ivrit-ai/knesset-plenums-whisper-training

Dataset Card for ivrit.ai - Knesset Plenums Whisper Training

This is a whisper formatted version of the ivrit.ai Knesset Plenums dataset.

Thid dataset was created by splitting long audio recordings, along with their respective transcriptions into 30s or less audio slices. Each surch slice represents one or more consecutive segments, along with timestamp token data and prev-slice transcription.

The code for this dataset prepearation process is available on the ivrit.ai ASR Training Github repo.

Dataset Details

Dataset Structure

Data Fields

Each example in the dataset contains:

• audio: An audio column containing:
  • bytes: The audio data encoded in MP3 format
  • path: A string identifier derived from the source entry ID
  • Sampling rate: Fixed at 16000 Hz
• transcript: A string containing the text with potentially Whisper-style timestamp tokens (e.g., <|0.00|>text<|2.40|>) if "has_timestamps" is true
• metadata: A dictionary containing:
  • seek: Float indicating the start time of this slice in the original source audio
  • source: String identifier for the source of the audio (Name of podcast, production system, etc.)
  • entry_id: Unique identifier for the source entry
  • quality_score: Segment median quality score
  • plenum_date: Date of the plenum
• has_prev: Boolean indicating if this slice has transcript from the previous slice within the audio source
• has_timestamps: Boolean indicating if the transcript contains timestamp tokens
• prev_transcript: String containing the transcript of the previous slice (empty if has_prev is false)