Datasets:

Benson
/

seamless-segment-dataset

	# Seamless Interaction — Emotion Segments

	This dataset is a derived, segmented version of the [Seamless Interaction Dataset](https://ai.meta.com/research/seamless-interaction/) (Meta). Each row is one short clip (segment) of a single participant with aligned video, audio, and emotion labels.

	---

	## Source dataset

	- Name: [Seamless Interaction Dataset](https://ai.meta.com/research/seamless-interaction/)
	- Provider: Meta Platforms, Inc. (FAIR Seamless Team)
	- Content: Large-scale face-to-face interactions (video, audio, transcripts, VAD, movement/emotion features from the Imitator model)
	- Original format: Per-participant files (e.g. one `.mp4`, `.wav`, `.npz`, `.json` per `file_id`) organized by label/split/batch/archive
	- HuggingFace (original): [facebook/seamless-interaction](https://huggingface.co/datasets/facebook/seamless-interaction)

	Only participants with imitator movement features (`has_imitator_movement=1` in the filelist) are used.

	---

	## How this dataset was created

	1. Input: The official filelist (e.g. `assets/filelist.csv`) is filtered to rows with `has_imitator_movement == 1`. Each such row corresponds to one participant (`file_id`).

	2. Per participant (script: `download_and_segment_participant.py`):
	- The participant’s data is downloaded from the source (S3) into a local directory.
	- Emotion runs: Per-frame dominant emotion is computed as `argmax(movement:emotion_scores)` (8 classes: Anger, Contempt, Disgust, Fear, Happiness, Neutral, Sadness, Surprise). Contiguous frames with the same dominant are grouped into runs.
	- Segmentation: Runs are split into segments of 3–10 seconds (by FPS). Only segments where the participant is speaking are kept:
	- At least 50% of the segment duration must overlap with speech intervals (from `metadata:vad` and `metadata:transcript`).
	- At least 3 seconds of continuous speech (with up to 2 s gaps merged as “thinking pause”) must fall inside the segment.
	- Segment label: For each segment, the segment-level dominant emotion is not the run label; it is `argmax(mean(emotion_scores[start_frame:end_frame], axis=0))` — i.e. average emotion scores over the segment, then argmax.
	- Extraction: For each segment, video and audio are cut with ffmpeg (output seek + re-encode for video so the clip is exactly the requested time window; audio copied). Per-segment metadata and emotion-related arrays (valence, arousal, emotion_scores, etc.) are written to the segment folder. Original full-length `.mp4`, `.wav`, and `.npz` are deleted after segmenting; only segment subfolders are kept.

	3. Upload (script: `batch_segment_and_upload_hf.py`):
	- The batch script iterates over all filelist participants (with the above filter), runs the segment script for each, collects all segment rows (video, audio, metadata, segment_data), and pushes to this HuggingFace dataset repo.
	- Progress is logged locally so runs can be resumed (already-processed participants are skipped).
	- The dataset is pushed every N successful participants (e.g. every 2). New segment rows are concatenated with any existing data on the Hub before each push.
	- Dataset card: This README is the dataset card; it is uploaded to the repo when the batch script pushes.

	Parameters used in the segmentation (defaults):

	- Segment length: 3–10 s (by FPS).
	- Min speech fraction: 0.5 (50% of segment must be speech).
	- Min continuous speech: 3 s (within segment, with 2 s merge gap for pauses).
	- VAD merge gap: 2 s (gaps ≤ 2 s merged into one “turn”; larger gaps split).

	---

	## Dataset structure

	Each row in this dataset is one segment (one clip of one participant).

