Datasets:

Hebrish
/

Background-Conversations

license: cc-by-4.0
language:
  - en
task_categories:
  - automatic-speech-recognition
pretty_name: Background Conversations
tags:
  - speaker-isolation
  - target-speaker-asr
  - background-speech
  - test-set
  - hebrish
configs:
  - config_name: default
    data_files:
      - split: test
        path: data/test-*.parquet

Background Conversations

Audio dataset, standalone. Kept apart from the master dataset and its split-outs. Every audio dataset as one tree, with hours: the audio index. How every dataset fits together: the dataset index.

A small test-only set for target-speaker ASR: one person (the foreground speaker) dictates into a phone while other people talk, a baby cries and household devices make noise in the same room. The transcript is the foreground speaker's words only. Everything else is what a model, or a speaker-isolation front end before it, should leave out.

It is never trained on. It is for checking how a dictation model, or a voice-isolation step, copes with real rooms.

Data

1 clip(s), 3.1 min, split test only.
Audio: FLAC, 48 kHz, 16-bit, mono, converted from phone voice notes (Opus).
text: foreground speech, verbatim, punctuated and cased. transcript_status is pending until the speaker has checked it against the audio, then reviewed.
model_hypothesis / asr_model: the untouched first-pass transcript the draft started from.
noise_flags: what is audible behind the speaker (background-speech, baby-crying, device-voice-prompt, ...).
extra (JSON text): background_sources (who or what is in the background), diarized (false: the background speech has no transcript or segment labels yet), source_file.

Columns follow the Hebrish audio format standard (audio.schema.json in this repo). There is no sentences tab: these are free speech, not read sentences.

Licence

CC-BY-4.0.