON (data is nested under "data" key)
with open(json_path) as f:
    data = json.load(f)

# Access metadata
print(data["watermark"])

# Load records into DataFrame
df = pd.DataFrame(data["data"])

6. Basic usage

print(f"Records: {len(df)}")
print(f"Columns: {df.columns.tolist()}")

# View first record
print(df.iloc[0])

# Label distribution
print(df["Speaker 1 Label"].value_counts())  # CSV columns
# or
print(df["speaker1Label"].value_counts())    # JSON columns

7. Playground scripts

The playground/ folder contains example scripts for exploring the dataset:

# Run the test script (requires uv)
cd playground
uv run python test_dataset.py

Versioning

Stable releases are tagged using semantic versioning.

To load a specific version:

from huggingface_hub import hf_hub_download

csv_path = hf_hub_download(
    repo_id="lalessandrorizzo/seamless-interactions-morph-annotations",
    filename="data.csv",
    revision="v1.0.0",  # specific version tag
    repo_type="dataset",
)

For the latest version:

csv_path = hf_hub_download(
    repo_id="lalessandrorizzo/seamless-interactions-morph-annotations",
    filename="data.csv",
    revision="main",  # latest
    repo_type="dataset",
)

Provenance and Rights

Annotations: Created by Alessandro Rizzo and collaborators, released under CC BY-NC 4.0
Underlying Videos: From the Seamless Interaction Dataset by Meta AI (separate license applies)

Note: This dataset contains annotations only. The underlying video data must be obtained separately from the original Seamless Interaction Dataset under its own license terms.

Ethical Considerations

Annotations describe observable behavioral signals, not inferred emotional states
The ontology uses descriptive, non-pathologising terminology
Annotator identifiers are included for reproducibility; contact the maintainer if you need anonymised versions

Limitations

Annotations reflect individual annotator judgments and may contain subjective interpretations
The "Morph A/B" classification is specific to the Seamless Interaction Dataset's experimental design
Behavioral signal annotations require video viewing for full context
This dataset does not include the source videos (obtain separately)

Changelog

v1.0.0 - Initial release (1,233 annotations)

Contact

Commercial licensing: l.alessandrorizzo@gmail.com
Research inquiries: l.alessandrorizzo@gmail.com

Seamless Interactions Annotation Dataset is a trademark of Alessandro Rizzo.

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