	\| Column \| Type \| Description \|
	\|---------------\|--------\|-------------\|
	\| `video` \| Video \| Segment video (MP4), same time window as `audio`. \|
	\| `audio` \| Audio \| Segment audio (WAV), 48 kHz. \|
	\| `file_id` \| string \| Original participant ID (e.g. `V00_S0809_I00000309_P0947`). \|
	\| `segment_id` \| string \| Segment name (e.g. `segment_000`). \|
	\| `label` \| string \| Dataset label: `improvised` or `naturalistic`. \|
	\| `split` \| string \| Split: `train`, `dev`, or `test`. \|
	\| `batch_idx` \| int \| Batch index from the original layout. \|
	\| `archive_idx` \| int \| Archive index from the original layout. \|
	\| `metadata` \| string \| JSON string: `start_frame`, `end_frame`, `start_time`, `end_time`, `duration_seconds`, `dominant_emotion_index`, `dominant_emotion_name`. \|
	\| `segment_data`\| string \| JSON string: full emotion-related data for the segment (dominant index/name, emotion_scores, valence, arousal, tokens, etc.; see original movement features). \|

	Emotion classes (index 0–7): Anger, Contempt, Disgust, Fear, Happiness, Neutral, Sadness, Surprise.

	---

	## Intended use

	- Training or evaluating models that use short, emotion-labeled audiovisual clips (e.g. emotion recognition, multimodal fusion).
	- Studying affect over 3–10 s speaking segments with aligned video, audio, and continuous emotion/valence/arousal.

	---

	## License and attribution

	This derived dataset follows the license and terms of the original Seamless Interaction Dataset (e.g. CC-BY-NC 4.0 or as specified by Meta). You must comply with the original dataset’s license and usage policy.

	Attribution: Segments are derived from the [Seamless Interaction Dataset](https://ai.meta.com/research/seamless-interaction/) by Meta Platforms, Inc. Processing and segmentation were done with the open-source [seamless_interaction](https://github.com/facebookresearch/seamless_interaction) scripts (`download_and_segment_participant.py`, `batch_segment_and_upload_hf.py`).

	---

	## How to load

	```python
	from datasets import load_dataset

	ds = load_dataset("YOUR_USERNAME/seamless-segment-dataset", split="train")
	# Each row: video, audio (48 kHz), file_id, segment_id, label, split, batch_idx, archive_idx, metadata (JSON string), segment_data (JSON string)
	```

	Parse metadata and segment_data with `json.loads(row["metadata"])` and `json.loads(row["segment_data"])` for numeric and emotion fields.

	# Seamless Interaction — Emotion Segments

	This dataset is a derived, segmented version of the [Seamless Interaction Dataset](https://ai.meta.com/research/seamless-interaction/) (Meta). Each row is one short clip (segment) of a single participant with aligned video, audio, and emotion labels.

	---

	## Source dataset

	- Name: [Seamless Interaction Dataset](https://ai.meta.com/research/seamless-interaction/)
	- Provider: Meta Platforms, Inc. (FAIR Seamless Team)
	- Content: Large-scale face-to-face interactions (video, audio, transcripts, VAD, movement/emotion features from the Imitator model)
	- Original format: Per-participant files (e.g. one `.mp4`, `.wav`, `.npz`, `.json` per `file_id`) organized by label/split/batch/archive
	- HuggingFace (original): [facebook/seamless-interaction](https://huggingface.co/datasets/facebook/seamless-interaction)

	Only participants with imitator movement features (`has_imitator_movement=1` in the filelist) are used.

	---

	## How this dataset was created

	1. Input: The official filelist (e.g. `assets/filelist.csv`) is filtered to rows with `has_imitator_movement == 1`. Each such row corresponds to one participant (`file_id`).

	2. Per participant (script: `download_and_segment_participant.py`):
	- The participant’s data is downloaded from the source (S3) into a local directory.
	- Emotion runs: Per-frame dominant emotion is computed as `argmax(movement:emotion_scores)` (8 classes: Anger, Contempt, Disgust, Fear, Happiness, Neutral, Sadness, Surprise). Contiguous frames with the same dominant are grouped into runs.
	- Segmentation: Runs are split into segments of 3–10 seconds (by FPS). Only segments where the participant is speaking are kept:
	- At least 50% of the segment duration must overlap with speech intervals (from `metadata:vad` and `metadata:transcript`).
	- At least 3 seconds of continuous speech (with up to 2 s gaps merged as “thinking pause”) must fall inside the segment.
	- Segment label: For each segment, the segment-level dominant emotion is not the run label; it is `argmax(mean(emotion_scores[start_frame:end_frame], axis=0))` — i.e. average emotion scores over the segment, then argmax.
	- Extraction: For each segment, video and audio are cut with ffmpeg (output seek + re-encode for video so the clip is exactly the requested time window; audio copied). Per-segment metadata and emotion-related arrays (valence, arousal, emotion_scores, etc.) are written to the segment folder. Original full-length `.mp4`, `.wav`, and `.npz` are deleted after segmenting; only segment subfolders are kept.

