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Trelis/1gb-test-energy-nopack

1gb-test-energy-nopack Speech dataset prepared with Trelis Studio. Statistics Metric Value Source files 5 Train samples 1125 Validation samples 50 Total duration 587.1 minutes Columns Column Type Description audio Audio Audio segment (16kHz) - speech only, silence stripped via VAD text string Plain transcription (no timestamps) - backwards compatible text_ts string Transcription WITH Whisper timestamp tokens… See the full description on the dataset page: https://huggingface.co/datasets/Trelis/1gb-test-energy-nopack.

sourceHugging Faceupdated 8mo agoView on Hugging Face
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1gb-test-energy-nopack

Speech dataset prepared with Trelis Studio.

Statistics

MetricValue
Source files5
Train samples1125
Validation samples50
Total duration587.1 minutes

Columns

ColumnTypeDescription
audioAudioAudio segment (16kHz) - speech only, silence stripped via VAD
textstringPlain transcription (no timestamps) - backwards compatible
text_tsstringTranscription WITH Whisper timestamp tokens (e.g., `<0.00>Hello<0.50>`)
start_timestringSegment start in original audio (HH:MM:SS.mmm)
end_timestringSegment end in original audio (HH:MM:SS.mmm)
speech_durationfloatDuration of speech in segment (excluding silence)
word_timestampslistWord-level timestamps (relative to speech-only audio)
source_filestringOriginal audio filename

VAD Processing

Audio segments are processed with Silero VAD to match faster-whisper inference:

  • —Silence is stripped from audio (only speech regions remain)
  • —Timestamps are relative to the concatenated speech audio
  • —This ensures training data matches inference behavior

Training Usage

For Whisper timestamp training, use the two-bucket approach:

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

Usage

python
from datasets import load_dataset

dataset = load_dataset("Trelis/1gb-test-energy-nopack")

Prepared with [Trelis Studio](https://studio.trelis.com)