README.md

metadata

tags:
  - audio
  - speech
  - whisper
  - dataset

test-draft-asr-e2e

Speech dataset prepared with Trelis Studio.

Statistics

Metric	Value
Source files	1
Train samples	4
Total duration	2.1 minutes

Columns

Column	Type	Description
audio	Audio	Audio segment (16kHz) - speech only, extracted from aligned regions
text	string	Plain transcription (no timestamps)
text_ts	string	Transcription with Whisper timestamp tokens
preconditioning	string	Previous segment's plain text (empty for first segment of each source file)
start_time	string	Segment start in original audio (HH:MM:SS.mmm)
end_time	string	Segment end in original audio (HH:MM:SS.mmm)
speech_duration	float	Duration of speech in segment (excluding silence)
word_timestamps	string	Word-level timestamps as JSON (relative to speech-only audio)
source_file	string	Original audio filename
language	string	ISO 639-1 language code for Whisper language token (e.g., en, el)

Speech Segmentation

For .txt transcripts, CTC alignment produces word-level timestamps that define speech boundaries. The full audio is treated as a single speech region and split at word boundaries into ~20s chunks. For .srt/.vtt files, transcript timestamps define segments. Timestamps are relative to the extracted speech audio within each chunk.

Training Usage

2-bucket approach (default)

• Bucket A (50%): Use text column - plain transcription without timestamps
• Bucket B (50%): Use text_ts column - transcription with Whisper timestamp tokens

4-bucket approach (with preconditioning)

• Bucket A (25%): text only (no timestamps, no preconditioning)
• Bucket A' (25%): preconditioning + text (previous segment context, no timestamps)
• Bucket B (25%): text_ts only (timestamps, no preconditioning)
• Bucket B' (25%): preconditioning + text_ts (previous segment context + timestamps)

Preconditioning prepends the previous segment's text to teach the model conversational continuity. Bucket ratios are configurable at training time.

Usage

from datasets import load_dataset

dataset = load_dataset("Trelis/test-draft-asr-e2e")

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Prepared with Trelis Studio