	3. Upload (script: `batch_segment_and_upload_hf.py`):
	- The batch script iterates over all filelist participants (with the above filter), runs the segment script for each, collects all segment rows (video, audio, metadata, segment_data), and pushes to this HuggingFace dataset repo.
	- Progress is logged locally so runs can be resumed (already-processed participants are skipped).
	- The dataset is pushed every N successful participants (e.g. every 2). New segment rows are concatenated with any existing data on the Hub before each push.
	- Dataset card: This README is the dataset card; it is uploaded to the repo when the batch script pushes.

	Parameters used in the segmentation (defaults):

	- Segment length: 3–10 s (by FPS).
	- Min speech fraction: 0.5 (50% of segment must be speech).
	- Min continuous speech: 3 s (within segment, with 2 s merge gap for pauses).
	- VAD merge gap: 2 s (gaps ≤ 2 s merged into one “turn”; larger gaps split).

	---

	## Dataset structure

	Each row in this dataset is one segment (one clip of one participant).

	\| Column \| Type \| Description \|
	\|---------------\|--------\|-------------\|
	\| `video` \| Video \| Segment video (MP4), same time window as `audio`. \|
	\| `audio` \| Audio \| Segment audio (WAV), 48 kHz. \|
	\| `file_id` \| string \| Original participant ID (e.g. `V00_S0809_I00000309_P0947`). \|
	\| `segment_id` \| string \| Segment name (e.g. `segment_000`). \|
	\| `label` \| string \| Dataset label: `improvised` or `naturalistic`. \|
	\| `split` \| string \| Split: `train`, `dev`, or `test`. \|
	\| `batch_idx` \| int \| Batch index from the original layout. \|
	\| `archive_idx` \| int \| Archive index from the original layout. \|
	\| `metadata` \| string \| JSON string: `start_frame`, `end_frame`, `start_time`, `end_time`, `duration_seconds`, `dominant_emotion_index`, `dominant_emotion_name`. \|
	\| `segment_data`\| string \| JSON string: full emotion-related data for the segment (dominant index/name, emotion_scores, valence, arousal, tokens, etc.; see original movement features). \|

	Emotion classes (index 0–7): Anger, Contempt, Disgust, Fear, Happiness, Neutral, Sadness, Surprise.

	---

	## Intended use

	- Training or evaluating models that use short, emotion-labeled audiovisual clips (e.g. emotion recognition, multimodal fusion).
	- Studying affect over 3–10 s speaking segments with aligned video, audio, and continuous emotion/valence/arousal.

	---

	## License and attribution

	This derived dataset follows the license and terms of the original Seamless Interaction Dataset (e.g. CC-BY-NC 4.0 or as specified by Meta). You must comply with the original dataset’s license and usage policy.

	Attribution: Segments are derived from the [Seamless Interaction Dataset](https://ai.meta.com/research/seamless-interaction/) by Meta Platforms, Inc. Processing and segmentation were done with the open-source [seamless_interaction](https://github.com/facebookresearch/seamless_interaction) scripts (`download_and_segment_participant.py`, `batch_segment_and_upload_hf.py`).

	---

	## How to load

	```python
	from datasets import load_dataset

	ds = load_dataset("YOUR_USERNAME/seamless-segment-dataset", split="train")
	# Each row: video, audio (48 kHz), file_id, segment_id, label, split, batch_idx, archive_idx, metadata (JSON string), segment_data (JSON string)
	```

	Parse metadata and segment_data with `json.loads(row["metadata"])` and `json.loads(row["segment_data"])` for numeric and emotion fields.