README.md

metadata

license: cc-by-4.0
language:
  - en
task_categories:
  - automatic-speech-recognition
  - text-classification
  - token-classification
tags:
  - voice-notes
  - task-extraction
  - disfluency
  - long-form
  - real-speech
pretty_name: Annotated Voice Notes
size_categories:
  - n<1K
dataset_info:
  features:
    - name: audio
      dtype: audio
    - name: asr_engine
      dtype: string
    - name: asr_settings
      struct:
        - name: disfluencies
          dtype: bool
        - name: language_code
          dtype: string
    - name: duration_s
      dtype: int64
    - name: id
      dtype: string
    - name: language
      dtype: string
    - name: notes
      list: string
    - name: purpose_explanation
      dtype: string
    - name: purposes
      list: string
    - name: recorded_at
      dtype: string
    - name: recording_context
      dtype: string
    - name: source_filename
      dtype: string
    - name: speakers
      dtype: int64
    - name: tags
      list: string
    - name: title
      dtype: string
    - name: transcript_raw
      dtype: string
    - name: word_count
      dtype: int64
  splits:
    - name: train
      num_bytes: 21446099
      num_examples: 1
  download_size: 21451787
  dataset_size: 21446099
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*

Annotated Voice Notes

A small, hand-picked set of real voice notes. It is kept small on purpose. Each note has its audio, a raw AssemblyAI transcript, and a written annotation that explains why it was included and what it is good for.

This is not a bulk corpus. Every recording was chosen because it shows something specific, such as tasks buried in rambling speech, heavy disfluency, or a long take. The annotation says what that is.

Fields

Field	Description
audio	The recording, in its original format (MP3)
id	Stable sample id (avn-NNNN)
title	Short description
recorded_at	ISO 8601 with offset
recording_context	How and where it was recorded
purposes	Machine-readable list of the uses it was included for
purpose_explanation	Why it is in the dataset
notes	Observations on structure, disfluencies and likely ASR errors
tags	Topic tags
duration_s, word_count	From the ASR output
transcript_raw	AssemblyAI output, uncorrected, with disfluencies kept
asr_engine, asr_settings	Model and settings that produced the transcript

Transcripts are raw. They are not corrected against the audio, so known and suspected ASR errors are listed in notes and left in the text.

Samples

id	Title	Length	Purposes
avn-0001	Apartment remaining-fixes walkthrough	22:19	task extraction, disfluency, long-form

Purposes vocabulary

• task_extraction: tasks expressed loosely, not as a clean list
• disfluency: fillers, false starts, self-corrections, restarts
• long_form_task_recording: several minutes of continuous task-bearing speech