README.md

---
dataset_info:
  features:
  - name: source
    dtype: string
  - name: audio
    dtype: audio
  - name: is_complete
    dtype: bool
  - name: transcript
    dtype: string
  splits:
  - name: train
configs:
- config_name: default
  data_files:
  - split: train
    path: data/*
license: bsd-2-clause
task_categories:
- automatic-speech-recognition
- voice-activity-detection
language:
- en
size_categories:
- 1K<n<10K
---

# Utterly

## Dataset Summary

**Utterly** is a speech dataset derived from *pipecat-ai/human_5_all* and *pipecat-ai/smart-turn-data-v3.1-train*. It contains over **7.1k recordings of complete and partial English utterances**, each augmented with **turn-level annotations**, including:

* Verbatim Whisper-generated transcripts
* End-of-turn (EoT) markers
* Speaker identifiers (Coming soon)

The dataset is designed to support research and development of speech and dialogue systems that require joint modeling of **speech recognition** and **conversational turn-taking**, such as streaming ASR systems, semantic end-of-turn detection and real-time conversational agents.

---

## Source Data

* **Base datasets**: 
  - *pipecat-ai/human_5_all*
  - *pipecat-ai/smart-turn-data-v3.1-train*
* **Language(s)**: English
* **Modality**: Audio (speech; mono-channel; sampled at 16kHz), Text
* **Interaction type**: Human conversational speech
* **Utterances**: 7,111
* **Speakers**: 500+

Dataset splits (e.g., train/validation/test) are not predefined and may be created by downstream users as needed. Deduplication was applied to the underlying audio sources to ensure dataset splits can be made without contamination.

---

## Annotation Details

* **Transcripts**

  * Generated automatically using **Whisper Large V3 Turbo**.
  * A subset of samples (\~200) was manually reviewed and corrected. The transcripts are estimated to have approximately a word error rate (WER) of **\~2.8%**.

* **End-of-Turn markers**

  * Human annotations inherited from the base datasets.

* **Speaker IDs**

  * Coming soon

---

## Dataset Structure

A typical data entry includes:

* `audio`: Path or reference to the audio utterance
* `transcript`: Text transcription of the utterance
* `speaker_id`: Identifier for the speaker (Coming soon)
* `is_completed`: Boolean or categorical flag indicating end-of-turn, i.e. turn completion

---

## Usage
In order to load the dataset from the hub, you can use the `datasets` library:

```python
ds = datasets.load_dataset(
    "ThBel/Utterly",
    split='train',
    streaming=True # (optional)
)

for row in ds:
    # Do something with the data
    print(row['audio']) # or row['is_complete'], row['transcript'], ...
```

Alternatively you may clone the `ThBel/Utterly` repository, and load the underlying parquet files using `pandas.read_parquet`.

---

## Intended Use Cases

The Utterly dataset is designed to support a range of speech and dialogue research tasks, including but not limited to:

* **Automatic Speech Recognition (ASR)** with embedded end-of-turn detection
* **Semantic end-of-turn modeling** using lexical and acoustic cues
* **Turn-taking and floor-control research** in conversational AI
* **Voice assistants and dialogue systems** requiring low-latency response timing

---

## Quality Considerations

* End-of-turn annotations involve human judgment and may reflect subjective interpretations of conversational completion.
* Transcription quality may vary depending on audio clarity and source conditions.
* Overlapping speech, interruptions, or disfluencies may introduce ambiguity in turn boundaries.

Users are encouraged to validate performance across multiple evaluation settings.

---

## Ethical Considerations

* The dataset consists of recorded human speech and should be used in accordance with the original dataset's licensing and consent terms.
* No additional personally identifying information beyond speaker IDs is introduced.
* Models trained on this dataset should avoid misuse related to surveillance or speaker profiling.

---

## Disclaimer and Licensing

Note that Utterly is a *derived dataset*. I am not the original creator of the source datasets and hold no rights over its content. This dataset is provided as-is for research purposes, and all credit goes to the original authors.

Annotations are released under the **BSD-2-Clause** license and are intended to be compatible with the licensing terms of the source datasets. 

---

## Citation

If you use the Utterly dataset in academic or commercial work, please reference the original datasets.