Datasets:

ThBel
/

Utterly

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:

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.